From 0ac12da9f339b54e2d541019cee226cc9ef54d01 Mon Sep 17 00:00:00 2001
From: UncleCode <unclecode@kidocode.com>
Date: Mon, 9 Jun 2025 23:18:27 +0800
Subject: [PATCH] feat: Major Chrome Extension overhaul with Click2Crawl,
 instant Schema extraction, and modular architecture
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

✨ New Features:
- Click2Crawl: Visual element selection with markdown conversion
  - Ctrl/Cmd+Click to select multiple elements
  - Visual text mode for WYSIWYG extraction
  - Real-time markdown preview with syntax highlighting
  - Export to .md file or clipboard

- Schema Builder Enhancement: Instant data extraction without LLMs
  - Test schemas directly in browser
  - See JSON results immediately
  - Export data or Python code
  - Cloud deployment ready (coming soon)

- Modular Architecture:
  - Separated into schemaBuilder.js, scriptBuilder.js, click2CrawlBuilder.js
  - Added contentAnalyzer.js and markdownConverter.js modules
  - Shared utilities and CSS reset system
  - Integrated marked.js for markdown rendering

🎨 UI/UX Improvements:
- Added edgy cloud announcement banner with seamless shimmer animation
- Direct, technical copy: "You don't need Puppeteer. You need Crawl4AI Cloud."
- Enhanced feature cards with emojis
- Fixed CSS conflicts with targeted reset approach
- Improved badge hover effects (red on hover)
- Added wrap toggle for code preview

📚 Documentation Updates:
- Split extraction diagrams into LLM and no-LLM versions
- Updated llms-full.txt with latest content
- Added versioned LLM context (v0.1.1)

🔧 Technical Enhancements:
- Refactored 3464 lines of monolithic content.js into modules
- Added proper event handling and cleanup
- Improved z-index management
- Better scroll position tracking for badges
- Enhanced error handling throughout

This release transforms the Chrome Extension from a simple tool into a powerful
visual data extraction suite, making web scraping accessible to everyone.
---
 .claude/settings.local.json                   |    6 +-
 .../apps/crawl4ai-assistant/assistant.css     |  524 +-
 .../content/click2CrawlBuilder.js             |  732 ++
 .../crawl4ai-assistant/content/content.js     | 3460 +-------
 .../content/contentAnalyzer.js                |  623 ++
 .../content/markdownConverter.js              |  718 ++
 .../crawl4ai-assistant/content/overlay.css    | 3088 +++++--
 .../content/schemaBuilder.js                  | 1812 ++++
 .../content/schemaBuilder_v1.js               |  608 ++
 .../content/scriptBuilder.js                  | 2515 ++++++
 .../content/shared/utils.js                   |  253 +
 docs/md_v2/apps/crawl4ai-assistant/index.html |  620 +-
 .../crawl4ai-assistant/libs/marked.min.js     |   69 +
 .../apps/crawl4ai-assistant/manifest.json     |   11 +-
 .../apps/crawl4ai-assistant/popup/popup.css   |    4 +
 .../apps/crawl4ai-assistant/popup/popup.html  |    8 +
 .../apps/crawl4ai-assistant/popup/popup.js    |   17 +
 docs/md_v2/apps/llmtxt/llmtxt.js              |   11 +-
 .../{extraction.txt => extraction-llm.txt}    |    0
 .../llm.txt/diagrams/extraction-no-llm.txt    |  478 +
 .../assets/llm.txt/txt/extraction-llm.txt     |  903 ++
 .../assets/llm.txt/txt/extraction-no-llm.txt  |  835 ++
 docs/md_v2/assets/llm.txt/txt/extraction.txt  |  788 --
 .../assets/llm.txt/txt/llms-full-v0.1.1.txt   | 7715 +++++++++++++++++
 docs/md_v2/assets/llm.txt/txt/llms-full.txt   | 4412 +++++++---
 25 files changed, 23686 insertions(+), 6524 deletions(-)
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/content/click2CrawlBuilder.js
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/content/contentAnalyzer.js
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/content/markdownConverter.js
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder.js
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder_v1.js
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/content/scriptBuilder.js
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/content/shared/utils.js
 create mode 100644 docs/md_v2/apps/crawl4ai-assistant/libs/marked.min.js
 rename docs/md_v2/assets/llm.txt/diagrams/{extraction.txt => extraction-llm.txt} (100%)
 create mode 100644 docs/md_v2/assets/llm.txt/diagrams/extraction-no-llm.txt
 create mode 100644 docs/md_v2/assets/llm.txt/txt/extraction-llm.txt
 create mode 100644 docs/md_v2/assets/llm.txt/txt/extraction-no-llm.txt
 delete mode 100644 docs/md_v2/assets/llm.txt/txt/extraction.txt
 create mode 100644 docs/md_v2/assets/llm.txt/txt/llms-full-v0.1.1.txt

diff --git a/.claude/settings.local.json b/.claude/settings.local.json
index 062eb7df..616bc6dd 100644
--- a/.claude/settings.local.json
+++ b/.claude/settings.local.json
@@ -16,7 +16,11 @@
       "Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg -A 5 -B 5 \"Script Builder\" docs/md_v2/apps/crawl4ai-assistant/)",
       "Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg -A 30 \"generateCode\\(events, format\\)\" docs/md_v2/apps/crawl4ai-assistant/content/content.js)",
       "Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg \"<style>\" docs/md_v2/apps/crawl4ai-assistant/index.html -A 5)",
-      "Bash(git checkout:*)"
+      "Bash(git checkout:*)",
+      "Bash(docker logs:*)",
+      "Bash(curl:*)",
+      "Bash(docker compose:*)",
+      "Bash(./test-final-integration.sh:*)"
     ]
   },
   "enableAllProjectMcpServers": false
diff --git a/docs/md_v2/apps/crawl4ai-assistant/assistant.css b/docs/md_v2/apps/crawl4ai-assistant/assistant.css
index f1a86123..a1154453 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/assistant.css
+++ b/docs/md_v2/apps/crawl4ai-assistant/assistant.css
@@ -626,6 +626,16 @@ code {
   background: var(--primary-pink);
 }
 
+.tool-status.new {
+  background: var(--primary-green);
+  animation: pulse 2s ease-in-out infinite;
+}
+
+@keyframes pulse {
+  0%, 100% { opacity: 1; }
+  50% { opacity: 0.8; }
+}
+
 /* Tool Details Panel */
 .tool-details {
   background: var(--bg-secondary);
@@ -1026,4 +1036,516 @@ code {
   .coming-soon-section h2 {
     font-size: 1.5rem;
   }
-}
\ No newline at end of file
+}
+/* Code Examples Grid Layout */
+.code-example > div[style*="grid"] {
+  min-height: 500px;
+}
+
+.code-example > div[style*="grid"] .terminal-window {
+  height: 100%;
+  display: flex;
+  flex-direction: column;
+}
+
+.code-example > div[style*="grid"] .terminal-content {
+  flex: 1;
+  overflow: auto;
+  max-height: 450px;
+}
+
+@media (max-width: 1200px) {
+  .code-example > div[style*="grid"] {
+    grid-template-columns: 1fr \!important;
+    gap: 12px \!important;
+  }
+}
+
+/* Cloud Banner Section (Thin Version) */
+.cloud-banner-section {
+  margin: 2rem 0 3rem 0;
+}
+
+.cloud-banner {
+  background: linear-gradient(135deg, rgba(15, 187, 170, 0.05) 0%, rgba(243, 128, 245, 0.05) 100%);
+  border: 1px solid rgba(15, 187, 170, 0.3);
+  border-radius: 12px;
+  padding: 1.5rem 2rem;
+  position: relative;
+  overflow: hidden;
+}
+
+.cloud-banner::before {
+  content: "";
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 200%;
+  height: 100%;
+  background: linear-gradient(90deg, 
+    transparent 0%, 
+    rgba(15, 187, 170, 0.1) 25%, 
+    transparent 50%,
+    rgba(15, 187, 170, 0.1) 75%,
+    transparent 100%
+  );
+  animation: cloud-shimmer 4s linear infinite;
+}
+
+@keyframes cloud-shimmer {
+  0% { transform: translateX(0); }
+  100% { transform: translateX(-50%); }
+}
+
+.cloud-banner-content {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 2rem;
+  position: relative;
+  z-index: 1;
+}
+
+
+.cloud-banner-text {
+  flex: 1;
+  text-align: left;
+}
+
+.cloud-banner-text h3 {
+  margin: 0;
+  font-size: 1.25rem;
+  color: var(--text-primary);
+  font-weight: 600;
+  letter-spacing: -0.02em;
+}
+
+.cloud-banner-text p {
+  margin: 0.25rem 0 0;
+  font-size: 0.875rem;
+  color: var(--text-secondary);
+}
+
+.cloud-banner-btn {
+  background: var(--primary-green);
+  color: var(--bg-dark);
+  border: none;
+  padding: 0.75rem 1.5rem;
+  font-size: 0.875rem;
+  font-weight: 600;
+  border-radius: 25px;
+  cursor: pointer;
+  transition: all 0.3s ease;
+  font-family: var(--font-primary);
+  white-space: nowrap;
+  flex-shrink: 0;
+}
+
+.cloud-banner-btn:hover {
+  background: #1fcbba;
+  transform: translateY(-2px);
+  box-shadow: 0 6px 20px rgba(15, 187, 170, 0.3);
+}
+
+@media (max-width: 768px) {
+  .cloud-banner-content {
+    flex-direction: column;
+    text-align: center;
+    gap: 1rem;
+  }
+  
+  .cloud-banner-text {
+    text-align: center;
+  }
+  
+  .cloud-banner-icon {
+    font-size: 2rem;
+  }
+  
+  .cloud-banner-text h3 {
+    font-size: 1.25rem;
+  }
+}
+
+/* Crawl4AI Cloud Section */
+.cloud-section {
+  margin: 5rem 0;
+}
+
+.cloud-announcement {
+  background: linear-gradient(135deg, #1a1a1a 0%, #2a2a2a 100%);
+  border: 2px solid var(--primary-green);
+  border-radius: 20px;
+  padding: 4rem 3rem;
+  position: relative;
+  overflow: hidden;
+  box-shadow: 0 20px 60px rgba(15, 187, 170, 0.2);
+  text-align: center;
+}
+
+.cloud-announcement::before {
+  content: "";
+  position: absolute;
+  top: -50%;
+  left: -50%;
+  width: 200%;
+  height: 450%;
+  background: radial-gradient(circle, rgba(15, 187, 170, 0.1) 0%, transparent 70%);
+  animation: rotate 20s linear infinite;
+}
+
+@keyframes rotate {
+  from { transform: rotate(0deg); }
+  to { transform: rotate(360deg); }
+}
+
+
+@keyframes float {
+  0%, 100% { transform: translateY(0); }
+  50% { transform: translateY(-10px); }
+}
+
+.cloud-announcement h2 {
+  font-size: 2.5rem;
+  margin: 0 0 0.5rem 0;
+  color: var(--text-primary);
+  font-weight: 700;
+  letter-spacing: -0.03em;
+  position: relative;
+  z-index: 1;
+}
+
+.cloud-tagline {
+  font-size: 1.25rem;
+  color: var(--text-secondary);
+  margin: 0.5rem 0 2rem;
+  position: relative;
+  z-index: 1;
+}
+
+.cloud-features-preview {
+  display: flex;
+  justify-content: center;
+  gap: 2rem;
+  margin: 2rem 0 3rem;
+  flex-wrap: wrap;
+  position: relative;
+  z-index: 1;
+}
+
+.cloud-feature-item {
+  font-size: 0.875rem;
+  color: var(--text-secondary);
+  font-family: var(--font-code);
+  padding: 0.5rem 1rem;
+  background: var(--bg-secondary);
+  border: 1px solid var(--border-color);
+  border-radius: 6px;
+}
+
+.cloud-cta-button {
+  background: var(--primary-green);
+  color: var(--bg-dark);
+  border: none;
+  padding: 0.875rem 2rem;
+  font-size: 1rem;
+  font-weight: 600;
+  border-radius: 6px;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  position: relative;
+  z-index: 1;
+  font-family: var(--font-primary);
+  text-transform: none;
+  letter-spacing: -0.01em;
+}
+
+
+.cloud-cta-button:hover {
+  transform: translateY(-2px);
+  box-shadow: 0 10px 30px rgba(15, 187, 170, 0.4);
+  background: #1fcbba;
+}
+
+.cloud-hint {
+  margin-top: 1.5rem;
+  font-size: 0.875rem;
+  color: var(--text-secondary);
+  position: relative;
+  z-index: 1;
+  font-style: italic;
+}
+
+/* Signup Overlay */
+.signup-overlay {
+  position: fixed;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: rgba(0, 0, 0, 0.9);
+  backdrop-filter: blur(10px);
+  z-index: 10000;
+  display: none;
+  align-items: center;
+  justify-content: center;
+  padding: 2rem;
+}
+
+.signup-overlay.active {
+  display: flex;
+}
+
+.signup-container {
+  background: var(--bg-secondary);
+  border: 2px solid var(--primary-green);
+  border-radius: 16px;
+  max-width: 600px;
+  width: 100%;
+  max-height: 90vh;
+  overflow: auto;
+  position: relative;
+  box-shadow: 0 20px 60px rgba(15, 187, 170, 0.3);
+}
+
+.close-signup {
+  position: absolute;
+  top: 1rem;
+  right: 1rem;
+  background: var(--bg-tertiary);
+  border: none;
+  color: var(--text-secondary);
+  width: 40px;
+  height: 40px;
+  border-radius: 50%;
+  font-size: 24px;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  z-index: 10;
+}
+
+.close-signup:hover {
+  background: var(--primary-pink);
+  color: var(--bg-dark);
+  transform: rotate(90deg);
+}
+
+.signup-content {
+  padding: 3rem;
+}
+
+.signup-content h3 {
+  font-size: 1.75rem;
+  margin: 0 0 0.5rem;
+  color: var(--text-primary);
+}
+
+.signup-content p {
+  color: var(--text-secondary);
+  margin-bottom: 2rem;
+}
+
+.waitlist-form {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+.form-field {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+}
+
+.form-field label {
+  font-size: 0.875rem;
+  color: var(--text-secondary);
+  text-transform: uppercase;
+  font-weight: 600;
+}
+
+.form-field input,
+.form-field select {
+  background: var(--bg-tertiary);
+  border: 1px solid var(--border-color);
+  color: var(--text-primary);
+  padding: 0.75rem 1rem;
+  border-radius: 8px;
+  font-size: 1rem;
+  font-family: var(--font-primary);
+  transition: all 0.2s ease;
+}
+
+.form-field input:focus,
+.form-field select:focus {
+  outline: none;
+  border-color: var(--primary-green);
+  box-shadow: 0 0 0 3px rgba(15, 187, 170, 0.2);
+}
+
+.submit-button {
+  background: var(--primary-green);
+  color: var(--bg-dark);
+  border: none;
+  padding: 1rem 2rem;
+  font-size: 1.125rem;
+  font-weight: 600;
+  border-radius: 8px;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  font-family: var(--font-primary);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.5rem;
+  margin-top: 1rem;
+}
+
+.submit-button:hover {
+  background: #1fcbba;
+  transform: translateY(-2px);
+  box-shadow: 0 8px 24px rgba(15, 187, 170, 0.3);
+}
+
+/* Crawling Animation */
+.crawl-animation {
+  padding: 3rem;
+  text-align: left;
+}
+
+.crawl-terminal {
+  margin-bottom: 2rem;
+}
+
+.crawl-terminal .terminal-content {
+  max-height: 400px;
+  overflow-y: auto;
+}
+
+.crawl-terminal code {
+  white-space: pre;
+  display: block;
+  line-height: 1.6;
+}
+
+.crawl-log {
+  color: var(--text-primary);
+  font-family: var(--font-code);
+}
+
+.crawl-log .log-init { color: #0fbbaa; }
+.crawl-log .log-fetch { color: #4169e1; }
+.crawl-log .log-scrape { color: #f380f5; }
+.crawl-log .log-extract { color: #ffbd2e; }
+.crawl-log .log-complete { color: #0fbbaa; }
+.crawl-log .log-success { color: #0fbbaa; }
+.crawl-log .log-time { color: #666; }
+
+.extracted-preview {
+  background: var(--bg-tertiary);
+  border-radius: 12px;
+  padding: 1.5rem;
+  margin-bottom: 2rem;
+}
+
+.extracted-preview h4 {
+  margin: 0 0 1rem;
+  color: var(--primary-green);
+  font-size: 1.25rem;
+}
+
+.json-preview {
+  background: var(--bg-dark);
+  border: 1px solid var(--border-color);
+  border-radius: 8px;
+  padding: 1rem;
+  overflow-x: auto;
+  max-height: 300px;
+}
+
+.json-preview code {
+  color: var(--text-primary);
+  font-size: 0.875rem;
+}
+
+.success-message {
+  text-align: center;
+  padding: 2rem;
+}
+
+.continue-button {
+  background: var(--primary-green);
+  color: var(--bg-dark);
+  border: none;
+  padding: 1rem 2rem;
+  font-size: 1.125rem;
+  font-weight: 600;
+  border-radius: 8px;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  font-family: var(--font-primary);
+  margin-top: 2rem;
+}
+
+.continue-button:hover {
+  background: #1fcbba;
+  transform: translateY(-2px);
+  box-shadow: 0 8px 24px rgba(15, 187, 170, 0.3);
+}
+
+.success-icon {
+  font-size: 4rem;
+  margin-bottom: 1rem;
+  animation: bounce 0.5s ease;
+}
+
+@keyframes bounce {
+  0%, 100% { transform: translateY(0); }
+  50% { transform: translateY(-20px); }
+}
+
+.success-message h3 {
+  font-size: 2rem;
+  margin: 0 0 1rem;
+  color: var(--primary-green);
+}
+
+.success-message ul {
+  list-style: none;
+  margin: 1.5rem 0;
+  padding: 0;
+  text-align: left;
+  max-width: 400px;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.success-message li {
+  padding: 0.5rem 0;
+  color: var(--text-primary);
+  font-size: 1.125rem;
+}
+
+.success-note {
+  color: var(--text-secondary);
+  font-size: 1rem;
+  margin-top: 2rem;
+  padding: 1rem;
+  background: var(--bg-tertiary);
+  border-radius: 8px;
+}
+
+@media (max-width: 768px) {
+  .cloud-announcement h2 {
+    font-size: 2rem;
+  }
+  
+  .cloud-features-preview {
+    flex-direction: column;
+    gap: 1rem;
+  }
+  
+  .signup-content {
+    padding: 2rem;
+  }
+}
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/click2CrawlBuilder.js b/docs/md_v2/apps/crawl4ai-assistant/content/click2CrawlBuilder.js
new file mode 100644
index 00000000..4fdcc40e
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/click2CrawlBuilder.js
@@ -0,0 +1,732 @@
+class Click2CrawlBuilder {
+  constructor() {
+    this.selectedElements = new Set();
+    this.highlightBoxes = new Map();
+    this.selectionMode = false;
+    this.toolbar = null;
+    this.previewPanel = null;
+    this.selectionCounter = 0;
+    this.markdownConverter = null;
+    this.contentAnalyzer = null;
+    
+    // Configuration options
+    this.options = {
+      includeImages: true,
+      preserveTables: true,
+      keepCodeFormatting: true,
+      simplifyLayout: false,
+      preserveLinks: true,
+      addSeparators: true,
+      includeXPath: false,
+      textOnly: false
+    };
+    
+    this.init();
+  }
+  
+  async init() {
+    // Initialize dependencies
+    this.markdownConverter = new MarkdownConverter();
+    this.contentAnalyzer = new ContentAnalyzer();
+    
+    this.createToolbar();
+    this.setupEventListeners();
+  }
+  
+  createToolbar() {
+    // Create floating toolbar
+    this.toolbar = document.createElement('div');
+    this.toolbar.className = 'c4ai-c2c-toolbar';
+    this.toolbar.innerHTML = `
+      <div class="c4ai-toolbar-header">
+        <div class="c4ai-toolbar-dots">
+          <span class="c4ai-dot c4ai-dot-red"></span>
+          <span class="c4ai-dot c4ai-dot-yellow"></span>
+          <span class="c4ai-dot c4ai-dot-green"></span>
+        </div>
+        <span class="c4ai-toolbar-title">Click2Crawl</span>
+        <button class="c4ai-close-btn" title="Close">×</button>
+      </div>
+      <div class="c4ai-toolbar-content">
+        <div class="c4ai-selection-info">
+          <span class="c4ai-selection-count">0 elements selected</span>
+          <button class="c4ai-clear-btn" title="Clear selection" disabled>Clear</button>
+        </div>
+        <div class="c4ai-toolbar-actions">
+          <button class="c4ai-preview-btn" disabled>Preview Markdown</button>
+          <button class="c4ai-copy-btn" disabled>Copy to Clipboard</button>
+        </div>
+        <div class="c4ai-toolbar-instructions">
+          <p>💡 <strong>Ctrl/Cmd + Click</strong> to select multiple elements</p>
+          <p>📝 Selected elements will be converted to clean markdown</p>
+          <p>⌨️ Press <strong>ESC</strong> to exit</p>
+        </div>
+      </div>
+    `;
+    
+    document.body.appendChild(this.toolbar);
+    makeDraggableByHeader(this.toolbar);
+    
+    // Position toolbar
+    this.toolbar.style.position = 'fixed';
+    this.toolbar.style.top = '20px';
+    this.toolbar.style.right = '20px';
+    this.toolbar.style.zIndex = '999999';
+  }
+  
+  setupEventListeners() {
+    // Close button
+    this.toolbar.querySelector('.c4ai-close-btn').addEventListener('click', () => {
+      this.deactivate();
+    });
+    
+    // Clear selection button
+    this.toolbar.querySelector('.c4ai-clear-btn').addEventListener('click', () => {
+      this.clearSelection();
+    });
+    
+    // Preview button
+    this.toolbar.querySelector('.c4ai-preview-btn').addEventListener('click', () => {
+      this.showPreview();
+    });
+    
+    // Copy button
+    this.toolbar.querySelector('.c4ai-copy-btn').addEventListener('click', () => {
+      this.copyToClipboard();
+    });
+    
+    // Document click handler for element selection
+    this.documentClickHandler = (event) => this.handleElementClick(event);
+    document.addEventListener('click', this.documentClickHandler, true);
+    
+    // Prevent default link behavior during selection mode
+    this.linkClickHandler = (event) => {
+      if (event.ctrlKey || event.metaKey) {
+        event.preventDefault();
+        event.stopPropagation();
+      }
+    };
+    document.addEventListener('click', this.linkClickHandler, true);
+    
+    // Hover effect
+    this.documentHoverHandler = (event) => this.handleElementHover(event);
+    document.addEventListener('mouseover', this.documentHoverHandler, true);
+    
+    // Remove hover on mouseout
+    this.documentMouseOutHandler = (event) => this.handleElementMouseOut(event);
+    document.addEventListener('mouseout', this.documentMouseOutHandler, true);
+    
+    // Keyboard shortcuts
+    this.keyboardHandler = (event) => this.handleKeyboard(event);
+    document.addEventListener('keydown', this.keyboardHandler);
+  }
+  
+  handleElementClick(event) {
+    // Check if Ctrl/Cmd is pressed
+    if (!event.ctrlKey && !event.metaKey) return;
+    
+    // Prevent default behavior
+    event.preventDefault();
+    event.stopPropagation();
+    
+    const element = event.target;
+    
+    // Don't select our own UI elements
+    if (element.closest('.c4ai-c2c-toolbar') || 
+        element.closest('.c4ai-c2c-preview') ||
+        element.closest('.c4ai-highlight-box')) {
+      return;
+    }
+    
+    // Toggle element selection
+    if (this.selectedElements.has(element)) {
+      this.deselectElement(element);
+    } else {
+      this.selectElement(element);
+    }
+    
+    this.updateUI();
+  }
+  
+  handleElementHover(event) {
+    const element = event.target;
+    
+    // Don't hover our own UI elements
+    if (element.closest('.c4ai-c2c-toolbar') || 
+        element.closest('.c4ai-c2c-preview') ||
+        element.closest('.c4ai-highlight-box') ||
+        element.hasAttribute('data-c4ai-badge')) {
+      return;
+    }
+    
+    // Add hover class
+    element.classList.add('c4ai-hover-candidate');
+  }
+  
+  handleElementMouseOut(event) {
+    const element = event.target;
+    element.classList.remove('c4ai-hover-candidate');
+  }
+  
+  handleKeyboard(event) {
+    // ESC to deactivate
+    if (event.key === 'Escape') {
+      this.deactivate();
+    }
+    // Ctrl/Cmd + A to select all visible elements
+    else if ((event.ctrlKey || event.metaKey) && event.key === 'a') {
+      event.preventDefault();
+      // Select all visible text-containing elements
+      const elements = document.querySelectorAll('p, h1, h2, h3, h4, h5, h6, li, td, th, div, span, article, section');
+      elements.forEach(el => {
+        if (el.textContent.trim() && this.isVisible(el) && !this.selectedElements.has(el)) {
+          this.selectElement(el);
+        }
+      });
+      this.updateUI();
+    }
+  }
+  
+  isVisible(element) {
+    const rect = element.getBoundingClientRect();
+    const style = window.getComputedStyle(element);
+    return rect.width > 0 && 
+           rect.height > 0 && 
+           style.display !== 'none' && 
+           style.visibility !== 'hidden' && 
+           style.opacity !== '0';
+  }
+  
+  selectElement(element) {
+    this.selectedElements.add(element);
+    
+    // Create highlight box
+    const box = this.createHighlightBox(element);
+    this.highlightBoxes.set(element, box);
+    
+    // Add selected class
+    element.classList.add('c4ai-selected');
+    
+    this.selectionCounter++;
+  }
+  
+  deselectElement(element) {
+    this.selectedElements.delete(element);
+    
+    // Remove highlight box (badge)
+    const badge = this.highlightBoxes.get(element);
+    if (badge) {
+      // Remove scroll/resize listeners
+      if (badge._updatePosition) {
+        window.removeEventListener('scroll', badge._updatePosition, true);
+        window.removeEventListener('resize', badge._updatePosition);
+      }
+      badge.remove();
+      this.highlightBoxes.delete(element);
+    }
+    
+    // Remove outline
+    element.style.outline = '';
+    element.style.outlineOffset = '';
+    
+    // Remove attributes
+    element.removeAttribute('data-c4ai-selection-order');
+    element.classList.remove('c4ai-selected');
+    
+    this.selectionCounter--;
+  }
+  
+  createHighlightBox(element) {
+    // Add a data attribute to track selection order
+    element.setAttribute('data-c4ai-selection-order', this.selectionCounter + 1);
+    
+    // Add selection outline directly to the element
+    element.style.outline = '2px solid #0fbbaa';
+    element.style.outlineOffset = '2px';
+    
+    // Create badge with fixed positioning
+    const badge = document.createElement('div');
+    badge.className = 'c4ai-selection-badge-fixed';
+    badge.textContent = this.selectionCounter + 1;
+    badge.setAttribute('data-c4ai-badge', 'true');
+    badge.title = 'Click to deselect';
+    
+    // Get element position and set badge position
+    const rect = element.getBoundingClientRect();
+    badge.style.cssText = `
+      position: fixed !important;
+      top: ${rect.top - 12}px !important;
+      left: ${rect.left - 12}px !important;
+      width: 24px !important;
+      height: 24px !important;
+      background: #0fbbaa !important;
+      color: #070708 !important;
+      border-radius: 50% !important;
+      display: flex !important;
+      align-items: center !important;
+      justify-content: center !important;
+      font-size: 12px !important;
+      font-weight: bold !important;
+      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif !important;
+      box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3) !important;
+      z-index: 999998 !important;
+      cursor: pointer !important;
+      transition: all 0.2s ease !important;
+      pointer-events: auto !important;
+      border: none !important;
+      padding: 0 !important;
+      margin: 0 !important;
+      line-height: 1 !important;
+      text-align: center !important;
+      text-decoration: none !important;
+      box-sizing: border-box !important;
+    `;
+    
+    // Add hover styles dynamically
+    badge.addEventListener('mouseenter', () => {
+      badge.style.setProperty('background', '#ff3c74', 'important');
+      badge.style.setProperty('transform', 'scale(1.1)', 'important');
+    });
+    
+    badge.addEventListener('mouseleave', () => {
+      badge.style.setProperty('background', '#0fbbaa', 'important');
+      badge.style.setProperty('transform', 'scale(1)', 'important');
+    });
+    
+    // Add click handler to badge for deselection
+    badge.addEventListener('click', (e) => {
+      e.stopPropagation();
+      e.preventDefault();
+      this.deselectElement(element);
+      this.updateUI();
+    });
+    
+    // Add scroll listener to update position
+    const updatePosition = () => {
+      const newRect = element.getBoundingClientRect();
+      badge.style.top = `${newRect.top - 12}px`;
+      badge.style.left = `${newRect.left - 12}px`;
+    };
+    
+    // Store the update function so we can remove it later
+    badge._updatePosition = updatePosition;
+    window.addEventListener('scroll', updatePosition, true);
+    window.addEventListener('resize', updatePosition);
+    
+    document.body.appendChild(badge);
+    
+    return badge;
+  }
+  
+  clearSelection() {
+    // Clear all selections
+    this.selectedElements.forEach(element => {
+      // Remove badge
+      const badge = this.highlightBoxes.get(element);
+      if (badge) {
+        // Remove scroll/resize listeners
+        if (badge._updatePosition) {
+          window.removeEventListener('scroll', badge._updatePosition, true);
+          window.removeEventListener('resize', badge._updatePosition);
+        }
+        badge.remove();
+      }
+      
+      // Remove outline
+      element.style.outline = '';
+      element.style.outlineOffset = '';
+      
+      // Remove attributes
+      element.removeAttribute('data-c4ai-selection-order');
+      element.classList.remove('c4ai-selected');
+    });
+    
+    this.selectedElements.clear();
+    this.highlightBoxes.clear();
+    this.selectionCounter = 0;
+    
+    this.updateUI();
+  }
+  
+  updateUI() {
+    const count = this.selectedElements.size;
+    
+    // Update selection count
+    this.toolbar.querySelector('.c4ai-selection-count').textContent = 
+      `${count} element${count !== 1 ? 's' : ''} selected`;
+    
+    // Enable/disable buttons
+    const hasSelection = count > 0;
+    this.toolbar.querySelector('.c4ai-preview-btn').disabled = !hasSelection;
+    this.toolbar.querySelector('.c4ai-copy-btn').disabled = !hasSelection;
+    this.toolbar.querySelector('.c4ai-clear-btn').disabled = !hasSelection;
+  }
+  
+  async showPreview() {
+    // Generate markdown from selected elements
+    const markdown = await this.generateMarkdown();
+    
+    // Create or update preview panel
+    if (!this.previewPanel) {
+      this.createPreviewPanel();
+    }
+    
+    await this.updatePreviewContent(markdown);
+    this.previewPanel.style.display = 'block';
+  }
+  
+  createPreviewPanel() {
+    this.previewPanel = document.createElement('div');
+    this.previewPanel.className = 'c4ai-c2c-preview';
+    this.previewPanel.innerHTML = `
+      <div class="c4ai-preview-header">
+        <div class="c4ai-toolbar-dots">
+          <span class="c4ai-dot c4ai-dot-red"></span>
+          <span class="c4ai-dot c4ai-dot-yellow"></span>
+          <span class="c4ai-dot c4ai-dot-green"></span>
+        </div>
+        <span class="c4ai-preview-title">Markdown Preview</span>
+        <button class="c4ai-preview-close">×</button>
+      </div>
+      <div class="c4ai-preview-options">
+        <label><input type="checkbox" name="textOnly"> 👁️ Visual Text Mode (As You See) TRY THIS!!!</label>
+        <label><input type="checkbox" name="includeImages" checked> Include Images</label>
+        <label><input type="checkbox" name="preserveTables" checked> Preserve Tables</label>
+        <label><input type="checkbox" name="preserveLinks" checked> Preserve Links</label>
+        <label><input type="checkbox" name="keepCodeFormatting" checked> Keep Code Formatting</label>
+        <label><input type="checkbox" name="simplifyLayout"> Simplify Layout</label>
+        <label><input type="checkbox" name="addSeparators" checked> Add Separators</label>
+        <label><input type="checkbox" name="includeXPath"> Include XPath Headers</label>
+      </div>
+      <div class="c4ai-preview-content">
+        <div class="c4ai-preview-tabs">
+          <button class="c4ai-tab active" data-tab="preview">Preview</button>
+          <button class="c4ai-tab" data-tab="markdown">Markdown</button>
+          <button class="c4ai-wrap-toggle" title="Toggle word wrap">↔️ Wrap</button>
+        </div>
+        <div class="c4ai-preview-pane active" data-pane="preview"></div>
+        <div class="c4ai-preview-pane" data-pane="markdown"></div>
+      </div>
+      <div class="c4ai-preview-actions">
+        <button class="c4ai-download-btn">Download .md</button>
+        <button class="c4ai-copy-markdown-btn">Copy Markdown</button>
+        <button class="c4ai-cloud-btn" disabled>Send to Cloud (Coming Soon)</button>
+      </div>
+    `;
+    
+    document.body.appendChild(this.previewPanel);
+    makeDraggableByHeader(this.previewPanel);
+    
+    // Position preview panel
+    this.previewPanel.style.position = 'fixed';
+    this.previewPanel.style.top = '50%';
+    this.previewPanel.style.left = '50%';
+    this.previewPanel.style.transform = 'translate(-50%, -50%)';
+    this.previewPanel.style.zIndex = '999999';
+    
+    this.setupPreviewEventListeners();
+  }
+  
+  setupPreviewEventListeners() {
+    // Close button
+    this.previewPanel.querySelector('.c4ai-preview-close').addEventListener('click', () => {
+      this.previewPanel.style.display = 'none';
+    });
+    
+    // Tab switching
+    this.previewPanel.querySelectorAll('.c4ai-tab').forEach(tab => {
+      tab.addEventListener('click', (e) => {
+        const tabName = e.target.dataset.tab;
+        this.switchPreviewTab(tabName);
+      });
+    });
+    
+    // Wrap toggle
+    const wrapToggle = this.previewPanel.querySelector('.c4ai-wrap-toggle');
+    wrapToggle.addEventListener('click', () => {
+      const panes = this.previewPanel.querySelectorAll('.c4ai-preview-pane');
+      panes.forEach(pane => {
+        pane.classList.toggle('wrap');
+      });
+      wrapToggle.classList.toggle('active');
+    });
+    
+    // Options change
+    this.previewPanel.querySelectorAll('input[type="checkbox"]').forEach(checkbox => {
+      checkbox.addEventListener('change', async (e) => {
+        this.options[e.target.name] = e.target.checked;
+        
+        // If text-only is enabled, automatically disable certain options
+        if (e.target.name === 'textOnly' && e.target.checked) {
+          // Update UI checkboxes
+          const preserveLinksCheckbox = this.previewPanel.querySelector('input[name="preserveLinks"]');
+          if (preserveLinksCheckbox) {
+            preserveLinksCheckbox.checked = false;
+            preserveLinksCheckbox.disabled = true;
+          }
+          
+          // Optionally disable images in text-only mode
+          const includeImagesCheckbox = this.previewPanel.querySelector('input[name="includeImages"]');
+          if (includeImagesCheckbox) {
+            includeImagesCheckbox.disabled = true;
+          }
+        } else if (e.target.name === 'textOnly' && !e.target.checked) {
+          // Re-enable options when text-only is disabled
+          const preserveLinksCheckbox = this.previewPanel.querySelector('input[name="preserveLinks"]');
+          if (preserveLinksCheckbox) {
+            preserveLinksCheckbox.disabled = false;
+          }
+          
+          const includeImagesCheckbox = this.previewPanel.querySelector('input[name="includeImages"]');
+          if (includeImagesCheckbox) {
+            includeImagesCheckbox.disabled = false;
+          }
+        }
+        
+        const markdown = await this.generateMarkdown();
+        await this.updatePreviewContent(markdown);
+      });
+    });
+    
+    // Action buttons
+    this.previewPanel.querySelector('.c4ai-copy-markdown-btn').addEventListener('click', () => {
+      this.copyToClipboard();
+    });
+    
+    this.previewPanel.querySelector('.c4ai-download-btn').addEventListener('click', () => {
+      this.downloadMarkdown();
+    });
+  }
+  
+  switchPreviewTab(tabName) {
+    // Update active tab
+    this.previewPanel.querySelectorAll('.c4ai-tab').forEach(tab => {
+      tab.classList.toggle('active', tab.dataset.tab === tabName);
+    });
+    
+    // Update active pane
+    this.previewPanel.querySelectorAll('.c4ai-preview-pane').forEach(pane => {
+      pane.classList.toggle('active', pane.dataset.pane === tabName);
+    });
+  }
+  
+  async updatePreviewContent(markdown) {
+    // Update markdown pane
+    const markdownPane = this.previewPanel.querySelector('[data-pane="markdown"]');
+    markdownPane.innerHTML = `<pre><code>${this.escapeHtml(markdown)}</code></pre>`;
+    
+    // Update preview pane using marked.js
+    const previewPane = this.previewPanel.querySelector('[data-pane="preview"]');
+    
+    // Configure marked options (marked.js is already loaded via manifest)
+    if (window.marked) {
+      marked.setOptions({
+        gfm: true,
+        breaks: true,
+        tables: true,
+        headerIds: false,
+        mangle: false
+      });
+      
+      // Render markdown to HTML
+      const html = marked.parse(markdown);
+      previewPane.innerHTML = `<div class="c4ai-markdown-preview">${html}</div>`;
+    } else {
+      // Fallback if marked.js is not available
+      previewPane.innerHTML = `<div class="c4ai-markdown-preview"><pre>${this.escapeHtml(markdown)}</pre></div>`;
+    }
+  }
+  
+  
+  escapeHtml(unsafe) {
+    return unsafe
+      .replace(/&/g, "&amp;")
+      .replace(/</g, "&lt;")
+      .replace(/>/g, "&gt;")
+      .replace(/"/g, "&quot;")
+      .replace(/'/g, "&#039;");
+  }
+  
+  async generateMarkdown() {
+    // Get selected elements as array
+    const elements = Array.from(this.selectedElements);
+    
+    // Sort elements by their selection order
+    const sortedElements = elements.sort((a, b) => {
+      const orderA = parseInt(a.getAttribute('data-c4ai-selection-order') || '0');
+      const orderB = parseInt(b.getAttribute('data-c4ai-selection-order') || '0');
+      return orderA - orderB;
+    });
+    
+    // Convert each element separately
+    const markdownParts = [];
+    
+    for (let i = 0; i < sortedElements.length; i++) {
+      const element = sortedElements[i];
+      
+      // Add XPath header if enabled
+      if (this.options.includeXPath) {
+        const xpath = this.getXPath(element);
+        markdownParts.push(`### Element ${i + 1} - XPath: \`${xpath}\`\n`);
+      }
+      
+      // Check if element is part of a table structure that should be processed specially
+      let elementsToConvert = [element];
+      
+      // If text-only mode and element is a TR, process the entire table for better context
+      if (this.options.textOnly && element.tagName === 'TR') {
+        const table = element.closest('table');
+        if (table && !sortedElements.includes(table)) {
+          // Only include this table row, not the whole table
+          elementsToConvert = [element];
+        }
+      }
+      
+      // Analyze and convert individual element
+      const analysis = await this.contentAnalyzer.analyze(elementsToConvert);
+      const markdown = await this.markdownConverter.convert(elementsToConvert, {
+        ...this.options,
+        analysis
+      });
+      
+      markdownParts.push(markdown.trim());
+      
+      // Add separator if enabled and not last element
+      if (this.options.addSeparators && i < sortedElements.length - 1) {
+        markdownParts.push('\n\n---\n\n');
+      }
+    }
+    
+    return markdownParts.join('\n\n');
+  }
+  
+  getXPath(element) {
+    if (element.id) {
+      return `//*[@id="${element.id}"]`;
+    }
+    
+    const parts = [];
+    let current = element;
+    
+    while (current && current.nodeType === Node.ELEMENT_NODE) {
+      let index = 0;
+      let sibling = current.previousSibling;
+      
+      while (sibling) {
+        if (sibling.nodeType === Node.ELEMENT_NODE && sibling.nodeName === current.nodeName) {
+          index++;
+        }
+        sibling = sibling.previousSibling;
+      }
+      
+      const tagName = current.nodeName.toLowerCase();
+      const part = index > 0 ? `${tagName}[${index + 1}]` : tagName;
+      parts.unshift(part);
+      
+      current = current.parentNode;
+    }
+    
+    return '/' + parts.join('/');
+  }
+  
+  sortElementsByPosition(elements) {
+    return elements.sort((a, b) => {
+      const position = a.compareDocumentPosition(b);
+      if (position & Node.DOCUMENT_POSITION_FOLLOWING) {
+        return -1;
+      } else if (position & Node.DOCUMENT_POSITION_PRECEDING) {
+        return 1;
+      }
+      return 0;
+    });
+  }
+  
+  async copyToClipboard() {
+    const markdown = await this.generateMarkdown();
+    
+    try {
+      await navigator.clipboard.writeText(markdown);
+      this.showNotification('Markdown copied to clipboard!');
+    } catch (err) {
+      console.error('Failed to copy:', err);
+      this.showNotification('Failed to copy. Please try again.', 'error');
+    }
+  }
+  
+  async downloadMarkdown() {
+    const markdown = await this.generateMarkdown();
+    const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, -5);
+    const filename = `crawl4ai-export-${timestamp}.md`;
+    
+    // Create blob and download
+    const blob = new Blob([markdown], { type: 'text/markdown' });
+    const url = URL.createObjectURL(blob);
+    
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = filename;
+    document.body.appendChild(a);
+    a.click();
+    document.body.removeChild(a);
+    URL.revokeObjectURL(url);
+    
+    this.showNotification(`Downloaded ${filename}`);
+  }
+  
+  showNotification(message, type = 'success') {
+    const notification = document.createElement('div');
+    notification.className = `c4ai-notification c4ai-notification-${type}`;
+    notification.textContent = message;
+    
+    document.body.appendChild(notification);
+    
+    // Animate in
+    setTimeout(() => notification.classList.add('show'), 10);
+    
+    // Remove after 3 seconds
+    setTimeout(() => {
+      notification.classList.remove('show');
+      setTimeout(() => notification.remove(), 300);
+    }, 3000);
+  }
+  
+  deactivate() {
+    // Remove event listeners
+    document.removeEventListener('click', this.documentClickHandler, true);
+    document.removeEventListener('click', this.linkClickHandler, true);
+    document.removeEventListener('mouseover', this.documentHoverHandler, true);
+    document.removeEventListener('mouseout', this.documentMouseOutHandler, true);
+    document.removeEventListener('keydown', this.keyboardHandler);
+    
+    // Clear selections
+    this.clearSelection();
+    
+    // Remove UI elements
+    if (this.toolbar) {
+      this.toolbar.remove();
+      this.toolbar = null;
+    }
+    
+    if (this.previewPanel) {
+      this.previewPanel.remove();
+      this.previewPanel = null;
+    }
+    
+    // Remove hover styles
+    document.querySelectorAll('.c4ai-hover-candidate').forEach(el => {
+      el.classList.remove('c4ai-hover-candidate');
+    });
+    
+    // Notify background script (with error handling)
+    try {
+      if (chrome.runtime && chrome.runtime.sendMessage) {
+        chrome.runtime.sendMessage({
+          action: 'c2cDeactivated'
+        });
+      }
+    } catch (error) {
+      // Extension context might be invalidated, ignore the error
+      console.log('Click2Crawl deactivated (extension context unavailable)');
+    }
+  }
+}
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/content.js b/docs/md_v2/apps/crawl4ai-assistant/content/content.js
index fa1e36a5..f20efe3b 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/content/content.js
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/content.js
@@ -1,3392 +1,78 @@
-// Content script for Crawl4AI Assistant
-class SchemaBuilder {
-  constructor() {
-    this.mode = null;
-    this.container = null;
-    this.fields = [];
-    this.overlay = null;
-    this.toolbar = null;
-    this.highlightBox = null;
-    this.selectedElements = new Set();
-    this.isPaused = false;
-    this.codeModal = null;
-    
-    this.handleMouseMove = this.handleMouseMove.bind(this);
-    this.handleClick = this.handleClick.bind(this);
-    this.handleKeyPress = this.handleKeyPress.bind(this);
-  }
+// Main content script for Crawl4AI Assistant
+// Coordinates between SchemaBuilder and ScriptBuilder
 
-  start() {
-    this.mode = 'container';
-    this.createOverlay();
-    this.createToolbar();
-    this.attachEventListeners();
-    this.updateToolbar();
-  }
-
-  stop() {
-    this.detachEventListeners();
-    this.overlay?.remove();
-    this.toolbar?.remove();
-    this.highlightBox?.remove();
-    this.removeAllHighlights();
-    this.mode = null;
-    this.container = null;
-    this.fields = [];
-    this.selectedElements.clear();
-  }
-
-  createOverlay() {
-    // Create highlight box
-    this.highlightBox = document.createElement('div');
-    this.highlightBox.className = 'c4ai-highlight-box';
-    document.body.appendChild(this.highlightBox);
-  }
-
-  createToolbar() {
-    this.toolbar = document.createElement('div');
-    this.toolbar.className = 'c4ai-toolbar';
-    this.toolbar.innerHTML = `
-      <div class="c4ai-toolbar-titlebar">
-        <div class="c4ai-titlebar-dots">
-          <button class="c4ai-dot c4ai-dot-close" id="c4ai-close"></button>
-          <button class="c4ai-dot c4ai-dot-minimize"></button>
-          <button class="c4ai-dot c4ai-dot-maximize"></button>
-        </div>
-        <img src="${chrome.runtime.getURL('icons/icon-16.png')}" class="c4ai-titlebar-icon" alt="Crawl4AI">
-        <div class="c4ai-titlebar-title">Crawl4AI Schema Builder</div>
-      </div>
-      <div class="c4ai-toolbar-content">
-        <div class="c4ai-toolbar-status">
-          <div class="c4ai-status-item">
-            <span class="c4ai-status-label">Mode:</span>
-            <span class="c4ai-status-value" id="c4ai-mode">Select Container</span>
-          </div>
-          <div class="c4ai-status-item">
-            <span class="c4ai-status-label">Container:</span>
-            <span class="c4ai-status-value" id="c4ai-container">Not selected</span>
-          </div>
-        </div>
-        <div class="c4ai-fields-list" id="c4ai-fields-list" style="display: none;">
-          <div class="c4ai-fields-header">Selected Fields:</div>
-          <ul class="c4ai-fields-items" id="c4ai-fields-items"></ul>
-        </div>
-        <div class="c4ai-toolbar-hint" id="c4ai-hint">
-          Click on a container element (e.g., product card, article, etc.)
-        </div>
-        <div class="c4ai-toolbar-actions">
-          <button id="c4ai-pause" class="c4ai-action-btn c4ai-pause-btn">
-            <span class="c4ai-pause-icon">⏸</span> Pause
-          </button>
-          <button id="c4ai-generate" class="c4ai-action-btn c4ai-generate-btn">
-            <span class="c4ai-generate-icon">⚡</span> Generate Code
-          </button>
-        </div>
-      </div>
-    `;
-    document.body.appendChild(this.toolbar);
-    
-    // Add event listeners for toolbar buttons
-    document.getElementById('c4ai-pause').addEventListener('click', () => this.togglePause());
-    document.getElementById('c4ai-generate').addEventListener('click', () => this.stopAndGenerate());
-    document.getElementById('c4ai-close').addEventListener('click', () => this.stop());
-    
-    // Make toolbar draggable
-    this.makeDraggable(this.toolbar);
-  }
-
-  attachEventListeners() {
-    document.addEventListener('mousemove', this.handleMouseMove, true);
-    document.addEventListener('click', this.handleClick, true);
-    document.addEventListener('keydown', this.handleKeyPress, true);
-  }
-
-  detachEventListeners() {
-    document.removeEventListener('mousemove', this.handleMouseMove, true);
-    document.removeEventListener('click', this.handleClick, true);
-    document.removeEventListener('keydown', this.handleKeyPress, true);
-  }
-
-  handleMouseMove(e) {
-    if (this.isPaused) return;
-    
-    const element = document.elementFromPoint(e.clientX, e.clientY);
-    if (element && !this.isOurElement(element)) {
-      this.highlightElement(element);
-    }
-  }
-
-  handleClick(e) {
-    if (this.isPaused) return;
-    
-    const element = e.target;
-    
-    if (this.isOurElement(element)) {
-      return;
-    }
-
-    e.preventDefault();
-    e.stopPropagation();
-
-    if (this.mode === 'container') {
-      this.selectContainer(element);
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-      this.selectField(element);
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-  handleKeyPress(e) {
-    if (e.key === 'Escape') {
-      this.stop();
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-  }
-
-  isOurElement(element) {
-    return element.classList.contains('c4ai-highlight-box') ||
-           element.classList.contains('c4ai-toolbar') ||
-           element.closest('.c4ai-toolbar') ||
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-  }
-
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-    let isDragging = false;
-    let startX, startY, initialX, initialY;
-    
-    const titlebar = element.querySelector('.c4ai-toolbar-titlebar');
-    
-    titlebar.addEventListener('mousedown', (e) => {
-      // Don't drag if clicking on buttons
-      if (e.target.classList.contains('c4ai-dot')) return;
-      
-      isDragging = true;
-      startX = e.clientX;
-      startY = e.clientY;
-      
-      const rect = element.getBoundingClientRect();
-      initialX = rect.left;
-      initialY = rect.top;
-      
-      element.style.transition = 'none';
-      titlebar.style.cursor = 'grabbing';
-    });
-    
-    document.addEventListener('mousemove', (e) => {
-      if (!isDragging) return;
-      
-      const deltaX = e.clientX - startX;
-      const deltaY = e.clientY - startY;
-      
-      element.style.left = `${initialX + deltaX}px`;
-      element.style.top = `${initialY + deltaY}px`;
-      element.style.right = 'auto';
-    });
-    
-    document.addEventListener('mouseup', () => {
-      if (isDragging) {
-        isDragging = false;
-        element.style.transition = '';
-        titlebar.style.cursor = 'grab';
-      }
-    });
-  }
-
-  makeDraggableByHeader(element) {
-    let isDragging = false;
-    let startX, startY, initialX, initialY;
-    
-    const header = element.querySelector('.c4ai-debugger-header');
-    if (!header) return;
-    
-    header.addEventListener('mousedown', (e) => {
-      // Don't drag if clicking on close button
-      if (e.target.id === 'c4ai-close-debugger' || e.target.closest('#c4ai-close-debugger')) return;
-      
-      isDragging = true;
-      startX = e.clientX;
-      startY = e.clientY;
-      
-      const rect = element.getBoundingClientRect();
-      initialX = rect.left;
-      initialY = rect.top;
-      
-      element.style.transition = 'none';
-      header.style.cursor = 'grabbing';
-    });
-    
-    document.addEventListener('mousemove', (e) => {
-      if (!isDragging) return;
-      
-      const deltaX = e.clientX - startX;
-      const deltaY = e.clientY - startY;
-      
-      element.style.left = `${initialX + deltaX}px`;
-      element.style.top = `${initialY + deltaY}px`;
-      element.style.right = 'auto';
-    });
-    
-    document.addEventListener('mouseup', () => {
-      if (isDragging) {
-        isDragging = false;
-        element.style.transition = '';
-        header.style.cursor = 'grab';
-      }
-    });
-  }
-
-  togglePause() {
-    this.isPaused = !this.isPaused;
-    const pauseBtn = document.getElementById('c4ai-pause');
-    if (this.isPaused) {
-      pauseBtn.innerHTML = '<span class="c4ai-play-icon">▶</span> Resume';
-      pauseBtn.classList.add('c4ai-paused');
-      this.highlightBox.style.display = 'none';
-    } else {
-      pauseBtn.innerHTML = '<span class="c4ai-pause-icon">⏸</span> Pause';
-      pauseBtn.classList.remove('c4ai-paused');
-    }
-  }
-
-  stopAndGenerate() {
-    if (!this.container || this.fields.length === 0) {
-      alert('Please select a container and at least one field before generating code.');
-      return;
-    }
-    
-    const code = this.generateCode();
-    this.showCodeModal(code);
-  }
-
-  highlightElement(element) {
-    const rect = element.getBoundingClientRect();
-    this.highlightBox.style.cssText = `
-      left: ${rect.left + window.scrollX}px;
-      top: ${rect.top + window.scrollY}px;
-      width: ${rect.width}px;
-      height: ${rect.height}px;
-      display: block;
-    `;
-
-    if (this.mode === 'container') {
-      this.highlightBox.className = 'c4ai-highlight-box c4ai-container-mode';
-    } else {
-      this.highlightBox.className = 'c4ai-highlight-box c4ai-field-mode';
-    }
-  }
-
-  selectContainer(element) {
-    // Remove previous container highlight
-    if (this.container) {
-      this.container.element.classList.remove('c4ai-selected-container');
-    }
-
-    this.container = {
-      element: element,
-      html: element.outerHTML,
-      selector: this.generateSelector(element),
-      tagName: element.tagName.toLowerCase()
-    };
-
-    element.classList.add('c4ai-selected-container');
-    this.mode = 'field';
-    this.updateToolbar();
-    this.updateStats();
-  }
-
-  selectField(element) {
-    // Don't select the container itself
-    if (element === this.container.element) {
-      return;
-    }
-
-    // Check if already selected - if so, deselect it
-    if (this.selectedElements.has(element)) {
-      this.deselectField(element);
-      return;
-    }
-
-    // Must be inside the container
-    if (!this.container.element.contains(element)) {
-      return;
-    }
-
-    this.showFieldDialog(element);
-  }
-
-  deselectField(element) {
-    // Remove from fields array
-    this.fields = this.fields.filter(f => f.element !== element);
-    
-    // Remove from selected elements set
-    this.selectedElements.delete(element);
-    
-    // Remove visual selection
-    element.classList.remove('c4ai-selected-field');
-    
-    // Update UI
-    this.updateToolbar();
-    this.updateStats();
-  }
-
-  showFieldDialog(element) {
-    const dialog = document.createElement('div');
-    dialog.className = 'c4ai-field-dialog';
-    
-    const rect = element.getBoundingClientRect();
-    dialog.style.cssText = `
-      left: ${rect.left + window.scrollX}px;
-      top: ${rect.bottom + window.scrollY + 10}px;
-    `;
-
-    dialog.innerHTML = `
-      <div class="c4ai-field-dialog-content">
-        <h4>Name this field:</h4>
-        <input type="text" id="c4ai-field-name" placeholder="e.g., title, price, description" autofocus>
-        <div class="c4ai-field-preview">
-          <strong>Content:</strong> ${element.textContent.trim().substring(0, 50)}...
-        </div>
-        <div class="c4ai-field-actions">
-          <button id="c4ai-field-save">Save</button>
-          <button id="c4ai-field-cancel">Cancel</button>
-        </div>
-      </div>
-    `;
-
-    document.body.appendChild(dialog);
-
-    const input = dialog.querySelector('#c4ai-field-name');
-    const saveBtn = dialog.querySelector('#c4ai-field-save');
-    const cancelBtn = dialog.querySelector('#c4ai-field-cancel');
-
-    const save = () => {
-      const fieldName = input.value.trim();
-      if (fieldName) {
-        this.fields.push({
-          name: fieldName,
-          value: element.textContent.trim(),
-          element: element,
-          selector: this.generateSelector(element, this.container.element)
-        });
-        
-        element.classList.add('c4ai-selected-field');
-        this.selectedElements.add(element);
-        this.updateToolbar();
-        this.updateStats();
-      }
-      dialog.remove();
-    };
-
-    const cancel = () => {
-      dialog.remove();
-    };
-
-    saveBtn.addEventListener('click', save);
-    cancelBtn.addEventListener('click', cancel);
-    input.addEventListener('keypress', (e) => {
-      if (e.key === 'Enter') save();
-      if (e.key === 'Escape') cancel();
-    });
-
-    input.focus();
-  }
-
-  generateSelector(element, context = document) {
-    // Try to generate a robust selector
-    if (element.id) {
-      return `#${CSS.escape(element.id)}`;
-    }
-
-    // Check for data attributes (most stable)
-    const dataAttrs = ['data-testid', 'data-id', 'data-test', 'data-cy'];
-    for (const attr of dataAttrs) {
-      const value = element.getAttribute(attr);
-      if (value) {
-        return `[${attr}="${value}"]`;
-      }
-    }
-
-    // Check for aria-label
-    if (element.getAttribute('aria-label')) {
-      return `[aria-label="${element.getAttribute('aria-label')}"]`;
-    }
-
-    // Try semantic HTML elements with text
-    const tagName = element.tagName.toLowerCase();
-    if (['button', 'a', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6'].includes(tagName)) {
-      const text = element.textContent.trim();
-      if (text && text.length < 50) {
-        // Use tag name with partial text match
-        return `${tagName}`;
-      }
-    }
-
-    // Check for simple, non-utility classes
-    const classes = Array.from(element.classList)
-      .filter(c => !c.startsWith('c4ai-')) // Exclude our classes
-      .filter(c => !c.includes('[') && !c.includes('(') && !c.includes(':')) // Exclude utility classes
-      .filter(c => c.length < 30); // Exclude very long classes
-    
-    if (classes.length > 0 && classes.length <= 3) {
-      const selector = classes.map(c => `.${CSS.escape(c)}`).join('');
-      try {
-        if (context.querySelectorAll(selector).length === 1) {
-          return selector;
-        }
-      } catch (e) {
-        // Invalid selector, continue
-      }
-    }
-
-    // Use nth-child with simple parent tag
-    const parent = element.parentElement;
-    if (parent && parent !== context) {
-      const siblings = Array.from(parent.children);
-      const index = siblings.indexOf(element) + 1;
-      // Just use parent tag name to avoid recursion
-      const parentTag = parent.tagName.toLowerCase();
-      return `${parentTag} > ${tagName}:nth-child(${index})`;
-    }
-
-    // Final fallback
-    return tagName;
-  }
-
-  updateToolbar() {
-    document.getElementById('c4ai-mode').textContent = 
-      this.mode === 'container' ? 'Select Container' : 'Select Fields';
-    
-    document.getElementById('c4ai-container').textContent = 
-      this.container ? `${this.container.tagName} ✓` : 'Not selected';
-
-    // Update fields list
-    const fieldsList = document.getElementById('c4ai-fields-list');
-    const fieldsItems = document.getElementById('c4ai-fields-items');
-    
-    if (this.fields.length > 0) {
-      fieldsList.style.display = 'block';
-      fieldsItems.innerHTML = this.fields.map(field => `
-        <li class="c4ai-field-item">
-          <span class="c4ai-field-name">${field.name}</span>
-          <span class="c4ai-field-value">${field.value.substring(0, 30)}${field.value.length > 30 ? '...' : ''}</span>
-        </li>
-      `).join('');
-    } else {
-      fieldsList.style.display = 'none';
-    }
-
-    const hint = document.getElementById('c4ai-hint');
-    if (this.mode === 'container') {
-      hint.textContent = 'Click on a container element (e.g., product card, article, etc.)';
-    } else if (this.fields.length === 0) {
-      hint.textContent = 'Click on fields inside the container to extract (title, price, etc.)';
-    } else {
-      hint.innerHTML = `Continue selecting fields or click <strong>Stop & Generate</strong> to finish.`;
-    }
-  }
-
-  updateStats() {
-    chrome.runtime.sendMessage({
-      action: 'updateStats',
-      stats: {
-        container: !!this.container,
-        fields: this.fields.length
-      }
-    });
-  }
-
-  removeAllHighlights() {
-    document.querySelectorAll('.c4ai-selected-container').forEach(el => {
-      el.classList.remove('c4ai-selected-container');
-    });
-    document.querySelectorAll('.c4ai-selected-field').forEach(el => {
-      el.classList.remove('c4ai-selected-field');
-    });
-  }
-
-  generateCode() {
-    const fieldDescriptions = this.fields.map(f => 
-      `- ${f.name} (example: "${f.value.substring(0, 50)}...")`
-    ).join('\n');
-
-    return `#!/usr/bin/env python3
-"""
-Generated by Crawl4AI Chrome Extension
-URL: ${window.location.href}
-Generated: ${new Date().toISOString()}
-"""
-
-import asyncio
-import json
-from pathlib import Path
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-
-# HTML snippet of the selected container element
-HTML_SNIPPET = """
-${this.container.html}
-"""
-
-# Extraction query based on your field selections
-EXTRACTION_QUERY = """
-Create a JSON CSS extraction schema to extract the following fields:
-${fieldDescriptions}
-
-The schema should handle multiple ${this.container.tagName} elements on the page.
-Each item should be extracted as a separate object in the results array.
-"""
-
-async def generate_schema():
-    """Generate extraction schema using LLM"""
-    print("🔧 Generating extraction schema...")
-    
-    try:
-        # Generate the schema using Crawl4AI's built-in LLM integration
-        schema = JsonCssExtractionStrategy.generate_schema(
-            html=HTML_SNIPPET,
-            query=EXTRACTION_QUERY,
-        )
-        
-        # Save the schema for reuse
-        schema_path = Path('generated_schema.json')
-        with open(schema_path, 'w') as f:
-            json.dump(schema, f, indent=2)
-        
-        print("✅ Schema generated successfully!")
-        print(f"📄 Schema saved to: {schema_path}")
-        print("\\nGenerated schema:")
-        print(json.dumps(schema, indent=2))
-        
-        return schema
-        
-    except Exception as e:
-        print(f"❌ Error generating schema: {e}")
-        return None
-
-async def test_extraction(url: str = "${window.location.href}"):
-    """Test the generated schema on the actual webpage"""
-    print("\\n🧪 Testing extraction on live webpage...")
-    
-    # Load the generated schema
-    try:
-        with open('generated_schema.json', 'r') as f:
-            schema = json.load(f)
-    except FileNotFoundError:
-        print("❌ Schema file not found. Run generate_schema() first.")
-        return
-    
-    # Configure browser
-    browser_config = BrowserConfig(
-        headless=True,
-        verbose=False
-    )
-    
-    # Configure extraction
-    crawler_config = CrawlerRunConfig(
-        extraction_strategy=JsonCssExtractionStrategy(schema=schema)
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        result = await crawler.arun(
-            url=url,
-            config=crawler_config
-        )
-        
-        if result.success and result.extracted_content:
-            data = json.loads(result.extracted_content)
-            print(f"\\n✅ Successfully extracted {len(data)} items!")
-            
-            # Save results
-            with open('extracted_data.json', 'w') as f:
-                json.dump(data, f, indent=2)
-            
-            # Show sample results
-            print("\\n📊 Sample results (first 2 items):")
-            for i, item in enumerate(data[:2], 1):
-                print(f"\\nItem {i}:")
-                for key, value in item.items():
-                    print(f"  {key}: {value}")
-        else:
-            print("❌ Extraction failed:", result.error_message)
-
-if __name__ == "__main__":
-    # Step 1: Generate the schema from HTML snippet
-    asyncio.run(generate_schema())
-    
-    # Step 2: Test extraction on the live webpage
-    # Uncomment the line below to test extraction:
-    # asyncio.run(test_extraction())
-    
-    print("\\n🎯 Next steps:")
-    print("1. Review the generated schema in 'generated_schema.json'")
-    print("2. Uncomment the test_extraction() line to test on the live site")
-    print("3. Use the schema in your Crawl4AI projects!")
-`;
-
-    return code;
-  }
-
-  showCodeModal(code) {
-    // Create modal
-    this.codeModal = document.createElement('div');
-    this.codeModal.className = 'c4ai-code-modal';
-    this.codeModal.innerHTML = `
-      <div class="c4ai-code-modal-content">
-        <div class="c4ai-code-modal-header">
-          <h2>Generated Python Code</h2>
-          <button class="c4ai-close-modal" id="c4ai-close-modal">✕</button>
-        </div>
-        <div class="c4ai-code-modal-body">
-          <pre class="c4ai-code-block"><code class="language-python">${this.escapeHtml(code)}</code></pre>
-        </div>
-        <div class="c4ai-code-modal-footer">
-          <button class="c4ai-action-btn c4ai-cloud-btn" id="c4ai-run-cloud" disabled>
-            <span>☁️</span> Run on C4AI Cloud (Coming Soon)
-          </button>
-          <button class="c4ai-action-btn c4ai-download-btn" id="c4ai-download-code">
-            <span>⬇</span> Download Code
-          </button>
-          <button class="c4ai-action-btn c4ai-copy-btn" id="c4ai-copy-code">
-            <span>📋</span> Copy to Clipboard
-          </button>
-        </div>
-      </div>
-    `;
-    
-    document.body.appendChild(this.codeModal);
-    
-    // Add event listeners
-    document.getElementById('c4ai-close-modal').addEventListener('click', () => {
-      this.codeModal.remove();
-      this.codeModal = null;
-      // Don't stop the capture session
-    });
-    
-    document.getElementById('c4ai-download-code').addEventListener('click', () => {
-      chrome.runtime.sendMessage({
-        action: 'downloadCode',
-        code: code,
-        filename: `crawl4ai_schema_${Date.now()}.py`
-      }, (response) => {
-        if (response && response.success) {
-          const btn = document.getElementById('c4ai-download-code');
-          const originalHTML = btn.innerHTML;
-          btn.innerHTML = '<span>✓</span> Downloaded!';
-          setTimeout(() => {
-            btn.innerHTML = originalHTML;
-          }, 2000);
-        } else {
-          console.error('Download failed:', response?.error);
-          alert('Download failed. Please check your browser settings.');
-        }
-      });
-    });
-    
-    document.getElementById('c4ai-copy-code').addEventListener('click', () => {
-      navigator.clipboard.writeText(code).then(() => {
-        const btn = document.getElementById('c4ai-copy-code');
-        btn.innerHTML = '<span>✓</span> Copied!';
-        setTimeout(() => {
-          btn.innerHTML = '<span>📋</span> Copy to Clipboard';
-        }, 2000);
-      });
-    });
-    
-    // Apply syntax highlighting if possible
-    this.applySyntaxHighlighting();
-  }
-
-  escapeHtml(text) {
-    const div = document.createElement('div');
-    div.textContent = text;
-    return div.innerHTML;
-  }
-
-  applySyntaxHighlighting() {
-    // Simple Python syntax highlighting - using a different approach
-    const codeElement = this.codeModal.querySelector('.language-python');
-    const code = codeElement.textContent;
-    
-    // Split by lines to handle line-by-line
-    const lines = code.split('\n');
-    const highlightedLines = lines.map(line => {
-      let highlightedLine = this.escapeHtml(line);
-      
-      // Skip if line is empty
-      if (!highlightedLine.trim()) return highlightedLine;
-      
-      // Comments (lines starting with #)
-      if (highlightedLine.trim().startsWith('#')) {
-        return `<span class="c4ai-comment">${highlightedLine}</span>`;
-      }
-      
-      // Triple quoted strings
-      if (highlightedLine.includes('"""')) {
-        highlightedLine = highlightedLine.replace(/(""".*?""")/g, '<span class="c4ai-string">$1</span>');
-      }
-      
-      // Regular strings - single and double quotes
-      highlightedLine = highlightedLine.replace(/(["'])([^"']*)\1/g, '<span class="c4ai-string">$1$2$1</span>');
-      
-      // Keywords - only highlight if not inside a string
-      const keywords = ['import', 'from', 'async', 'def', 'await', 'try', 'except', 'with', 'as', 'for', 'if', 'else', 'elif', 'return', 'print', 'open', 'and', 'or', 'not', 'in', 'is', 'class', 'self', 'None', 'True', 'False', '__name__', '__main__'];
-      
-      keywords.forEach(keyword => {
-        // Use word boundaries and lookahead/lookbehind to ensure we're not in a string
-        const regex = new RegExp(`\\b(${keyword})\\b(?![^<]*</span>)`, 'g');
-        highlightedLine = highlightedLine.replace(regex, '<span class="c4ai-keyword">$1</span>');
-      });
-      
-      // Functions (word followed by parenthesis)
-      highlightedLine = highlightedLine.replace(/\b([a-zA-Z_]\w*)\s*\(/g, '<span class="c4ai-function">$1</span>(');
-      
-      return highlightedLine;
-    });
-    
-    codeElement.innerHTML = highlightedLines.join('\n');
-  }
-}
-
-// Script Builder for recording user actions
-class ScriptBuilder {
-  constructor() {
-    this.mode = 'recording';
-    this.isRecording = false;
-    this.isPaused = false;
-    this.rawEvents = [];
-    this.groupedEvents = [];
-    this.startTime = 0;
-    this.lastEventTime = 0;
-    this.toolbar = null;
-    this.keyBuffer = [];
-    this.keyBufferTimeout = null;
-    this.scrollAccumulator = { direction: null, amount: 0, startTime: 0 };
-    this.processedEventIndices = new Set();
-    this.outputFormat = 'js'; // 'js' or 'c4a'
-    this.timelineModal = null;
-    this.recordingIndicator = null;
-  }
-
-  start() {
-    // Don't start recording immediately, just show the toolbar
-    this.createToolbar();
-    this.showStartScreen();
-  }
-  
-  showStartScreen() {
-    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
-    toolbarContent.innerHTML = `
-      <div class="c4ai-toolbar-status">
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Script Builder</span>
-          <span class="c4ai-status-value">Ready</span>
-        </div>
-      </div>
-      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
-        Click "Start Recording" to begin capturing your actions. <span style="color: #ff3c74; font-size: 11px;">(Alpha - May contain bugs)</span>
-      </div>
-      <div class="c4ai-toolbar-actions c4ai-start-actions">
-        <button id="c4ai-start-recording" class="c4ai-action-btn c4ai-primary-btn">
-          <span>🔴</span> Start Recording
-        </button>
-        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
-          <span>📂</span> Saved Flows
-        </button>
-      </div>
-    `;
-    
-    // Add event listeners
-    document.getElementById('c4ai-start-recording').addEventListener('click', () => this.startRecording());
-    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
-  }
-  
-  startRecording() {
-    this.isRecording = true;
-    this.startTime = Date.now();
-    this.lastEventTime = this.startTime;
-    this.createRecordingIndicator();
-    this.injectEventCapture();
-    
-    // Update toolbar to show recording controls
-    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
-    toolbarContent.innerHTML = `
-      <div class="c4ai-toolbar-status">
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Actions:</span>
-          <span class="c4ai-status-value" id="c4ai-action-count">0</span>
-        </div>
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Format:</span>
-          <select id="c4ai-output-format" class="c4ai-format-select">
-            <option value="js">JavaScript</option>
-            <option value="c4a">C4A Script</option>
-          </select>
-        </div>
-      </div>
-      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
-        Recording your actions... Click, type, and scroll to build your script.
-      </div>
-      <div class="c4ai-toolbar-actions c4ai-recording-actions">
-        <button id="c4ai-add-wait" class="c4ai-action-btn c4ai-secondary-btn">
-          <span class="c4ai-wait-icon">⏱</span> Add Wait
-        </button>
-        <button id="c4ai-pause-recording" class="c4ai-action-btn c4ai-secondary-btn">
-          <span class="c4ai-pause-icon">⏸</span> Pause
-        </button>
-        <button id="c4ai-stop-generate" class="c4ai-action-btn c4ai-primary-btn">
-          <span class="c4ai-generate-icon">⚡</span> Stop & Generate
-        </button>
-        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
-          <span>📂</span> Saved Flows
-        </button>
-      </div>
-    `;
-    
-    // Re-add event listeners
-    document.getElementById('c4ai-pause-recording').addEventListener('click', () => this.togglePause());
-    document.getElementById('c4ai-stop-generate').addEventListener('click', () => this.stopAndGenerate());
-    document.getElementById('c4ai-add-wait').addEventListener('click', () => this.showWaitDialog());
-    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
-    document.getElementById('c4ai-output-format').addEventListener('change', (e) => {
-      this.outputFormat = e.target.value;
-    });
-    
-    this.updateToolbar();
-  }
-
-  stop() {
-    this.isRecording = false;
-    this.removeEventCapture();
-    this.toolbar?.remove();
-    this.recordingIndicator?.remove();
-    this.timelineModal?.remove();
-    this.rawEvents = [];
-    this.groupedEvents = [];
-    this.processedEventIndices.clear();
-  }
-
-  createRecordingIndicator() {
-    this.recordingIndicator = document.createElement('div');
-    this.recordingIndicator.className = 'c4ai-recording-indicator';
-    this.recordingIndicator.innerHTML = `
-      <div class="c4ai-recording-dot"></div>
-      <span>Recording</span>
-    `;
-    document.body.appendChild(this.recordingIndicator);
-  }
-
-  createToolbar() {
-    this.toolbar = document.createElement('div');
-    this.toolbar.className = 'c4ai-script-toolbar';
-    this.toolbar.innerHTML = `
-      <div class="c4ai-toolbar-titlebar">
-        <div class="c4ai-titlebar-dots">
-          <button class="c4ai-dot c4ai-dot-close" id="c4ai-script-close"></button>
-          <button class="c4ai-dot c4ai-dot-minimize"></button>
-          <button class="c4ai-dot c4ai-dot-maximize"></button>
-        </div>
-        <img src="${chrome.runtime.getURL('icons/icon-16.png')}" class="c4ai-titlebar-icon" alt="Crawl4AI">
-        <div class="c4ai-titlebar-title">Crawl4AI Script Builder <span style="color: #ff3c74; font-size: 10px; margin-left: 8px;">(ALPHA)</span></div>
-      </div>
-      <div class="c4ai-toolbar-content" id="c4ai-toolbar-content">
-        <!-- Content will be dynamically updated -->
-      </div>
-    `;
-    document.body.appendChild(this.toolbar);
-
-    // Add close button listener
-    document.getElementById('c4ai-script-close').addEventListener('click', () => this.stop());
-
-    // Make toolbar draggable
-    this.makeDraggable(this.toolbar);
-  }
-
-  showStartScreen() {
-    const content = document.getElementById('c4ai-toolbar-content');
-    content.innerHTML = `
-      <div class="c4ai-start-screen">
-        <div class="c4ai-welcome-message">
-          <h3>Welcome to Script Builder</h3>
-          <p>Record your actions to create automation scripts. <span style="color: #ff3c74;">Alpha version - may contain bugs</span></p>
-        </div>
-        <div class="c4ai-start-actions">
-          <button id="c4ai-start-recording" class="c4ai-action-btn c4ai-primary-btn">
-            <span>🔴</span> Start Recording
-          </button>
-          <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
-            <span>📂</span> Saved Flows
-          </button>
-        </div>
-      </div>
-    `;
-    
-    // Add event listeners
-    document.getElementById('c4ai-start-recording').addEventListener('click', () => this.startRecording());
-    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
-  }
-
-  showRecordingUI() {
-    const content = document.getElementById('c4ai-toolbar-content');
-    content.innerHTML = `
-      <div class="c4ai-toolbar-status">
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Actions:</span>
-          <span class="c4ai-status-value" id="c4ai-action-count">0</span>
-        </div>
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Format:</span>
-          <select id="c4ai-output-format" class="c4ai-format-select">
-            <option value="js">JavaScript</option>
-            <option value="c4a">C4A Script</option>
-          </select>
-        </div>
-      </div>
-      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
-        Recording your actions... Click, type, and scroll to build your script.
-      </div>
-      <div class="c4ai-toolbar-actions">
-        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
-          <span>📂</span> Saved Flows
-        </button>
-        <button id="c4ai-add-wait" class="c4ai-action-btn c4ai-secondary-btn">
-          <span class="c4ai-wait-icon">⏱</span> Add Wait
-        </button>
-        <button id="c4ai-pause-recording" class="c4ai-action-btn c4ai-pause-btn">
-          <span class="c4ai-pause-icon">⏸</span> Pause
-        </button>
-        <button id="c4ai-stop-generate" class="c4ai-action-btn c4ai-primary-btn">
-          <span class="c4ai-generate-icon">⚡</span> Stop & Generate
-        </button>
-      </div>
-    `;
-    
-    // Re-add event listeners
-    document.getElementById('c4ai-pause-recording').addEventListener('click', () => this.togglePause());
-    document.getElementById('c4ai-stop-generate').addEventListener('click', () => this.stopAndGenerate());
-    document.getElementById('c4ai-add-wait').addEventListener('click', () => this.showWaitDialog());
-    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
-    document.getElementById('c4ai-output-format').addEventListener('change', (e) => {
-      this.outputFormat = e.target.value;
-    });
-  }
-
-  makeDraggable(element) {
-    let isDragging = false;
-    let startX, startY, initialX, initialY;
-    
-    const titlebar = element.querySelector('.c4ai-toolbar-titlebar');
-    
-    titlebar.addEventListener('mousedown', (e) => {
-      if (e.target.classList.contains('c4ai-dot')) return;
-      
-      isDragging = true;
-      startX = e.clientX;
-      startY = e.clientY;
-      
-      const rect = element.getBoundingClientRect();
-      initialX = rect.left;
-      initialY = rect.top;
-      
-      element.style.transition = 'none';
-      titlebar.style.cursor = 'grabbing';
-    });
-    
-    document.addEventListener('mousemove', (e) => {
-      if (!isDragging) return;
-      
-      const deltaX = e.clientX - startX;
-      const deltaY = e.clientY - startY;
-      
-      element.style.left = `${initialX + deltaX}px`;
-      element.style.top = `${initialY + deltaY}px`;
-      element.style.right = 'auto';
-    });
-    
-    document.addEventListener('mouseup', () => {
-      if (isDragging) {
-        isDragging = false;
-        element.style.transition = '';
-        titlebar.style.cursor = 'grab';
-      }
-    });
-  }
-
-  makeDraggableByHeader(element) {
-    let isDragging = false;
-    let startX, startY, initialX, initialY;
-    
-    const header = element.querySelector('.c4ai-debugger-header');
-    if (!header) return;
-    
-    header.addEventListener('mousedown', (e) => {
-      // Don't drag if clicking on close button
-      if (e.target.id === 'c4ai-close-debugger' || e.target.closest('#c4ai-close-debugger')) return;
-      
-      isDragging = true;
-      startX = e.clientX;
-      startY = e.clientY;
-      
-      const rect = element.getBoundingClientRect();
-      initialX = rect.left;
-      initialY = rect.top;
-      
-      element.style.transition = 'none';
-      header.style.cursor = 'grabbing';
-    });
-    
-    document.addEventListener('mousemove', (e) => {
-      if (!isDragging) return;
-      
-      const deltaX = e.clientX - startX;
-      const deltaY = e.clientY - startY;
-      
-      element.style.left = `${initialX + deltaX}px`;
-      element.style.top = `${initialY + deltaY}px`;
-      element.style.right = 'auto';
-    });
-    
-    document.addEventListener('mouseup', () => {
-      if (isDragging) {
-        isDragging = false;
-        element.style.transition = '';
-        header.style.cursor = 'grab';
-      }
-    });
-  }
-
-  togglePause() {
-    this.isPaused = !this.isPaused;
-    const pauseBtn = document.getElementById('c4ai-pause-recording');
-    const recordingIndicator = document.querySelector('.c4ai-recording-indicator');
-    
-    if (this.isPaused) {
-      pauseBtn.innerHTML = '<span class="c4ai-play-icon">▶</span> Resume';
-      pauseBtn.classList.add('c4ai-paused');
-      recordingIndicator.classList.add('c4ai-paused');
-      document.getElementById('c4ai-script-hint').textContent = 'Recording paused. Click Resume to continue.';
-    } else {
-      pauseBtn.innerHTML = '<span class="c4ai-pause-icon">⏸</span> Pause';
-      pauseBtn.classList.remove('c4ai-paused');
-      recordingIndicator.classList.remove('c4ai-paused');
-      document.getElementById('c4ai-script-hint').textContent = 'Recording your actions... Click, type, and scroll to build your script.';
-    }
-  }
-
-  showWaitDialog() {
-    const dialog = document.createElement('div');
-    dialog.className = 'c4ai-wait-dialog';
-    dialog.innerHTML = `
-      <div class="c4ai-wait-dialog-content">
-        <h4>Add Wait Command</h4>
-        <div class="c4ai-wait-options">
-          <label class="c4ai-wait-option">
-            <input type="radio" name="wait-type" value="time" checked>
-            <span>Wait for time (seconds)</span>
-          </label>
-          <label class="c4ai-wait-option">
-            <input type="radio" name="wait-type" value="selector">
-            <span>Wait for element</span>
-          </label>
-        </div>
-        <div class="c4ai-wait-input" id="c4ai-wait-time-input">
-          <input type="number" id="c4ai-wait-seconds" min="0.5" step="0.5" value="2" placeholder="Seconds">
-        </div>
-        <div class="c4ai-wait-input c4ai-hidden" id="c4ai-wait-selector-input">
-          <p>Click on an element to wait for</p>
-        </div>
-        <div class="c4ai-wait-actions">
-          <button id="c4ai-wait-cancel" class="c4ai-action-btn">Cancel</button>
-          <button id="c4ai-wait-add" class="c4ai-action-btn c4ai-primary">Add Wait</button>
-        </div>
-      </div>
-    `;
-    document.body.appendChild(dialog);
-
-    // Handle radio button changes
-    dialog.querySelectorAll('input[name="wait-type"]').forEach(radio => {
-      radio.addEventListener('change', (e) => {
-        const timeInput = document.getElementById('c4ai-wait-time-input');
-        const selectorInput = document.getElementById('c4ai-wait-selector-input');
-        
-        if (e.target.value === 'time') {
-          timeInput.classList.remove('c4ai-hidden');
-          selectorInput.classList.add('c4ai-hidden');
-        } else {
-          timeInput.classList.add('c4ai-hidden');
-          selectorInput.classList.remove('c4ai-hidden');
-          this.waitForElementSelection(dialog);
-        }
-      });
-    });
-
-    // Handle buttons
-    document.getElementById('c4ai-wait-cancel').addEventListener('click', () => {
-      dialog.remove();
-    });
-
-    document.getElementById('c4ai-wait-add').addEventListener('click', () => {
-      const waitType = dialog.querySelector('input[name="wait-type"]:checked').value;
-      
-      if (waitType === 'time') {
-        const seconds = parseFloat(document.getElementById('c4ai-wait-seconds').value) || 2;
-        this.addWaitCommand('time', seconds);
-        dialog.remove();
-      }
-    });
-  }
-
-  waitForElementSelection(dialog) {
-    this.isPaused = true; // Pause recording during element selection
-    
-    const highlightBox = document.createElement('div');
-    highlightBox.className = 'c4ai-highlight-box c4ai-wait-mode';
-    document.body.appendChild(highlightBox);
-
-    const handleMouseMove = (e) => {
-      const element = document.elementFromPoint(e.clientX, e.clientY);
-      if (element && !this.isOurElement(element)) {
-        this.highlightElement(element, highlightBox);
-      }
-    };
-
-    const handleClick = (e) => {
-      const element = e.target;
-      if (this.isOurElement(element)) return;
-
-      e.preventDefault();
-      e.stopPropagation();
-
-      const selector = this.getElementSelector(element);
-      this.addWaitCommand('selector', selector);
-
-      // Cleanup
-      document.removeEventListener('mousemove', handleMouseMove, true);
-      document.removeEventListener('click', handleClick, true);
-      highlightBox.remove();
-      dialog.remove();
-      this.isPaused = false;
-    };
-
-    document.addEventListener('mousemove', handleMouseMove, true);
-    document.addEventListener('click', handleClick, true);
-  }
-
-  addWaitCommand(type, value) {
-    const waitEvent = {
-      type: 'WAIT',
-      waitType: type,
-      value: value,
-      time: (Date.now() - this.startTime) / 1000
-    };
-
-    this.groupedEvents.push(waitEvent);
-    this.updateToolbar();
-  }
-
-  highlightElement(element, highlightBox) {
-    if (!highlightBox) {
-      highlightBox = document.querySelector('.c4ai-highlight-box');
-    }
-    
-    const rect = element.getBoundingClientRect();
-    highlightBox.style.display = 'block';
-    highlightBox.style.top = `${rect.top + window.scrollY}px`;
-    highlightBox.style.left = `${rect.left + window.scrollX}px`;
-    highlightBox.style.width = `${rect.width}px`;
-    highlightBox.style.height = `${rect.height}px`;
-  }
-
-  isOurElement(element) {
-    return element.classList.contains('c4ai-script-toolbar') ||
-           element.closest('.c4ai-script-toolbar') ||
-           element.classList.contains('c4ai-wait-dialog') ||
-           element.closest('.c4ai-wait-dialog') ||
-           element.classList.contains('c4ai-highlight-box') ||
-           element.classList.contains('c4ai-timeline-modal') ||
-           element.closest('.c4ai-timeline-modal');
-  }
-
-  getElementSelector(element) {
-    // Priority: ID > unique class > tag with position
-    if (element.id) {
-      return `#${element.id}`;
-    }
-
-    if (element.className && typeof element.className === 'string') {
-      const classes = element.className.split(' ').filter(c => c && !c.startsWith('c4ai-'));
-      if (classes.length > 0) {
-        const selector = `.${classes[0]}`;
-        if (document.querySelectorAll(selector).length === 1) {
-          return selector;
-        }
-      }
-    }
-
-    // Build a path selector
-    const path = [];
-    let current = element;
-
-    while (current && current !== document.body) {
-      const tagName = current.tagName.toLowerCase();
-      const parent = current.parentElement;
-      
-      if (parent) {
-        const siblings = Array.from(parent.children);
-        const index = siblings.indexOf(current) + 1;
-        
-        if (siblings.filter(s => s.tagName === current.tagName).length > 1) {
-          path.unshift(`${tagName}:nth-child(${index})`);
-        } else {
-          path.unshift(tagName);
-        }
-      } else {
-        path.unshift(tagName);
-      }
-      
-      current = parent;
-    }
-
-    return path.join(' > ');
-  }
-
-  injectEventCapture() {
-    // Use content script context instead of injecting inline script
-    if (window.__c4aiScriptRecordingActive) return;
-    window.__c4aiScriptRecordingActive = true;
-    
-    const captureEvent = (type, event) => {
-      // Skip events from our own UI
-      if (event.target.classList && 
-          (event.target.classList.contains('c4ai-script-toolbar') ||
-           event.target.closest('.c4ai-script-toolbar') ||
-           event.target.classList.contains('c4ai-wait-dialog') ||
-           event.target.closest('.c4ai-wait-dialog') ||
-           event.target.classList.contains('c4ai-timeline-modal') ||
-           event.target.closest('.c4ai-timeline-modal'))) {
-        return;
-      }
-      
-      const data = {
-        type: type,
-        timestamp: Date.now(),
-        targetTag: event.target.tagName,
-        targetId: event.target.id,
-        targetClass: event.target.className,
-        targetSelector: this.getElementSelector(event.target),
-        targetType: event.target.type
-      };
-      
-      switch(type) {
-        case 'click':
-        case 'dblclick':
-        case 'contextmenu':
-          data.x = event.clientX;
-          data.y = event.clientY;
-          break;
-        case 'keydown':
-        case 'keyup':
-          data.key = event.key;
-          data.code = event.code;
-          data.ctrlKey = event.ctrlKey;
-          data.shiftKey = event.shiftKey;
-          data.altKey = event.altKey;
-          data.metaKey = event.metaKey; // Add meta key support
-          break;
-        case 'input':
-        case 'change':
-          // Check if it's a contenteditable element
-          if (event.target.contentEditable === 'true' || 
-              event.target.getAttribute('contenteditable') === 'true') {
-            data.value = event.target.textContent || event.target.innerText;
-            data.isContentEditable = true;
-          } else {
-            data.value = event.target.value;
-          }
-          data.inputType = event.inputType;
-          if (event.target.type === 'checkbox' || event.target.type === 'radio') {
-            data.checked = event.target.checked;
-          }
-          if (event.target.tagName === 'SELECT') {
-            data.selectedText = event.target.options[event.target.selectedIndex]?.text || '';
-          }
-          break;
-        case 'scroll':
-          // Capture which element was scrolled
-          if (event.target === window || event.target === document || event.target === document.body) {
-            data.isWindowScroll = true;
-            data.scrollTop = window.scrollY;
-            data.scrollLeft = window.scrollX;
-          } else {
-            data.isWindowScroll = false;
-            data.scrollTop = event.target.scrollTop;
-            data.scrollLeft = event.target.scrollLeft;
-            data.scrollHeight = event.target.scrollHeight;
-            data.scrollWidth = event.target.scrollWidth;
-          }
-          break;
-        case 'wheel':
-          data.deltaY = event.deltaY || 0;
-          data.deltaX = event.deltaX || 0;
-          // Check if the target element is scrollable
-          const isScrollable = (el) => {
-            const hasScrollableContent = el.scrollHeight > el.clientHeight || el.scrollWidth > el.clientWidth;
-            const overflowY = window.getComputedStyle(el).overflowY;
-            const overflowX = window.getComputedStyle(el).overflowX;
-            const canScroll = (overflowY !== 'visible' && overflowY !== 'hidden') || 
-                              (overflowX !== 'visible' && overflowX !== 'hidden');
-            return hasScrollableContent && canScroll;
-          };
-          
-          // Find the actual scrollable element
-          let scrollTarget = event.target;
-          while (scrollTarget && scrollTarget !== document.body) {
-            if (isScrollable(scrollTarget)) {
-              break;
-            }
-            scrollTarget = scrollTarget.parentElement;
-          }
-          
-          if (scrollTarget && scrollTarget !== document.body && isScrollable(scrollTarget)) {
-            data.isWindowScroll = false;
-            data.scrollTop = scrollTarget.scrollTop;
-            data.scrollLeft = scrollTarget.scrollLeft;
-            data.targetSelector = this.getElementSelector(scrollTarget);
-          } else {
-            data.isWindowScroll = true;
-            data.scrollTop = window.scrollY;
-            data.scrollLeft = window.scrollX;
-          }
-          break;
-      }
-      
-      // Handle the event directly instead of using postMessage
-      if (this.isRecording && !this.isPaused) {
-        this.handleRecordedEvent(data);
-      }
-    };
-    
-    // Store bound event handlers for later removal
-    this.eventHandlers = {
-      click: (e) => captureEvent('click', e),
-      dblclick: (e) => captureEvent('dblclick', e),
-      contextmenu: (e) => captureEvent('contextmenu', e),
-      keydown: (e) => captureEvent('keydown', e),
-      input: (e) => captureEvent('input', e),
-      change: (e) => captureEvent('change', e),
-      scroll: (e) => captureEvent('scroll', e)
-    };
-    
-    // Add event listeners
-    Object.entries(this.eventHandlers).forEach(([eventType, handler]) => {
-      document.addEventListener(eventType, handler, true);
-    });
-    
-    // Cleanup on page unload
-    window.addEventListener('beforeunload', () => {
-      this.removeEventCapture();
-    });
-  }
-
-  removeEventCapture() {
-    window.__c4aiScriptRecordingActive = false;
-    
-    // Remove event listeners if they exist
-    if (this.eventHandlers) {
-      Object.entries(this.eventHandlers).forEach(([eventType, handler]) => {
-        document.removeEventListener(eventType, handler, true);
-      });
-      this.eventHandlers = null;
-    }
-  }
-
-  handleRecordedEvent(event) {
-    // Skip events from our own UI
-    if (event.targetClass && typeof event.targetClass === 'string' && event.targetClass.includes('c4ai-')) return;
-
-    // Add time since start
-    event.timeSinceStart = (event.timestamp - this.startTime) / 1000;
-    
-    // Store raw event
-    this.rawEvents.push(event);
-
-    // Process event based on type
-    if (event.type === 'keydown') {
-      // Handle keyboard shortcuts (with modifier keys)
-      if (event.metaKey || event.ctrlKey || event.altKey) {
-        // Flush any pending keystrokes
-        if (this.keyBuffer.length > 0) {
-          this.flushKeyBuffer();
-        }
-        
-        // Create keyboard shortcut command
-        const command = this.eventToCommand(event);
-        if (command) {
-          this.groupedEvents.push(command);
-          this.updateToolbar();
-        }
-      } else if (this.shouldGroupKeystrokes(event)) {
-        // Regular typing
-        this.keyBuffer.push(event);
-        
-        if (this.keyBufferTimeout) {
-          clearTimeout(this.keyBufferTimeout);
-        }
-        
-        this.keyBufferTimeout = setTimeout(() => {
-          this.flushKeyBuffer();
-          this.updateToolbar();
-        }, 500);
-      } else if (event.key === 'Delete' || event.key === 'Backspace') {
-        // Handle Delete and Backspace as individual commands when not part of typing
-        if (this.keyBuffer.length > 0) {
-          this.flushKeyBuffer();
-        }
-        
-        const command = this.eventToCommand(event);
-        if (command) {
-          this.groupedEvents.push(command);
-          this.updateToolbar();
-        }
-      }
-    } else if (event.type === 'scroll' || event.type === 'wheel') {
-      this.handleScrollEvent(event);
-    } else if (event.type === 'click' || event.type === 'dblclick' || event.type === 'contextmenu') {
-      // Flush any pending keystrokes before click
-      if (this.keyBuffer.length > 0) {
-        this.flushKeyBuffer();
-      }
-      
-      const command = this.eventToCommand(event);
-      if (command) {
-        this.groupedEvents.push(command);
-        this.updateToolbar();
-      }
-    } else if (event.type === 'change') {
-      // Handle select, checkbox, radio changes
-      const tagName = event.targetTag?.toLowerCase();
-      if (tagName === 'select' || 
-          (tagName === 'input' && (event.targetType === 'checkbox' || event.targetType === 'radio'))) {
-        const command = this.eventToCommand(event);
-        if (command) {
-          this.groupedEvents.push(command);
-          this.updateToolbar();
-        }
-      }
-    }
-  }
-
-  shouldGroupKeystrokes(event) {
-    // Don't group if modifier keys are pressed (except shift for capitals)
-    if (event.metaKey || event.ctrlKey || event.altKey) {
-      return false;
-    }
-    
-    return event.key && (
-      event.key.length === 1 ||
-      event.key === ' ' ||
-      event.key === 'Enter' ||
-      event.key === 'Tab' ||
-      event.key === 'Backspace' ||
-      event.key === 'Delete'
-    );
-  }
-
-  flushKeyBuffer() {
-    if (this.keyBuffer.length === 0) return;
-    
-    const text = this.keyBuffer.map(e => {
-      switch(e.key) {
-        case ' ': return ' ';
-        case 'Enter': return '\\n';
-        case 'Tab': return '\\t';
-        case 'Backspace': return '';
-        case 'Delete': return '';
-        default: return e.key;
-      }
-    }).join('');
-    
-    if (text.length === 0) {
-      this.keyBuffer = [];
-      return;
-    }
-    
-    const firstEvent = this.keyBuffer[0];
-    const lastEvent = this.keyBuffer[this.keyBuffer.length - 1];
-    
-    // Check if this is a SET command pattern
-    const firstKeystrokeIndex = this.rawEvents.indexOf(firstEvent);
-    let commandType = 'TYPE';
-    
-    if (firstKeystrokeIndex > 0) {
-      const prevEvent = this.rawEvents[firstKeystrokeIndex - 1];
-      
-      // Check for click before typing
-      if (prevEvent && prevEvent.type === 'click' && 
-          prevEvent.targetSelector === firstEvent.targetSelector) {
-        commandType = 'SET';
-      }
-      
-      // Check for Cmd+A or Ctrl+A (select all) before typing
-      if (prevEvent && prevEvent.type === 'keydown' && 
-          prevEvent.key === 'a' && (prevEvent.metaKey || prevEvent.ctrlKey) &&
-          prevEvent.targetSelector === firstEvent.targetSelector) {
-        commandType = 'SET';
-      }
-    }
-    
-    // Also check within the last few events for select-all pattern
-    if (commandType === 'TYPE' && firstKeystrokeIndex > 1) {
-      // Look back up to 3 events for select-all
-      for (let i = Math.max(0, firstKeystrokeIndex - 3); i < firstKeystrokeIndex; i++) {
-        const event = this.rawEvents[i];
-        if (event && event.type === 'keydown' && 
-            event.key === 'a' && (event.metaKey || event.ctrlKey) &&
-            event.targetSelector === firstEvent.targetSelector) {
-          commandType = 'SET';
-          break;
-        }
-      }
-    }
-    
-    this.groupedEvents.push({
-      type: commandType,
-      selector: firstEvent.targetSelector,
-      value: text,
-      time: firstEvent.timeSinceStart
-    });
-    
-    this.keyBuffer = [];
-  }
-
-  handleScrollEvent(event) {
-    const direction = event.deltaY > 0 ? 'DOWN' : 'UP';
-    const amount = Math.abs(event.deltaY);
-    
-    // Remove previous scroll in same direction within 0.5s for the same element
-    this.groupedEvents = this.groupedEvents.filter(e => 
-      !(e.type === 'SCROLL' && 
-        e.direction === direction && 
-        e.selector === (event.targetSelector || 'window') &&
-        parseFloat(e.time) > parseFloat(event.timeSinceStart) - 0.5)
-    );
-    
-    this.groupedEvents.push({
-      type: 'SCROLL',
-      direction: direction,
-      amount: amount,
-      selector: event.targetSelector || 'window',
-      isWindowScroll: event.isWindowScroll !== false,
-      time: event.timeSinceStart
-    });
-    
-    this.updateToolbar();
-  }
-
-  eventToCommand(event) {
-    switch (event.type) {
-      case 'click':
-        return {
-          type: 'CLICK',
-          selector: event.targetSelector,
-          time: event.timeSinceStart
-        };
-      case 'dblclick':
-        return {
-          type: 'DOUBLE_CLICK',
-          selector: event.targetSelector,
-          time: event.timeSinceStart
-        };
-      case 'contextmenu':
-        return {
-          type: 'RIGHT_CLICK',
-          selector: event.targetSelector,
-          time: event.timeSinceStart
-        };
-      case 'keydown':
-        // Handle keyboard shortcuts
-        if (event.metaKey || event.ctrlKey || event.altKey) {
-          return {
-            type: 'KEYBOARD_SHORTCUT',
-            key: event.key,
-            code: event.code,
-            metaKey: event.metaKey,
-            ctrlKey: event.ctrlKey,
-            altKey: event.altKey,
-            shiftKey: event.shiftKey,
-            time: event.timeSinceStart
-          };
-        } else if (event.key === 'Delete' || event.key === 'Backspace') {
-          // Handle Delete/Backspace as special keys
-          return {
-            type: 'KEY_PRESS',
-            key: event.key,
-            time: event.timeSinceStart
-          };
-        }
-        break;
-      case 'change':
-        if (event.targetTag === 'SELECT') {
-          return {
-            type: 'SET',
-            selector: event.targetSelector,
-            value: event.value,
-            time: event.timeSinceStart
-          };
-        } else if (event.targetType === 'checkbox' || event.targetType === 'radio') {
-          return {
-            type: 'CLICK',
-            selector: event.targetSelector,
-            time: event.timeSinceStart
-          };
-        }
-        break;
-    }
-    return null;
-  }
-
-  updateToolbar() {
-    const actionCount = document.getElementById('c4ai-action-count');
-    if (actionCount) {
-      actionCount.textContent = this.groupedEvents.length;
-    }
-  }
-
-  stopAndGenerate() {
-    // Flush any pending events
-    if (this.keyBufferTimeout) {
-      clearTimeout(this.keyBufferTimeout);
-      this.flushKeyBuffer();
-    }
-
-    this.isRecording = false;
-    this.recordingIndicator?.remove();
-    this.showRecordingSummary();
-  }
-
-  showRecordingSummary() {
-    // Update toolbar to show summary
-    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
-    
-    toolbarContent.innerHTML = `
-      <div class="c4ai-toolbar-status">
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Recording Complete</span>
-          <span class="c4ai-status-value">${this.groupedEvents.length} actions</span>
-        </div>
-      </div>
-      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
-        Recording stopped. You can replay, save, or generate code.
-      </div>
-      <div class="c4ai-toolbar-actions c4ai-summary-actions">
-        <button id="c4ai-replay" class="c4ai-action-btn c4ai-replay-btn">
-          <span>▶</span> Replay
-        </button>
-        <button id="c4ai-save-flow" class="c4ai-action-btn c4ai-save-btn">
-          <span>💾</span> Save Flow
-        </button>
-        <button id="c4ai-show-timeline" class="c4ai-action-btn c4ai-timeline-btn">
-          <span>📋</span> Review & Generate
-        </button>
-        <button id="c4ai-record-again" class="c4ai-action-btn c4ai-record-btn">
-          <span>🔄</span> Record Again
-        </button>
-      </div>
-    `;
-
-    // Add event listeners
-    document.getElementById('c4ai-replay')?.addEventListener('click', () => this.replayRecording());
-    document.getElementById('c4ai-save-flow')?.addEventListener('click', () => this.saveFlow());
-    document.getElementById('c4ai-show-timeline')?.addEventListener('click', () => this.showTimeline());
-    document.getElementById('c4ai-record-again')?.addEventListener('click', () => this.recordAgain());
-  }
-
-  showTimeline() {
-    this.timelineModal = document.createElement('div');
-    this.timelineModal.className = 'c4ai-timeline-modal';
-    this.timelineModal.innerHTML = `
-      <div class="c4ai-timeline-content">
-        <div class="c4ai-timeline-header">
-          <h2>Review Your Actions</h2>
-          <button class="c4ai-close-modal" id="c4ai-close-timeline">✕</button>
-        </div>
-        <div class="c4ai-timeline-body">
-          <div class="c4ai-timeline-controls">
-            <button class="c4ai-action-btn" id="c4ai-select-all">Select All</button>
-            <button class="c4ai-action-btn" id="c4ai-clear-all">Clear All</button>
-          </div>
-          <div class="c4ai-timeline-events" id="c4ai-timeline-events">
-            ${this.renderTimelineEvents()}
-          </div>
-        </div>
-        <div class="c4ai-timeline-footer">
-          <select id="c4ai-final-format" class="c4ai-format-select">
-            <option value="js" ${this.outputFormat === 'js' ? 'selected' : ''}>JavaScript</option>
-            <option value="c4a" ${this.outputFormat === 'c4a' ? 'selected' : ''}>C4A Script</option>
-          </select>
-          <button class="c4ai-action-btn c4ai-download-btn" id="c4ai-download-script">
-            <span>⬇</span> Generate & Download
-          </button>
-        </div>
-      </div>
-    `;
-    document.body.appendChild(this.timelineModal);
-
-    // Event listeners
-    document.getElementById('c4ai-close-timeline').addEventListener('click', () => {
-      this.timelineModal.remove();
-      // Don't stop the toolbar, just close the modal
-    });
-
-    document.getElementById('c4ai-select-all').addEventListener('click', () => {
-      document.querySelectorAll('.c4ai-event-checkbox').forEach(cb => cb.checked = true);
-    });
-
-    document.getElementById('c4ai-clear-all').addEventListener('click', () => {
-      document.querySelectorAll('.c4ai-event-checkbox').forEach(cb => cb.checked = false);
-    });
-
-    document.getElementById('c4ai-download-script').addEventListener('click', () => {
-      this.generateAndDownload();
-    });
-  }
-
-  renderTimelineEvents() {
-    return this.groupedEvents.map((event, index) => {
-      const detail = this.getEventDetail(event);
-      return `
-        <div class="c4ai-timeline-event">
-          <input type="checkbox" class="c4ai-event-checkbox" id="event-${index}" checked>
-          <label for="event-${index}" class="c4ai-event-label">
-            <span class="c4ai-event-time">${event.time.toFixed(1)}s</span>
-            <span class="c4ai-event-type">${event.type.replace(/_/g, ' ')}</span>
-            <span class="c4ai-event-detail" title="${detail.replace(/"/g, '&quot;')}">${detail}</span>
-          </label>
-        </div>
-      `;
-    }).join('');
-  }
-
-  getEventDetail(event) {
-    switch (event.type) {
-      case 'CLICK':
-      case 'DOUBLE_CLICK':
-      case 'RIGHT_CLICK':
-        return event.selector;
-      case 'TYPE':
-      case 'SET':
-        return `${event.selector} = "${event.value.substring(0, 30)}${event.value.length > 30 ? '...' : ''}"`;
-      case 'SCROLL':
-        if (event.isWindowScroll || event.selector === 'window') {
-          return `${event.direction} ${event.amount}px`;
-        } else {
-          return `${event.selector} ${event.direction} ${event.amount}px`;
-        }
-      case 'WAIT':
-        return event.waitType === 'time' ? `${event.value}s` : event.value;
-      case 'KEYBOARD_SHORTCUT':
-        const keys = [];
-        if (event.metaKey) keys.push('Cmd');
-        if (event.ctrlKey) keys.push('Ctrl');
-        if (event.altKey) keys.push('Alt');
-        if (event.shiftKey) keys.push('Shift');
-        keys.push(event.key.toUpperCase());
-        return keys.join('+');
-      case 'KEY_PRESS':
-        return event.key;
-      default:
-        return '';
-    }
-  }
-
-  generateAndDownload() {
-    const format = document.getElementById('c4ai-final-format').value;
-    const selectedEvents = [];
-    
-    document.querySelectorAll('.c4ai-event-checkbox').forEach((cb, index) => {
-      if (cb.checked && this.groupedEvents[index]) {
-        selectedEvents.push(this.groupedEvents[index]);
-      }
-    });
-
-    if (selectedEvents.length === 0) {
-      alert('Please select at least one action');
-      return;
-    }
-
-    const code = this.generateCode(selectedEvents, format);
-    
-    chrome.runtime.sendMessage({
-      action: 'downloadScript',
-      code: code,
-      format: format,
-      filename: `crawl4ai_script_${Date.now()}.py`
-    }, (response) => {
-      if (response && response.success) {
-        this.timelineModal.remove();
-        // Don't stop the toolbar after download
-        // Show success message in toolbar
-        document.getElementById('c4ai-script-hint').textContent = '✅ Script downloaded successfully!';
-      }
-    });
-  }
-
-  generateCode(events, format) {
-    let scriptCode;
-    
-    if (format === 'js') {
-      scriptCode = this.generateJavaScript(events);
-    } else {
-      scriptCode = this.generateC4AScript(events);
-    }
-
-    return this.generatePythonTemplate(scriptCode, format);
-  }
-
-  generateJavaScript(events) {
-    const commands = events.map(event => {
-      switch (event.type) {
-        case 'CLICK':
-          return `document.querySelector('${event.selector}').click();`;
-        case 'DOUBLE_CLICK':
-          return `const el = document.querySelector('${event.selector}');\nconst evt = new MouseEvent('dblclick', {bubbles: true});\nel.dispatchEvent(evt);`;
-        case 'RIGHT_CLICK':
-          return `const el = document.querySelector('${event.selector}');\nconst evt = new MouseEvent('contextmenu', {bubbles: true});\nel.dispatchEvent(evt);`;
-        case 'TYPE':
-          return this.generateTypeCode(event, 'append');
-        case 'SET':
-          return this.generateTypeCode(event, 'set');
-        case 'SCROLL':
-          if (event.isWindowScroll || event.selector === 'window') {
-            return `window.scrollBy(0, ${event.direction === 'DOWN' ? event.amount : -event.amount});`;
-          } else {
-            const scrollAmount = event.direction === 'DOWN' ? event.amount : -event.amount;
-            return `// Scroll element
-const scrollEl = document.querySelector('${event.selector}');
-if (scrollEl) {
-  scrollEl.scrollTop += ${scrollAmount};
-}`;
-          }
-        case 'WAIT':
-          if (event.waitType === 'time') {
-            return `await new Promise(resolve => setTimeout(resolve, ${event.value * 1000}));`;
-          } else {
-            return `// Wait for element: ${event.value}\nawait new Promise((resolve) => {\n  const checkElement = setInterval(() => {\n    if (document.querySelector('${event.value}')) {\n      clearInterval(checkElement);\n      resolve();\n    }\n  }, 100);\n  setTimeout(() => { clearInterval(checkElement); resolve(); }, 5000);\n});`;
-          }
-        case 'KEYBOARD_SHORTCUT':
-          const modifiers = [];
-          if (event.metaKey) modifiers.push('metaKey: true');
-          if (event.ctrlKey) modifiers.push('ctrlKey: true');
-          if (event.altKey) modifiers.push('altKey: true');
-          if (event.shiftKey) modifiers.push('shiftKey: true');
-          return `// Keyboard shortcut: ${this.getEventDetail(event)}\ndocument.dispatchEvent(new KeyboardEvent('keydown', {key: '${event.key}', ${modifiers.join(', ')}}));`;
-        case 'KEY_PRESS':
-          return `// Press ${event.key} key\ndocument.dispatchEvent(new KeyboardEvent('keydown', {key: '${event.key}'}));`;
-        default:
-          return `// Unknown action: ${event.type}`;
-      }
-    });
-
-    return commands.join('\\n');
-  }
-
-  generateTypeCode(event, mode) {
-    const value = event.value.replace(/'/g, "\\'").replace(/\n/g, '\\n');
-    
-    return `// ${mode === 'set' ? 'Set' : 'Type'} text
-const el = document.querySelector('${event.selector}');
-el.focus();
-el.click();
-
-// Check if contenteditable
-if (el.contentEditable === 'true' || el.getAttribute('contenteditable') === 'true') {
-  // Handle contenteditable element
-  ${mode === 'set' ? 'el.textContent = "";' : ''}
-  const selection = window.getSelection();
-  const range = document.createRange();
-  range.selectNodeContents(el);
-  range.collapse(${mode === 'set' ? 'true' : 'false'});
-  selection.removeAllRanges();
-  selection.addRange(range);
-  
-  // Type each character
-  '${value}'.split('').forEach(char => {
-    document.execCommand('insertText', false, char);
-    el.dispatchEvent(new InputEvent('input', {bubbles: true, inputType: 'insertText', data: char}));
-  });
-} else if (el.tagName === 'INPUT' || el.tagName === 'TEXTAREA') {
-  // Handle input/textarea
-  ${mode === 'set' ? `el.value = '${value}';` : `el.value += '${value}';`}
-  el.dispatchEvent(new Event('input', {bubbles: true}));
-  el.dispatchEvent(new Event('change', {bubbles: true}));
-}
-el.dispatchEvent(new Event('blur', {bubbles: true}));`;
-    }
-
-  generateC4AScript(events) {
-    const commands = events.map(event => {
-      switch (event.type) {
-        case 'CLICK':
-          return `CLICK \`${event.selector}\``;
-        case 'DOUBLE_CLICK':
-          return `DOUBLE_CLICK \`${event.selector}\``;
-        case 'RIGHT_CLICK':
-          return `RIGHT_CLICK \`${event.selector}\``;
-        case 'TYPE':
-          return `TYPE "${event.value}"`;
-        case 'SET':
-          return `SET \`${event.selector}\` "${event.value}"`;
-        case 'SCROLL':
-          if (event.isWindowScroll || event.selector === 'window') {
-            return `SCROLL ${event.direction} ${event.amount}`;
-          } else {
-            return `SCROLL \`${event.selector}\` ${event.direction} ${event.amount}`;
-          }
-        case 'WAIT':
-          if (event.waitType === 'time') {
-            return `WAIT ${event.value}`;
-          } else {
-            return `WAIT \`${event.value}\` 5`;
-          }
-        case 'KEYBOARD_SHORTCUT':
-          return `# Keyboard shortcut: ${this.getEventDetail(event)}\nKEY "${this.getEventDetail(event)}"`;
-        case 'KEY_PRESS':
-          return `KEY "${event.key}"`;
-        default:
-          return `# Unknown: ${event.type}`;
-      }
-    });
-
-    return commands.join('\\n');
-  }
-
-  async replayRecording() {
-    if (this.groupedEvents.length === 0) {
-      alert('No actions to replay');
-      return;
-    }
-
-    // Create debugger window instead of simple overlay
-    this.createDebuggerWindow();
-  }
-
-  createReplayOverlay() {
-    // Create visual indicator for current action
-    this.replayIndicator = document.createElement('div');
-    this.replayIndicator.className = 'c4ai-replay-indicator';
-    this.replayIndicator.innerHTML = `
-      <div class="c4ai-replay-content">
-        <div class="c4ai-replay-header">
-          <span class="c4ai-replay-icon">▶️</span>
-          <span class="c4ai-replay-title">Replaying Actions</span>
-        </div>
-        <div class="c4ai-replay-progress">
-          <div class="c4ai-replay-progress-bar" id="c4ai-replay-progress"></div>
-        </div>
-        <div class="c4ai-replay-status" id="c4ai-replay-status">
-          Preparing...
-        </div>
-        <button class="c4ai-replay-stop" id="c4ai-replay-stop">Stop Replay</button>
-      </div>
-    `;
-    document.body.appendChild(this.replayIndicator);
-    
-    // Create highlight overlay for showing where actions happen
-    this.replayHighlight = document.createElement('div');
-    this.replayHighlight.className = 'c4ai-replay-highlight';
-    document.body.appendChild(this.replayHighlight);
-    
-    // Add stop button listener
-    document.getElementById('c4ai-replay-stop').addEventListener('click', () => {
-      this.stopReplay();
-    });
-  }
-
-  async executeReplaySequence() {
-    const events = this.groupedEvents;
-    const totalEvents = events.length;
-    
-    for (let i = 0; i < totalEvents; i++) {
-      if (!this.isReplaying) break;
-      
-      this.replayIndex = i;
-      const event = events[i];
-      const progress = ((i + 1) / totalEvents) * 100;
-      
-      // Update progress
-      document.getElementById('c4ai-replay-progress').style.width = `${progress}%`;
-      document.getElementById('c4ai-replay-status').textContent = 
-        `Action ${i + 1}/${totalEvents}: ${this.getReplayActionDescription(event)}`;
-      
-      // Execute the action with visual feedback
-      await this.executeReplayAction(event);
-      
-      // Wait between actions (except for the last one)
-      if (i < totalEvents - 1) {
-        const nextEvent = events[i + 1];
-        const waitTime = this.calculateWaitTime(event, nextEvent);
-        await this.wait(waitTime);
-      }
-    }
-    
-    // Replay complete
-    this.stopReplay(true);
-  }
-
-  async executeReplayAction(event) {
-    switch (event.type) {
-      case 'CLICK':
-      case 'DOUBLE_CLICK':
-      case 'RIGHT_CLICK':
-        await this.replayClickAction(event);
-        break;
-      case 'TYPE':
-      case 'SET':
-        await this.replayTypeAction(event);
-        break;
-      case 'SCROLL':
-        await this.replayScrollAction(event);
-        break;
-      case 'WAIT':
-        await this.replayWaitAction(event);
-        break;
-      case 'KEYBOARD_SHORTCUT':
-        await this.replayKeyboardShortcut(event);
-        break;
-      case 'KEY_PRESS':
-        await this.replayKeyPress(event);
-        break;
-    }
-  }
-
-  async replayClickAction(event) {
-    const element = document.querySelector(event.selector);
-    if (element) {
-      // Highlight the element
-      this.highlightReplayElement(element);
-      
-      // Show click animation
-      this.showClickAnimation(element);
-      
-      // Wait a bit for visual effect
-      await this.wait(300);
-    } else {
-      console.warn(`Element not found for selector: ${event.selector}`);
-    }
-  }
-
-  async replayTypeAction(event) {
-    const element = document.querySelector(event.selector);
-    if (element) {
-      // Highlight the input field
-      this.highlightReplayElement(element);
-      
-      // Show typing animation
-      const originalValue = element.value || '';
-      
-      if (event.type === 'SET') {
-        // Clear and set new value with animation
-        element.value = '';
-        await this.wait(200);
-        
-        // Type character by character
-        for (let i = 0; i < event.value.length; i++) {
-          element.value = event.value.substring(0, i + 1);
-          await this.wait(50); // Typing speed
-        }
-      } else {
-        // Append text with typing animation
-        const startLength = originalValue.length;
-        for (let i = 0; i < event.value.length; i++) {
-          element.value = originalValue + event.value.substring(0, i + 1);
-          await this.wait(50);
-        }
-      }
-    }
-  }
-
-  async replayScrollAction(event) {
-    const scrollAmount = event.direction === 'DOWN' ? event.amount : -event.amount;
-    
-    // Animate scroll
-    const startY = window.scrollY;
-    const targetY = startY + scrollAmount;
-    const duration = 500; // ms
-    const startTime = Date.now();
-    
-    const animateScroll = () => {
-      const elapsed = Date.now() - startTime;
-      const progress = Math.min(elapsed / duration, 1);
-      const easeProgress = this.easeInOut(progress);
-      
-      window.scrollTo(0, startY + (targetY - startY) * easeProgress);
-      
-      if (progress < 1 && this.isReplaying) {
-        requestAnimationFrame(animateScroll);
-      }
-    };
-    
-    animateScroll();
-    await this.wait(duration);
-  }
-
-  async replayWaitAction(event) {
-    if (event.waitType === 'time') {
-      document.getElementById('c4ai-replay-status').textContent = 
-        `Waiting ${event.value} seconds...`;
-      await this.wait(event.value * 1000);
-    } else {
-      // Wait for element
-      document.getElementById('c4ai-replay-status').textContent = 
-        `Waiting for element: ${event.value}`;
-      await this.waitForElement(event.value, 5000); // 5 second timeout
-    }
-  }
-
-  async replayKeyboardShortcut(event) {
-    const keys = [];
-    if (event.metaKey) keys.push('Cmd');
-    if (event.ctrlKey) keys.push('Ctrl');
-    if (event.altKey) keys.push('Alt');
-    if (event.shiftKey) keys.push('Shift');
-    keys.push(event.key.toUpperCase());
-    
-    // Show keyboard shortcut overlay
-    this.showKeyboardOverlay(keys.join('+'));
-    await this.wait(1000);
-  }
-
-  async replayKeyPress(event) {
-    this.showKeyboardOverlay(event.key);
-    await this.wait(500);
-  }
-
-  highlightReplayElement(element) {
-    const rect = element.getBoundingClientRect();
-    this.replayHighlight.style.display = 'block';
-    this.replayHighlight.style.top = `${rect.top + window.scrollY}px`;
-    this.replayHighlight.style.left = `${rect.left + window.scrollX}px`;
-    this.replayHighlight.style.width = `${rect.width}px`;
-    this.replayHighlight.style.height = `${rect.height}px`;
-    
-    // Fade out after a moment
-    setTimeout(() => {
-      this.replayHighlight.style.display = 'none';
-    }, 800);
-  }
-
-  showClickAnimation(element) {
-    const rect = element.getBoundingClientRect();
-    const centerX = rect.left + rect.width / 2;
-    const centerY = rect.top + rect.height / 2;
-    
-    const clickIndicator = document.createElement('div');
-    clickIndicator.className = 'c4ai-click-indicator';
-    clickIndicator.style.left = `${centerX}px`;
-    clickIndicator.style.top = `${centerY}px`;
-    document.body.appendChild(clickIndicator);
-    
-    // Remove after animation
-    setTimeout(() => clickIndicator.remove(), 600);
-  }
-
-  showKeyboardOverlay(keys) {
-    const overlay = document.createElement('div');
-    overlay.className = 'c4ai-keyboard-overlay';
-    overlay.textContent = keys;
-    document.body.appendChild(overlay);
-    
-    // Fade in
-    setTimeout(() => overlay.classList.add('visible'), 10);
-    
-    // Fade out and remove
-    setTimeout(() => {
-      overlay.classList.remove('visible');
-      setTimeout(() => overlay.remove(), 300);
-    }, 700);
-  }
-
-  getReplayActionDescription(event) {
-    switch (event.type) {
-      case 'CLICK': return `Click on ${event.selector}`;
-      case 'DOUBLE_CLICK': return `Double-click on ${event.selector}`;
-      case 'RIGHT_CLICK': return `Right-click on ${event.selector}`;
-      case 'TYPE': return `Type "${event.value.substring(0, 20)}..."`;
-      case 'SET': return `Set value in ${event.selector}`;
-      case 'SCROLL': 
-        if (event.isWindowScroll || event.selector === 'window') {
-          return `Scroll ${event.direction} ${event.amount}px`;
-        } else {
-          return `Scroll ${event.selector} ${event.direction} ${event.amount}px`;
-        }
-      case 'WAIT': return event.waitType === 'time' ? 
-        `Wait ${event.value}s` : `Wait for ${event.value}`;
-      case 'KEYBOARD_SHORTCUT': return `Press ${this.getEventDetail(event)}`;
-      case 'KEY_PRESS': return `Press ${event.key}`;
-      default: return event.type;
-    }
-  }
-
-  calculateWaitTime(currentEvent, nextEvent) {
-    // Use timing from recording if available
-    if (nextEvent.time && currentEvent.time) {
-      const timeDiff = (nextEvent.time - currentEvent.time) * 1000; // Convert to ms
-      // Cap wait time between 100ms and 2000ms for better replay experience
-      return Math.min(Math.max(timeDiff, 100), 2000);
-    }
-    return 500; // Default wait time
-  }
-
-  async wait(ms) {
-    return new Promise(resolve => setTimeout(resolve, ms));
-  }
-
-  async waitForElement(selector, timeout = 5000) {
-    const startTime = Date.now();
-    while (Date.now() - startTime < timeout) {
-      const element = document.querySelector(selector);
-      if (element) return element;
-      await this.wait(100);
-    }
-    return null;
-  }
-
-  easeInOut(t) {
-    return t < 0.5 ? 2 * t * t : -1 + (4 - 2 * t) * t;
-  }
-
-  stopReplay(completed = false) {
-    this.isReplaying = false;
-    
-    // Remove replay UI
-    this.replayIndicator?.remove();
-    this.replayHighlight?.remove();
-    
-    // Update toolbar
-    if (completed) {
-      document.getElementById('c4ai-script-hint').textContent = '✅ Replay completed!';
-    } else {
-      document.getElementById('c4ai-script-hint').textContent = '⏹️ Replay stopped';
-    }
-    
-    // Re-enable replay button
-    const replayBtn = document.getElementById('c4ai-replay');
-    if (replayBtn) {
-      replayBtn.disabled = false;
-      replayBtn.innerHTML = '<span>▶</span> Replay';
-    }
-  }
-
-  createDebuggerWindow() {
-    // Create debugger modal
-    this.debuggerModal = document.createElement('div');
-    this.debuggerModal.className = 'c4ai-debugger-modal';
-    
-    // Set initial position for dragging
-    this.debuggerModal.style.position = 'fixed';
-    this.debuggerModal.style.top = '50px';
-    this.debuggerModal.style.right = '20px';
-    
-    this.debuggerModal.innerHTML = `
-      <div class="c4ai-debugger-content">
-        <div class="c4ai-debugger-header">
-          <h2>Action Debugger <span style="color: #ff3c74; font-size: 12px;">(ALPHA)</span></h2>
-          <button class="c4ai-close-modal" id="c4ai-close-debugger">✕</button>
-        </div>
-        <div class="c4ai-debugger-body">
-          <div class="c4ai-debugger-controls">
-            <button class="c4ai-debug-btn c4ai-run-btn" id="c4ai-debug-run" title="Run to end or next breakpoint">
-              <span>▶</span> Run
-            </button>
-            <button class="c4ai-debug-btn c4ai-step-btn" id="c4ai-debug-step" title="Execute next action">
-              <span>⏭</span> Step
-            </button>
-            <button class="c4ai-debug-btn c4ai-pause-btn" id="c4ai-debug-pause" disabled title="Pause execution">
-              <span>⏸</span> Pause
-            </button>
-            <button class="c4ai-debug-btn c4ai-stop-btn" id="c4ai-debug-stop" title="Stop debugging">
-              <span>⏹</span> Stop
-            </button>
-            <button class="c4ai-debug-btn c4ai-restart-btn" id="c4ai-debug-restart" title="Restart from beginning">
-              <span>🔄</span> Restart
-            </button>
-          </div>
-          <div class="c4ai-debugger-status">
-            <span class="c4ai-debug-label">Status:</span>
-            <span class="c4ai-debug-value" id="c4ai-debug-status">Ready</span>
-          </div>
-          <div class="c4ai-debugger-actions" id="c4ai-debugger-actions">
-            ${this.renderDebuggerActions()}
-          </div>
-        </div>
-      </div>
-    `;
-    
-    document.body.appendChild(this.debuggerModal);
-    
-    // Initialize debugger state
-    this.debuggerState = {
-      currentIndex: 0,
-      isRunning: false,
-      isPaused: false,
-      breakpoints: new Set(),
-      editedEvents: [...this.groupedEvents] // Copy for editing
-    };
-    
-    // Create replay highlight element
-    this.replayHighlight = document.createElement('div');
-    this.replayHighlight.className = 'c4ai-replay-highlight';
-    document.body.appendChild(this.replayHighlight);
-    
-    // Add event listeners
-    this.attachDebuggerListeners();
-    
-    // Make debugger draggable by the header
-    this.makeDraggableByHeader(this.debuggerModal);
-  }
-
-  renderDebuggerActions() {
-    const events = this.debuggerState ? this.debuggerState.editedEvents : this.groupedEvents;
-    return events.map((event, index) => {
-      const detail = this.getEventDetail(event);
-      return `
-        <div class="c4ai-debug-action ${index === 0 ? 'c4ai-current' : ''}" data-index="${index}">
-          <div class="c4ai-debug-action-left">
-            <input type="checkbox" class="c4ai-breakpoint-checkbox" data-index="${index}" title="Toggle breakpoint">
-            <span class="c4ai-action-number">${index + 1}</span>
-            <span class="c4ai-action-indicator">➤</span>
-          </div>
-          <div class="c4ai-debug-action-content">
-            <div class="c4ai-action-type">${event.type.replace(/_/g, ' ')}</div>
-            <div class="c4ai-action-detail" contenteditable="true" data-index="${index}" data-field="detail">
-              ${detail}
-            </div>
-          </div>
-          <div class="c4ai-debug-action-right">
-            <button class="c4ai-action-play" data-index="${index}" title="Execute this action">▶</button>
-            <button class="c4ai-action-edit" data-index="${index}" title="Edit action">✏️</button>
-            <button class="c4ai-action-delete" data-index="${index}" title="Delete action">🗑</button>
-          </div>
-        </div>
-      `;
-    }).join('');
-  }
-
-  attachDebuggerListeners() {
-    // Control buttons
-    document.getElementById('c4ai-debug-run').addEventListener('click', () => this.debugRun());
-    document.getElementById('c4ai-debug-step').addEventListener('click', () => this.debugStep());
-    document.getElementById('c4ai-debug-pause').addEventListener('click', () => this.debugPause());
-    document.getElementById('c4ai-debug-stop').addEventListener('click', () => this.debugStop());
-    document.getElementById('c4ai-debug-restart').addEventListener('click', () => this.debugRestart());
-    document.getElementById('c4ai-close-debugger').addEventListener('click', () => this.closeDebugger());
-    
-    // Breakpoint checkboxes
-    document.querySelectorAll('.c4ai-breakpoint-checkbox').forEach(cb => {
-      cb.addEventListener('change', (e) => {
-        const index = parseInt(e.target.dataset.index);
-        if (e.target.checked) {
-          this.debuggerState.breakpoints.add(index);
-        } else {
-          this.debuggerState.breakpoints.delete(index);
-        }
-        this.updateBreakpointVisual(index, e.target.checked);
-      });
-    });
-    
-    // Play buttons
-    document.querySelectorAll('.c4ai-action-play').forEach(btn => {
-      btn.addEventListener('click', (e) => {
-        const index = parseInt(e.target.dataset.index);
-        this.executeActionOnPage(this.debuggerState.editedEvents[index], index);
-      });
-    });
-    
-    // Delete buttons
-    document.querySelectorAll('.c4ai-action-delete').forEach(btn => {
-      btn.addEventListener('click', (e) => {
-        const index = parseInt(e.target.dataset.index);
-        this.deleteDebugAction(index);
-      });
-    });
-    
-    // Edit buttons
-    document.querySelectorAll('.c4ai-action-edit').forEach(btn => {
-      btn.addEventListener('click', (e) => {
-        const index = parseInt(e.target.dataset.index);
-        this.editDebugAction(index);
-      });
-    });
-    
-    // Inline editing
-    document.querySelectorAll('.c4ai-action-detail[contenteditable]').forEach(elem => {
-      elem.addEventListener('blur', (e) => {
-        const index = parseInt(e.target.dataset.index);
-        this.updateActionDetail(index, e.target.textContent);
-      });
-    });
-  }
-
-  async debugRun() {
-    this.debuggerState.isRunning = true;
-    this.debuggerState.isPaused = false;
-    this.updateDebugControls();
-    this.updateDebugStatus('Running...');
-    
-    const startIndex = this.debuggerState.currentIndex;
-    const events = this.debuggerState.editedEvents;
-    
-    for (let i = startIndex; i < events.length; i++) {
-      if (!this.debuggerState.isRunning || this.debuggerState.isPaused) break;
-      
-      // Update current action
-      this.setCurrentAction(i);
-      
-      // Execute the action
-      await this.executeDebugAction(events[i], i);
-      
-      // Check for breakpoint on next action
-      if (i < events.length - 1 && this.debuggerState.breakpoints.has(i + 1)) {
-        this.debuggerState.currentIndex = i + 1;
-        this.debugPause();
-        this.updateDebugStatus(`Stopped at breakpoint ${i + 2}`);
-        break;
-      }
-      
-      this.debuggerState.currentIndex = i + 1;
-    }
-    
-    if (this.debuggerState.currentIndex >= events.length) {
-      this.debugStop(true);
-    }
-  }
-
-  async debugStep() {
-    if (this.debuggerState.currentIndex >= this.debuggerState.editedEvents.length) {
-      this.updateDebugStatus('End of actions');
-      return;
-    }
-    
-    this.updateDebugStatus('Stepping...');
-    this.setCurrentAction(this.debuggerState.currentIndex);
-    
-    await this.executeDebugAction(
-      this.debuggerState.editedEvents[this.debuggerState.currentIndex],
-      this.debuggerState.currentIndex
-    );
-    
-    this.debuggerState.currentIndex++;
-    
-    if (this.debuggerState.currentIndex >= this.debuggerState.editedEvents.length) {
-      this.updateDebugStatus('Completed');
-    } else {
-      this.updateDebugStatus('Ready');
-    }
-  }
-
-  debugPause() {
-    this.debuggerState.isPaused = true;
-    this.debuggerState.isRunning = false;
-    this.updateDebugControls();
-    this.updateDebugStatus('Paused');
-  }
-
-  debugStop(completed = false) {
-    this.debuggerState.isRunning = false;
-    this.debuggerState.isPaused = false;
-    this.updateDebugControls();
-    
-    if (completed) {
-      this.updateDebugStatus('Completed');
-    } else {
-      this.updateDebugStatus('Stopped');
-    }
-  }
-
-  debugRestart() {
-    this.debuggerState.currentIndex = 0;
-    this.debuggerState.isRunning = false;
-    this.debuggerState.isPaused = false;
-    this.setCurrentAction(0);
-    this.updateDebugControls();
-    this.updateDebugStatus('Ready');
-  }
-
-  closeDebugger() {
-    this.debuggerModal?.remove();
-    this.replayHighlight?.remove();
-    this.debuggerState = null;
-  }
-
-  setCurrentAction(index) {
-    // Remove previous current marker
-    document.querySelectorAll('.c4ai-debug-action').forEach(elem => {
-      elem.classList.remove('c4ai-current');
-    });
-    
-    // Add current marker
-    const currentElem = document.querySelector(`.c4ai-debug-action[data-index="${index}"]`);
-    if (currentElem) {
-      currentElem.classList.add('c4ai-current');
-      
-      // Better scrolling into view
-      const scrollContainer = document.querySelector('.c4ai-debugger-actions');
-      if (scrollContainer) {
-        const containerRect = scrollContainer.getBoundingClientRect();
-        const elemRect = currentElem.getBoundingClientRect();
-        
-        // Check if element is out of view
-        if (elemRect.top < containerRect.top || elemRect.bottom > containerRect.bottom) {
-          // Scroll to center the element
-          const scrollTop = currentElem.offsetTop - scrollContainer.offsetTop - (scrollContainer.clientHeight / 2) + (currentElem.clientHeight / 2);
-          scrollContainer.scrollTo({
-            top: scrollTop,
-            behavior: 'smooth'
-          });
-        }
-      }
-    }
-    
-    this.debuggerState.currentIndex = index;
-  }
-
-  updateDebugControls() {
-    const runBtn = document.getElementById('c4ai-debug-run');
-    const stepBtn = document.getElementById('c4ai-debug-step');
-    const pauseBtn = document.getElementById('c4ai-debug-pause');
-    
-    if (this.debuggerState.isRunning) {
-      runBtn.disabled = true;
-      stepBtn.disabled = true;
-      pauseBtn.disabled = false;
-    } else {
-      runBtn.disabled = false;
-      stepBtn.disabled = false;
-      pauseBtn.disabled = true;
-    }
-  }
-
-  updateDebugStatus(status) {
-    document.getElementById('c4ai-debug-status').textContent = status;
-  }
-
-  updateBreakpointVisual(index, hasBreakpoint) {
-    const actionElem = document.querySelector(`.c4ai-debug-action[data-index="${index}"]`);
-    if (actionElem) {
-      if (hasBreakpoint) {
-        actionElem.classList.add('has-breakpoint');
-      } else {
-        actionElem.classList.remove('has-breakpoint');
-      }
-    }
-  }
-
-  deleteDebugAction(index) {
-    if (confirm('Delete this action?')) {
-      this.debuggerState.editedEvents.splice(index, 1);
-      this.debuggerState.breakpoints = new Set(
-        [...this.debuggerState.breakpoints]
-          .filter(bp => bp !== index)
-          .map(bp => bp > index ? bp - 1 : bp)
-      );
-      
-      // Re-render actions
-      document.getElementById('c4ai-debugger-actions').innerHTML = this.renderDebuggerActions();
-      this.attachDebuggerListeners();
-      
-      // Adjust current index if needed
-      if (this.debuggerState.currentIndex > index) {
-        this.debuggerState.currentIndex--;
-      }
-      this.setCurrentAction(Math.min(this.debuggerState.currentIndex, this.debuggerState.editedEvents.length - 1));
-    }
-  }
-
-  editDebugAction(index) {
-    const event = this.debuggerState.editedEvents[index];
-    const dialog = document.createElement('div');
-    dialog.className = 'c4ai-edit-dialog';
-    dialog.innerHTML = `
-      <div class="c4ai-edit-content">
-        <h3>Edit Action</h3>
-        <div class="c4ai-edit-field">
-          <label>Type:</label>
-          <select id="c4ai-edit-type">
-            <option value="CLICK" ${event.type === 'CLICK' ? 'selected' : ''}>Click</option>
-            <option value="TYPE" ${event.type === 'TYPE' ? 'selected' : ''}>Type</option>
-            <option value="SET" ${event.type === 'SET' ? 'selected' : ''}>Set</option>
-            <option value="SCROLL" ${event.type === 'SCROLL' ? 'selected' : ''}>Scroll</option>
-            <option value="WAIT" ${event.type === 'WAIT' ? 'selected' : ''}>Wait</option>
-          </select>
-        </div>
-        <div class="c4ai-edit-field">
-          <label>Selector/Value:</label>
-          <input type="text" id="c4ai-edit-value" value="${event.selector || event.value || ''}">
-        </div>
-        <div class="c4ai-edit-actions">
-          <button id="c4ai-edit-save">Save</button>
-          <button id="c4ai-edit-cancel">Cancel</button>
-        </div>
-      </div>
-    `;
-    
-    document.body.appendChild(dialog);
-    
-    document.getElementById('c4ai-edit-save').addEventListener('click', () => {
-      const newType = document.getElementById('c4ai-edit-type').value;
-      const newValue = document.getElementById('c4ai-edit-value').value;
-      
-      // Update event based on type
-      this.debuggerState.editedEvents[index] = {
-        ...event,
-        type: newType,
-        selector: ['CLICK', 'TYPE', 'SET'].includes(newType) ? newValue : event.selector,
-        value: ['TYPE', 'SET', 'WAIT'].includes(newType) ? newValue : event.value
-      };
-      
-      // Re-render
-      document.getElementById('c4ai-debugger-actions').innerHTML = this.renderDebuggerActions();
-      this.attachDebuggerListeners();
-      dialog.remove();
-    });
-    
-    document.getElementById('c4ai-edit-cancel').addEventListener('click', () => {
-      dialog.remove();
-    });
-  }
-
-  async executeDebugAction(event, index) {
-    this.updateDebugStatus(`Executing: ${this.getEventDetail(event)}`);
-    
-    // Execute the action on the page
-    await this.executeActionOnPage(event, index);
-    
-    // Add small delay between actions for visibility
-    await this.wait(300);
-  }
-
-  async executeActionOnPage(event, index) {
-    try {
-      // Set current action for visual feedback
-      this.setCurrentAction(index);
-      
-      switch (event.type) {
-        case 'CLICK':
-        case 'DOUBLE_CLICK':
-        case 'RIGHT_CLICK':
-          await this.executeClick(event);
-          break;
-        case 'TYPE':
-        case 'SET':
-          await this.executeType(event);
-          break;
-        case 'SCROLL':
-          await this.executeScroll(event);
-          break;
-        case 'WAIT':
-          await this.executeWait(event);
-          break;
-        case 'KEYBOARD_SHORTCUT':
-          await this.executeKeyboardShortcut(event);
-          break;
-        case 'KEY_PRESS':
-          await this.executeKeyPress(event);
-          break;
-      }
-      
-      this.updateDebugStatus(`Executed: ${this.getEventDetail(event)}`);
-    } catch (error) {
-      console.error('Error executing action:', error);
-      this.updateDebugStatus(`Error: ${error.message}`);
-    }
-  }
-
-  async executeClick(event) {
-    const element = document.querySelector(event.selector);
-    if (!element) {
-      throw new Error(`Element not found: ${event.selector}`);
-    }
-    
-    // Highlight element briefly
-    this.highlightReplayElement(element);
-    
-    // Create and dispatch the appropriate mouse event
-    const eventType = {
-      'CLICK': 'click',
-      'DOUBLE_CLICK': 'dblclick',
-      'RIGHT_CLICK': 'contextmenu'
-    }[event.type];
-    
-    const mouseEvent = new MouseEvent(eventType, {
-      view: window,
-      bubbles: true,
-      cancelable: true,
-      buttons: event.type === 'RIGHT_CLICK' ? 2 : 1
-    });
-    
-    element.dispatchEvent(mouseEvent);
-    
-    // Also try clicking for better compatibility
-    if (event.type === 'CLICK' && typeof element.click === 'function') {
-      element.click();
-    }
-  }
-
-  async executeType(event) {
-    const element = document.querySelector(event.selector);
-    if (!element) {
-      throw new Error(`Element not found: ${event.selector}`);
-    }
-    
-    // Highlight element
-    this.highlightReplayElement(element);
-    
-    // Make sure element is visible and focused
-    element.scrollIntoView({ behavior: 'smooth', block: 'center' });
-    await this.wait(100);
-    
-    // Focus the element
-    element.focus();
-    element.click(); // Some inputs need a click to properly focus
-    
-    // Check if it's a contenteditable element
-    const isContentEditable = element.contentEditable === 'true' || 
-                             element.getAttribute('contenteditable') === 'true' ||
-                             element.closest('[contenteditable="true"]');
-    
-    if (isContentEditable) {
-      // Handle contenteditable elements
-      await this.typeInContentEditable(element, event);
-    } else if (element.tagName === 'INPUT' || element.tagName === 'TEXTAREA') {
-      // Handle regular input/textarea elements
-      await this.typeInInput(element, event);
-    } else {
-      // Try contenteditable approach for other elements
-      await this.typeInContentEditable(element, event);
-    }
-  }
-
-  async typeInInput(element, event) {
-    if (event.type === 'SET') {
-      // Clear and set value
-      element.value = '';
-      
-      // Dispatch events
-      element.dispatchEvent(new Event('focus', { bubbles: true }));
-      element.dispatchEvent(new Event('input', { bubbles: true }));
-      
-      // Type character by character for realism
-      for (let i = 0; i < event.value.length; i++) {
-        element.value = event.value.substring(0, i + 1);
-        
-        // Dispatch multiple events for better compatibility
-        element.dispatchEvent(new Event('input', { bubbles: true }));
-        element.dispatchEvent(new InputEvent('input', { 
-          bubbles: true,
-          cancelable: true,
-          inputType: 'insertText',
-          data: event.value[i]
-        }));
-        
-        await this.wait(30);
-      }
-    } else {
-      // TYPE - append to existing value
-      const startValue = element.value || '';
-      for (let i = 0; i < event.value.length; i++) {
-        element.value = startValue + event.value.substring(0, i + 1);
-        
-        // Dispatch multiple events
-        element.dispatchEvent(new Event('input', { bubbles: true }));
-        element.dispatchEvent(new InputEvent('input', { 
-          bubbles: true,
-          cancelable: true,
-          inputType: 'insertText',
-          data: event.value[i]
-        }));
-        
-        await this.wait(30);
-      }
-    }
-    
-    // Dispatch change and blur events
-    element.dispatchEvent(new Event('change', { bubbles: true }));
-    element.dispatchEvent(new Event('blur', { bubbles: true }));
-  }
-
-  async typeInContentEditable(element, event) {
-    // Focus and prepare the element
-    element.focus();
-    
-    // Create a range and selection
-    const selection = window.getSelection();
-    const range = document.createRange();
-    
-    if (event.type === 'SET') {
-      // Clear existing content
-      element.textContent = '';
-      
-      // Set cursor at the beginning
-      range.selectNodeContents(element);
-      range.collapse(true);
-      selection.removeAllRanges();
-      selection.addRange(range);
-    } else {
-      // Move cursor to the end for TYPE
-      range.selectNodeContents(element);
-      range.collapse(false);
-      selection.removeAllRanges();
-      selection.addRange(range);
-    }
-    
-    // Type each character using keyboard events
-    for (let i = 0; i < event.value.length; i++) {
-      const char = event.value[i];
-      
-      // Dispatch keydown
-      const keydownEvent = new KeyboardEvent('keydown', {
-        key: char,
-        char: char,
-        keyCode: char.charCodeAt(0),
-        which: char.charCodeAt(0),
-        bubbles: true,
-        cancelable: true
-      });
-      element.dispatchEvent(keydownEvent);
-      
-      // Insert the character
-      if (!keydownEvent.defaultPrevented) {
-        document.execCommand('insertText', false, char);
-      }
-      
-      // Dispatch keyup
-      const keyupEvent = new KeyboardEvent('keyup', {
-        key: char,
-        char: char,
-        keyCode: char.charCodeAt(0),
-        which: char.charCodeAt(0),
-        bubbles: true,
-        cancelable: true
-      });
-      element.dispatchEvent(keyupEvent);
-      
-      // Dispatch input event
-      element.dispatchEvent(new InputEvent('input', {
-        bubbles: true,
-        cancelable: true,
-        inputType: 'insertText',
-        data: char
-      }));
-      
-      await this.wait(30);
-    }
-    
-    // Dispatch blur event
-    element.dispatchEvent(new Event('blur', { bubbles: true }));
-  }
-
-  async executeScroll(event) {
-    const scrollAmount = event.direction === 'DOWN' ? event.amount : -event.amount;
-    const duration = 300;
-    const startTime = Date.now();
-    
-    if (event.isWindowScroll || event.selector === 'window') {
-      // Window scroll
-      const startY = window.scrollY;
-      const targetY = startY + scrollAmount;
-      
-      const animateScroll = () => {
-        const elapsed = Date.now() - startTime;
-        const progress = Math.min(elapsed / duration, 1);
-        const easeProgress = this.easeInOut(progress);
-        
-        window.scrollTo(0, startY + (targetY - startY) * easeProgress);
-        
-        if (progress < 1) {
-          requestAnimationFrame(animateScroll);
-        }
-      };
-      
-      animateScroll();
-    } else {
-      // Element scroll
-      const element = document.querySelector(event.selector);
-      if (!element) {
-        throw new Error(`Scrollable element not found: ${event.selector}`);
-      }
-      
-      // Highlight the scrollable element
-      this.highlightReplayElement(element);
-      
-      const startY = element.scrollTop;
-      const targetY = startY + scrollAmount;
-      
-      const animateScroll = () => {
-        const elapsed = Date.now() - startTime;
-        const progress = Math.min(elapsed / duration, 1);
-        const easeProgress = this.easeInOut(progress);
-        
-        element.scrollTop = startY + (targetY - startY) * easeProgress;
-        
-        if (progress < 1) {
-          requestAnimationFrame(animateScroll);
-        }
-      };
-      
-      animateScroll();
-    }
-    
-    await this.wait(duration);
-  }
-
-  async executeWait(event) {
-    if (event.waitType === 'time') {
-      await this.wait(event.value * 1000);
-    } else {
-      // Wait for element to appear
-      const timeout = 5000;
-      const startTime = Date.now();
-      
-      while (Date.now() - startTime < timeout) {
-        const element = document.querySelector(event.value);
-        if (element) {
-          this.highlightReplayElement(element);
-          return;
-        }
-        await this.wait(100);
-      }
-      
-      throw new Error(`Element not found after ${timeout}ms: ${event.value}`);
-    }
-  }
-
-  async executeKeyboardShortcut(event) {
-    // Show keyboard overlay
-    this.showKeyboardOverlay(this.getEventDetail(event));
-    
-    // Create and dispatch keyboard event
-    const keyEvent = new KeyboardEvent('keydown', {
-      key: event.key,
-      code: event.code,
-      ctrlKey: event.ctrlKey,
-      metaKey: event.metaKey,
-      altKey: event.altKey,
-      shiftKey: event.shiftKey,
-      bubbles: true,
-      cancelable: true
-    });
-    
-    document.activeElement.dispatchEvent(keyEvent);
-    
-    // Some shortcuts need keyup as well
-    const keyUpEvent = new KeyboardEvent('keyup', {
-      key: event.key,
-      code: event.code,
-      ctrlKey: event.ctrlKey,
-      metaKey: event.metaKey,
-      altKey: event.altKey,
-      shiftKey: event.shiftKey,
-      bubbles: true,
-      cancelable: true
-    });
-    
-    document.activeElement.dispatchEvent(keyUpEvent);
-    
-    await this.wait(500);
-  }
-
-  async executeKeyPress(event) {
-    this.showKeyboardOverlay(event.key);
-    
-    const activeElement = document.activeElement;
-    
-    if (event.key === 'Delete' || event.key === 'Backspace') {
-      // Handle delete/backspace on input elements
-      if (activeElement.tagName === 'INPUT' || activeElement.tagName === 'TEXTAREA') {
-        const start = activeElement.selectionStart;
-        const end = activeElement.selectionEnd;
-        
-        if (start !== end) {
-          // Delete selection
-          activeElement.value = activeElement.value.substring(0, start) + activeElement.value.substring(end);
-          activeElement.selectionStart = activeElement.selectionEnd = start;
-        } else if (event.key === 'Backspace' && start > 0) {
-          // Delete character before cursor
-          activeElement.value = activeElement.value.substring(0, start - 1) + activeElement.value.substring(start);
-          activeElement.selectionStart = activeElement.selectionEnd = start - 1;
-        } else if (event.key === 'Delete' && start < activeElement.value.length) {
-          // Delete character after cursor
-          activeElement.value = activeElement.value.substring(0, start) + activeElement.value.substring(start + 1);
-        }
-        
-        activeElement.dispatchEvent(new Event('input', { bubbles: true }));
-      }
-    } else {
-      // Regular key press
-      const keyEvent = new KeyboardEvent('keydown', {
-        key: event.key,
-        bubbles: true,
-        cancelable: true
-      });
-      
-      activeElement.dispatchEvent(keyEvent);
-    }
-    
-    await this.wait(300);
-  }
-
-  async saveFlow() {
-    // Get flow name from user
-    const flowName = prompt('Enter a name for this flow:');
-    if (!flowName || !flowName.trim()) {
-      return;
-    }
-
-    // Get current domain
-    const domain = window.location.hostname;
-    
-    // Create flow object
-    const flow = {
-      id: Date.now().toString(),
-      name: flowName.trim(),
-      domain: domain,
-      url: window.location.href,
-      events: this.groupedEvents,
-      createdAt: new Date().toISOString(),
-      outputFormat: this.outputFormat
-    };
-
-    // Get existing flows for this domain
-    const storageKey = `c4ai_flows_${domain}`;
-    chrome.storage.local.get(storageKey, (result) => {
-      const flows = result[storageKey] || [];
-      flows.push(flow);
-      
-      // Save updated flows
-      chrome.storage.local.set({ [storageKey]: flows }, () => {
-        if (chrome.runtime.lastError) {
-          alert('Failed to save flow: ' + chrome.runtime.lastError.message);
-        } else {
-          alert(`Flow "${flowName}" saved successfully!`);
-          // Update hint text
-          document.getElementById('c4ai-script-hint').textContent = `✅ Flow "${flowName}" saved!`;
-        }
-      });
-    });
-  }
-
-  showSavedFlows() {
-    const domain = window.location.hostname;
-    const storageKey = `c4ai_flows_${domain}`;
-    
-    chrome.storage.local.get(storageKey, (result) => {
-      const flows = result[storageKey] || [];
-      
-      // Create modal
-      const modal = document.createElement('div');
-      modal.className = 'c4ai-saved-flows-modal';
-      modal.innerHTML = `
-        <div class="c4ai-saved-flows-content">
-          <div class="c4ai-saved-flows-header">
-            <h2>Saved Flows for ${domain}</h2>
-            <button class="c4ai-close-modal" id="c4ai-close-flows">✕</button>
-          </div>
-          <div class="c4ai-saved-flows-body">
-            ${flows.length === 0 ? 
-              '<p class="c4ai-no-flows">No saved flows for this domain yet. Record and save your first flow!</p>' :
-              `<div class="c4ai-flows-list">
-                ${flows.map(flow => `
-                  <div class="c4ai-flow-item" data-flow-id="${flow.id}">
-                    <div class="c4ai-flow-info">
-                      <h3>${flow.name}</h3>
-                      <p class="c4ai-flow-meta">
-                        <span>${flow.events.length} actions</span>
-                        <span>•</span>
-                        <span>${new Date(flow.createdAt).toLocaleDateString()}</span>
-                      </p>
-                    </div>
-                    <div class="c4ai-flow-actions">
-                      <button class="c4ai-action-btn c4ai-load-flow-btn" data-flow-id="${flow.id}">
-                        <span>▶</span> Load
-                      </button>
-                      <button class="c4ai-action-btn c4ai-delete-flow-btn" data-flow-id="${flow.id}">
-                        <span>🗑</span>
-                      </button>
-                    </div>
-                  </div>
-                `).join('')}
-              </div>`
-            }
-          </div>
-        </div>
-      `;
-      
-      document.body.appendChild(modal);
-      
-      // Add event listeners
-      document.getElementById('c4ai-close-flows').addEventListener('click', () => {
-        modal.remove();
-      });
-      
-      // Load flow buttons
-      modal.querySelectorAll('.c4ai-load-flow-btn').forEach(btn => {
-        btn.addEventListener('click', (e) => {
-          const flowId = e.target.closest('button').dataset.flowId;
-          const flow = flows.find(f => f.id === flowId);
-          if (flow) {
-            this.loadFlow(flow);
-            modal.remove();
-          }
-        });
-      });
-      
-      // Delete flow buttons
-      modal.querySelectorAll('.c4ai-delete-flow-btn').forEach(btn => {
-        btn.addEventListener('click', (e) => {
-          const flowId = e.target.closest('button').dataset.flowId;
-          if (confirm('Are you sure you want to delete this flow?')) {
-            this.deleteFlow(flowId);
-            modal.remove();
-            this.showSavedFlows(); // Refresh the modal
-          }
-        });
-      });
-    });
-  }
-
-  loadFlow(flow) {
-    // Stop any current recording
-    this.isRecording = false;
-    this.removeEventCapture();
-    
-    // Load the flow's events
-    this.groupedEvents = [...flow.events];
-    this.outputFormat = flow.outputFormat || 'js';
-    
-    // Show the recording summary UI
-    this.showRecordingSummary();
-    
-    // Update hint
-    document.getElementById('c4ai-script-hint').textContent = `Loaded flow: "${flow.name}"`;
-  }
-
-  deleteFlow(flowId) {
-    const domain = window.location.hostname;
-    const storageKey = `c4ai_flows_${domain}`;
-    
-    chrome.storage.local.get(storageKey, (result) => {
-      const flows = result[storageKey] || [];
-      const updatedFlows = flows.filter(f => f.id !== flowId);
-      
-      chrome.storage.local.set({ [storageKey]: updatedFlows }, () => {
-        if (chrome.runtime.lastError) {
-          alert('Failed to delete flow: ' + chrome.runtime.lastError.message);
-        }
-      });
-    });
-  }
-
-  recordAgain() {
-    // Show confirmation dialog
-    if (!confirm('Are you sure you want to start a new recording? This will clear the current recording.')) {
-      return;
-    }
-    
-    // Clear current recording and start fresh
-    this.groupedEvents = [];
-    this.rawEvents = [];
-    this.processedEventIndices.clear();
-    this.keyBuffer = [];
-    
-    // Don't create a new toolbar, just update the existing one
-    this.isRecording = true;
-    this.startTime = Date.now();
-    this.lastEventTime = this.startTime;
-    
-    // Reset toolbar content to recording state
-    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
-    toolbarContent.innerHTML = `
-      <div class="c4ai-toolbar-status">
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Actions:</span>
-          <span class="c4ai-status-value" id="c4ai-action-count">0</span>
-        </div>
-        <div class="c4ai-status-item">
-          <span class="c4ai-status-label">Format:</span>
-          <select id="c4ai-output-format" class="c4ai-format-select">
-            <option value="js">JavaScript</option>
-            <option value="c4a">C4A Script</option>
-          </select>
-        </div>
-      </div>
-      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
-        Recording your actions... Click, type, and scroll to build your script.
-      </div>
-      <div class="c4ai-toolbar-actions">
-        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-flows-btn">
-          <span>📂</span> Saved Flows
-        </button>
-        <button id="c4ai-add-wait" class="c4ai-action-btn c4ai-wait-btn">
-          <span class="c4ai-wait-icon">⏱</span> Add Wait
-        </button>
-        <button id="c4ai-pause-recording" class="c4ai-action-btn c4ai-pause-btn">
-          <span class="c4ai-pause-icon">⏸</span> Pause
-        </button>
-        <button id="c4ai-stop-generate" class="c4ai-action-btn c4ai-generate-btn">
-          <span class="c4ai-generate-icon">⚡</span> Stop & Generate
-        </button>
-      </div>
-    `;
-    
-    // Re-add event listeners
-    document.getElementById('c4ai-pause-recording').addEventListener('click', () => this.togglePause());
-    document.getElementById('c4ai-stop-generate').addEventListener('click', () => this.stopAndGenerate());
-    document.getElementById('c4ai-add-wait').addEventListener('click', () => this.showWaitDialog());
-    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
-    document.getElementById('c4ai-output-format').addEventListener('change', (e) => {
-      this.outputFormat = e.target.value;
-    });
-    
-    this.createRecordingIndicator();
-    this.updateToolbar();
-  }
-
-  generatePythonTemplate(scriptCode, format) {
-    const currentUrl = window.location.href;
-    const timestamp = new Date().toISOString();
-    
-    if (format === 'js') {
-      return `#!/usr/bin/env python3
-"""
-Generated by Crawl4AI Chrome Extension - Script Builder (ALPHA)
-URL: ${currentUrl}
-Generated: ${timestamp}
-"""
-
-import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-
-# JavaScript code to execute
-JS_SCRIPT = """
-${scriptCode}
-"""
-
-async def run_automation():
-    """Run the recorded automation script"""
-    
-    # Configure browser
-    browser_config = BrowserConfig(
-        headless=False,  # Set to True for headless mode
-        verbose=True
-    )
-    
-    # Configure crawler with JavaScript execution
-    crawler_config = CrawlerRunConfig(
-        js_code=JS_SCRIPT,
-        wait_for="js:() => document.readyState === 'complete'",
-        page_timeout=30000  # 30 seconds timeout
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        result = await crawler.arun(
-            url="${currentUrl}",
-            config=crawler_config
-        )
-        
-        if result.success:
-            print("✅ Automation completed successfully!")
-            print(f"Final URL: {result.url}")
-            # You can access the final HTML with result.html
-            # Or extracted content with result.cleaned_html
-        else:
-            print("❌ Automation failed:", result.error_message)
-
-if __name__ == "__main__":
-    asyncio.run(run_automation())
-`;
-    } else {
-      // C4A Script format
-      return `#!/usr/bin/env python3
-"""
-Generated by Crawl4AI Chrome Extension - Script Builder (ALPHA)
-URL: ${currentUrl}
-Generated: ${timestamp}
-"""
-
-import asyncio
-from pathlib import Path
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-
-# C4A Script commands
-C4A_SCRIPT = """
-${scriptCode}
-"""
-
-# Save the C4A script for reference
-script_path = Path('automation_script.c4a')
-with open(script_path, 'w') as f:
-    f.write(C4A_SCRIPT)
-
-print(f"💾 C4A Script saved to: {script_path}")
-print("\\n📜 Generated C4A Script:")
-print(C4A_SCRIPT)
-
-# Note: To execute C4A scripts, you'll need to use the C4A Script compiler
-# Example:
-# from crawl4ai.script import C4ACompiler
-# compiler = C4ACompiler()
-# js_code = compiler.compile(C4A_SCRIPT)
-# 
-# Then use js_code in CrawlerRunConfig as shown in the JavaScript example above
-
-print("\\n💡 To execute this C4A script, compile it to JavaScript first!")
-`;
-    }
-  }
-}
-
-// Initialize
-let schemaBuilder = null;
-let scriptBuilder = null;
+let activeBuilder = null;
 
 // Listen for messages from popup
-chrome.runtime.onMessage.addListener((message, sender, sendResponse) => {
-  switch (message.action) {
-    case 'startSchemaCapture':
-      if (!schemaBuilder) {
-        schemaBuilder = new SchemaBuilder();
-      }
-      schemaBuilder.start();
-      sendResponse({ success: true });
-      break;
+chrome.runtime.onMessage.addListener((request, sender, sendResponse) => {
+  if (request.action === 'startCapture') {
+    if (activeBuilder) {
+      console.log('Stopping existing capture session');
+      activeBuilder.stop();
+      activeBuilder = null;
+    }
 
-    case 'startScriptCapture':
-      if (!scriptBuilder) {
-        scriptBuilder = new ScriptBuilder();
-      }
-      scriptBuilder.start();
-      sendResponse({ success: true });
-      break;
-      
-    case 'stopCapture':
-      if (schemaBuilder) {
-        schemaBuilder.stop();
-        schemaBuilder = null;
-      }
-      if (scriptBuilder) {
-        scriptBuilder.stop();
-        scriptBuilder = null;
-      }
-      sendResponse({ success: true });
-      break;
-      
-    case 'generateCode':
-      if (schemaBuilder) {
-        const code = schemaBuilder.generateCode();
-        schemaBuilder.showCodeModal(code);
-      }
-      sendResponse({ success: true });
-      break;
+    if (request.mode === 'schema') {
+      console.log('Starting Schema Builder');
+      activeBuilder = new SchemaBuilder();
+      activeBuilder.start();
+    } else if (request.mode === 'script') {
+      console.log('Starting Script Builder');
+      activeBuilder = new ScriptBuilder();
+      activeBuilder.start();
+    }
+    
+    sendResponse({ success: true });
+  } else if (request.action === 'stopCapture') {
+    if (activeBuilder) {
+      activeBuilder.stop();
+      activeBuilder = null;
+    }
+    sendResponse({ success: true });
+  } else if (request.action === 'startSchemaCapture') {
+    if (activeBuilder) {
+      activeBuilder.deactivate?.();
+      activeBuilder = null;
+    }
+    console.log('Starting Schema Builder');
+    activeBuilder = new SchemaBuilder();
+    activeBuilder.start();
+    sendResponse({ success: true });
+  } else if (request.action === 'startScriptCapture') {
+    if (activeBuilder) {
+      activeBuilder.deactivate?.();
+      activeBuilder = null;
+    }
+    console.log('Starting Script Builder');
+    activeBuilder = new ScriptBuilder();
+    activeBuilder.start();
+    sendResponse({ success: true });
+  } else if (request.action === 'startClick2Crawl') {
+    if (activeBuilder) {
+      activeBuilder.deactivate?.();
+      activeBuilder = null;
+    }
+    console.log('Starting Click2Crawl');
+    activeBuilder = new Click2CrawlBuilder();
+    sendResponse({ success: true });
+  } else if (request.action === 'generateCode') {
+    if (activeBuilder && activeBuilder.generateCode) {
+      activeBuilder.generateCode();
+    }
+    sendResponse({ success: true });
   }
-  
-  return true;
-});
\ No newline at end of file
+});
+
+// Cleanup on page unload
+window.addEventListener('beforeunload', () => {
+  if (activeBuilder) {
+    if (activeBuilder.deactivate) {
+      activeBuilder.deactivate();
+    } else if (activeBuilder.stop) {
+      activeBuilder.stop();
+    }
+    activeBuilder = null;
+  }
+});
+
+console.log('Crawl4AI Assistant content script loaded');
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/contentAnalyzer.js b/docs/md_v2/apps/crawl4ai-assistant/content/contentAnalyzer.js
new file mode 100644
index 00000000..a4ca3054
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/contentAnalyzer.js
@@ -0,0 +1,623 @@
+class ContentAnalyzer {
+  constructor() {
+    this.patterns = {
+      article: ['article', 'main', 'content', 'post', 'entry'],
+      navigation: ['nav', 'menu', 'navigation', 'breadcrumb'],
+      sidebar: ['sidebar', 'aside', 'widget'],
+      header: ['header', 'masthead', 'banner'],
+      footer: ['footer', 'copyright', 'contact'],
+      list: ['list', 'items', 'results', 'products', 'cards'],
+      table: ['table', 'grid', 'data'],
+      media: ['gallery', 'carousel', 'slideshow', 'video', 'media']
+    };
+  }
+  
+  async analyze(elements) {
+    const analysis = {
+      structure: await this.analyzeStructure(elements),
+      contentType: this.identifyContentType(elements),
+      hierarchy: this.buildHierarchy(elements),
+      mediaAssets: this.collectMediaAssets(elements),
+      textDensity: this.calculateTextDensity(elements),
+      semanticRegions: this.identifySemanticRegions(elements),
+      relationships: this.analyzeRelationships(elements),
+      metadata: this.extractMetadata(elements)
+    };
+    
+    return analysis;
+  }
+  
+  analyzeStructure(elements) {
+    const structure = {
+      hasHeadings: false,
+      hasLists: false,
+      hasTables: false,
+      hasMedia: false,
+      hasCode: false,
+      hasLinks: false,
+      layout: 'linear', // linear, grid, mixed
+      depth: 0,
+      elementTypes: new Map()
+    };
+    
+    // Analyze each element
+    for (const element of elements) {
+      this.analyzeElementStructure(element, structure);
+    }
+    
+    // Determine layout type
+    structure.layout = this.determineLayout(elements);
+    
+    // Calculate max depth
+    structure.depth = this.calculateMaxDepth(elements);
+    
+    return structure;
+  }
+  
+  analyzeElementStructure(element, structure, visited = new Set()) {
+    if (visited.has(element)) return;
+    visited.add(element);
+    
+    const tagName = element.tagName;
+    
+    // Update element type count
+    structure.elementTypes.set(
+      tagName, 
+      (structure.elementTypes.get(tagName) || 0) + 1
+    );
+    
+    // Check for specific structures
+    if (/^H[1-6]$/.test(tagName)) {
+      structure.hasHeadings = true;
+    } else if (['UL', 'OL', 'DL'].includes(tagName)) {
+      structure.hasLists = true;
+    } else if (tagName === 'TABLE') {
+      structure.hasTables = true;
+    } else if (['IMG', 'VIDEO', 'IFRAME', 'PICTURE'].includes(tagName)) {
+      structure.hasMedia = true;
+    } else if (['CODE', 'PRE'].includes(tagName)) {
+      structure.hasCode = true;
+    } else if (tagName === 'A') {
+      structure.hasLinks = true;
+    }
+    
+    // Analyze children
+    for (const child of element.children) {
+      this.analyzeElementStructure(child, structure, visited);
+    }
+  }
+  
+  identifyContentType(elements) {
+    const scores = {
+      article: 0,
+      list: 0,
+      table: 0,
+      form: 0,
+      media: 0,
+      mixed: 0
+    };
+    
+    for (const element of elements) {
+      // Score based on element types and classes
+      const tagName = element.tagName;
+      const className = element.className.toLowerCase();
+      const id = element.id.toLowerCase();
+      
+      // Check for article patterns
+      if (tagName === 'ARTICLE' || 
+          this.matchesPattern(className + ' ' + id, this.patterns.article)) {
+        scores.article += 10;
+      }
+      
+      // Check for list patterns
+      if (['UL', 'OL'].includes(tagName) || 
+          this.matchesPattern(className, this.patterns.list)) {
+        scores.list += 5;
+      }
+      
+      // Check for table
+      if (tagName === 'TABLE') {
+        scores.table += 10;
+      }
+      
+      // Check for form
+      if (tagName === 'FORM' || element.querySelector('input, select, textarea')) {
+        scores.form += 5;
+      }
+      
+      // Check for media gallery
+      if (this.matchesPattern(className, this.patterns.media) ||
+          element.querySelectorAll('img, video').length > 3) {
+        scores.media += 5;
+      }
+    }
+    
+    // Determine primary content type
+    const maxScore = Math.max(...Object.values(scores));
+    if (maxScore === 0) return 'unknown';
+    
+    for (const [type, score] of Object.entries(scores)) {
+      if (score === maxScore) {
+        return type;
+      }
+    }
+    
+    return 'mixed';
+  }
+  
+  buildHierarchy(elements) {
+    const hierarchy = {
+      root: null,
+      levels: [],
+      headingStructure: []
+    };
+    
+    // Find common ancestor
+    if (elements.length > 0) {
+      hierarchy.root = this.findCommonAncestor(elements);
+    }
+    
+    // Build heading hierarchy
+    const headings = [];
+    for (const element of elements) {
+      const foundHeadings = element.querySelectorAll('h1, h2, h3, h4, h5, h6');
+      headings.push(...Array.from(foundHeadings));
+    }
+    
+    // Sort headings by document position
+    headings.sort((a, b) => {
+      const position = a.compareDocumentPosition(b);
+      if (position & Node.DOCUMENT_POSITION_FOLLOWING) {
+        return -1;
+      } else if (position & Node.DOCUMENT_POSITION_PRECEDING) {
+        return 1;
+      }
+      return 0;
+    });
+    
+    // Build heading structure
+    let currentLevel = 0;
+    const stack = [];
+    
+    for (const heading of headings) {
+      const level = parseInt(heading.tagName.substring(1));
+      const item = {
+        level,
+        text: heading.textContent.trim(),
+        element: heading,
+        children: []
+      };
+      
+      // Find parent in stack
+      while (stack.length > 0 && stack[stack.length - 1].level >= level) {
+        stack.pop();
+      }
+      
+      if (stack.length > 0) {
+        stack[stack.length - 1].children.push(item);
+      } else {
+        hierarchy.headingStructure.push(item);
+      }
+      
+      stack.push(item);
+    }
+    
+    return hierarchy;
+  }
+  
+  collectMediaAssets(elements) {
+    const media = {
+      images: [],
+      videos: [],
+      iframes: [],
+      audio: []
+    };
+    
+    for (const element of elements) {
+      // Collect images
+      const images = element.querySelectorAll('img');
+      for (const img of images) {
+        media.images.push({
+          src: img.src,
+          alt: img.alt,
+          title: img.title,
+          width: img.width,
+          height: img.height,
+          element: img
+        });
+      }
+      
+      // Collect videos
+      const videos = element.querySelectorAll('video');
+      for (const video of videos) {
+        media.videos.push({
+          src: video.src,
+          poster: video.poster,
+          width: video.width,
+          height: video.height,
+          element: video
+        });
+      }
+      
+      // Collect iframes
+      const iframes = element.querySelectorAll('iframe');
+      for (const iframe of iframes) {
+        media.iframes.push({
+          src: iframe.src,
+          width: iframe.width,
+          height: iframe.height,
+          title: iframe.title,
+          element: iframe
+        });
+      }
+      
+      // Collect audio
+      const audios = element.querySelectorAll('audio');
+      for (const audio of audios) {
+        media.audio.push({
+          src: audio.src,
+          element: audio
+        });
+      }
+    }
+    
+    return media;
+  }
+  
+  calculateTextDensity(elements) {
+    let totalText = 0;
+    let totalElements = 0;
+    let linkText = 0;
+    let codeText = 0;
+    
+    for (const element of elements) {
+      const stats = this.getTextStats(element);
+      totalText += stats.textLength;
+      totalElements += stats.elementCount;
+      linkText += stats.linkTextLength;
+      codeText += stats.codeTextLength;
+    }
+    
+    return {
+      textLength: totalText,
+      elementCount: totalElements,
+      averageTextPerElement: totalElements > 0 ? totalText / totalElements : 0,
+      linkDensity: totalText > 0 ? linkText / totalText : 0,
+      codeDensity: totalText > 0 ? codeText / totalText : 0
+    };
+  }
+  
+  getTextStats(element, visited = new Set()) {
+    if (visited.has(element)) {
+      return { textLength: 0, elementCount: 0, linkTextLength: 0, codeTextLength: 0 };
+    }
+    visited.add(element);
+    
+    let stats = {
+      textLength: 0,
+      elementCount: 1,
+      linkTextLength: 0,
+      codeTextLength: 0
+    };
+    
+    // Get direct text content
+    for (const node of element.childNodes) {
+      if (node.nodeType === Node.TEXT_NODE) {
+        const text = node.textContent.trim();
+        stats.textLength += text.length;
+        
+        // Check if this text is within a link
+        if (element.tagName === 'A') {
+          stats.linkTextLength += text.length;
+        }
+        
+        // Check if this text is within code
+        if (['CODE', 'PRE'].includes(element.tagName)) {
+          stats.codeTextLength += text.length;
+        }
+      }
+    }
+    
+    // Process children
+    for (const child of element.children) {
+      const childStats = this.getTextStats(child, visited);
+      stats.textLength += childStats.textLength;
+      stats.elementCount += childStats.elementCount;
+      stats.linkTextLength += childStats.linkTextLength;
+      stats.codeTextLength += childStats.codeTextLength;
+    }
+    
+    return stats;
+  }
+  
+  identifySemanticRegions(elements) {
+    const regions = {
+      headers: [],
+      navigation: [],
+      main: [],
+      sidebars: [],
+      footers: [],
+      articles: []
+    };
+    
+    for (const element of elements) {
+      // Check element and its ancestors for semantic regions
+      let current = element;
+      while (current) {
+        const tagName = current.tagName;
+        const className = current.className.toLowerCase();
+        const role = current.getAttribute('role');
+        
+        // Check semantic HTML5 elements
+        if (tagName === 'HEADER' || role === 'banner') {
+          regions.headers.push(current);
+        } else if (tagName === 'NAV' || role === 'navigation') {
+          regions.navigation.push(current);
+        } else if (tagName === 'MAIN' || role === 'main') {
+          regions.main.push(current);
+        } else if (tagName === 'ASIDE' || role === 'complementary') {
+          regions.sidebars.push(current);
+        } else if (tagName === 'FOOTER' || role === 'contentinfo') {
+          regions.footers.push(current);
+        } else if (tagName === 'ARTICLE' || role === 'article') {
+          regions.articles.push(current);
+        }
+        
+        // Check class patterns
+        if (this.matchesPattern(className, this.patterns.header)) {
+          regions.headers.push(current);
+        } else if (this.matchesPattern(className, this.patterns.navigation)) {
+          regions.navigation.push(current);
+        } else if (this.matchesPattern(className, this.patterns.sidebar)) {
+          regions.sidebars.push(current);
+        } else if (this.matchesPattern(className, this.patterns.footer)) {
+          regions.footers.push(current);
+        }
+        
+        current = current.parentElement;
+      }
+    }
+    
+    // Deduplicate
+    for (const key of Object.keys(regions)) {
+      regions[key] = Array.from(new Set(regions[key]));
+    }
+    
+    return regions;
+  }
+  
+  analyzeRelationships(elements) {
+    const relationships = {
+      siblings: [],
+      parents: [],
+      children: [],
+      relatedByClass: new Map(),
+      relatedByStructure: []
+    };
+    
+    // Find sibling relationships
+    for (let i = 0; i < elements.length; i++) {
+      for (let j = i + 1; j < elements.length; j++) {
+        if (elements[i].parentElement === elements[j].parentElement) {
+          relationships.siblings.push([elements[i], elements[j]]);
+        }
+      }
+    }
+    
+    // Find parent-child relationships
+    for (const element of elements) {
+      for (const other of elements) {
+        if (element !== other) {
+          if (element.contains(other)) {
+            relationships.parents.push({ parent: element, child: other });
+          } else if (other.contains(element)) {
+            relationships.children.push({ parent: other, child: element });
+          }
+        }
+      }
+    }
+    
+    // Group by similar classes
+    for (const element of elements) {
+      const classes = Array.from(element.classList);
+      for (const className of classes) {
+        if (!relationships.relatedByClass.has(className)) {
+          relationships.relatedByClass.set(className, []);
+        }
+        relationships.relatedByClass.get(className).push(element);
+      }
+    }
+    
+    // Find structurally similar elements
+    for (let i = 0; i < elements.length; i++) {
+      for (let j = i + 1; j < elements.length; j++) {
+        if (this.areStructurallySimilar(elements[i], elements[j])) {
+          relationships.relatedByStructure.push([elements[i], elements[j]]);
+        }
+      }
+    }
+    
+    return relationships;
+  }
+  
+  areStructurallySimilar(element1, element2) {
+    // Same tag name
+    if (element1.tagName !== element2.tagName) {
+      return false;
+    }
+    
+    // Similar class structure
+    const classes1 = Array.from(element1.classList).sort();
+    const classes2 = Array.from(element2.classList).sort();
+    
+    // At least 50% overlap in classes
+    const intersection = classes1.filter(c => classes2.includes(c));
+    const union = Array.from(new Set([...classes1, ...classes2]));
+    
+    if (union.length > 0 && intersection.length / union.length >= 0.5) {
+      return true;
+    }
+    
+    // Similar child structure
+    if (element1.children.length === element2.children.length) {
+      const childTags1 = Array.from(element1.children).map(c => c.tagName).sort();
+      const childTags2 = Array.from(element2.children).map(c => c.tagName).sort();
+      
+      if (JSON.stringify(childTags1) === JSON.stringify(childTags2)) {
+        return true;
+      }
+    }
+    
+    return false;
+  }
+  
+  extractMetadata(elements) {
+    const metadata = {
+      title: null,
+      description: null,
+      author: null,
+      date: null,
+      tags: [],
+      microdata: []
+    };
+    
+    for (const element of elements) {
+      // Look for title
+      const h1 = element.querySelector('h1');
+      if (h1 && !metadata.title) {
+        metadata.title = h1.textContent.trim();
+      }
+      
+      // Look for meta information
+      const metaElements = element.querySelectorAll('[itemprop], [property], [name]');
+      for (const meta of metaElements) {
+        const prop = meta.getAttribute('itemprop') || 
+                    meta.getAttribute('property') || 
+                    meta.getAttribute('name');
+        const content = meta.getAttribute('content') || meta.textContent.trim();
+        
+        if (prop && content) {
+          if (prop.includes('author')) {
+            metadata.author = content;
+          } else if (prop.includes('date') || prop.includes('time')) {
+            metadata.date = content;
+          } else if (prop.includes('description')) {
+            metadata.description = content;
+          } else if (prop.includes('tag') || prop.includes('keyword')) {
+            metadata.tags.push(content);
+          }
+          
+          metadata.microdata.push({ property: prop, value: content });
+        }
+      }
+      
+      // Look for time elements
+      const timeElements = element.querySelectorAll('time');
+      for (const time of timeElements) {
+        if (!metadata.date && time.dateTime) {
+          metadata.date = time.dateTime;
+        }
+      }
+    }
+    
+    return metadata;
+  }
+  
+  determineLayout(elements) {
+    // Check if elements form a grid
+    const positions = elements.map(el => {
+      const rect = el.getBoundingClientRect();
+      return { x: rect.left, y: rect.top, width: rect.width, height: rect.height };
+    });
+    
+    // Check for grid layout (multiple elements on same row)
+    const rows = new Map();
+    for (const pos of positions) {
+      const row = Math.round(pos.y / 10) * 10; // Round to nearest 10px
+      if (!rows.has(row)) {
+        rows.set(row, []);
+      }
+      rows.get(row).push(pos);
+    }
+    
+    // If multiple elements share rows, it's likely a grid
+    const hasGrid = Array.from(rows.values()).some(row => row.length > 1);
+    
+    if (hasGrid) {
+      return 'grid';
+    }
+    
+    // Check for mixed layout (significant variation in widths)
+    const widths = positions.map(p => p.width);
+    const avgWidth = widths.reduce((a, b) => a + b, 0) / widths.length;
+    const variance = widths.reduce((sum, w) => sum + Math.pow(w - avgWidth, 2), 0) / widths.length;
+    const stdDev = Math.sqrt(variance);
+    
+    if (stdDev / avgWidth > 0.3) {
+      return 'mixed';
+    }
+    
+    return 'linear';
+  }
+  
+  calculateMaxDepth(elements) {
+    let maxDepth = 0;
+    
+    for (const element of elements) {
+      const depth = this.getElementDepth(element);
+      maxDepth = Math.max(maxDepth, depth);
+    }
+    
+    return maxDepth;
+  }
+  
+  getElementDepth(element, depth = 0) {
+    if (element.children.length === 0) {
+      return depth;
+    }
+    
+    let maxChildDepth = depth;
+    for (const child of element.children) {
+      const childDepth = this.getElementDepth(child, depth + 1);
+      maxChildDepth = Math.max(maxChildDepth, childDepth);
+    }
+    
+    return maxChildDepth;
+  }
+  
+  findCommonAncestor(elements) {
+    if (elements.length === 0) return null;
+    if (elements.length === 1) return elements[0].parentElement;
+    
+    // Start with the first element's ancestors
+    let ancestor = elements[0];
+    const ancestors = [];
+    
+    while (ancestor) {
+      ancestors.push(ancestor);
+      ancestor = ancestor.parentElement;
+    }
+    
+    // Find the deepest common ancestor
+    for (const ancestorCandidate of ancestors) {
+      let isCommon = true;
+      
+      for (const element of elements) {
+        if (!ancestorCandidate.contains(element)) {
+          isCommon = false;
+          break;
+        }
+      }
+      
+      if (isCommon) {
+        return ancestorCandidate;
+      }
+    }
+    
+    return document.body;
+  }
+  
+  matchesPattern(text, patterns) {
+    return patterns.some(pattern => text.includes(pattern));
+  }
+}
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/markdownConverter.js b/docs/md_v2/apps/crawl4ai-assistant/content/markdownConverter.js
new file mode 100644
index 00000000..a2c486f2
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/markdownConverter.js
@@ -0,0 +1,718 @@
+class MarkdownConverter {
+  constructor() {
+    // Conversion handlers for different element types
+    this.converters = {
+      'H1': async (el, ctx) => await this.convertHeading(el, 1, ctx),
+      'H2': async (el, ctx) => await this.convertHeading(el, 2, ctx),
+      'H3': async (el, ctx) => await this.convertHeading(el, 3, ctx),
+      'H4': async (el, ctx) => await this.convertHeading(el, 4, ctx),
+      'H5': async (el, ctx) => await this.convertHeading(el, 5, ctx),
+      'H6': async (el, ctx) => await this.convertHeading(el, 6, ctx),
+      'P': async (el, ctx) => await this.convertParagraph(el, ctx),
+      'A': async (el, ctx) => await this.convertLink(el, ctx),
+      'IMG': async (el, ctx) => await this.convertImage(el, ctx),
+      'UL': async (el, ctx) => await this.convertList(el, 'ul', ctx),
+      'OL': async (el, ctx) => await this.convertList(el, 'ol', ctx),
+      'LI': async (el, ctx) => await this.convertListItem(el, ctx),
+      'TABLE': async (el, ctx) => await this.convertTable(el, ctx),
+      'BLOCKQUOTE': async (el, ctx) => await this.convertBlockquote(el, ctx),
+      'PRE': async (el, ctx) => await this.convertPreformatted(el, ctx),
+      'CODE': async (el, ctx) => await this.convertCode(el, ctx),
+      'HR': async (el, ctx) => '\n---\n',
+      'BR': async (el, ctx) => '  \n',
+      'STRONG': async (el, ctx) => `**${await this.getTextContent(el, ctx)}**`,
+      'B': async (el, ctx) => `**${await this.getTextContent(el, ctx)}**`,
+      'EM': async (el, ctx) => `*${await this.getTextContent(el, ctx)}*`,
+      'I': async (el, ctx) => `*${await this.getTextContent(el, ctx)}*`,
+      'DEL': async (el, ctx) => `~~${await this.getTextContent(el, ctx)}~~`,
+      'S': async (el, ctx) => `~~${await this.getTextContent(el, ctx)}~~`,
+      'DIV': async (el, ctx) => await this.convertDiv(el, ctx),
+      'SPAN': async (el, ctx) => await this.convertSpan(el, ctx),
+      'ARTICLE': async (el, ctx) => await this.convertArticle(el, ctx),
+      'SECTION': async (el, ctx) => await this.convertSection(el, ctx),
+      'FIGURE': async (el, ctx) => await this.convertFigure(el, ctx),
+      'FIGCAPTION': async (el, ctx) => await this.convertFigCaption(el, ctx),
+      'VIDEO': async (el, ctx) => await this.convertVideo(el, ctx),
+      'IFRAME': async (el, ctx) => await this.convertIframe(el, ctx),
+      'DL': async (el, ctx) => await this.convertDefinitionList(el, ctx),
+      'DT': async (el, ctx) => await this.convertDefinitionTerm(el, ctx),
+      'DD': async (el, ctx) => await this.convertDefinitionDescription(el, ctx),
+      'TR': async (el, ctx) => await this.convertTableRow(el, ctx)
+    };
+    
+    // Maintain context during conversion
+    this.conversionContext = {
+      listDepth: 0,
+      inTable: false,
+      inCode: false,
+      preserveWhitespace: false,
+      references: [],
+      imageCount: 0,
+      linkCount: 0
+    };
+  }
+  
+  async convert(elements, options = {}) {
+    // Reset context
+    this.resetContext();
+    
+    // Apply options
+    this.options = {
+      includeImages: true,
+      preserveTables: true,
+      keepCodeFormatting: true,
+      simplifyLayout: false,
+      preserveLinks: true,
+      ...options
+    };
+    
+    // Convert elements
+    const markdownParts = [];
+    
+    for (const element of elements) {
+      const markdown = await this.convertElement(element, this.conversionContext);
+      if (markdown.trim()) {
+        markdownParts.push(markdown);
+      }
+    }
+    
+    // Join parts with appropriate spacing
+    let result = markdownParts.join('\n\n');
+    
+    // Add references if using reference-style links
+    if (this.conversionContext.references.length > 0) {
+      result += '\n\n' + this.generateReferences();
+    }
+    
+    // Post-process to clean up
+    result = this.postProcess(result);
+    
+    return result;
+  }
+  
+  resetContext() {
+    this.conversionContext = {
+      listDepth: 0,
+      inTable: false,
+      inCode: false,
+      preserveWhitespace: false,
+      references: [],
+      imageCount: 0,
+      linkCount: 0
+    };
+  }
+  
+  async convertElement(element, context) {
+    // Skip hidden elements
+    if (this.isHidden(element)) {
+      return '';
+    }
+    
+    // Skip script and style elements
+    if (['SCRIPT', 'STYLE', 'NOSCRIPT'].includes(element.tagName)) {
+      return '';
+    }
+    
+    // Get converter for this element type
+    const converter = this.converters[element.tagName];
+    
+    if (converter) {
+      return await converter(element, context);
+    } else {
+      // For unknown elements, process children
+      return await this.processChildren(element, context);
+    }
+  }
+  
+  async processChildren(element, context) {
+    const parts = [];
+    
+    for (const child of element.childNodes) {
+      if (child.nodeType === Node.TEXT_NODE) {
+        const text = this.processTextNode(child, context);
+        if (text) {
+          parts.push(text);
+        }
+      } else if (child.nodeType === Node.ELEMENT_NODE) {
+        const markdown = await this.convertElement(child, context);
+        if (markdown) {
+          parts.push(markdown);
+        }
+      }
+    }
+    
+    return parts.join('');
+  }
+  
+  processTextNode(node, context) {
+    let text = node.textContent;
+    
+    // Preserve whitespace in code blocks
+    if (!context.preserveWhitespace && !context.inCode) {
+      // Normalize whitespace
+      text = text.replace(/\s+/g, ' ');
+      
+      // Trim if at block boundaries
+      if (this.isBlockBoundary(node.previousSibling)) {
+        text = text.trimStart();
+      }
+      if (this.isBlockBoundary(node.nextSibling)) {
+        text = text.trimEnd();
+      }
+    }
+    
+    // Escape markdown characters
+    if (!context.inCode) {
+      text = this.escapeMarkdown(text);
+    }
+    
+    return text;
+  }
+  
+  isBlockBoundary(node) {
+    if (!node || node.nodeType !== Node.ELEMENT_NODE) {
+      return true;
+    }
+    
+    const blockElements = [
+      'DIV', 'P', 'H1', 'H2', 'H3', 'H4', 'H5', 'H6',
+      'UL', 'OL', 'LI', 'BLOCKQUOTE', 'PRE', 'TABLE',
+      'HR', 'ARTICLE', 'SECTION', 'HEADER', 'FOOTER',
+      'NAV', 'ASIDE', 'MAIN'
+    ];
+    
+    return blockElements.includes(node.tagName);
+  }
+  
+  escapeMarkdown(text) {
+    // In text-only mode, don't escape characters
+    if (this.options.textOnly) {
+      return text;
+    }
+    
+    // Escape special markdown characters
+    return text
+      .replace(/\\/g, '\\\\')
+      .replace(/\*/g, '\\*')
+      .replace(/_/g, '\\_')
+      .replace(/\[/g, '\\[')
+      .replace(/\]/g, '\\]')
+      .replace(/\(/g, '\\(')
+      .replace(/\)/g, '\\)')
+      .replace(/\#/g, '\\#')
+      .replace(/\+/g, '\\+')
+      .replace(/\-/g, '\\-')
+      .replace(/\./g, '\\.')
+      .replace(/\!/g, '\\!')
+      .replace(/\|/g, '\\|');
+  }
+  
+  async convertHeading(element, level, context) {
+    const text = await this.getTextContent(element, context);
+    return '#'.repeat(level) + ' ' + text + '\n';
+  }
+  
+  async convertParagraph(element, context) {
+    const content = await this.processChildren(element, context);
+    return content.trim() ? content + '\n' : '';
+  }
+  
+  async convertLink(element, context) {
+    if (!this.options.preserveLinks || this.options.textOnly) {
+      return await this.getTextContent(element, context);
+    }
+    
+    const text = await this.getTextContent(element, context);
+    const href = element.getAttribute('href');
+    const title = element.getAttribute('title');
+    
+    if (!href) {
+      return text;
+    }
+    
+    // Convert relative URLs to absolute
+    const absoluteUrl = this.makeAbsoluteUrl(href);
+    
+    // Use reference-style links for cleaner markdown
+    if (text && absoluteUrl) {
+      if (title) {
+        return `[${text}](${absoluteUrl} "${title}")`;
+      } else {
+        return `[${text}](${absoluteUrl})`;
+      }
+    }
+    
+    return text;
+  }
+  
+  async convertImage(element, context) {
+    if (!this.options.includeImages || this.options.textOnly) {
+      // In text-only mode, return alt text if available
+      if (this.options.textOnly) {
+        const alt = element.getAttribute('alt');
+        return alt ? `[Image: ${alt}]` : '';
+      }
+      return '';
+    }
+    
+    const src = element.getAttribute('src');
+    const alt = element.getAttribute('alt') || '';
+    const title = element.getAttribute('title');
+    
+    if (!src) {
+      return '';
+    }
+    
+    // Convert relative URLs to absolute
+    const absoluteUrl = this.makeAbsoluteUrl(src);
+    
+    if (title) {
+      return `![${alt}](${absoluteUrl} "${title}")`;
+    } else {
+      return `![${alt}](${absoluteUrl})`;
+    }
+  }
+  
+  async convertList(element, type, context) {
+    const oldDepth = context.listDepth;
+    context.listDepth++;
+    
+    const items = [];
+    for (const child of element.children) {
+      if (child.tagName === 'LI') {
+        const markdown = await this.convertListItem(child, { ...context, listType: type });
+        if (markdown) {
+          items.push(markdown);
+        }
+      }
+    }
+    
+    context.listDepth = oldDepth;
+    
+    return items.join('\n') + (context.listDepth === 0 ? '\n' : '');
+  }
+  
+  async convertListItem(element, context) {
+    const indent = '  '.repeat(Math.max(0, context.listDepth - 1));
+    const bullet = context.listType === 'ol' ? '1.' : '-';
+    const content = (await this.processChildren(element, context)).trim();
+    
+    return `${indent}${bullet} ${content}`;
+  }
+  
+  async convertTable(element, context) {
+    if (!this.options.preserveTables || this.options.textOnly) {
+      // Fallback to simple text representation
+      return await this.convertTableToText(element, context);
+    }
+    
+    const rows = [];
+    const headerRows = [];
+    let maxCols = 0;
+    
+    // Process table rows
+    for (const child of element.children) {
+      if (child.tagName === 'THEAD') {
+        for (const row of child.children) {
+          if (row.tagName === 'TR') {
+            const cells = await this.processTableRow(row, context);
+            headerRows.push(cells);
+            maxCols = Math.max(maxCols, cells.length);
+          }
+        }
+      } else if (child.tagName === 'TBODY') {
+        for (const row of child.children) {
+          if (row.tagName === 'TR') {
+            const cells = await this.processTableRow(row, context);
+            rows.push(cells);
+            maxCols = Math.max(maxCols, cells.length);
+          }
+        }
+      } else if (child.tagName === 'TR') {
+        const cells = await this.processTableRow(child, context);
+        rows.push(cells);
+        maxCols = Math.max(maxCols, cells.length);
+      }
+    }
+    
+    // Build markdown table
+    const markdownRows = [];
+    
+    // Add headers
+    if (headerRows.length > 0) {
+      for (const headerRow of headerRows) {
+        const paddedRow = this.padTableRow(headerRow, maxCols);
+        markdownRows.push('| ' + paddedRow.join(' | ') + ' |');
+      }
+      
+      // Add separator
+      const separator = Array(maxCols).fill('---');
+      markdownRows.push('| ' + separator.join(' | ') + ' |');
+    }
+    
+    // Add body rows
+    for (const row of rows) {
+      const paddedRow = this.padTableRow(row, maxCols);
+      markdownRows.push('| ' + paddedRow.join(' | ') + ' |');
+    }
+    
+    return markdownRows.join('\n') + '\n';
+  }
+  
+  async processTableRow(row, context) {
+    const cells = [];
+    
+    for (const cell of row.children) {
+      if (cell.tagName === 'TD' || cell.tagName === 'TH') {
+        const content = (await this.getTextContent(cell, context)).trim();
+        cells.push(content);
+      }
+    }
+    
+    return cells;
+  }
+  
+  async convertTableRow(element, context) {
+    // Convert a single table row to markdown
+    if (this.options.textOnly) {
+      const cells = await this.processTableRow(element, context);
+      return cells.join(' ');
+    }
+    
+    // For non-text-only mode, create a simple table representation
+    const cells = await this.processTableRow(element, context);
+    return '| ' + cells.join(' | ') + ' |';
+  }
+  
+  padTableRow(row, targetLength) {
+    const padded = [...row];
+    while (padded.length < targetLength) {
+      padded.push('');
+    }
+    return padded;
+  }
+  
+  async convertTableToText(element, context) {
+    // Convert table to clean text representation
+    const lines = [];
+    const rows = element.querySelectorAll('tr');
+    
+    for (const row of rows) {
+      const cells = row.querySelectorAll('td, th');
+      const cellTexts = [];
+      
+      for (const cell of cells) {
+        const text = (await this.getTextContent(cell, context)).trim();
+        if (text) {
+          cellTexts.push(text);
+        }
+      }
+      
+      if (cellTexts.length > 0) {
+        // Join cells with space, handling common patterns
+        lines.push(cellTexts.join(' '));
+      }
+    }
+    
+    return lines.join('\n');
+  }
+  
+  async convertBlockquote(element, context) {
+    const lines = (await this.processChildren(element, context)).trim().split('\n');
+    return lines.map(line => '> ' + line).join('\n') + '\n';
+  }
+  
+  async convertPreformatted(element, context) {
+    const oldInCode = context.inCode;
+    const oldPreserveWhitespace = context.preserveWhitespace;
+    
+    context.inCode = true;
+    context.preserveWhitespace = true;
+    
+    let content = '';
+    let language = '';
+    
+    // Check if this is a code block with language
+    const codeElement = element.querySelector('code');
+    if (codeElement) {
+      // Try to detect language from class
+      const className = codeElement.className;
+      const langMatch = className.match(/language-(\w+)/);
+      if (langMatch) {
+        language = langMatch[1];
+      }
+      
+      content = codeElement.textContent;
+    } else {
+      content = element.textContent;
+    }
+    
+    context.inCode = oldInCode;
+    context.preserveWhitespace = oldPreserveWhitespace;
+    
+    // Use fenced code blocks
+    return '```' + language + '\n' + content + '\n```\n';
+  }
+  
+  async convertCode(element, context) {
+    if (element.parentElement && element.parentElement.tagName === 'PRE') {
+      // Already handled by convertPreformatted
+      return element.textContent;
+    }
+    
+    const content = element.textContent;
+    return '`' + content + '`';
+  }
+  
+  async convertDiv(element, context) {
+    // Check for special div types
+    if (element.className.includes('code-block') || 
+        element.className.includes('highlight')) {
+      return await this.convertPreformatted(element, context);
+    }
+    
+    const content = await this.processChildren(element, context);
+    return content.trim() ? content + '\n' : '';
+  }
+  
+  async convertSpan(element, context) {
+    // Check for special span types
+    if (element.className.includes('code') || 
+        element.className.includes('inline-code')) {
+      return this.convertCode(element, context);
+    }
+    
+    return await this.processChildren(element, context);
+  }
+  
+  async convertArticle(element, context) {
+    const content = await this.processChildren(element, context);
+    return content.trim() ? content + '\n' : '';
+  }
+  
+  async convertSection(element, context) {
+    const content = await this.processChildren(element, context);
+    return content.trim() ? content + '\n' : '';
+  }
+  
+  async convertFigure(element, context) {
+    const content = await this.processChildren(element, context);
+    return content.trim() ? content + '\n' : '';
+  }
+  
+  async convertFigCaption(element, context) {
+    const caption = await this.getTextContent(element, context);
+    return caption ? '\n*' + caption + '*\n' : '';
+  }
+  
+  async convertVideo(element, context) {
+    const title = element.getAttribute('title') || 'Video';
+    
+    if (this.options.textOnly) {
+      return `[Video: ${title}]`;
+    }
+    
+    const src = element.getAttribute('src');
+    const poster = element.getAttribute('poster');
+    
+    if (!src) {
+      return '';
+    }
+    
+    // Convert to markdown with poster image if available
+    if (poster) {
+      const absolutePoster = this.makeAbsoluteUrl(poster);
+      const absoluteSrc = this.makeAbsoluteUrl(src);
+      return `[![${title}](${absolutePoster})](${absoluteSrc})`;
+    } else {
+      const absoluteSrc = this.makeAbsoluteUrl(src);
+      return `[${title}](${absoluteSrc})`;
+    }
+  }
+  
+  async convertIframe(element, context) {
+    const title = element.getAttribute('title') || 'Embedded content';
+    
+    if (this.options.textOnly) {
+      const src = element.getAttribute('src') || '';
+      if (src.includes('youtube.com') || src.includes('youtu.be')) {
+        return `[Video: ${title}]`;
+      } else if (src.includes('vimeo.com')) {
+        return `[Video: ${title}]`;
+      } else {
+        return `[Embedded: ${title}]`;
+      }
+    }
+    
+    const src = element.getAttribute('src');
+    if (!src) {
+      return '';
+    }
+    
+    // Check for common embeds
+    if (src.includes('youtube.com') || src.includes('youtu.be')) {
+      return `[▶️ ${title}](${src})`;
+    } else if (src.includes('vimeo.com')) {
+      return `[▶️ ${title}](${src})`;
+    } else {
+      return `[${title}](${src})`;
+    }
+  }
+  
+  async convertDefinitionList(element, context) {
+    return await this.processChildren(element, context) + '\n';
+  }
+  
+  async convertDefinitionTerm(element, context) {
+    const term = await this.getTextContent(element, context);
+    return '**' + term + '**\n';
+  }
+  
+  async convertDefinitionDescription(element, context) {
+    const description = await this.processChildren(element, context);
+    return ': ' + description + '\n';
+  }
+  
+  async getTextContent(element, context) {
+    // Special handling for elements that might contain other markdown
+    if (context.inCode) {
+      return element.textContent;
+    }
+    
+    return await this.processChildren(element, context);
+  }
+  
+  makeAbsoluteUrl(url) {
+    if (!url) return '';
+    
+    try {
+      // Check if already absolute
+      if (url.startsWith('http://') || url.startsWith('https://')) {
+        return url;
+      }
+      
+      // Handle protocol-relative URLs
+      if (url.startsWith('//')) {
+        return window.location.protocol + url;
+      }
+      
+      // Convert relative to absolute
+      const base = window.location.origin;
+      const path = window.location.pathname;
+      
+      if (url.startsWith('/')) {
+        return base + url;
+      } else {
+        // Relative to current path
+        const pathDir = path.substring(0, path.lastIndexOf('/') + 1);
+        return base + pathDir + url;
+      }
+    } catch (e) {
+      return url;
+    }
+  }
+  
+  isHidden(element) {
+    const style = window.getComputedStyle(element);
+    return style.display === 'none' || 
+           style.visibility === 'hidden' || 
+           style.opacity === '0';
+  }
+  
+  generateReferences() {
+    return this.conversionContext.references
+      .map((ref, index) => `[${index + 1}]: ${ref.url}`)
+      .join('\n');
+  }
+  
+  postProcess(markdown) {
+    // Apply text-only specific processing
+    if (this.options.textOnly) {
+      markdown = this.postProcessTextOnly(markdown);
+    }
+    
+    // Clean up excessive newlines
+    markdown = markdown.replace(/\n{3,}/g, '\n\n');
+    
+    // Clean up spaces before punctuation
+    markdown = markdown.replace(/ +([.,;:!?])/g, '$1');
+    
+    // Ensure proper spacing around headers
+    markdown = markdown.replace(/\n(#{1,6} )/g, '\n\n$1');
+    markdown = markdown.replace(/(#{1,6} .+)\n(?![\n#])/g, '$1\n\n');
+    
+    // Clean up list spacing
+    markdown = markdown.replace(/\n\n(-|\d+\.) /g, '\n$1 ');
+    
+    // Trim final result
+    return markdown.trim();
+  }
+  
+  postProcessTextOnly(markdown) {
+    // Smart pattern recognition for common formats
+    const lines = markdown.split('\n');
+    const processedLines = [];
+    let inMetadata = false;
+    let currentItem = null;
+    
+    for (let i = 0; i < lines.length; i++) {
+      const line = lines[i].trim();
+      if (!line) {
+        processedLines.push('');
+        continue;
+      }
+      
+      // Detect numbered list items (common in HN, Reddit, etc.)
+      const numberPattern = /^(\d+)\.\s*(.+)$/;
+      const numberMatch = line.match(numberPattern);
+      
+      if (numberMatch) {
+        // Start of a new numbered item
+        inMetadata = false;
+        currentItem = numberMatch[1];
+        const content = numberMatch[2];
+        
+        // Check if content has domain in parentheses
+        const domainPattern = /^(.+?)\s*\(([^)]+)\)\s*(.*)$/;
+        const domainMatch = content.match(domainPattern);
+        
+        if (domainMatch) {
+          const [, title, domain, rest] = domainMatch;
+          processedLines.push(`${currentItem}. **${title.trim()}** (${domain})`);
+          if (rest.trim()) {
+            processedLines.push(`   ${rest.trim()}`);
+            inMetadata = true;
+          }
+        } else {
+          processedLines.push(`${currentItem}. **${content}**`);
+        }
+      } else if (line.match(/\b(points?|by|ago|hide|comments?)\b/i) && currentItem) {
+        // This looks like metadata for the current item
+        const cleanedLine = line
+          .replace(/\s+/g, ' ')
+          .replace(/\s*\|\s*/g, ' | ')
+          .trim();
+        processedLines.push(`   ${cleanedLine}`);
+        inMetadata = true;
+      } else if (inMetadata && line.length < 100) {
+        // Continue metadata if we're in metadata mode and line is short
+        processedLines.push(`   ${line}`);
+      } else {
+        // Regular content
+        inMetadata = false;
+        processedLines.push(line);
+      }
+    }
+    
+    // Clean up the output
+    let result = processedLines.join('\n');
+    
+    // Remove excessive blank lines
+    result = result.replace(/\n{3,}/g, '\n\n');
+    
+    // Ensure proper spacing after numbered items
+    result = result.replace(/^(\d+\..+)$\n^(?!\s)/gm, '$1\n\n');
+    
+    return result;
+  }
+}
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/overlay.css b/docs/md_v2/apps/crawl4ai-assistant/content/overlay.css
index d8185772..af8235e9 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/content/overlay.css
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/overlay.css
@@ -2,1657 +2,2839 @@
 
 /* Font Face Definitions - Note: Fonts will be loaded from extension URL in content script */
 
+/* Font Face Definitions for DankMono */
+@font-face {
+  font-family: 'Dank Mono';
+  src: url('chrome-extension://__MSG_@@extension_id__/assets/DankMono-Regular.woff2') format('woff2');
+  font-weight: 400;
+  font-style: normal;
+  font-display: swap;
+}
+
+@font-face {
+  font-family: 'Dank Mono';
+  src: url('chrome-extension://__MSG_@@extension_id__/assets/DankMono-Bold.woff2') format('woff2');
+  font-weight: 700;
+  font-style: normal;
+  font-display: swap;
+}
+
+@font-face {
+  font-family: 'Dank Mono';
+  src: url('chrome-extension://__MSG_@@extension_id__/assets/DankMono-Italic.woff2') format('woff2');
+  font-weight: 400;
+  font-style: italic;
+  font-display: swap;
+}
+
 :root {
-  --font-primary: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace;
-  --font-code: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace;
+    --font-primary: "Dank Mono", Monaco, "Cascadia Code", "Roboto Mono", Consolas, "Courier New", monospace;
+    --font-code: "Dank Mono", Monaco, "Cascadia Code", "Roboto Mono", Consolas, "Courier New", monospace;
 }
 
 /* Highlight box for hovering */
 .c4ai-highlight-box {
-  position: absolute;
-  pointer-events: none;
-  z-index: 999999;
-  transition: all 0.1s ease;
-  display: none;
+    position: absolute;
+    pointer-events: none;
+    z-index: 999999;
+    transition: all 0.1s ease;
+    display: none;
 }
 
 .c4ai-highlight-box.c4ai-container-mode {
-  border: 3px dashed #0fbbaa;
-  background: rgba(15, 187, 170, 0.1);
-  box-shadow: 0 0 0 2px rgba(15, 187, 170, 0.3);
+    border: 3px dashed #0fbbaa;
+    background: rgba(15, 187, 170, 0.1);
+    box-shadow: 0 0 0 2px rgba(15, 187, 170, 0.3);
 }
 
 .c4ai-highlight-box.c4ai-field-mode {
-  border: 2px dashed #f380f5;
-  background: rgba(243, 128, 245, 0.1);
-  box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.3);
+    border: 2px dashed #f380f5;
+    background: rgba(243, 128, 245, 0.1);
+    box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.3);
 }
 
-/* Selected elements */
+/* Selected elements - keeping for backward compatibility */
 .c4ai-selected-container {
-  outline: 3px solid #0fbbaa !important;
-  outline-offset: 2px;
-  background: rgba(15, 187, 170, 0.05) !important;
+    outline: 3px solid #0fbbaa !important;
+    outline-offset: 2px;
+    background: rgba(15, 187, 170, 0.05) !important;
 }
 
 .c4ai-selected-field {
-  outline: 2px solid #f380f5 !important;
-  outline-offset: 1px;
-  background: rgba(243, 128, 245, 0.1) !important;
-  position: relative;
+    outline: 2px solid #f380f5 !important;
+    outline-offset: 1px;
+    background: rgba(243, 128, 245, 0.1) !important;
+    position: relative;
+}
+
+/* Selected box for permanent selection */
+.c4ai-selected-box {
+    position: absolute;
+    pointer-events: none;
+    z-index: 999998;
+    transition: all 0.1s ease;
+}
+
+.c4ai-selected-box.c4ai-selected-container {
+    border: 3px solid #0fbbaa;
+    background: rgba(15, 187, 170, 0.05);
+    box-shadow: 0 0 0 2px rgba(15, 187, 170, 0.3);
+}
+
+.c4ai-selected-box.c4ai-selected-field {
+    border: 2px solid #f380f5;
+    background: rgba(243, 128, 245, 0.1);
+    box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.3);
 }
 
 .c4ai-selected-field::after {
-  content: attr(data-c4ai-field);
-  position: absolute;
-  top: -24px;
-  left: 0;
-  background: #f380f5;
-  color: #000;
-  padding: 2px 8px;
-  border-radius: 4px;
-  font-size: 12px;
-  font-weight: bold;
-  font-family: var(--font-primary);
-  z-index: 999999;
-  white-space: nowrap;
+    content: attr(data-c4ai-field);
+    position: absolute;
+    top: -24px;
+    left: 0;
+    background: #f380f5;
+    color: #000;
+    padding: 2px 8px;
+    border-radius: 4px;
+    font-size: 12px;
+    font-weight: bold;
+    font-family: var(--font-primary);
+    z-index: 999999;
+    white-space: nowrap;
 }
 
 /* Toolbar */
 .c4ai-toolbar {
-  position: fixed;
-  top: 20px;
-  right: 20px;
-  background: #070708;
-  border: 1px solid #3f3f44;
-  border-radius: 8px;
-  z-index: 999999;
-  font-family: var(--font-primary);
-  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
-  width: 320px;
-  color: #e8e9ed;
-  overflow: hidden;
+    position: fixed;
+    top: 20px;
+    right: 20px;
+    background: #070708;
+    border: 1px solid #3f3f44;
+    border-radius: 8px;
+    z-index: 2147483647; /* Maximum z-index */
+    font-family: var(--font-primary);
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
+    width: 320px;
+    color: #e8e9ed;
+    overflow: hidden;
+    pointer-events: auto; /* Ensure clicks work */
 }
 
 .c4ai-toolbar * {
-  font-family: inherit;
-  box-sizing: border-box;
+    font-family: inherit;
+    box-sizing: border-box;
 }
 
 /* macOS-style titlebar */
 .c4ai-toolbar-titlebar {
-  display: flex;
-  align-items: center;
-  padding: 12px;
-  background: #1a1a1a;
-  border-bottom: 1px solid #3f3f44;
-  cursor: grab;
-  user-select: none;
+    display: flex;
+    align-items: center;
+    padding: 12px;
+    background: #1a1a1a;
+    border-bottom: 1px solid #3f3f44;
+    cursor: grab;
+    user-select: none;
 }
 
 .c4ai-titlebar-dots {
-  display: flex;
-  gap: 8px;
-  flex-shrink: 0;
+    display: flex;
+    gap: 8px;
+    flex-shrink: 0;
 }
 
 .c4ai-dot {
-  width: 12px;
-  height: 12px;
-  border-radius: 50%;
-  border: none;
-  cursor: pointer;
-  transition: opacity 0.2s ease;
-  padding: 0;
+    width: 12px;
+    height: 12px;
+    border-radius: 50%;
+    border: none;
+    cursor: pointer;
+    transition: opacity 0.2s ease;
+    padding: 0;
 }
 
 .c4ai-dot-close {
-  background: #ff5f57;
+    background: #ff5f57;
 }
 
 .c4ai-dot-close:hover {
-  background: #ff3030;
+    background: #ff3030;
 }
 
 .c4ai-dot-minimize {
-  background: #ffbd2e;
+    background: #ffbd2e;
 }
 
 .c4ai-dot-minimize:hover {
-  background: #ffaa00;
+    background: #ffaa00;
 }
 
 .c4ai-dot-maximize {
-  background: #28ca42;
+    background: #28ca42;
 }
 
 .c4ai-dot-maximize:hover {
-  background: #1eb533;
+    background: #1eb533;
 }
 
 .c4ai-titlebar-icon {
-  width: 16px;
-  height: 16px;
-  margin-left: 8px;
-  margin-right: 8px;
+    width: 16px;
+    height: 16px;
+    margin-left: 8px;
+    margin-right: 8px;
 }
 
 .c4ai-titlebar-title {
-  flex: 1;
-  text-align: left;
-  font-size: 13px;
-  font-weight: 600;
-  color: #e8e9ed;
+    flex: 1;
+    text-align: left;
+    font-size: 13px;
+    font-weight: 600;
+    color: #e8e9ed;
 }
 
 .c4ai-toolbar-content {
-  padding: 16px;
+    padding: 16px;
 }
 
 .c4ai-toolbar-status {
-  display: flex;
-  flex-direction: column;
-  gap: 8px;
-  margin-bottom: 16px;
-  padding: 12px;
-  background: #3f3f44;
-  border-radius: 8px;
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+    margin-bottom: 16px;
+    padding: 12px;
+    background: #3f3f44;
+    border-radius: 8px;
 }
 
 .c4ai-status-item {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    gap: 8px;
+}
+
+#c4ai-container-item {
+    align-items: center;
+}
+
+#c4ai-container-item .c4ai-status-value {
+    flex: 1;
+}
+
+#c4ai-selector-display {
+    padding-left: 0;
+    padding-right: 0;
 }
 
 .c4ai-status-label {
-  font-size: 12px;
-  color: #a3abba;
-  text-transform: uppercase;
+    font-size: 12px;
+    color: #a3abba;
+    text-transform: uppercase;
 }
 
 .c4ai-status-value {
-  font-size: 14px;
-  font-weight: 600;
-  color: #e8e9ed;
+    font-size: 14px;
+    font-weight: 600;
+    color: #e8e9ed;
+}
+
+/* Parent level controls */
+.c4ai-parent-controls {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.c4ai-parent-btn {
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    color: #e8e9ed;
+    width: 24px;
+    height: 24px;
+    border-radius: 4px;
+    font-size: 14px;
+    font-weight: bold;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-family: var(--font-primary);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.c4ai-parent-btn:hover {
+    background: #4f4f54;
+    border-color: #0fbbaa;
+}
+
+.c4ai-parent-value {
+    min-width: 20px;
+    text-align: center;
+    font-weight: 600;
+    color: #0fbbaa;
+}
+
+/* Container Selector Display */
+.c4ai-container-selector {
+    font-family: var(--font-code);
+    font-size: 11px;
+    color: #666;
+    background: #1a1a1a;
+    padding: 6px 10px;
+    border-radius: 4px;
+    border: 1px solid #2a2a2a;
+    margin-top: 4px;
+    word-break: break-all;
+    line-height: 1.4;
 }
 
 .c4ai-toolbar-hint {
     margin-top: 16px;
-  font-size: 13px;
-  line-height: 1.4;
-  color: #d5cec0;
-  padding: 12px;
-  background: #3f3f44;
-  border-radius: 8px;
-  border-left: 3px solid #0fbbaa;
+    font-size: 13px;
+    line-height: 1.4;
+    color: #d5cec0;
+    padding: 12px;
+    background: #3f3f44;
+    border-radius: 8px;
+    border-left: 3px solid #0fbbaa;
 }
 
 .c4ai-toolbar-hint strong {
-  color: #0fbbaa;
-  font-weight: 600;
+    color: #0fbbaa;
+    font-weight: 600;
 }
 
 /* Fields list */
 .c4ai-fields-list {
-  margin-top: 16px;
-  padding: 12px;
-  background: #3f3f44;
-  border-radius: 8px;
-  border: 1px solid #3f3f44;
+    margin-top: 16px;
+    padding: 12px;
+    background: #3f3f44;
+    border-radius: 8px;
+    border: 1px solid #3f3f44;
 }
 
 .c4ai-fields-header {
-  font-size: 12px;
-  text-transform: uppercase;
-  color: #a3abba;
-  margin-bottom: 8px;
-  font-weight: 600;
+    font-size: 12px;
+    text-transform: uppercase;
+    color: #a3abba;
+    margin-bottom: 8px;
+    font-weight: 600;
 }
 
 .c4ai-fields-items {
-  list-style: none;
-  margin: 0;
-  padding: 0;
+    list-style: none;
+    margin: 0;
+    padding: 0;
 }
 
-.c4ai-field-item {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 6px 8px;
-  margin-bottom: 4px;
-  background: #070708;
-  border-radius: 4px;
-  font-size: 12px;
-}
-
-.c4ai-field-name {
-  font-weight: 600;
-  color: #f380f5;
-  margin-right: 8px;
-}
-
-.c4ai-field-value {
-  color: #a3abba;
-  font-size: 11px;
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
-  flex: 1;
-  text-align: right;
-}
-
-/* Field naming dialog */
+/* Enhanced Field Dialog */
 .c4ai-field-dialog {
-  position: absolute;
-  background: #070708;
-  border: 2px solid #f380f5;
-  border-radius: 12px;
-  padding: 16px;
-  z-index: 999999;
-  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
-  font-family: -apple-system, BlinkMacSystemFont, sans-serif;
-  color: #e8e9ed;
-  min-width: 300px;
+    position: absolute;
+    background: #070708;
+    border: 2px solid #f380f5;
+    border-radius: 12px;
+    padding: 20px;
+    z-index: 999999;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
+    font-family: var(--font-primary);
+    color: #e8e9ed;
+    min-width: 400px;
+    max-width: 500px;
 }
 
 .c4ai-field-dialog h4 {
-  margin: 0 0 12px 0;
-  font-size: 14px;
-  color: #e8e9ed;
+    margin: 0 0 16px 0;
+    font-size: 16px;
+    color: #e8e9ed;
+    font-weight: 600;
 }
 
-.c4ai-field-dialog input {
-  width: 100%;
-  padding: 8px 12px;
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  border-radius: 6px;
-  color: #e8e9ed;
-  font-size: 14px;
-  margin-bottom: 12px;
-  outline: none;
+.c4ai-field-input {
+    margin-bottom: 16px;
 }
 
-.c4ai-field-dialog input:focus {
-  border-color: #f380f5;
-  box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.2);
+.c4ai-field-input label {
+    display: block;
+    font-size: 12px;
+    color: #a3abba;
+    margin-bottom: 6px;
+    text-transform: uppercase;
+    font-weight: 600;
+}
+
+.c4ai-field-input input,
+.c4ai-field-input select {
+    width: 100%;
+    padding: 8px 12px;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    border-radius: 6px;
+    color: #e8e9ed;
+    font-size: 14px;
+    outline: none;
+    font-family: var(--font-primary);
+}
+
+.c4ai-field-input input {
+    width: auto;
+}
+
+.c4ai-field-input input:focus,
+.c4ai-field-input select:focus {
+    border-color: #f380f5;
+    box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.2);
 }
 
 .c4ai-field-preview {
-  font-size: 12px;
-  color: #999;
-  margin-bottom: 12px;
-  padding: 8px;
-  background: #1a1a1a;
-  border-radius: 6px;
-  word-break: break-word;
+    margin-bottom: 16px;
+    padding: 12px;
+    background: #1a1a1a;
+    border-radius: 6px;
+    border: 1px solid #2a2a2a;
 }
 
 .c4ai-field-preview strong {
-  color: #ccc;
+    display: block;
+    font-size: 12px;
+    color: #a3abba;
+    margin-bottom: 6px;
+    text-transform: uppercase;
+}
+
+#c4ai-preview-value {
+    font-size: 13px;
+    color: #e8e9ed;
+    word-break: break-word;
+    font-family: var(--font-code);
+}
+
+.c4ai-field-selector {
+    margin-bottom: 16px;
+    padding: 12px;
+    background: #1a1a1a;
+    border-radius: 6px;
+    border: 1px solid #2a2a2a;
+}
+
+/* .c4ai-toolbar .c4ai-field-selector {
+
+} */
+
+.c4ai-field-selector strong {
+    display: block;
+    font-size: 12px;
+    color: #a3abba;
+    margin-bottom: 6px;
+    text-transform: uppercase;
+}
+
+#c4ai-selector-preview {
+    font-size: 12px;
+    color: #0fbbaa;
+    font-family: var(--font-code);
+    word-break: break-all;
+    word-break: break-word;
+}
+
+.c4ai-field-preview strong {
+    color: #ccc;
 }
 
 .c4ai-field-actions {
-  display: flex;
-  gap: 8px;
-  justify-content: flex-end;
+    display: flex;
+    gap: 8px;
+    justify-content: flex-end;
 }
 
 .c4ai-field-actions button {
-  padding: 6px 16px;
-  border: none;
-  border-radius: 6px;
-  font-size: 13px;
-  font-weight: 600;
-  cursor: pointer;
-  transition: all 0.2s ease;
+    padding: 6px 10px;
+    border: none;
+    border-radius: 6px;
+    font-size: 13px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
 }
 
 #c4ai-field-save {
-  background: #f380f5;
-  color: #070708;
+    background: #f380f5;
+    color: #070708;
 }
 
 #c4ai-field-save:hover {
-  background: #e370e5;
-  transform: translateY(-1px);
+    background: #e370e5;
+    transform: translateY(-1px);
 }
 
 #c4ai-field-cancel {
-  background: #3f3f44;
-  color: #e8e9ed;
+    background: #3f3f44;
+    color: #e8e9ed;
 }
 
 #c4ai-field-cancel:hover {
-  background: #4f4f54;
+    background: #4f4f54;
 }
 
 /* Toolbar action buttons */
 .c4ai-toolbar-actions {
-  display: grid;
-  grid-template-columns: 1fr 1fr;
-  gap: 8px;
-  margin-top: 16px;
-  padding-top: 16px;
-  border-top: 1px solid #2a2a2a;
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 8px;
+    margin-top: 16px;
+    padding-top: 16px;
+    border-top: 1px solid #2a2a2a;
+}
+
+/* Mode toggle buttons */
+.c4ai-mode-btn {
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    padding: 8px 16px;
+    border-radius: 6px;
+    color: #a3abba;
+    font-size: 13px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 6px;
+}
+
+.c4ai-mode-btn:hover:not(:disabled) {
+    background: #4f4f54;
+    border-color: #5f5f64;
+    color: #e8e9ed;
+}
+
+.c4ai-mode-btn.c4ai-active {
+    background: #0fbbaa;
+    border-color: #0fbbaa;
+    color: #070708;
+}
+
+.c4ai-mode-btn.c4ai-active:hover {
+    background: #1fcbba;
+    border-color: #1fcbba;
+}
+
+.c4ai-mode-btn:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.c4ai-mode-btn:disabled:hover {
+    background: #3f3f44;
+    border-color: #3f3f44;
+    color: #a3abba;
 }
 
 .c4ai-action-btn {
-  flex: 1;
-  padding: 8px 16px;
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  border-radius: 6px;
-  color: #e8e9ed;
-  font-size: 13px;
-  font-weight: 600;
-  cursor: pointer;
-  transition: all 0.2s ease;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  gap: 6px;
+    flex: 1;
+    padding: 8px 16px;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    border-radius: 6px;
+    color: #e8e9ed;
+    font-size: 13px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 6px;
 }
 
 .c4ai-action-btn:hover {
-  background: #4f4f54;
-  border-color: #5f5f64;
-  transform: translateY(-1px);
+    background: #4f4f54;
+    border-color: #5f5f64;
+    transform: translateY(-1px);
 }
 
 /* Primary button styling */
 .c4ai-primary-btn {
-  background: #0fbbaa;
-  border-color: #0fbbaa;
-  color: #070708;
+    background: #0fbbaa;
+    border-color: #0fbbaa;
+    color: #070708;
 }
 
 .c4ai-primary-btn:hover {
-  background: #1fcbba;
-  border-color: #1fcbba;
+    background: #1fcbba;
+    border-color: #1fcbba;
+}
+
+.c4ai-primary-btn.c4ai-active {
+    background: #f380f5;
+    border-color: #f380f5;
+    color: #070708;
+}
+
+.c4ai-primary-btn.c4ai-active:hover {
+    background: #e370e5;
+    border-color: #e370e5;
 }
 
 /* Secondary button styling */
 .c4ai-secondary-btn {
-  background: #2a2a2a;
-  border-color: #2a2a2a;
-  color: #a3abba;
+    background: #2a2a2a;
+    border-color: #2a2a2a;
+    color: #a3abba;
 }
 
 .c4ai-secondary-btn:hover {
-  background: #3a3a3a;
-  border-color: #3a3a3a;
-  color: #e8e9ed;
+    background: #3a3a3a;
+    border-color: #3a3a3a;
+    color: #e8e9ed;
 }
 
 .c4ai-pause-btn {
-  background: #f380f5;
-  border-color: #f380f5;
-  color: #070708;
+    background: #f380f5;
+    border-color: #f380f5;
+    color: #070708;
 }
 
 .c4ai-pause-btn:hover {
-  background: #e370e5;
+    background: #e370e5;
 }
 
 .c4ai-pause-btn.c4ai-paused {
-  background: #ff3c74;
-  border-color: #ff3c74;
+    background: #ff3c74;
+    border-color: #ff3c74;
 }
 
 .c4ai-generate-btn {
-  background: #0fbbaa;
-  border-color: #0fbbaa;
-  color: #070708;
+    background: #0fbbaa;
+    border-color: #0fbbaa;
+    color: #070708;
 }
 
 .c4ai-generate-btn:hover {
-  background: #1fcbba;
+    background: #1fcbba;
 }
 
 /* Code modal */
 .c4ai-code-modal {
-  position: fixed;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  background: rgba(0, 0, 0, 0.8);
-  z-index: 999999;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  padding: 20px;
+    position: fixed;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.8);
+    z-index: 999999;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: 20px;
 }
 
 .c4ai-code-modal-content {
-  background: #070708;
-  border: 2px solid #3f3f44;
-  border-radius: 16px;
-  width: 90%;
-  max-width: 900px;
-  max-height: 90vh;
-  display: flex;
-  flex-direction: column;
-  box-shadow: 0 16px 64px rgba(0, 0, 0, 0.9);
+    background: #070708;
+    border: 2px solid #3f3f44;
+    border-radius: 16px;
+    width: 90%;
+    max-width: 900px;
+    max-height: 90vh;
+    display: flex;
+    flex-direction: column;
+    box-shadow: 0 16px 64px rgba(0, 0, 0, 0.9);
 }
 
 .c4ai-code-modal-header {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 20px 24px;
-  border-bottom: 1px solid #2a2a2a;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 20px 24px;
+    border-bottom: 1px solid #2a2a2a;
 }
 
 .c4ai-code-modal-header h2 {
-  margin: 0;
-  font-size: 20px;
-  color: #fff;
-  font-family: -apple-system, BlinkMacSystemFont, sans-serif;
+    margin: 0;
+    font-size: 20px;
+    color: #fff;
+    font-family: -apple-system, BlinkMacSystemFont, sans-serif;
 }
 
 .c4ai-close-modal {
-  background: none;
-  border: none;
-  color: #999;
-  font-size: 24px;
-  cursor: pointer;
-  padding: 0;
-  width: 32px;
-  height: 32px;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  border-radius: 4px;
-  transition: all 0.2s ease;
+    background: none;
+    border: none;
+    color: #999;
+    font-size: 24px;
+    cursor: pointer;
+    padding: 0;
+    width: 32px;
+    height: 32px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 4px;
+    transition: all 0.2s ease;
 }
 
 .c4ai-close-modal:hover {
-  background: #2a2a2a;
-  color: #fff;
+    background: #2a2a2a;
+    color: #fff;
 }
 
 .c4ai-code-modal-body {
-  flex: 1;
-  overflow: auto;
-  padding: 24px;
+    flex: 1;
+    overflow: auto;
+    padding: 24px;
 }
 
 .c4ai-code-block {
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  border-radius: 8px;
-  padding: 20px;
-  overflow-x: auto;
-  margin: 0;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    border-radius: 8px;
+    padding: 20px;
+    overflow-x: auto;
+    margin: 0;
 }
 
 .c4ai-code-block code {
-  font-family: var(--font-code);
-  font-size: 13px;
-  line-height: 1.6;
-  color: #e8e9ed;
-  white-space: pre;
-  display: block;
+    font-family: var(--font-code);
+    font-size: 13px;
+    line-height: 1.6;
+    color: #e8e9ed;
+    white-space: pre;
+    display: block;
 }
 
 /* Syntax highlighting */
 .c4ai-keyword {
-  color: #0fbbaa;
-  font-weight: bold;
+    color: #0fbbaa;
+    font-weight: bold;
 }
 
 .c4ai-string {
-  color: #f380f5;
+    color: #f380f5;
 }
 
 .c4ai-comment {
-  color: #a3abba;
-  font-style: italic;
+    color: #a3abba;
+    font-style: italic;
 }
 
 .c4ai-function {
-  color: #ff3c74;
+    color: #ff3c74;
 }
 
 .c4ai-code-modal-footer {
-  display: flex;
-  gap: 12px;
-  padding: 20px 24px;
-  border-top: 1px solid #2a2a2a;
-  justify-content: flex-end;
+    display: flex;
+    gap: 12px;
+    padding: 20px 24px;
+    border-top: 1px solid #2a2a2a;
+    justify-content: flex-end;
 }
 
 .c4ai-download-btn {
-  background: #0fbbaa;
-  color: #070708;
-  border: none;
+    background: #0fbbaa;
+    color: #070708;
+    border: none;
 }
 
 .c4ai-download-btn:hover {
-  background: #1fcbba;
+    background: #1fcbba;
 }
 
 .c4ai-copy-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
+    background: #3f3f44;
+    border-color: #3f3f44;
 }
 
 .c4ai-copy-btn:hover {
-  background: #4f4f54;
-  border-color: #5f5f64;
+    background: #4f4f54;
+    border-color: #5f5f64;
 }
 
 .c4ai-cloud-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
-  opacity: 0.6;
-  cursor: not-allowed;
+    background: #3f3f44;
+    border-color: #3f3f44;
+    opacity: 0.6;
+    cursor: not-allowed;
 }
 
 .c4ai-cloud-btn:disabled {
-  opacity: 0.6;
-  cursor: not-allowed;
+    opacity: 0.6;
+    cursor: not-allowed;
 }
 
 .c4ai-cloud-btn:hover:disabled {
-  background: #3f3f44;
-  transform: none;
+    background: #3f3f44;
+    transform: none;
+}
+
+/* Cloud Modal Styles */
+.c4ai-cloud-modal-content {
+    background: #070708;
+    border: 2px solid #0fbbaa;
+    border-radius: 16px;
+    width: 90%;
+    max-width: 600px;
+    max-height: 90vh;
+    display: flex;
+    flex-direction: column;
+    box-shadow: 0 16px 64px rgba(15, 187, 170, 0.2);
+    position: relative;
+    overflow: hidden;
+}
+
+.c4ai-cloud-header {
+    text-align: center;
+    padding: 30px 24px 20px;
+    background: linear-gradient(135deg, #0a0a0a 0%, #1a1a1a 100%);
+    border-bottom: 1px solid #2a2a2a;
+}
+
+.c4ai-cloud-icon {
+    font-size: 64px;
+    margin-bottom: 16px;
+    animation: cloud-float 3s ease-in-out infinite;
+}
+
+@keyframes cloud-float {
+    0%,
+    100% {
+        transform: translateY(0);
+    }
+    50% {
+        transform: translateY(-10px);
+    }
+}
+
+.c4ai-cloud-header h2 {
+    margin: 0;
+    font-size: 28px;
+    color: #fff;
+    font-weight: 700;
+    font-family: var(--font-primary);
+}
+
+.c4ai-cloud-body {
+    padding: 30px;
+    overflow-y: auto;
+}
+
+.c4ai-cloud-features h3 {
+    margin: 0 0 16px 0;
+    font-size: 20px;
+    color: #0fbbaa;
+    font-family: var(--font-primary);
+}
+
+.c4ai-cloud-features p {
+    margin: 0 0 20px 0;
+    color: #a3abba;
+    font-size: 15px;
+    line-height: 1.6;
+    font-family: var(--font-primary);
+}
+
+.c4ai-cloud-features ul {
+    list-style: none;
+    margin: 0;
+    padding: 0;
+}
+
+.c4ai-cloud-features li {
+    padding: 12px 0;
+    border-bottom: 1px solid #2a2a2a;
+    color: #e8e9ed;
+    font-size: 14px;
+    line-height: 1.5;
+    font-family: var(--font-primary);
+}
+
+.c4ai-cloud-features li:last-child {
+    border-bottom: none;
+}
+
+.c4ai-cloud-features li strong {
+    color: #fff;
+}
+
+.c4ai-cloud-cta {
+    display: flex;
+    flex-direction: column;
+    margin-top: 30px;
+    padding: 24px;
+    background: #1a1a1a;
+    border-radius: 12px;
+    text-align: center;
+    border: 1px solid #2a2a2a;
+}
+
+.c4ai-cloud-cta p {
+    margin: 0 0 16px 0;
+    color: #e8e9ed;
+    font-size: 16px;
+    font-weight: 500;
+    font-family: var(--font-primary);
+}
+
+.c4ai-waitlist-btn {
+    padding: 12px 32px;
+    font-size: 16px;
+    background: #0fbbaa;
+    color: #070708;
+    border: none;
+    font-weight: 600;
+    font-family: var(--font-primary);
+}
+
+.c4ai-waitlist-btn:hover {
+    background: #1fcbba;
+    transform: translateY(-2px);
+    box-shadow: 0 8px 24px rgba(15, 187, 170, 0.4);
+}
+
+.c4ai-cloud-modal-content .c4ai-close-modal {
+    position: absolute;
+    top: 20px;
+    right: 20px;
+    background: #2a2a2a;
+    color: #999;
+    border: none;
+    width: 36px;
+    height: 36px;
+    border-radius: 50%;
+    font-size: 20px;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: all 0.2s ease;
+    z-index: 10;
+}
+
+.c4ai-cloud-modal-content .c4ai-close-modal:hover {
+    background: #ff3c74;
+    color: #fff;
+    transform: rotate(90deg);
+}
+
+/* Enhanced Schema Builder Styles */
+.c4ai-section-header {
+    font-size: 12px;
+    text-transform: uppercase;
+    color: #a3abba;
+    font-weight: 600;
+    margin: 16px 0 12px 0;
+    padding: 0 4px;
+}
+
+.c4ai-schema-section,
+.c4ai-actions-section,
+.c4ai-stats-section {
+    margin-bottom: 16px;
+}
+
+.c4ai-fields-list {
+    background: #3f3f44;
+    border-radius: 8px;
+    padding: 8px;
+    max-height: 200px;
+    overflow-y: auto;
+}
+
+.c4ai-field-item {
+    display: flex;
+    flex-direction: column;
+    padding: 10px;
+    margin-bottom: 8px;
+    background: #070708;
+    border-radius: 6px;
+    border: 1px solid #2a2a2a;
+    transition: all 0.2s ease;
+}
+
+.c4ai-field-item:hover {
+    border-color: #3f3f44;
+}
+
+.c4ai-field-item:last-child {
+    margin-bottom: 0;
+}
+
+.c4ai-field-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    margin-bottom: 4px;
+}
+
+.c4ai-field-icon {
+    font-size: 14px;
+    margin-right: 6px;
+}
+
+.c4ai-field-name {
+    font-weight: 600;
+    color: #f380f5;
+    font-size: 13px;
+    flex: 1;
+}
+
+.c4ai-field-actions {
+    display: flex;
+    gap: 4px;
+}
+
+.c4ai-field-edit,
+.c4ai-field-delete {
+    background: none;
+    border: none;
+    color: #666;
+    cursor: pointer;
+    font-size: 14px;
+    padding: 4px 6px;
+    border-radius: 4px;
+    transition: all 0.2s ease;
+}
+
+.c4ai-field-edit:hover {
+    background: #3f3f44;
+    color: #0fbbaa;
+}
+
+.c4ai-field-delete:hover {
+    background: #ff3c74;
+    color: #fff;
+}
+
+.c4ai-toolbar .c4ai-field-selector {
+    font-size: 11px;
+    color: #666;
+    font-family: var(--font-code);
+    padding: 4px 6px;
+    /* margin-left: 20px; */
+    background: #1a1a1a;
+    border: 1px solid transparent;
+    border-radius: 4px;
+    outline: none;
+    transition: all 0.2s ease;
+    margin-bottom: 0px;
+}
+
+.c4ai-toolbar .c4ai-field-selector:hover {
+    border-color: #2a2a2a;
+}
+
+.c4ai-toolbar .c4ai-field-selector:focus {
+    border-color: #3f3f44;
+    color: #a3abba;
+}
+
+/* Toolbar Actions Grid */
+.c4ai-toolbar-actions {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 8px;
+}
+
+.c4ai-preview-btn,
+.c4ai-test-btn,
+.c4ai-export-btn {
+    padding: 8px 12px;
+    font-size: 12px;
+}
+
+/* Actions grid layout - 2 rows */
+.c4ai-actions-section .c4ai-toolbar-actions {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 8px;
+}
+
+/* First two buttons span full width */
+.c4ai-actions-section .c4ai-toolbar-actions button:nth-child(1),
+.c4ai-actions-section .c4ai-toolbar-actions button:nth-child(2) {
+    grid-column: span 1;
+}
+
+/* Cloud button special styling */
+.c4ai-cloud-btn {
+    background: #3f3f44;
+    border-color: #3f3f44;
+    color: #0fbbaa;
+    grid-column: 1 / -1;
+    font-weight: 600;
+}
+
+.c4ai-cloud-btn:hover:not(:disabled) {
+    background: #0fbbaa;
+    border-color: #0fbbaa;
+    color: #070708;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 16px rgba(15, 187, 170, 0.3);
+}
+
+.c4ai-actions-section .c4ai-toolbar-actions button {
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+/* Stats Section */
+.c4ai-stats {
+    background: #3f3f44;
+    border-radius: 8px;
+    padding: 12px;
+}
+
+.c4ai-stat-item {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    font-size: 13px;
+    margin-bottom: 8px;
+}
+
+.c4ai-stat-item:last-child {
+    margin-bottom: 0;
+}
+
+.c4ai-stat-item span:first-child {
+    color: #a3abba;
+}
+
+.c4ai-stat-item span:last-child {
+    color: #e8e9ed;
+    font-weight: 600;
+}
+
+/* Toolbar Footer */
+.c4ai-toolbar-footer {
+    margin-top: 16px;
+    padding-top: 16px;
+    border-top: 1px solid #2a2a2a;
+    display: flex;
+    gap: 8px;
+}
+
+/* Preview Elements */
+.c4ai-preview-container {
+    outline: 3px dashed #0fbbaa !important;
+    outline-offset: 4px;
+    background: rgba(15, 187, 170, 0.05) !important;
+}
+
+.c4ai-preview-field-success {
+    outline: 2px solid #4169e1 !important;
+    background: rgba(65, 105, 225, 0.1) !important;
+}
+
+.c4ai-preview-field-error {
+    outline: 2px solid #ff3c74 !important;
+    background: rgba(255, 60, 116, 0.1) !important;
+}
+
+.c4ai-preview-badge {
+    position: absolute;
+    background: #0fbbaa;
+    color: #070708;
+    width: 24px;
+    height: 24px;
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 12px;
+    font-weight: bold;
+    font-family: var(--font-primary);
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3);
+}
+
+/* Primary button style */
+.c4ai-primary {
+    background: #f380f5 !important;
+    color: #070708 !important;
+    border: none !important;
+    font-weight: 600;
+}
+
+.c4ai-primary:hover {
+    background: #e370e5 !important;
+    transform: translateY(-1px);
 }
 
 /* Script Builder Styles */
 .c4ai-script-toolbar {
-  position: fixed;
-  bottom: 20px;
-  right: 20px;
-  background: #070708;
-  border: 1px solid #3f3f44;
-  border-radius: 8px;
-  z-index: 999999;
-  font-family: var(--font-primary);
-  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
-  width: 360px;
-  color: #e8e9ed;
-  overflow: hidden;
+    position: fixed;
+    bottom: 20px;
+    right: 20px;
+    background: #070708;
+    border: 1px solid #3f3f44;
+    border-radius: 8px;
+    z-index: 999999;
+    font-family: var(--font-primary);
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
+    width: 360px;
+    color: #e8e9ed;
+    overflow: hidden;
 }
 
 /* Recording Indicator */
 .c4ai-recording-indicator {
-  position: fixed;
-  top: 20px;
-  left: 50%;
-  transform: translateX(-50%);
-  background: #1a1a1a;
-  border: 1px solid #ff3c74;
-  border-radius: 20px;
-  padding: 8px 16px;
-  display: flex;
-  align-items: center;
-  gap: 8px;
-  z-index: 999999;
-  font-family: var(--font-primary);
-  color: #ff3c74;
-  font-size: 14px;
-  font-weight: 600;
-  box-shadow: 0 4px 16px rgba(255, 60, 116, 0.3);
+    position: fixed;
+    top: 20px;
+    left: 50%;
+    transform: translateX(-50%);
+    background: #1a1a1a;
+    border: 1px solid #ff3c74;
+    border-radius: 20px;
+    padding: 8px 16px;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    z-index: 999999;
+    font-family: var(--font-primary);
+    color: #ff3c74;
+    font-size: 14px;
+    font-weight: 600;
+    box-shadow: 0 4px 16px rgba(255, 60, 116, 0.3);
 }
 
 .c4ai-recording-dot {
-  width: 8px;
-  height: 8px;
-  background: #ff3c74;
-  border-radius: 50%;
-  animation: c4ai-pulse 1.5s ease-in-out infinite;
+    width: 8px;
+    height: 8px;
+    background: #ff3c74;
+    border-radius: 50%;
+    animation: c4ai-pulse 1.5s ease-in-out infinite;
 }
 
 @keyframes c4ai-pulse {
-  0%, 100% { opacity: 1; transform: scale(1); }
-  50% { opacity: 0.6; transform: scale(0.8); }
+    0%,
+    100% {
+        opacity: 1;
+        transform: scale(1);
+    }
+    50% {
+        opacity: 0.6;
+        transform: scale(0.8);
+    }
 }
 
 .c4ai-recording-indicator.c4ai-paused .c4ai-recording-dot {
-  animation: none;
-  background: #666;
+    animation: none;
+    background: #666;
 }
 
 /* Format Select */
 .c4ai-format-select {
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  color: #e8e9ed;
-  padding: 4px 8px;
-  border-radius: 4px;
-  font-size: 12px;
-  font-family: var(--font-primary);
-  outline: none;
-  cursor: pointer;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    color: #e8e9ed;
+    padding: 4px 8px;
+    border-radius: 4px;
+    font-size: 12px;
+    font-family: var(--font-primary);
+    outline: none;
+    cursor: pointer;
 }
 
 .c4ai-format-select:focus {
-  border-color: #0fbbaa;
+    border-color: #0fbbaa;
 }
 
 /* Wait Button */
 .c4ai-wait-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
+    background: #3f3f44;
+    border-color: #3f3f44;
 }
 
 .c4ai-wait-btn:hover {
-  background: #4f4f54;
-  border-color: #5f5f64;
+    background: #4f4f54;
+    border-color: #5f5f64;
 }
 
 /* Wait Dialog */
 .c4ai-wait-dialog {
-  position: fixed;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%);
-  z-index: 999999;
-  font-family: var(--font-primary);
+    position: fixed;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    z-index: 999999;
+    font-family: var(--font-primary);
 }
 
 .c4ai-wait-dialog-content {
-  background: #070708;
-  border: 2px solid #f380f5;
-  border-radius: 12px;
-  padding: 20px;
-  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
-  color: #e8e9ed;
-  min-width: 350px;
+    background: #070708;
+    border: 2px solid #f380f5;
+    border-radius: 12px;
+    padding: 20px;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
+    color: #e8e9ed;
+    min-width: 350px;
 }
 
 .c4ai-wait-dialog h4 {
-  margin: 0 0 16px 0;
-  font-size: 16px;
-  color: #e8e9ed;
+    margin: 0 0 16px 0;
+    font-size: 16px;
+    color: #e8e9ed;
 }
 
 .c4ai-wait-options {
-  display: flex;
-  flex-direction: column;
-  gap: 12px;
-  margin-bottom: 16px;
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+    margin-bottom: 16px;
 }
 
 .c4ai-wait-option {
-  display: flex;
-  align-items: center;
-  gap: 8px;
-  cursor: pointer;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    cursor: pointer;
 }
 
 .c4ai-wait-option input[type="radio"] {
-  cursor: pointer;
+    cursor: pointer;
 }
 
 .c4ai-wait-input {
-  margin-bottom: 16px;
+    margin-bottom: 16px;
 }
 
 .c4ai-wait-input input[type="number"] {
-  width: 100%;
-  padding: 8px 12px;
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  border-radius: 6px;
-  color: #e8e9ed;
-  font-size: 14px;
-  outline: none;
-  font-family: var(--font-primary);
+    width: 100%;
+    padding: 8px 12px;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    border-radius: 6px;
+    color: #e8e9ed;
+    font-size: 14px;
+    outline: none;
+    font-family: var(--font-primary);
 }
 
 .c4ai-wait-input input[type="number"]:focus {
-  border-color: #f380f5;
-  box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.2);
+    border-color: #f380f5;
+    box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.2);
 }
 
 .c4ai-wait-input p {
-  margin: 0;
-  color: #a3abba;
-  font-size: 13px;
+    margin: 0;
+    color: #a3abba;
+    font-size: 13px;
 }
 
 .c4ai-wait-actions {
-  display: flex;
-  gap: 8px;
-  justify-content: flex-end;
+    display: flex;
+    gap: 8px;
+    justify-content: flex-end;
 }
 
 .c4ai-hidden {
-  display: none !important;
+    display: none !important;
 }
 
 .c4ai-primary {
-  background: #f380f5 !important;
-  color: #070708 !important;
+    background: #f380f5 !important;
+    color: #070708 !important;
 }
 
 .c4ai-primary:hover {
-  background: #e370e5 !important;
+    background: #e370e5 !important;
 }
 
 /* Wait Mode Highlight */
 .c4ai-highlight-box.c4ai-wait-mode {
-  border: 2px dashed #f380f5 !important;
-  background: rgba(243, 128, 245, 0.1) !important;
-  box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.3) !important;
+    border: 2px dashed #f380f5 !important;
+    background: rgba(243, 128, 245, 0.1) !important;
+    box-shadow: 0 0 0 2px rgba(243, 128, 245, 0.3) !important;
 }
 
 /* Timeline Modal */
 .c4ai-timeline-modal {
-  position: fixed;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  background: rgba(0, 0, 0, 0.8);
-  z-index: 999999;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  padding: 20px;
-  font-family: var(--font-primary);
+    position: fixed;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.8);
+    z-index: 999999;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: 20px;
+    font-family: var(--font-primary);
 }
 
 .c4ai-timeline-content {
-  background: #070708;
-  border: 2px solid #3f3f44;
-  border-radius: 16px;
-  width: 90%;
-  max-width: 700px;
-  max-height: 80vh;
-  display: flex;
-  flex-direction: column;
-  box-shadow: 0 16px 64px rgba(0, 0, 0, 0.9);
+    background: #070708;
+    border: 2px solid #3f3f44;
+    border-radius: 16px;
+    width: 90%;
+    max-width: 700px;
+    max-height: 80vh;
+    display: flex;
+    flex-direction: column;
+    box-shadow: 0 16px 64px rgba(0, 0, 0, 0.9);
 }
 
 .c4ai-timeline-header {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 20px 24px;
-  border-bottom: 1px solid #2a2a2a;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 20px 24px;
+    border-bottom: 1px solid #2a2a2a;
 }
 
 .c4ai-timeline-header h2 {
-  margin: 0;
-  font-size: 18px;
-  color: #fff;
+    margin: 0;
+    font-size: 18px;
+    color: #fff;
 }
 
 .c4ai-timeline-body {
-  flex: 1;
-  overflow-y: auto;
-  padding: 24px;
+    flex: 1;
+    overflow-y: auto;
+    padding: 24px;
 }
 
 .c4ai-timeline-controls {
-  display: flex;
-  gap: 8px;
-  margin-bottom: 16px;
+    display: flex;
+    gap: 8px;
+    margin-bottom: 16px;
 }
 
 .c4ai-timeline-events {
-  display: flex;
-  flex-direction: column;
-  gap: 8px;
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
 }
 
 .c4ai-timeline-event {
-  display: flex;
-  align-items: center;
-  gap: 12px;
-  padding: 12px;
-  background: #3f3f44;
-  border-radius: 8px;
-  border: 1px solid #3f3f44;
-  transition: all 0.2s ease;
+    display: flex;
+    align-items: center;
+    gap: 12px;
+    padding: 12px;
+    background: #3f3f44;
+    border-radius: 8px;
+    border: 1px solid #3f3f44;
+    transition: all 0.2s ease;
 }
 
 .c4ai-timeline-event:hover {
-  border-color: #0fbbaa;
+    border-color: #0fbbaa;
 }
 
 .c4ai-event-checkbox {
-  cursor: pointer;
+    cursor: pointer;
 }
 
 .c4ai-event-label {
-  flex: 1;
-  display: flex;
-  align-items: center;
-  gap: 12px;
-  cursor: pointer;
-  font-size: 13px;
+    flex: 1;
+    display: flex;
+    align-items: center;
+    gap: 12px;
+    cursor: pointer;
+    font-size: 13px;
 }
 
 .c4ai-event-time {
-  color: #a3abba;
-  font-size: 12px;
-  min-width: 50px;
+    color: #a3abba;
+    font-size: 12px;
+    min-width: 50px;
 }
 
 .c4ai-event-type {
-  background: #0fbbaa;
-  color: #070708;
-  padding: 2px 8px;
-  border-radius: 4px;
-  font-size: 11px;
-  font-weight: 600;
-  min-width: 80px;
-  text-align: center;
+    background: #0fbbaa;
+    color: #070708;
+    padding: 2px 8px;
+    border-radius: 4px;
+    font-size: 11px;
+    font-weight: 600;
+    min-width: 80px;
+    text-align: center;
 }
 
 .c4ai-event-detail {
-  color: #e8e9ed;
-  flex: 1;
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
-  max-width: 400px; /* Prevent extremely long selectors from breaking layout */
+    color: #e8e9ed;
+    flex: 1;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    max-width: 400px; /* Prevent extremely long selectors from breaking layout */
 }
 
 .c4ai-timeline-footer {
-  display: flex;
-  gap: 12px;
-  padding: 20px 24px;
-  border-top: 1px solid #2a2a2a;
-  align-items: center;
-  justify-content: space-between;
+    display: flex;
+    gap: 12px;
+    padding: 20px 24px;
+    border-top: 1px solid #2a2a2a;
+    align-items: center;
+    justify-content: space-between;
 }
 
 /* Pause button state */
 .c4ai-pause-btn.c4ai-paused {
-  background: #ff3c74;
-  border-color: #ff3c74;
+    background: #ff3c74;
+    border-color: #ff3c74;
 }
 
 /* Summary actions - 2x2 grid layout */
 .c4ai-summary-actions {
-  display: grid !important;
-  grid-template-columns: 1fr 1fr;
-  gap: 8px !important;
-  margin-top: 16px;
-  padding-top: 16px;
-  border-top: 1px solid #2a2a2a;
+    display: grid !important;
+    grid-template-columns: 1fr 1fr;
+    gap: 8px !important;
+    margin-top: 16px;
+    padding-top: 16px;
+    border-top: 1px solid #2a2a2a;
 }
 
 /* Start screen styles */
 .c4ai-start-screen {
-  padding: 20px 0;
+    padding: 20px 0;
 }
 
 .c4ai-welcome-message {
-  text-align: center;
-  margin-bottom: 20px;
+    text-align: center;
+    margin-bottom: 20px;
 }
 
 .c4ai-welcome-message h3 {
-  margin: 0 0 8px 0;
-  font-size: 16px;
-  color: #e8e9ed;
+    margin: 0 0 8px 0;
+    font-size: 16px;
+    color: #e8e9ed;
 }
 
 .c4ai-welcome-message p {
-  margin: 0;
-  font-size: 13px;
-  color: #a3abba;
-  line-height: 1.5;
+    margin: 0;
+    font-size: 13px;
+    color: #a3abba;
+    line-height: 1.5;
 }
 
 .c4ai-start-actions {
-  display: flex;
-  gap: 8px;
+    display: flex;
+    gap: 8px;
 }
 
 /* Start screen styles */
 .c4ai-start-screen {
-  padding: 20px;
-  text-align: center;
+    padding: 20px;
+    text-align: center;
 }
 
 .c4ai-welcome-message {
-  margin-bottom: 24px;
+    margin-bottom: 24px;
 }
 
 .c4ai-welcome-message h3 {
-  margin: 0 0 8px 0;
-  font-size: 16px;
-  color: #e8e9ed;
+    margin: 0 0 8px 0;
+    font-size: 16px;
+    color: #e8e9ed;
 }
 
 .c4ai-welcome-message p {
-  margin: 0;
-  font-size: 13px;
-  color: #a3abba;
-  line-height: 1.4;
+    margin: 0;
+    font-size: 13px;
+    color: #a3abba;
+    line-height: 1.4;
 }
 
 .c4ai-start-actions {
-  display: flex;
-  flex-direction: column;
-  gap: 12px;
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
 }
 
 /* Primary and Secondary button styles */
 .c4ai-primary-btn {
-  background: #0fbbaa !important;
-  border-color: #0fbbaa !important;
-  color: #070708 !important;
-  font-weight: 600;
+    background: #0fbbaa !important;
+    border-color: #0fbbaa !important;
+    color: #070708 !important;
+    font-weight: 600;
 }
 
 .c4ai-primary-btn:hover {
-  background: #1fcbba !important;
-  transform: translateY(-1px);
-  box-shadow: 0 4px 16px rgba(15, 187, 170, 0.3);
+    background: #1fcbba !important;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 16px rgba(15, 187, 170, 0.3);
 }
 
 .c4ai-secondary-btn {
-  background: #2a2a2a !important;
-  border-color: #2a2a2a !important;
-  color: #a3abba !important;
+    background: #2a2a2a !important;
+    border-color: #2a2a2a !important;
+    color: #a3abba !important;
 }
 
 .c4ai-secondary-btn:hover {
-  background: #3a3a3a !important;
-  border-color: #3a3a3a !important;
-  color: #e8e9ed !important;
+    background: #3a3a3a !important;
+    border-color: #3a3a3a !important;
+    color: #e8e9ed !important;
 }
 
 .c4ai-replay-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
-  color: #a3abba;
+    background: #3f3f44;
+    border-color: #3f3f44;
+    color: #a3abba;
 }
 
 .c4ai-replay-btn:hover {
-  background: #4f4f54;
-  border-color: #5f5f64;
+    background: #4f4f54;
+    border-color: #5f5f64;
 }
 
 .c4ai-save-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
-  color: #a3abba;
+    background: #3f3f44;
+    border-color: #3f3f44;
+    color: #a3abba;
 }
 
 .c4ai-save-btn:hover {
-  background: #4f4f54;
-  border-color: #5f5f64;
+    background: #4f4f54;
+    border-color: #5f5f64;
 }
 
 .c4ai-timeline-btn {
-  background: #e370e5  !important;
-  border-color: #e370e5  !important;
-  color: #070708 !important;
-  font-weight: 600;
+    background: #e370e5 !important;
+    border-color: #e370e5 !important;
+    color: #070708 !important;
+    font-weight: 600;
 }
 
 .c4ai-timeline-btn:hover {
-  background: #e77ee9  !important;
-  transform: translateY(-1px);
-  box-shadow: 0 4px 16px rgba(173, 15, 187, 0.3);
+    background: #e77ee9 !important;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 16px rgba(173, 15, 187, 0.3);
 }
 
 .c4ai-record-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
-  color: #a3abba;
+    background: #3f3f44;
+    border-color: #3f3f44;
+    color: #a3abba;
 }
 
 .c4ai-record-btn:hover {
-  background: #4f4f54;
-  border-color: #5f5f64;
+    background: #4f4f54;
+    border-color: #5f5f64;
 }
 
 /* Disabled button state */
 .c4ai-action-btn:disabled {
-  opacity: 0.5;
-  cursor: not-allowed;
+    opacity: 0.5;
+    cursor: not-allowed;
 }
 
 .c4ai-action-btn:disabled:hover {
-  transform: none;
-  box-shadow: none;
+    transform: none;
+    box-shadow: none;
 }
 
 /* Saved Flows button styling */
 .c4ai-flows-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
-  color: #a3abba;
+    background: #3f3f44;
+    border-color: #3f3f44;
+    color: #a3abba;
 }
 
 .c4ai-flows-btn:hover {
-  background: #4f4f54;
-  border-color: #5f5f64;
+    background: #4f4f54;
+    border-color: #5f5f64;
 }
 
 /* Saved Flows Modal */
 .c4ai-saved-flows-modal {
-  position: fixed;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  background: rgba(0, 0, 0, 0.8);
-  z-index: 999999;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  padding: 20px;
-  font-family: var(--font-primary);
+    position: fixed;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.8);
+    z-index: 999999;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: 20px;
+    font-family: var(--font-primary);
 }
 
 .c4ai-saved-flows-content {
-  background: #070708;
-  border: 2px solid #3f3f44;
-  border-radius: 16px;
-  width: 90%;
-  max-width: 600px;
-  max-height: 70vh;
-  display: flex;
-  flex-direction: column;
-  box-shadow: 0 16px 64px rgba(0, 0, 0, 0.9);
+    background: #070708;
+    border: 2px solid #3f3f44;
+    border-radius: 16px;
+    width: 90%;
+    max-width: 600px;
+    max-height: 70vh;
+    display: flex;
+    flex-direction: column;
+    box-shadow: 0 16px 64px rgba(0, 0, 0, 0.9);
 }
 
 .c4ai-saved-flows-header {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 20px 24px;
-  border-bottom: 1px solid #2a2a2a;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 20px 24px;
+    border-bottom: 1px solid #2a2a2a;
 }
 
 .c4ai-saved-flows-header h2 {
-  margin: 0;
-  font-size: 18px;
-  color: #fff;
+    margin: 0;
+    font-size: 18px;
+    color: #fff;
 }
 
 .c4ai-saved-flows-body {
-  flex: 1;
-  overflow-y: auto;
-  padding: 24px;
+    flex: 1;
+    overflow-y: auto;
+    padding: 24px;
 }
 
 .c4ai-no-flows {
-  text-align: center;
-  color: #a3abba;
-  font-size: 14px;
-  padding: 40px 20px;
+    text-align: center;
+    color: #a3abba;
+    font-size: 14px;
+    padding: 40px 20px;
 }
 
 .c4ai-flows-list {
-  display: flex;
-  flex-direction: column;
-  gap: 12px;
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
 }
 
 .c4ai-flow-item {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  border-radius: 8px;
-  padding: 16px;
-  transition: all 0.2s ease;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    border-radius: 8px;
+    padding: 16px;
+    transition: all 0.2s ease;
 }
 
 .c4ai-flow-item:hover {
-  border-color: #0fbbaa;
+    border-color: #0fbbaa;
 }
 
 .c4ai-flow-info h3 {
-  margin: 0 0 8px 0;
-  font-size: 16px;
-  color: #e8e9ed;
+    margin: 0 0 8px 0;
+    font-size: 16px;
+    color: #e8e9ed;
 }
 
 .c4ai-flow-meta {
-  font-size: 12px;
-  color: #a3abba;
-  display: flex;
-  gap: 8px;
-  margin: 0;
+    font-size: 12px;
+    color: #a3abba;
+    display: flex;
+    gap: 8px;
+    margin: 0;
 }
 
 .c4ai-flow-actions {
-  display: flex;
-  gap: 8px;
+    display: flex;
+    gap: 8px;
 }
 
 .c4ai-load-flow-btn {
-  background: #0fbbaa !important;
-  border-color: #0fbbaa !important;
-  color: #070708 !important;
-  padding: 6px 12px;
-  font-size: 12px;
+    background: #0fbbaa !important;
+    border-color: #0fbbaa !important;
+    color: #070708 !important;
+    padding: 6px 12px;
+    font-size: 12px;
 }
 
 .c4ai-load-flow-btn:hover {
-  background: #1fcbba !important;
+    background: #1fcbba !important;
 }
 
 .c4ai-delete-flow-btn {
-  background: #3f3f44;
-  border-color: #3f3f44;
-  color: #ff3c74;
-  padding: 6px 12px;
-  font-size: 12px;
+    background: #3f3f44;
+    border-color: #3f3f44;
+    color: #ff3c74;
+    padding: 6px 12px;
+    font-size: 12px;
 }
 
 .c4ai-delete-flow-btn:hover {
-  background: #ff3c74;
-  color: #070708;
-  border-color: #ff3c74;
+    background: #ff3c74;
+    color: #070708;
+    border-color: #ff3c74;
 }
 
 /* Replay Indicator */
 .c4ai-replay-indicator {
-  position: fixed;
-  bottom: 100px;
-  left: 50%;
-  transform: translateX(-50%);
-  background: #070708;
-  border: 2px solid #0fbbaa;
-  border-radius: 12px;
-  padding: 20px;
-  z-index: 999999;
-  font-family: var(--font-primary);
-  box-shadow: 0 8px 32px rgba(15, 187, 170, 0.3);
-  min-width: 300px;
+    position: fixed;
+    bottom: 100px;
+    left: 50%;
+    transform: translateX(-50%);
+    background: #070708;
+    border: 2px solid #0fbbaa;
+    border-radius: 12px;
+    padding: 20px;
+    z-index: 999999;
+    font-family: var(--font-primary);
+    box-shadow: 0 8px 32px rgba(15, 187, 170, 0.3);
+    min-width: 300px;
 }
 
 .c4ai-replay-content {
-  display: flex;
-  flex-direction: column;
-  gap: 12px;
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
 }
 
 .c4ai-replay-header {
-  display: flex;
-  align-items: center;
-  gap: 8px;
-  font-size: 16px;
-  font-weight: 600;
-  color: #0fbbaa;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    font-size: 16px;
+    font-weight: 600;
+    color: #0fbbaa;
 }
 
 .c4ai-replay-icon {
-  font-size: 20px;
+    font-size: 20px;
 }
 
 .c4ai-replay-progress {
-  height: 6px;
-  background: #3f3f44;
-  border-radius: 3px;
-  overflow: hidden;
+    height: 6px;
+    background: #3f3f44;
+    border-radius: 3px;
+    overflow: hidden;
 }
 
 .c4ai-replay-progress-bar {
-  height: 100%;
-  background: #0fbbaa;
-  width: 0%;
-  transition: width 0.3s ease;
+    height: 100%;
+    background: #0fbbaa;
+    width: 0%;
+    transition: width 0.3s ease;
 }
 
 .c4ai-replay-status {
-  font-size: 13px;
-  color: #e8e9ed;
-  text-align: center;
+    font-size: 13px;
+    color: #e8e9ed;
+    text-align: center;
 }
 
 .c4ai-replay-stop {
-  background: #ff3c74;
-  border: none;
-  color: #070708;
-  padding: 8px 16px;
-  border-radius: 6px;
-  font-size: 13px;
-  font-weight: 600;
-  cursor: pointer;
-  transition: all 0.2s ease;
-  font-family: var(--font-primary);
+    background: #ff3c74;
+    border: none;
+    color: #070708;
+    padding: 8px 16px;
+    border-radius: 6px;
+    font-size: 13px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-family: var(--font-primary);
 }
 
 .c4ai-replay-stop:hover {
-  background: #ff5c84;
-  transform: translateY(-1px);
+    background: #ff5c84;
+    transform: translateY(-1px);
 }
 
 /* Replay Highlight */
 .c4ai-replay-highlight {
-  position: absolute;
-  pointer-events: none;
-  z-index: 999998;
-  border: 3px solid #0fbbaa;
-  background: rgba(15, 187, 170, 0.2);
-  box-shadow: 0 0 0 4px rgba(15, 187, 170, 0.3);
-  transition: all 0.3s ease;
-  display: none;
+    position: absolute;
+    pointer-events: none;
+    z-index: 999998;
+    border: 3px solid #0fbbaa;
+    background: rgba(15, 187, 170, 0.2);
+    box-shadow: 0 0 0 4px rgba(15, 187, 170, 0.3);
+    transition: all 0.3s ease;
+    display: none;
 }
 
 /* Click Indicator Animation */
 .c4ai-click-indicator {
-  position: fixed;
-  pointer-events: none;
-  z-index: 999999;
-  width: 20px;
-  height: 20px;
-  border: 3px solid #0fbbaa;
-  border-radius: 50%;
-  transform: translate(-50%, -50%);
-  animation: c4ai-click-pulse 0.6s ease-out;
+    position: fixed;
+    pointer-events: none;
+    z-index: 999999;
+    width: 20px;
+    height: 20px;
+    border: 3px solid #0fbbaa;
+    border-radius: 50%;
+    transform: translate(-50%, -50%);
+    animation: c4ai-click-pulse 0.6s ease-out;
 }
 
 @keyframes c4ai-click-pulse {
-  0% {
-    width: 20px;
-    height: 20px;
-    opacity: 1;
-  }
-  100% {
-    width: 60px;
-    height: 60px;
-    opacity: 0;
-  }
+    0% {
+        width: 20px;
+        height: 20px;
+        opacity: 1;
+    }
+    100% {
+        width: 60px;
+        height: 60px;
+        opacity: 0;
+    }
 }
 
 /* Keyboard Overlay */
 .c4ai-keyboard-overlay {
-  position: fixed;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%);
-  background: #070708;
-  border: 2px solid #f380f5;
-  border-radius: 8px;
-  padding: 20px 40px;
-  font-size: 24px;
-  font-weight: 600;
-  color: #f380f5;
-  z-index: 999999;
-  font-family: var(--font-primary);
-  box-shadow: 0 8px 32px rgba(243, 128, 245, 0.3);
-  opacity: 0;
-  transition: opacity 0.3s ease;
+    position: fixed;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    background: #070708;
+    border: 2px solid #f380f5;
+    border-radius: 8px;
+    padding: 20px 40px;
+    font-size: 24px;
+    font-weight: 600;
+    color: #f380f5;
+    z-index: 999999;
+    font-family: var(--font-primary);
+    box-shadow: 0 8px 32px rgba(243, 128, 245, 0.3);
+    opacity: 0;
+    transition: opacity 0.3s ease;
 }
 
 .c4ai-keyboard-overlay.visible {
-  opacity: 1;
+    opacity: 1;
 }
 
 /* Debugger Modal */
 .c4ai-debugger-modal {
-  position: fixed;
-  top: 50px;
-  right: 20px;
-  background: #070708;
-  border: 2px solid #3f3f44;
-  border-radius: 12px;
-  z-index: 999999;
-  font-family: var(--font-primary);
-  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
-  width: 500px;
-  max-height: 80vh;
-  display: flex;
-  flex-direction: column;
-  overflow: hidden;
+    position: fixed;
+    top: 50px;
+    right: 20px;
+    background: #070708;
+    border: 2px solid #3f3f44;
+    border-radius: 12px;
+    z-index: 999999;
+    font-family: var(--font-primary);
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
+    width: 500px;
+    max-height: 80vh;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
 }
 
 .c4ai-debugger-content {
-  display: flex;
-  flex-direction: column;
-  height: 100%;
-  overflow: hidden;
+    display: flex;
+    flex-direction: column;
+    height: 100%;
+    overflow: hidden;
 }
 
 .c4ai-debugger-header {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 16px 20px;
-  border-bottom: 1px solid #3f3f44;
-  background: #1a1a1a;
-  border-radius: 12px 12px 0 0;
-  cursor: grab;
-  user-select: none;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 16px 20px;
+    border-bottom: 1px solid #3f3f44;
+    background: #1a1a1a;
+    border-radius: 12px 12px 0 0;
+    cursor: grab;
+    user-select: none;
 }
 
 .c4ai-debugger-header h2 {
-  margin: 0;
-  font-size: 16px;
-  color: #e8e9ed;
+    margin: 0;
+    font-size: 16px;
+    color: #e8e9ed;
 }
 
 .c4ai-debugger-body {
-  flex: 1;
-  display: flex;
-  flex-direction: column;
-  overflow: hidden;
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
 }
 
 .c4ai-debugger-controls {
-  display: flex;
-  gap: 8px;
-  padding: 16px;
-  background: #1a1a1a;
-  border-bottom: 1px solid #3f3f44;
+    display: flex;
+    gap: 8px;
+    padding: 16px;
+    background: #1a1a1a;
+    border-bottom: 1px solid #3f3f44;
 }
 
 .c4ai-debug-btn {
-  padding: 8px 12px;
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  border-radius: 6px;
-  color: #e8e9ed;
-  font-size: 13px;
-  font-weight: 600;
-  cursor: pointer;
-  transition: all 0.2s ease;
-  display: flex;
-  align-items: center;
-  gap: 6px;
-  font-family: var(--font-primary);
+    padding: 8px 12px;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    border-radius: 6px;
+    color: #e8e9ed;
+    font-size: 13px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    font-family: var(--font-primary);
 }
 
 .c4ai-debug-btn:hover:not(:disabled) {
-  background: #4f4f54;
-  border-color: #5f5f64;
-  transform: translateY(-1px);
+    background: #4f4f54;
+    border-color: #5f5f64;
+    transform: translateY(-1px);
 }
 
 .c4ai-debug-btn:disabled {
-  opacity: 0.5;
-  cursor: not-allowed;
+    opacity: 0.5;
+    cursor: not-allowed;
 }
 
 .c4ai-run-btn:not(:disabled) {
-  background: #0fbbaa;
-  border-color: #0fbbaa;
-  color: #070708;
+    background: #0fbbaa;
+    border-color: #0fbbaa;
+    color: #070708;
 }
 
 .c4ai-run-btn:hover:not(:disabled) {
-  background: #1fcbba;
+    background: #1fcbba;
 }
 
 .c4ai-stop-btn {
-  background: #ff3c74;
-  border-color: #ff3c74;
-  color: #070708;
+    background: #ff3c74;
+    border-color: #ff3c74;
+    color: #070708;
 }
 
 .c4ai-stop-btn:hover {
-  background: #ff5c84;
+    background: #ff5c84;
 }
 
 .c4ai-debugger-status {
-  padding: 12px 16px;
-  background: #3f3f44;
-  border-bottom: 1px solid #3f3f44;
-  display: flex;
-  align-items: center;
-  gap: 8px;
+    padding: 12px 16px;
+    background: #3f3f44;
+    border-bottom: 1px solid #3f3f44;
+    display: flex;
+    align-items: center;
+    gap: 8px;
 }
 
 .c4ai-debug-label {
-  font-size: 12px;
-  color: #a3abba;
-  text-transform: uppercase;
+    font-size: 12px;
+    color: #a3abba;
+    text-transform: uppercase;
 }
 
 .c4ai-debug-value {
-  font-size: 14px;
-  color: #0fbbaa;
-  font-weight: 600;
+    font-size: 14px;
+    color: #0fbbaa;
+    font-weight: 600;
 }
 
 .c4ai-debugger-actions {
-  flex: 1;
-  overflow-y: auto;
-  padding: 8px;
-  overflow-x: hidden;
+    flex: 1;
+    overflow-y: auto;
+    padding: 8px;
+    overflow-x: hidden;
 }
 
 /* Debug Action Item */
 .c4ai-debug-action {
-  display: flex;
-  align-items: center;
-  gap: 8px;
-  padding: 10px;
-  margin-bottom: 4px;
-  background: #1a1a1a;
-  border: 2px solid transparent;
-  border-radius: 8px;
-  transition: all 0.2s ease;
-  position: relative;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 10px;
+    margin-bottom: 4px;
+    background: #1a1a1a;
+    border: 2px solid transparent;
+    border-radius: 8px;
+    transition: all 0.2s ease;
+    position: relative;
 }
 
 .c4ai-debug-action:hover {
-  background: #2a2a2a;
+    background: #2a2a2a;
 }
 
 .c4ai-debug-action.c4ai-current {
-  border-color: #0fbbaa;
-  background: rgba(15, 187, 170, 0.1);
+    border-color: #0fbbaa;
+    background: rgba(15, 187, 170, 0.1);
 }
 
 .c4ai-debug-action.has-breakpoint {
-  border-left: 4px solid #ff3c74;
-  padding-left: 8px;
+    border-left: 4px solid #ff3c74;
+    padding-left: 8px;
 }
 
 .c4ai-debug-action-left {
-  display: flex;
-  align-items: center;
-  gap: 4px;
-  flex-shrink: 0;
-  width: 60px;
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    flex-shrink: 0;
+    width: 60px;
 }
 
 .c4ai-breakpoint-checkbox {
-  cursor: pointer;
+    cursor: pointer;
 }
 
 .c4ai-action-number {
-  font-size: 12px;
-  color: #a3abba;
-  min-width: 20px;
-  text-align: center;
+    font-size: 12px;
+    color: #a3abba;
+    min-width: 20px;
+    text-align: center;
 }
 
 .c4ai-action-indicator {
-  color: #0fbbaa;
-  font-size: 12px;
-  opacity: 0;
-  transition: opacity 0.2s ease;
-  width: 12px;
+    color: #0fbbaa;
+    font-size: 12px;
+    opacity: 0;
+    transition: opacity 0.2s ease;
+    width: 12px;
 }
 
 .c4ai-debug-action.c4ai-current .c4ai-action-indicator {
-  opacity: 1;
+    opacity: 1;
 }
 
 .c4ai-debug-action-content {
-  flex: 1;
-  display: flex;
-  flex-direction: column;
-  gap: 2px;
-  min-width: 0;
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    gap: 2px;
+    min-width: 0;
 }
 
 .c4ai-action-type {
-  font-size: 11px;
-  text-transform: uppercase;
-  color: #0fbbaa;
-  font-weight: 600;
+    font-size: 11px;
+    text-transform: uppercase;
+    color: #0fbbaa;
+    font-weight: 600;
 }
 
 .c4ai-action-detail {
-  font-size: 12px;
-  color: #e8e9ed;
-  padding: 3px 6px;
-  background: #070708;
-  border-radius: 4px;
-  border: 1px solid transparent;
-  transition: all 0.2s ease;
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
-  max-width: 100%;
+    font-size: 12px;
+    color: #e8e9ed;
+    padding: 3px 6px;
+    background: #070708;
+    border-radius: 4px;
+    border: 1px solid transparent;
+    transition: all 0.2s ease;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    max-width: 100%;
 }
 
 .c4ai-action-detail[contenteditable]:hover {
-  border-color: #3f3f44;
+    border-color: #3f3f44;
 }
 
 .c4ai-action-detail[contenteditable]:focus {
-  outline: none;
-  border-color: #0fbbaa;
-  white-space: normal;
+    outline: none;
+    border-color: #0fbbaa;
+    white-space: normal;
 }
 
 .c4ai-debug-action-right {
-  display: flex;
-  gap: 4px;
-  opacity: 0;
-  transition: opacity 0.2s ease;
+    display: flex;
+    gap: 4px;
+    opacity: 0;
+    transition: opacity 0.2s ease;
 }
 
 .c4ai-debug-action:hover .c4ai-debug-action-right {
-  opacity: 1;
+    opacity: 1;
 }
 
 .c4ai-action-edit,
 .c4ai-action-delete,
 .c4ai-action-play {
-  background: none;
-  border: none;
-  cursor: pointer;
-  font-size: 16px;
-  padding: 4px;
-  border-radius: 4px;
-  transition: all 0.2s ease;
+    background: none;
+    border: none;
+    cursor: pointer;
+    font-size: 16px;
+    padding: 4px;
+    border-radius: 4px;
+    transition: all 0.2s ease;
 }
 
 .c4ai-action-edit:hover {
-  background: #3f3f44;
+    background: #3f3f44;
 }
 
 .c4ai-action-delete:hover {
-  background: #ff3c74;
+    background: #ff3c74;
 }
 
 .c4ai-action-play {
-  color: #0fbbaa;
+    color: #0fbbaa;
 }
 
 .c4ai-action-play:hover {
-  background: #0fbbaa;
-  color: #070708;
+    background: #0fbbaa;
+    color: #070708;
 }
 
 /* Edit Dialog */
 .c4ai-edit-dialog {
-  position: fixed;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  background: rgba(0, 0, 0, 0.8);
-  z-index: 999999;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  font-family: var(--font-primary);
+    position: fixed;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.8);
+    z-index: 999999;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-family: var(--font-primary);
 }
 
 .c4ai-edit-content {
-  background: #070708;
-  border: 2px solid #3f3f44;
-  border-radius: 12px;
-  padding: 24px;
-  min-width: 400px;
-  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
+    background: #070708;
+    border: 2px solid #3f3f44;
+    border-radius: 12px;
+    padding: 24px;
+    min-width: 400px;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.8);
 }
 
 .c4ai-edit-content h3 {
-  margin: 0 0 20px 0;
-  font-size: 18px;
-  color: #e8e9ed;
+    margin: 0 0 20px 0;
+    font-size: 18px;
+    color: #e8e9ed;
 }
 
 .c4ai-edit-field {
-  margin-bottom: 16px;
+    margin-bottom: 16px;
 }
 
 .c4ai-edit-field label {
-  display: block;
-  font-size: 12px;
-  color: #a3abba;
-  margin-bottom: 8px;
-  text-transform: uppercase;
+    display: block;
+    font-size: 12px;
+    color: #a3abba;
+    margin-bottom: 8px;
+    text-transform: uppercase;
 }
 
 .c4ai-edit-field select,
 .c4ai-edit-field input {
-  width: 100%;
-  padding: 8px 12px;
-  background: #3f3f44;
-  border: 1px solid #3f3f44;
-  border-radius: 6px;
-  color: #e8e9ed;
-  font-size: 14px;
-  font-family: var(--font-primary);
-  outline: none;
+    width: 100%;
+    padding: 8px 12px;
+    background: #3f3f44;
+    border: 1px solid #3f3f44;
+    border-radius: 6px;
+    color: #e8e9ed;
+    font-size: 14px;
+    font-family: var(--font-primary);
+    outline: none;
 }
 
 .c4ai-edit-field select:focus,
 .c4ai-edit-field input:focus {
-  border-color: #0fbbaa;
+    border-color: #0fbbaa;
 }
 
 .c4ai-edit-actions {
-  display: flex;
-  gap: 8px;
-  justify-content: flex-end;
-  margin-top: 20px;
+    display: flex;
+    gap: 8px;
+    justify-content: flex-end;
+    margin-top: 20px;
 }
 
 .c4ai-edit-actions button {
-  padding: 8px 20px;
-  border: none;
-  border-radius: 6px;
-  font-size: 13px;
-  font-weight: 600;
-  cursor: pointer;
-  transition: all 0.2s ease;
-  font-family: var(--font-primary);
+    padding: 8px 20px;
+    border: none;
+    border-radius: 6px;
+    font-size: 13px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-family: var(--font-primary);
 }
 
 #c4ai-edit-save {
-  background: #0fbbaa;
-  color: #070708;
+    background: #0fbbaa;
+    color: #070708;
 }
 
 #c4ai-edit-save:hover {
-  background: #1fcbba;
+    background: #1fcbba;
 }
 
 #c4ai-edit-cancel {
-  background: #3f3f44;
-  color: #e8e9ed;
+    background: #3f3f44;
+    color: #e8e9ed;
 }
 
 #c4ai-edit-cancel:hover {
-  background: #4f4f54;
-}
\ No newline at end of file
+    background: #4f4f54;
+}
+
+/* Click2Crawl (C2C) Styles */
+.c4ai-c2c-toolbar {
+    position: fixed;
+    background: #1a1a1a;
+    border: 1px solid #3f3f44;
+    border-radius: 12px;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4);
+    font-family: var(--font-primary);
+    font-size: 13px;
+    width: 320px;
+    z-index: 999999;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 12px 16px;
+    background: #252527;
+    border-bottom: 1px solid #3f3f44;
+    border-radius: 12px 12px 0 0;
+    cursor: grab;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-header:active {
+    cursor: grabbing;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-title {
+    color: #e8e9ed;
+    font-weight: 600;
+    font-size: 14px;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-content {
+    padding: 16px;
+}
+
+.c4ai-c2c-toolbar .c4ai-selection-info {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 16px;
+    padding: 8px 12px;
+    background: #252527;
+    border-radius: 8px;
+}
+
+.c4ai-c2c-toolbar .c4ai-selection-count {
+    color: #0fbbaa;
+    font-weight: 600;
+}
+
+.c4ai-c2c-toolbar .c4ai-clear-btn {
+    background: transparent;
+    border: 1px solid #3f3f44;
+    color: #a3abba;
+    padding: 4px 12px;
+    border-radius: 6px;
+    font-size: 12px;
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.c4ai-c2c-toolbar .c4ai-clear-btn:hover:not(:disabled) {
+    border-color: #ff3c74;
+    color: #ff3c74;
+}
+
+.c4ai-c2c-toolbar .c4ai-clear-btn:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-actions {
+    display: flex;
+    gap: 8px;
+    margin-bottom: 16px;
+}
+
+.c4ai-c2c-toolbar .c4ai-preview-btn,
+.c4ai-c2c-toolbar .c4ai-copy-btn {
+    flex: 1;
+    padding: 10px 16px;
+    border-radius: 8px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    border: none;
+}
+
+.c4ai-c2c-toolbar .c4ai-preview-btn {
+    background: #0fbbaa;
+    color: #070708;
+}
+
+.c4ai-c2c-toolbar .c4ai-preview-btn:hover:not(:disabled) {
+    background: #0d9f92;
+    transform: translateY(-1px);
+}
+
+.c4ai-c2c-toolbar .c4ai-copy-btn {
+    background: #252527;
+    color: #e8e9ed;
+    border: 1px solid #3f3f44;
+}
+
+.c4ai-c2c-toolbar .c4ai-copy-btn:hover:not(:disabled) {
+    border-color: #0fbbaa;
+    color: #0fbbaa;
+}
+
+.c4ai-c2c-toolbar button:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+    transform: none !important;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-instructions {
+    background: #252527;
+    border-radius: 8px;
+    padding: 12px;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-instructions p {
+    margin: 6px 0;
+    color: #a3abba;
+    font-size: 12px;
+    line-height: 1.5;
+}
+
+.c4ai-c2c-toolbar .c4ai-toolbar-instructions strong {
+    color: #e8e9ed;
+}
+
+/* C2C Selection Highlight */
+.c4ai-highlight-box.c4ai-c2c-selection {
+    border: 2px solid #0fbbaa;
+    background: rgba(15, 187, 170, 0.1);
+    box-shadow: 0 0 0 2px rgba(15, 187, 170, 0.2);
+    display: block;
+}
+
+.c4ai-selection-badge {
+    position: absolute;
+    top: -8px;
+    right: -8px;
+    background: #0fbbaa;
+    color: #070708;
+    width: 24px;
+    height: 24px;
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 12px;
+    font-weight: bold;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3);
+}
+
+/* Note: Badge styles are now inline to prevent CSS conflicts */
+
+/* C2C Hover State */
+.c4ai-hover-candidate {
+    outline: 2px dashed #0fbbaa !important;
+    outline-offset: 2px;
+    cursor: pointer !important;
+}
+
+/* C2C Preview Panel */
+.c4ai-c2c-preview {
+    position: fixed;
+    background: #1a1a1a;
+    border: 1px solid #3f3f44;
+    border-radius: 12px;
+    box-shadow: 0 16px 48px rgba(0, 0, 0, 0.5);
+    font-family: var(--font-primary);
+    width: 600px;
+    max-width: 90vw;
+    max-height: 80vh;
+    display: flex;
+    flex-direction: column;
+    z-index: 999999;
+}
+
+.c4ai-c2c-preview .c4ai-preview-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 12px 16px;
+    background: #252527;
+    border-bottom: 1px solid #3f3f44;
+    border-radius: 12px 12px 0 0;
+    cursor: grab;
+}
+
+.c4ai-c2c-preview .c4ai-preview-header:active {
+    cursor: grabbing;
+}
+
+.c4ai-c2c-preview .c4ai-preview-title {
+    color: #e8e9ed;
+    font-weight: 600;
+    font-size: 14px;
+}
+
+.c4ai-c2c-preview .c4ai-preview-close {
+    background: transparent;
+    border: none;
+    color: #a3abba;
+    font-size: 24px;
+    cursor: pointer;
+    padding: 0;
+    width: 32px;
+    height: 32px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 6px;
+    transition: all 0.2s ease;
+}
+
+.c4ai-c2c-preview .c4ai-preview-close:hover {
+    background: #ff3c74;
+    color: #fff;
+}
+
+.c4ai-c2c-preview .c4ai-preview-options {
+    display: flex;
+    gap: 16px;
+    padding: 12px 16px;
+    background: #252527;
+    border-bottom: 1px solid #3f3f44;
+    flex-wrap: wrap;
+}
+
+.c4ai-c2c-preview .c4ai-preview-options label {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    color: #a3abba;
+    font-size: 12px;
+    cursor: pointer;
+}
+
+/* Highlight the Visual Text Mode option */
+.c4ai-c2c-preview .c4ai-preview-options label:first-child {
+    color: #0fbbaa;
+    font-weight: 600;
+    padding-bottom: 8px;
+    border-bottom: 1px solid #3f3f44;
+    margin-bottom: 8px;
+    width: 100%;
+}
+
+.c4ai-c2c-preview .c4ai-preview-options input[type="checkbox"] {
+    width: 16px;
+    height: 16px;
+    cursor: pointer;
+}
+
+.c4ai-c2c-preview .c4ai-preview-content {
+    flex: 1;
+    overflow: hidden;
+    display: flex;
+    flex-direction: column;
+    max-height: 60vh;
+}
+
+.c4ai-c2c-preview .c4ai-preview-tabs {
+    display: flex;
+    align-items: center;
+    background: #252527;
+    border-bottom: 1px solid #3f3f44;
+}
+
+.c4ai-c2c-preview .c4ai-tab {
+    flex: 1;
+    padding: 12px;
+    background: transparent;
+    border: none;
+    color: #a3abba;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    position: relative;
+}
+
+.c4ai-c2c-preview .c4ai-tab:hover {
+    color: #e8e9ed;
+}
+
+.c4ai-c2c-preview .c4ai-tab.active {
+    color: #0fbbaa;
+}
+
+.c4ai-c2c-preview .c4ai-tab.active::after {
+    content: "";
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    height: 2px;
+    background: #0fbbaa;
+}
+
+.c4ai-c2c-preview .c4ai-wrap-toggle {
+    margin-left: auto;
+    padding: 8px 16px;
+    background: transparent;
+    border: 1px solid #3f3f44;
+    color: #a3abba;
+    font-size: 12px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    border-radius: 6px;
+    margin-right: 8px;
+}
+
+.c4ai-c2c-preview .c4ai-wrap-toggle:hover {
+    border-color: #0fbbaa;
+    color: #0fbbaa;
+}
+
+.c4ai-c2c-preview .c4ai-wrap-toggle.active {
+    background: #0fbbaa;
+    color: #070708;
+    border-color: #0fbbaa;
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane {
+    display: none;
+    flex: 1;
+    overflow-y: auto;
+    overflow-x: hidden;
+    padding: 16px;
+    max-height: calc(60vh - 120px); /* Account for header, tabs, and actions */
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane.active {
+    display: block;
+}
+
+/* Custom scrollbar for preview pane */
+.c4ai-c2c-preview .c4ai-preview-pane::-webkit-scrollbar {
+    width: 8px;
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane::-webkit-scrollbar-track {
+    background: #252527;
+    border-radius: 4px;
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane::-webkit-scrollbar-thumb {
+    background: #3f3f44;
+    border-radius: 4px;
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane::-webkit-scrollbar-thumb:hover {
+    background: #4f4f54;
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane pre {
+    margin: 0;
+    white-space: pre;
+    overflow-x: auto;
+    font-family: var(--font-code);
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane.wrap pre {
+    white-space: pre-wrap;
+    word-wrap: break-word;
+    overflow-x: hidden;
+}
+
+.c4ai-c2c-preview .c4ai-preview-pane code {
+    color: #e8e9ed;
+    font-family: var(--font-code);
+    font-size: 13px;
+    line-height: 1.6;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview {
+    color: #e8e9ed;
+    line-height: 1.6;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview h1,
+.c4ai-c2c-preview .c4ai-markdown-preview h2,
+.c4ai-c2c-preview .c4ai-markdown-preview h3,
+.c4ai-c2c-preview .c4ai-markdown-preview h4,
+.c4ai-c2c-preview .c4ai-markdown-preview h5,
+.c4ai-c2c-preview .c4ai-markdown-preview h6 {
+    color: #fff;
+    margin: 16px 0 8px 0;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview a {
+    color: #0fbbaa;
+    text-decoration: none;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview a:hover {
+    text-decoration: underline;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview code {
+    background: #252527;
+    padding: 2px 6px;
+    border-radius: 4px;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview pre {
+    background: #252527;
+    padding: 12px;
+    border-radius: 8px;
+    overflow-x: auto;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview pre code {
+    background: none;
+    padding: 0;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview table {
+    border-collapse: collapse;
+    width: 100%;
+    margin: 16px 0;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview th,
+.c4ai-c2c-preview .c4ai-markdown-preview td {
+    border: 1px solid #3f3f44;
+    padding: 8px 12px;
+    text-align: left;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview th {
+    background: #252527;
+    font-weight: 600;
+}
+
+.c4ai-c2c-preview .c4ai-markdown-preview blockquote {
+    border-left: 4px solid #0fbbaa;
+    margin: 16px 0;
+    padding-left: 16px;
+    color: #a3abba;
+}
+
+.c4ai-c2c-preview .c4ai-preview-actions {
+    display: flex;
+    gap: 8px;
+    padding: 16px;
+    background: #252527;
+    border-top: 1px solid #3f3f44;
+    border-radius: 0 0 12px 12px;
+}
+
+.c4ai-c2c-preview .c4ai-download-btn,
+.c4ai-c2c-preview .c4ai-copy-markdown-btn,
+.c4ai-c2c-preview .c4ai-cloud-btn {
+    flex: 1;
+    padding: 10px 16px;
+    border-radius: 8px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    border: none;
+    font-size: 13px;
+}
+
+.c4ai-c2c-preview .c4ai-download-btn {
+    background: #252527;
+    color: #e8e9ed;
+    border: 1px solid #3f3f44;
+}
+
+.c4ai-c2c-preview .c4ai-download-btn:hover {
+    border-color: #0fbbaa;
+    color: #0fbbaa;
+}
+
+.c4ai-c2c-preview .c4ai-copy-markdown-btn {
+    background: #0fbbaa;
+    color: #070708;
+}
+
+.c4ai-c2c-preview .c4ai-copy-markdown-btn:hover {
+    background: #0d9f92;
+}
+
+.c4ai-c2c-preview .c4ai-cloud-btn {
+    background: #3f3f44;
+    color: #666;
+    cursor: not-allowed;
+}
+
+/* Navigation Buttons */
+.c4ai-nav-btn {
+    background: #3f3f44;
+    color: #e8e9ed;
+    border: none;
+    width: 24px;
+    height: 24px;
+    border-radius: 4px;
+    font-size: 14px;
+    font-weight: bold;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-family: var(--font-primary);
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    margin-left: 4px;
+}
+
+.c4ai-nav-btn-small {
+    width: 20px;
+    height: 20px;
+    font-size: 12px;
+}
+
+.c4ai-nav-btn:hover:not(:disabled) {
+    background: #4f4f54;
+    border-color: #0fbbaa;
+}
+
+.c4ai-nav-btn:disabled {
+    background: #2a2a2a;
+    color: #666;
+    cursor: not-allowed;
+    opacity: 0.5;
+}
+
+.c4ai-nav-btn:disabled:hover {
+    background: #2a2a2a;
+    border-color: transparent;
+}
+
+.c4ai-nav-btn.c4ai-nav-deselect {
+    background: #ff3c74;
+    color: #fff;
+}
+
+.c4ai-nav-btn.c4ai-nav-deselect:hover {
+    background: #ff5c84;
+}
+
+/* C2C Notification Enhancement */
+.c4ai-notification {
+    position: fixed;
+    bottom: 24px;
+    right: 24px;
+    background: #1e1e1e;
+    color: #e8e9ed;
+    padding: 12px 20px;
+    border-radius: 8px;
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
+    font-family: var(--font-primary);
+    font-size: 14px;
+    z-index: 999999;
+    transform: translateY(100px);
+    opacity: 0;
+    transition: all 0.3s ease;
+}
+
+.c4ai-notification.show {
+    transform: translateY(0);
+    opacity: 1;
+}
+
+.c4ai-notification.c4ai-notification-success {
+    border-left: 4px solid #0fbbaa;
+}
+
+.c4ai-notification.c4ai-notification-error {
+    border-left: 4px solid #ff3c74;
+}
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder.js b/docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder.js
new file mode 100644
index 00000000..7bbb7a80
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder.js
@@ -0,0 +1,1812 @@
+// Enhanced SchemaBuilder class for Crawl4AI Chrome Extension
+
+// Singleton instance to prevent multiple toolbars
+let schemaBuilderInstance = null;
+
+class SchemaBuilder {
+  constructor() {
+    // Prevent multiple instances
+    if (schemaBuilderInstance) {
+      schemaBuilderInstance.stop();
+    }
+    schemaBuilderInstance = this;
+    
+    this.container = null;
+    this.fields = [];
+    this.overlay = null;
+    this.toolbar = null;
+    this.highlightBox = null; // For hover preview
+    this.selectedBox = null; // For selected element
+    this.currentElement = null; // Currently hovered element
+    this.selectedElement = null; // Currently selected element (container)
+    this.selectedElements = new Set();
+    this.inspectingFields = false; // Field inspection mode
+    this.codeModal = null;
+    this.previewMode = false;
+    this.previewElements = [];
+    this.schema = null;
+    this.parentLevels = 1; // Default parent levels for base container
+    
+    this.handleMouseMove = this.handleMouseMove.bind(this);
+    this.handleClick = this.handleClick.bind(this);
+    this.handleKeyPress = this.handleKeyPress.bind(this);
+    this.handleMouseLeave = this.handleMouseLeave.bind(this);
+  }
+
+  start() {
+    this.createOverlay();
+    this.createToolbar();
+    this.attachEventListeners();
+    this.updateToolbar();
+  }
+
+  stop() {
+    this.detachEventListeners();
+    this.overlay?.remove();
+    this.toolbar?.remove();
+    this.highlightBox?.remove();
+    this.selectedBox?.remove();
+    this.removeAllHighlights();
+    this.clearPreview();
+    this.container = null;
+    this.fields = [];
+    this.selectedElements.clear();
+    this.schema = null;
+    this.currentElement = null;
+    this.selectedElement = null;
+    this.inspectingFields = false;
+    this.parentLevels = 1;
+    
+    // Clear singleton reference
+    if (schemaBuilderInstance === this) {
+      schemaBuilderInstance = null;
+    }
+  }
+  
+  // Alias for content script compatibility
+  deactivate() {
+    this.stop();
+  }
+
+  createOverlay() {
+    // Create highlight box for hover preview
+    this.highlightBox = document.createElement('div');
+    this.highlightBox.className = 'c4ai-highlight-box';
+    document.body.appendChild(this.highlightBox);
+    
+    // Create selected box for permanent selection
+    this.selectedBox = document.createElement('div');
+    this.selectedBox.className = 'c4ai-selected-box';
+    this.selectedBox.style.display = 'none';
+    document.body.appendChild(this.selectedBox);
+  }
+
+  createToolbar() {
+    // Remove any existing toolbar first
+    const existingToolbar = document.querySelector('.c4ai-toolbar');
+    if (existingToolbar) {
+      existingToolbar.remove();
+    }
+    
+    this.toolbar = document.createElement('div');
+    this.toolbar.className = 'c4ai-toolbar';
+    this.toolbar.innerHTML = `
+      <div class="c4ai-toolbar-titlebar">
+        <div class="c4ai-titlebar-dots">
+          <button class="c4ai-dot c4ai-dot-close" id="c4ai-close"></button>
+          <button class="c4ai-dot c4ai-dot-minimize"></button>
+          <button class="c4ai-dot c4ai-dot-maximize"></button>
+        </div>
+        <img src="${chrome.runtime.getURL('icons/icon-16.png')}" class="c4ai-titlebar-icon" alt="Crawl4AI">
+        <div class="c4ai-titlebar-title">🔧 Schema Builder</div>
+      </div>
+      <div class="c4ai-toolbar-content">
+        <div class="c4ai-toolbar-status">
+          <div class="c4ai-status-item">
+            <span class="c4ai-status-label">Mode:</span>
+            <span class="c4ai-status-value" id="c4ai-mode">Select Container</span>
+          </div>
+          <div class="c4ai-status-item" id="c4ai-container-item" style="display: none;">
+            <span class="c4ai-status-label">Container:</span>
+            <span class="c4ai-status-value" id="c4ai-container">Not selected</span>
+            <button class="c4ai-nav-btn c4ai-nav-btn-small" id="c4ai-nav-up" title="Select parent">↑</button>
+            <button class="c4ai-nav-btn c4ai-nav-btn-small" id="c4ai-nav-down" title="Select child">↓</button>
+            <button class="c4ai-nav-btn c4ai-nav-btn-small c4ai-nav-deselect" id="c4ai-nav-close" title="Deselect">×</button>
+          </div>
+          <div class="c4ai-status-item" id="c4ai-selector-display" style="display: none;">
+            <div class="c4ai-container-selector" id="c4ai-container-selector"></div>
+          </div>
+          <div class="c4ai-status-item" id="c4ai-parent-levels" style="display: none;">
+            <span class="c4ai-status-label">Parent Levels:</span>
+            <div class="c4ai-parent-controls">
+              <button class="c4ai-parent-btn" id="c4ai-parent-minus">-</button>
+              <span class="c4ai-parent-value" id="c4ai-parent-value">1</span>
+              <button class="c4ai-parent-btn" id="c4ai-parent-plus">+</button>
+            </div>
+          </div>
+        </div>
+        
+        <div class="c4ai-schema-section" id="c4ai-schema-section" style="display: none;">
+          <div class="c4ai-section-header">
+            <span>SCHEMA FIELDS (<span id="c4ai-field-count">0</span>)</span>
+          </div>
+          <div class="c4ai-fields-list" id="c4ai-fields-list"></div>
+        </div>
+        
+        <div class="c4ai-actions-section" id="c4ai-actions-section" style="display: none;">
+          <div class="c4ai-section-header">ACTIONS</div>
+          <div class="c4ai-toolbar-actions">
+            <button id="c4ai-preview" class="c4ai-action-btn c4ai-preview-btn" disabled>
+              <span>👁️</span> Preview Matches
+            </button>
+            <button id="c4ai-test" class="c4ai-action-btn c4ai-test-btn" disabled>
+              <span>🧪</span> Test Schema
+            </button>
+            <button id="c4ai-deploy-cloud" class="c4ai-action-btn c4ai-export-btn c4ai-cloud-btn" disabled>
+              <span>☁️</span> Deploy
+            </button>
+            <button id="c4ai-export-schema" class="c4ai-action-btn c4ai-export-btn" disabled>
+              <span>📄</span> Schema
+            </button>
+            <button id="c4ai-export-data" class="c4ai-action-btn c4ai-export-btn" disabled>
+              <span>📊</span> Data
+            </button>
+          </div>
+        </div>
+        
+        <div class="c4ai-stats-section" id="c4ai-stats-section" style="display: none;">
+          <div class="c4ai-section-header">STATS</div>
+          <div class="c4ai-stats">
+            <div class="c4ai-stat-item">
+              <span>Matches Found:</span>
+              <span id="c4ai-matches-count">0 items</span>
+            </div>
+            <div class="c4ai-stat-item">
+              <span>Schema Valid:</span>
+              <span id="c4ai-schema-valid">Not tested</span>
+            </div>
+          </div>
+        </div>
+        
+        <div class="c4ai-toolbar-hint" id="c4ai-hint">
+          Click on a container element (e.g., product card, article, etc.)
+        </div>
+        
+        <div class="c4ai-toolbar-footer" id="c4ai-footer-section" style="display: none;">
+          <button id="c4ai-inspect-fields" class="c4ai-action-btn c4ai-primary-btn">
+            <span>🏷️</span> Fields
+          </button>
+        </div>
+      </div>
+    `;
+    document.body.appendChild(this.toolbar);
+    
+    // Force toolbar to top of z-index stack
+    this.toolbar.style.zIndex = '2147483647'; // Maximum z-index
+    
+    // Add event listeners for toolbar buttons with error handling
+    const addClickHandler = (id, handler) => {
+      const element = document.getElementById(id);
+      if (element) {
+        element.addEventListener('click', (e) => {
+          e.preventDefault();
+          e.stopPropagation();
+          handler();
+        });
+      }
+    };
+    
+    // Add all event listeners
+      addClickHandler('c4ai-inspect-fields', () => this.toggleFieldInspection());
+      addClickHandler('c4ai-preview', () => this.togglePreview());
+      addClickHandler('c4ai-test', () => this.testSchema());
+      addClickHandler('c4ai-export-schema', () => this.exportSchema());
+      addClickHandler('c4ai-export-data', () => this.exportData());
+      addClickHandler('c4ai-deploy-cloud', () => this.deployToCloud());
+      addClickHandler('c4ai-close', () => this.stop());
+      
+      // Navigation controls
+      addClickHandler('c4ai-nav-up', () => this.navigateUp());
+      addClickHandler('c4ai-nav-down', () => this.navigateDown());
+      addClickHandler('c4ai-nav-close', () => this.deselectContainer());
+      
+      // Parent level controls
+      addClickHandler('c4ai-parent-minus', () => this.adjustParentLevels(-1));
+      addClickHandler('c4ai-parent-plus', () => this.adjustParentLevels(1));
+    
+    // Make toolbar draggable
+    if (window.C4AI_Utils && window.C4AI_Utils.makeDraggable) {
+      window.C4AI_Utils.makeDraggable(this.toolbar);
+    }
+  }
+
+  attachEventListeners() {
+    document.addEventListener('mousemove', this.handleMouseMove, true);
+    document.addEventListener('click', this.handleClick, true);
+    document.addEventListener('keydown', this.handleKeyPress, true);
+    document.addEventListener('mouseleave', this.handleMouseLeave, true);
+  }
+
+  detachEventListeners() {
+    document.removeEventListener('mousemove', this.handleMouseMove, true);
+    document.removeEventListener('click', this.handleClick, true);
+    document.removeEventListener('keydown', this.handleKeyPress, true);
+    document.removeEventListener('mouseleave', this.handleMouseLeave, true);
+  }
+
+  handleMouseMove(e) {
+    const element = document.elementFromPoint(e.clientX, e.clientY);
+    
+    // Don't highlight if hovering over our UI elements
+    if (this.isOurElement(element)) {
+      this.highlightBox.style.display = 'none';
+      return;
+    }
+    
+    // Only show highlight if:
+    // 1. No container selected (selection mode)
+    // 2. Or inspecting fields inside container
+    if (!this.container || (this.inspectingFields && this.container)) {
+      if (element) {
+        // If inspecting fields, only highlight elements inside container
+        if (this.inspectingFields && !this.container.element.contains(element)) {
+          this.highlightBox.style.display = 'none';
+          return;
+        }
+        
+        this.currentElement = element;
+        this.highlightElement(element);
+      }
+    } else {
+      // Container selected but not inspecting fields - no highlight
+      this.highlightBox.style.display = 'none';
+    }
+  }
+  
+  handleMouseLeave(e) {
+    // Hide highlight when mouse leaves
+    if (e.target === document) {
+      this.highlightBox.style.display = 'none';
+    }
+  }
+
+  handleClick(e) {
+    const element = e.target;
+    
+    // Check if clicking on our UI elements
+    if (this.isOurElement(element)) {
+      return; // Let toolbar clicks work normally
+    }
+
+    // Use current element
+    const targetElement = this.currentElement || element;
+
+    if (!this.container) {
+      // Container selection mode - prevent default
+      e.preventDefault();
+      e.stopPropagation();
+      this.selectContainer(targetElement);
+    } else if (this.inspectingFields && this.container.element.contains(targetElement)) {
+      // Field selection mode AND clicking inside container - prevent default
+      e.preventDefault();
+      e.stopPropagation();
+      this.selectField(targetElement);
+    }
+    // Otherwise, let the click work normally
+  }
+
+  handleKeyPress(e) {
+    if (e.key === 'Escape') {
+      this.stop();
+    }
+  }
+
+  isOurElement(element) {
+    return window.C4AI_Utils.isOurElement(element) || 
+           (this.selectedBox && element === this.selectedBox);
+  }
+  
+  showSelectedBox(element) {
+    if (!element) return;
+    
+    const rect = element.getBoundingClientRect();
+    this.selectedBox.style.cssText = `
+      position: absolute;
+      left: ${rect.left + window.scrollX}px;
+      top: ${rect.top + window.scrollY}px;
+      width: ${rect.width}px;
+      height: ${rect.height}px;
+      display: block;
+    `;
+    
+    this.selectedBox.className = 'c4ai-selected-box c4ai-selected-container';
+  }
+  
+  updateNavButtonStates() {
+    const upBtn = document.getElementById('c4ai-nav-up');
+    const downBtn = document.getElementById('c4ai-nav-down');
+    
+    if (this.selectedElement) {
+      // Disable up button if no parent or parent is body
+      upBtn.disabled = !this.selectedElement.parentElement || this.selectedElement.parentElement === document.body;
+      
+      // Disable down button if no children
+      downBtn.disabled = this.selectedElement.children.length === 0;
+    }
+  }
+  
+  navigateUp() {
+    if (!this.selectedElement || !this.selectedElement.parentElement) return;
+    
+    const parent = this.selectedElement.parentElement;
+    if (parent === document.body) return;
+    
+    // Update selected element and container
+    this.selectedElement = parent;
+    this.container.element = parent;
+    this.container.tagName = parent.tagName.toLowerCase();
+    this.container.selector = this.generateContainerSelector(parent);
+    
+    // Update visual selection
+    this.showSelectedBox(parent);
+    this.updateNavButtonStates();
+    this.updateToolbar();
+    this.updateStats();
+  }
+  
+  navigateDown() {
+    if (!this.selectedElement || this.selectedElement.children.length === 0) return;
+    
+    const firstChild = this.selectedElement.children[0];
+    
+    // Update selected element and container
+    this.selectedElement = firstChild;
+    this.container.element = firstChild;
+    this.container.tagName = firstChild.tagName.toLowerCase();
+    this.container.selector = this.generateContainerSelector(firstChild);
+    
+    // Update visual selection
+    this.showSelectedBox(firstChild);
+    this.updateNavButtonStates();
+    this.updateToolbar();
+    this.updateStats();
+  }
+  
+  deselectContainer() {
+    if (this.container) {
+      // Remove visual selection
+      this.container.element.classList.remove('c4ai-selected-container');
+      this.selectedBox.style.display = 'none';
+      
+      // Clear container and related state
+      this.container = null;
+      this.selectedElement = null;
+      this.inspectingFields = false;
+      
+      // Clear all fields
+      this.fields.forEach(field => {
+        field.element.classList.remove('c4ai-selected-field');
+        field.element.removeAttribute('data-c4ai-field');
+      });
+      this.fields = [];
+      this.selectedElements.clear();
+      
+      this.updateToolbar();
+      this.updateStats();
+    }
+  }
+  
+  toggleFieldInspection() {
+    this.inspectingFields = !this.inspectingFields;
+    const fieldsBtn = document.getElementById('c4ai-inspect-fields');
+    
+    if (this.inspectingFields) {
+      fieldsBtn.classList.add('c4ai-active');
+      fieldsBtn.innerHTML = '<span>✓</span> Fields';
+    } else {
+      fieldsBtn.classList.remove('c4ai-active');
+      fieldsBtn.innerHTML = '<span>🏷️</span> Fields';
+      this.highlightBox.style.display = 'none';
+    }
+    
+    this.updateToolbar();
+  }
+
+
+  // Legacy method - kept for compatibility but now redirects to test schema
+  stopAndGenerate() {
+    this.testSchema();
+  }
+
+  highlightElement(element) {
+    const rect = element.getBoundingClientRect();
+    this.highlightBox.style.cssText = `
+      left: ${rect.left + window.scrollX}px;
+      top: ${rect.top + window.scrollY}px;
+      width: ${rect.width}px;
+      height: ${rect.height}px;
+      display: block;
+    `;
+
+    if (!this.container) {
+      // Container selection mode
+      this.highlightBox.className = 'c4ai-highlight-box c4ai-container-mode';
+    } else {
+      // Field selection mode
+      this.highlightBox.className = 'c4ai-highlight-box c4ai-field-mode';
+    }
+  }
+
+  selectContainer(element) {
+    // Remove previous container highlight
+    if (this.container) {
+      this.container.element.classList.remove('c4ai-selected-container');
+    }
+
+    this.container = {
+      element: element,
+      html: element.outerHTML,
+      selector: this.generateContainerSelector(element),
+      tagName: element.tagName.toLowerCase()
+    };
+
+    element.classList.add('c4ai-selected-container');
+    this.selectedElement = element;
+    this.showSelectedBox(element);
+    
+    // Hide hover highlight after selection
+    this.highlightBox.style.display = 'none';
+    
+    // Update navigation button states
+    this.updateNavButtonStates();
+    this.updateToolbar();
+    this.updateStats();
+  }
+
+  selectField(element) {
+    // Don't select the container itself
+    if (element === this.container.element) {
+      return;
+    }
+
+    // Check if already selected - if so, deselect it
+    if (this.selectedElements.has(element)) {
+      this.deselectField(element);
+      return;
+    }
+
+    // Must be inside the container
+    if (!this.container.element.contains(element)) {
+      return;
+    }
+
+    this.showFieldDialog(element);
+  }
+
+  deselectField(element) {
+    // Remove from fields array
+    this.fields = this.fields.filter(f => f.element !== element);
+    
+    // Remove from selected elements set
+    this.selectedElements.delete(element);
+    
+    // Remove visual selection
+    element.classList.remove('c4ai-selected-field');
+    
+    // Update UI
+    this.updateToolbar();
+    this.updateStats();
+  }
+
+  showFieldDialog(element) {
+    const dialog = document.createElement('div');
+    dialog.className = 'c4ai-field-dialog';
+    
+    const rect = element.getBoundingClientRect();
+    dialog.style.cssText = `
+      left: ${rect.left + window.scrollX}px;
+      top: ${rect.bottom + window.scrollY + 10}px;
+    `;
+
+    // Get available attributes
+    const attributes = this.getElementAttributes(element);
+    const attributeOptions = attributes.map(attr => 
+      `<option value="${attr.name}">${attr.name}: "${attr.value.substring(0, 30)}${attr.value.length > 30 ? '...' : ''}"</option>`
+    ).join('');
+
+    dialog.innerHTML = `
+      <div class="c4ai-field-dialog-content">
+        <h4>Configure Field</h4>
+        
+        <div class="c4ai-field-input">
+          <label>Field Name:</label>
+          <input type="text" id="c4ai-field-name" placeholder="e.g., title, price, description" autofocus>
+        </div>
+        
+        <div class="c4ai-field-input">
+          <label>Field Type:</label>
+          <select id="c4ai-field-type">
+            <option value="text">Text Content</option>
+            <option value="attribute">Attribute</option>
+            <option value="link">Link (href)</option>
+            <option value="image">Image (src)</option>
+            <option value="list">List</option>
+            <option value="nested">Nested Object</option>
+          </select>
+        </div>
+        
+        <div class="c4ai-field-input" id="c4ai-attribute-select" style="display: none;">
+          <label>Select Attribute:</label>
+          <select id="c4ai-field-attribute">
+            ${attributeOptions}
+          </select>
+        </div>
+        
+        <div class="c4ai-field-preview">
+          <strong>Preview Value:</strong>
+          <div id="c4ai-preview-value">${element.textContent.trim().substring(0, 100)}</div>
+        </div>
+        
+        <div class="c4ai-field-selector">
+          <strong>Selector (auto-generated):</strong>
+          <div id="c4ai-selector-preview">${this.generateSmartSelector(element, this.container.element)}</div>
+        </div>
+        
+        <div class="c4ai-field-actions">
+          <button id="c4ai-field-save" class="c4ai-primary">✓ Save</button>
+          <button id="c4ai-field-cancel">✗ Cancel</button>
+        </div>
+      </div>
+    `;
+
+    document.body.appendChild(dialog);
+
+    const nameInput = dialog.querySelector('#c4ai-field-name');
+    const typeSelect = dialog.querySelector('#c4ai-field-type');
+    const attributeSelect = dialog.querySelector('#c4ai-field-attribute');
+    const attributeContainer = dialog.querySelector('#c4ai-attribute-select');
+    const previewValue = dialog.querySelector('#c4ai-preview-value');
+    const saveBtn = dialog.querySelector('#c4ai-field-save');
+    const cancelBtn = dialog.querySelector('#c4ai-field-cancel');
+
+    // Update preview based on type selection
+    const updatePreview = () => {
+      const type = typeSelect.value;
+      let value = '';
+      
+      switch(type) {
+        case 'text':
+          value = element.textContent.trim();
+          attributeContainer.style.display = 'none';
+          break;
+        case 'attribute':
+          attributeContainer.style.display = 'block';
+          value = element.getAttribute(attributeSelect.value) || '';
+          break;
+        case 'link':
+          value = element.getAttribute('href') || element.querySelector('a')?.getAttribute('href') || '';
+          attributeContainer.style.display = 'none';
+          break;
+        case 'image':
+          value = element.getAttribute('src') || element.querySelector('img')?.getAttribute('src') || '';
+          attributeContainer.style.display = 'none';
+          break;
+        case 'list':
+          const listItems = element.querySelectorAll('li, option');
+          value = `[${listItems.length} items]`;
+          attributeContainer.style.display = 'none';
+          break;
+        case 'nested':
+          value = '[Complex nested structure]';
+          attributeContainer.style.display = 'none';
+          break;
+      }
+      
+      previewValue.textContent = value.substring(0, 100) + (value.length > 100 ? '...' : '');
+    };
+
+    typeSelect.addEventListener('change', updatePreview);
+    attributeSelect.addEventListener('change', updatePreview);
+
+    const save = () => {
+      const fieldName = nameInput.value.trim();
+      if (fieldName) {
+        const type = typeSelect.value;
+        const selector = this.generateSmartSelector(element, this.container.element);
+        
+        const field = {
+          name: fieldName,
+          type: type,
+          selector: selector,
+          element: element,
+          value: previewValue.textContent
+        };
+        
+        // Add attribute if needed
+        if (type === 'attribute') {
+          field.attribute = attributeSelect.value;
+        } else if (type === 'link') {
+          field.type = 'attribute';
+          field.attribute = 'href';
+        } else if (type === 'image') {
+          field.type = 'attribute';
+          field.attribute = 'src';
+        }
+        
+        this.fields.push(field);
+        element.classList.add('c4ai-selected-field');
+        element.setAttribute('data-c4ai-field', fieldName);
+        this.selectedElements.add(element);
+        this.updateToolbar();
+        this.updateStats();
+        this.generateSchema();
+      }
+      dialog.remove(); // Always close dialog
+    };
+
+    const cancel = () => {
+      dialog.remove();
+    };
+
+    saveBtn.addEventListener('click', save);
+    cancelBtn.addEventListener('click', cancel);
+    nameInput.addEventListener('keypress', (e) => {
+      if (e.key === 'Enter') save();
+      if (e.key === 'Escape') cancel();
+    });
+
+    nameInput.focus();
+  }
+
+  adjustParentLevels(delta) {
+    if (!this.container) return;
+    
+    const newLevel = this.parentLevels + delta;
+    if (newLevel < 0 || newLevel > 5) return;
+    
+    this.parentLevels = newLevel;
+    document.getElementById('c4ai-parent-value').textContent = newLevel;
+    
+    // Update container selector with new parent levels
+    this.updateContainerSelector();
+  }
+  
+  updateContainerSelector() {
+    if (!this.container || !this.selectedElement) return;
+    
+    this.container.selector = this.generateContainerSelector(this.selectedElement);
+    this.container.element = this.selectedElement;
+    
+    // Update the schema
+    this.generateSchema();
+    
+    // Update display
+    const containerDisplay = document.getElementById('c4ai-container');
+    // containerDisplay.textContent = `${this.container.tagName} (${this.parentLevels} levels)`;
+    containerDisplay.textContent = `${this.container.tagName}`;
+    
+    // Update selector display
+    const containerSelector = document.getElementById('c4ai-container-selector');
+    if (containerSelector) {
+      containerSelector.textContent = this.container.selector;
+    }
+  }
+  
+  generateContainerSelector(element) {
+    // For container, include parent levels
+    let current = element;
+    const parts = [];
+    
+    // Start from the target element
+    for (let i = 0; i <= this.parentLevels; i++) {
+      if (!current || current === document.body) break;
+      
+      const selector = this.generateSingleElementSelector(current);
+      parts.unshift(selector);
+      
+      if (i < this.parentLevels) {
+        current = current.parentElement;
+      }
+    }
+    
+    // If we have parent levels, show them clearly
+    if (this.parentLevels > 0 && parts.length > 1) {
+      // Make it clear which part is the container
+      const containerPart = parts[parts.length - 1];
+      const parentParts = parts.slice(0, -1);
+      return parentParts.join(' > ') + ' > ' + containerPart;
+    }
+    
+    return parts.join(' > ');
+  }
+  
+  generateSingleElementSelector(element) {
+    // Generate selector for a single element
+    if (element.id) {
+      return `#${CSS.escape(element.id)}`;
+    }
+
+    // Check for data attributes (most stable)
+    const dataAttrs = ['data-testid', 'data-id', 'data-test', 'data-cy'];
+    for (const attr of dataAttrs) {
+      const value = element.getAttribute(attr);
+      if (value) {
+        return `[${attr}="${value}"]`;
+      }
+    }
+
+    // Check for aria-label
+    if (element.getAttribute('aria-label')) {
+      return `[aria-label="${element.getAttribute('aria-label')}"]`;
+    }
+
+    const tagName = element.tagName.toLowerCase();
+    
+    // Check for simple, non-utility classes
+    const classes = Array.from(element.classList)
+      .filter(c => !c.startsWith('c4ai-')) // Exclude our classes
+      .filter(c => !c.includes('[') && !c.includes('(') && !c.includes(':')) // Exclude utility classes
+      .filter(c => c.length < 30); // Exclude very long classes
+    
+    if (classes.length > 0 && classes.length <= 2) {
+      return tagName + classes.map(c => `.${CSS.escape(c)}`).join('');
+    }
+    
+    return tagName;
+  }
+
+  generateSelector(element, context = document) {
+    // Try to generate a robust selector
+    if (element.id) {
+      return `#${CSS.escape(element.id)}`;
+    }
+
+    // Check for data attributes (most stable)
+    const dataAttrs = ['data-testid', 'data-id', 'data-test', 'data-cy'];
+    for (const attr of dataAttrs) {
+      const value = element.getAttribute(attr);
+      if (value) {
+        return `[${attr}="${value}"]`;
+      }
+    }
+
+    // Check for aria-label
+    if (element.getAttribute('aria-label')) {
+      return `[aria-label="${element.getAttribute('aria-label')}"]`;
+    }
+
+    // Try semantic HTML elements with text
+    const tagName = element.tagName.toLowerCase();
+    if (['button', 'a', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6'].includes(tagName)) {
+      const text = element.textContent.trim();
+      if (text && text.length < 50) {
+        // Use tag name with partial text match
+        return `${tagName}`;
+      }
+    }
+
+    // Check for simple, non-utility classes
+    const classes = Array.from(element.classList)
+      .filter(c => !c.startsWith('c4ai-')) // Exclude our classes
+      .filter(c => !c.includes('[') && !c.includes('(') && !c.includes(':')) // Exclude utility classes
+      .filter(c => c.length < 30); // Exclude very long classes
+    
+    if (classes.length > 0 && classes.length <= 3) {
+      const selector = classes.map(c => `.${CSS.escape(c)}`).join('');
+      try {
+        if (context.querySelectorAll(selector).length === 1) {
+          return selector;
+        }
+      } catch (e) {
+        // Invalid selector, continue
+      }
+    }
+
+    // Use nth-child with simple parent tag
+    const parent = element.parentElement;
+    if (parent && parent !== context) {
+      const siblings = Array.from(parent.children);
+      const index = siblings.indexOf(element) + 1;
+      // Just use parent tag name to avoid recursion
+      const parentTag = parent.tagName.toLowerCase();
+      return `${parentTag} > ${tagName}:nth-child(${index})`;
+    }
+
+    // Final fallback
+    return tagName;
+  }
+
+  updateToolbar() {
+    // Update mode display
+    if (!this.container) {
+      document.getElementById('c4ai-mode').textContent = 'Select Container';
+    } else if (this.inspectingFields) {
+      document.getElementById('c4ai-mode').textContent = 'Select Fields';
+    } else {
+      document.getElementById('c4ai-mode').textContent = 'Container Selected';
+    }
+    
+    // Show/hide container info and controls
+    const containerItem = document.getElementById('c4ai-container-item');
+    const parentLevelControls = document.getElementById('c4ai-parent-levels');
+    const footerSection = document.getElementById('c4ai-footer-section');
+    const selectorDisplay = document.getElementById('c4ai-selector-display');
+    const containerSelector = document.getElementById('c4ai-container-selector');
+    
+    if (this.container) {
+      containerItem.style.display = 'flex';
+      parentLevelControls.style.display = 'flex';
+      footerSection.style.display = 'flex';
+      selectorDisplay.style.display = 'block';
+      
+      // Update container display
+      document.getElementById('c4ai-container').textContent = 
+        `${this.container.tagName} (${this.parentLevels} levels)`;
+      
+      // Update selector display
+      containerSelector.textContent = this.container.selector;
+    } else {
+      containerItem.style.display = 'none';
+      parentLevelControls.style.display = 'none';
+      footerSection.style.display = 'none';
+      selectorDisplay.style.display = 'none';
+    }
+
+    // Show/hide sections based on state
+    const schemaSection = document.getElementById('c4ai-schema-section');
+    const actionsSection = document.getElementById('c4ai-actions-section');
+    const statsSection = document.getElementById('c4ai-stats-section');
+    
+    if (this.fields.length > 0) {
+      schemaSection.style.display = 'block';
+      actionsSection.style.display = 'block';
+      statsSection.style.display = 'block';
+      
+      // Update field count
+      document.getElementById('c4ai-field-count').textContent = this.fields.length;
+      
+      // Update fields list with enhanced UI
+      const fieldsList = document.getElementById('c4ai-fields-list');
+      fieldsList.innerHTML = this.fields.map((field, index) => {
+        const icon = this.getFieldIcon(field.type);
+        return `
+          <div class="c4ai-field-item" data-index="${index}">
+            <div class="c4ai-field-header">
+              <span class="c4ai-field-icon">${icon}</span>
+              <span class="c4ai-field-name">${field.name}</span>
+              <div class="c4ai-field-actions">
+                <button class="c4ai-field-edit" data-index="${index}" title="Edit field">✏️</button>
+                <button class="c4ai-field-delete" data-index="${index}" title="Remove field">×</button>
+              </div>
+            </div>
+            <div class="c4ai-field-selector" contenteditable="true" data-index="${index}">${field.selector}</div>
+          </div>
+        `;
+      }).join('');
+      
+      // Add event handlers
+      fieldsList.querySelectorAll('.c4ai-field-delete').forEach(btn => {
+        btn.addEventListener('click', (e) => {
+          const index = parseInt(e.target.dataset.index);
+          this.removeField(index);
+        });
+      });
+      
+      fieldsList.querySelectorAll('.c4ai-field-edit').forEach(btn => {
+        btn.addEventListener('click', (e) => {
+          const index = parseInt(e.target.dataset.index);
+          this.editField(index);
+        });
+      });
+      
+      fieldsList.querySelectorAll('.c4ai-field-selector').forEach(selector => {
+        selector.addEventListener('blur', (e) => {
+          const index = parseInt(e.target.dataset.index);
+          const newSelector = e.target.textContent.trim();
+          if (newSelector && this.fields[index]) {
+            this.fields[index].selector = newSelector;
+            this.generateSchema();
+          }
+        });
+        
+        selector.addEventListener('keydown', (e) => {
+          if (e.key === 'Enter') {
+            e.preventDefault();
+            e.target.blur();
+          }
+        });
+      });
+      
+      // Enable action buttons
+      document.getElementById('c4ai-preview').disabled = false;
+      document.getElementById('c4ai-test').disabled = false;
+      document.getElementById('c4ai-export-schema').disabled = false;
+      document.getElementById('c4ai-export-data').disabled = false;
+      document.getElementById('c4ai-deploy-cloud').disabled = false;
+    } else {
+      schemaSection.style.display = 'none';
+      actionsSection.style.display = 'none';
+      statsSection.style.display = 'none';
+    }
+
+    const hint = document.getElementById('c4ai-hint');
+    if (!this.container) {
+      hint.textContent = 'Click on a container element (e.g., product card, article, etc.)';
+    } else if (this.inspectingFields && this.fields.length === 0) {
+      hint.textContent = 'Click on fields inside the container to extract (title, price, etc.)';
+    } else if (this.inspectingFields) {
+      hint.innerHTML = `Continue selecting fields or click Fields button to stop.`;
+    } else if (this.fields.length === 0) {
+      hint.innerHTML = `Click <strong>Fields</strong> button to start selecting fields.`;
+    } else {
+      hint.innerHTML = `Use action buttons above or click <strong>Fields</strong> to add more.`;
+    }
+  }
+
+  getFieldIcon(type) {
+    const icons = {
+      'text': '📝',
+      'attribute': '📊',
+      'link': '🔗',
+      'image': '🖼️',
+      'list': '📚',
+      'nested': '📁'
+    };
+    return icons[type] || '📝';
+  }
+
+  removeField(index) {
+    const field = this.fields[index];
+    
+    // Remove from arrays
+    this.fields.splice(index, 1);
+    
+    // Remove visual selection
+    field.element.classList.remove('c4ai-selected-field');
+    field.element.removeAttribute('data-c4ai-field');
+    this.selectedElements.delete(field.element);
+    
+    // Update UI
+    this.updateToolbar();
+    this.updateStats();
+    this.generateSchema();
+  }
+  
+  editField(index) {
+    const field = this.fields[index];
+    if (!field) return;
+    
+    // Re-show the field dialog with existing values
+    const dialog = document.createElement('div');
+    dialog.className = 'c4ai-field-dialog';
+    
+    const rect = field.element.getBoundingClientRect();
+    dialog.style.cssText = `
+      left: ${rect.left + window.scrollX}px;
+      top: ${rect.bottom + window.scrollY + 10}px;
+    `;
+
+    // Get available attributes
+    const attributes = this.getElementAttributes(field.element);
+    const attributeOptions = attributes.map(attr => 
+      `<option value="${attr.name}" ${field.attribute === attr.name ? 'selected' : ''}>${attr.name}: "${attr.value.substring(0, 30)}${attr.value.length > 30 ? '...' : ''}"</option>`
+    ).join('');
+
+    dialog.innerHTML = `
+      <div class="c4ai-field-dialog-content">
+        <h4>Edit Field</h4>
+        
+        <div class="c4ai-field-input">
+          <label>Field Name:</label>
+          <input type="text" id="c4ai-field-name" value="${field.name}" placeholder="e.g., title, price, description" autofocus>
+        </div>
+        
+        <div class="c4ai-field-input">
+          <label>Field Type:</label>
+          <select id="c4ai-field-type">
+            <option value="text" ${field.type === 'text' ? 'selected' : ''}>Text Content</option>
+            <option value="attribute" ${field.type === 'attribute' ? 'selected' : ''}>Attribute</option>
+            <option value="link" ${field.type === 'link' ? 'selected' : ''}>Link (href)</option>
+            <option value="image" ${field.type === 'image' ? 'selected' : ''}>Image (src)</option>
+            <option value="list" ${field.type === 'list' ? 'selected' : ''}>List</option>
+            <option value="nested" ${field.type === 'nested' ? 'selected' : ''}>Nested Object</option>
+          </select>
+        </div>
+        
+        <div class="c4ai-field-input" id="c4ai-attribute-select" style="display: ${field.type === 'attribute' ? 'block' : 'none'};">
+          <label>Select Attribute:</label>
+          <select id="c4ai-field-attribute">
+            ${attributeOptions}
+          </select>
+        </div>
+        
+        <div class="c4ai-field-preview">
+          <strong>Preview Value:</strong>
+          <div id="c4ai-preview-value">${field.value}</div>
+        </div>
+        
+        <div class="c4ai-field-selector">
+          <strong>Selector (auto-generated):</strong>
+          <div id="c4ai-selector-preview">${field.selector}</div>
+        </div>
+        
+        <div class="c4ai-field-actions">
+          <button id="c4ai-field-save" class="c4ai-primary">✓ Update</button>
+          <button id="c4ai-field-cancel">✗ Cancel</button>
+        </div>
+      </div>
+    `;
+
+    document.body.appendChild(dialog);
+
+    const nameInput = dialog.querySelector('#c4ai-field-name');
+    const typeSelect = dialog.querySelector('#c4ai-field-type');
+    const attributeSelect = dialog.querySelector('#c4ai-field-attribute');
+    const attributeContainer = dialog.querySelector('#c4ai-attribute-select');
+    const previewValue = dialog.querySelector('#c4ai-preview-value');
+    const saveBtn = dialog.querySelector('#c4ai-field-save');
+    const cancelBtn = dialog.querySelector('#c4ai-field-cancel');
+
+    // Update preview based on type selection
+    const updatePreview = () => {
+      const type = typeSelect.value;
+      let value = '';
+      
+      switch(type) {
+        case 'text':
+          value = field.element.textContent.trim();
+          attributeContainer.style.display = 'none';
+          break;
+        case 'attribute':
+          attributeContainer.style.display = 'block';
+          value = field.element.getAttribute(attributeSelect.value) || '';
+          break;
+        case 'link':
+          value = field.element.getAttribute('href') || field.element.querySelector('a')?.getAttribute('href') || '';
+          attributeContainer.style.display = 'none';
+          break;
+        case 'image':
+          value = field.element.getAttribute('src') || field.element.querySelector('img')?.getAttribute('src') || '';
+          attributeContainer.style.display = 'none';
+          break;
+        case 'list':
+          const listItems = field.element.querySelectorAll('li, option');
+          value = `[${listItems.length} items]`;
+          attributeContainer.style.display = 'none';
+          break;
+        case 'nested':
+          value = '[Complex nested structure]';
+          attributeContainer.style.display = 'none';
+          break;
+      }
+      
+      previewValue.textContent = value.substring(0, 100) + (value.length > 100 ? '...' : '');
+    };
+
+    typeSelect.addEventListener('change', updatePreview);
+    attributeSelect.addEventListener('change', updatePreview);
+
+    const save = () => {
+      const fieldName = nameInput.value.trim();
+      if (fieldName) {
+        const type = typeSelect.value;
+        
+        // Update field
+        field.name = fieldName;
+        field.type = type;
+        field.value = previewValue.textContent;
+        
+        // Update attribute if needed
+        if (type === 'attribute') {
+          field.attribute = attributeSelect.value;
+        } else if (type === 'link') {
+          field.type = 'attribute';
+          field.attribute = 'href';
+        } else if (type === 'image') {
+          field.type = 'attribute';
+          field.attribute = 'src';
+        } else {
+          delete field.attribute;
+        }
+        
+        // Update element attribute
+        field.element.setAttribute('data-c4ai-field', fieldName);
+        
+        this.updateToolbar();
+        this.updateStats();
+        this.generateSchema();
+      }
+      dialog.remove();
+    };
+
+    const cancel = () => {
+      dialog.remove();
+    };
+
+    saveBtn.addEventListener('click', save);
+    cancelBtn.addEventListener('click', cancel);
+    nameInput.addEventListener('keypress', (e) => {
+      if (e.key === 'Enter') save();
+      if (e.key === 'Escape') cancel();
+    });
+
+    nameInput.focus();
+    nameInput.select();
+  }
+
+  updateStats() {
+    chrome.runtime.sendMessage({
+      action: 'updateStats',
+      stats: {
+        container: !!this.container,
+        fields: this.fields.length
+      }
+    });
+  }
+
+  removeAllHighlights() {
+    document.querySelectorAll('.c4ai-selected-container').forEach(el => {
+      el.classList.remove('c4ai-selected-container');
+    });
+    document.querySelectorAll('.c4ai-selected-field').forEach(el => {
+      el.classList.remove('c4ai-selected-field');
+    });
+  }
+
+  // New helper methods for enhanced functionality
+  getElementAttributes(element) {
+    const attributes = [];
+    for (const attr of element.attributes) {
+      attributes.push({
+        name: attr.name,
+        value: attr.value
+      });
+    }
+    return attributes;
+  }
+
+  generateSmartSelector(element, container) {
+    // Smart selector generation with 2-level parent context
+    const parts = [];
+    let current = element;
+    let depth = 0;
+    
+    // Build path from element up to container (max 3 levels)
+    while (current && current !== container && depth < 3) {
+      let selector = current.tagName.toLowerCase();
+      
+      // Add ID if available
+      if (current.id && !current.id.includes(':') && !current.id.includes('[')) {
+        selector = `#${CSS.escape(current.id)}`;
+        parts.unshift(selector);
+        break; // ID is unique enough
+      }
+      
+      // Add classes (filter out dynamic/utility classes)
+      const classes = Array.from(current.classList)
+        .filter(c => !c.startsWith('c4ai-'))
+        .filter(c => !c.includes('[') && !c.includes('(') && !c.includes(':'))
+        .filter(c => c.length < 30)
+        .slice(0, 2); // Max 2 classes
+      
+      if (classes.length > 0) {
+        selector += classes.map(c => `.${CSS.escape(c)}`).join('');
+      }
+      
+      // Add data attributes for more specificity
+      const dataAttrs = ['data-testid', 'data-id', 'data-test'];
+      for (const attr of dataAttrs) {
+        if (current.hasAttribute(attr)) {
+          selector += `[${attr}="${CSS.escape(current.getAttribute(attr))}"]`;
+          break;
+        }
+      }
+      
+      // Add nth-child if needed for disambiguation
+      if (current.parentElement && depth === 0) {
+        const siblings = Array.from(current.parentElement.children);
+        const sameTagSiblings = siblings.filter(s => s.tagName === current.tagName);
+        if (sameTagSiblings.length > 1) {
+          const index = sameTagSiblings.indexOf(current) + 1;
+          selector += `:nth-of-type(${index})`;
+        }
+      }
+      
+      parts.unshift(selector);
+      current = current.parentElement;
+      depth++;
+    }
+    
+    // Create relative selector from container
+    const fullSelector = parts.join(' > ');
+    
+    // Test selector uniqueness within container
+    try {
+      const matches = container.querySelectorAll(fullSelector);
+      if (matches.length === 1 && matches[0] === element) {
+        return fullSelector;
+      }
+    } catch (e) {
+      // Invalid selector, continue with fallback
+    }
+    
+    // Fallback to simple selector
+    return parts[parts.length - 1] || element.tagName.toLowerCase();
+  }
+
+  generateSchema() {
+    if (!this.container || this.fields.length === 0) {
+      return null;
+    }
+    
+    // Build schema object
+    this.schema = {
+      name: `${window.location.hostname} Schema`,
+      baseSelector: this.container.selector,
+      fields: this.fields.map(field => {
+        const schemaField = {
+          name: field.name,
+          selector: field.selector,
+          type: field.type
+        };
+        
+        if (field.attribute) {
+          schemaField.attribute = field.attribute;
+        }
+        
+        return schemaField;
+      })
+    };
+    
+    return this.schema;
+  }
+
+  togglePreview() {
+    this.previewMode = !this.previewMode;
+    const previewBtn = document.getElementById('c4ai-preview');
+    
+    if (this.previewMode) {
+      previewBtn.innerHTML = '<span>🔄</span> Hide Preview';
+      this.showPreview();
+    } else {
+      previewBtn.innerHTML = '<span>👁️</span> Preview Matches';
+      this.clearPreview();
+    }
+  }
+
+  showPreview() {
+    if (!this.schema) {
+      this.generateSchema();
+    }
+    
+    this.clearPreview();
+    
+    // Find all matching containers
+    const containers = document.querySelectorAll(this.schema.baseSelector);
+    let successCount = 0;
+    
+    containers.forEach((container, index) => {
+      // Highlight container
+      const containerBox = document.createElement('div');
+      containerBox.className = 'c4ai-preview-container';
+      const rect = container.getBoundingClientRect();
+      containerBox.style.cssText = `
+        position: absolute;
+        left: ${rect.left + window.scrollX}px;
+        top: ${rect.top + window.scrollY}px;
+        width: ${rect.width}px;
+        height: ${rect.height}px;
+        pointer-events: none;
+        z-index: 999997;
+      `;
+      document.body.appendChild(containerBox);
+      this.previewElements.push(containerBox);
+      
+      // Check each field
+      let fieldsFound = 0;
+      this.schema.fields.forEach(field => {
+        try {
+          const fieldElement = container.querySelector(field.selector);
+          if (fieldElement) {
+            fieldsFound++;
+            // Highlight successful field
+            const fieldBox = document.createElement('div');
+            fieldBox.className = 'c4ai-preview-field-success';
+            const fieldRect = fieldElement.getBoundingClientRect();
+            fieldBox.style.cssText = `
+              position: absolute;
+              left: ${fieldRect.left + window.scrollX}px;
+              top: ${fieldRect.top + window.scrollY}px;
+              width: ${fieldRect.width}px;
+              height: ${fieldRect.height}px;
+              pointer-events: none;
+              z-index: 999998;
+            `;
+            document.body.appendChild(fieldBox);
+            this.previewElements.push(fieldBox);
+          }
+        } catch (e) {
+          // Invalid selector
+        }
+      });
+      
+      // Add count badge
+      const badge = document.createElement('div');
+      badge.className = 'c4ai-preview-badge';
+      badge.textContent = `${index + 1}`;
+      badge.style.cssText = `
+        position: absolute;
+        left: ${rect.left + window.scrollX - 20}px;
+        top: ${rect.top + window.scrollY - 20}px;
+        z-index: 999999;
+      `;
+      document.body.appendChild(badge);
+      this.previewElements.push(badge);
+      
+      if (fieldsFound === this.schema.fields.length) {
+        successCount++;
+      }
+    });
+    
+    // Update stats
+    document.getElementById('c4ai-matches-count').textContent = `${containers.length} items`;
+    document.getElementById('c4ai-schema-valid').textContent = 
+      successCount === containers.length ? '✓ Yes' : `⚠️ Partial (${successCount}/${containers.length})`;
+  }
+
+  clearPreview() {
+    this.previewElements.forEach(el => el.remove());
+    this.previewElements = [];
+  }
+
+  async testSchema() {
+    if (!this.schema) {
+      this.generateSchema();
+    }
+    
+    // Extract data using schema
+    const results = [];
+    const containers = document.querySelectorAll(this.schema.baseSelector);
+    
+    containers.forEach(container => {
+      const item = {};
+      
+      this.schema.fields.forEach(field => {
+        try {
+          const element = container.querySelector(field.selector);
+          if (element) {
+            if (field.type === 'text') {
+              item[field.name] = element.textContent.trim();
+            } else if (field.type === 'attribute' && field.attribute) {
+              item[field.name] = element.getAttribute(field.attribute);
+            }
+          } else {
+            item[field.name] = null;
+          }
+        } catch (e) {
+          item[field.name] = null;
+        }
+      });
+      
+      results.push(item);
+    });
+    
+    // Show results modal
+    this.showResultsModal(results);
+  }
+
+  showResultsModal(data) {
+    const modal = document.createElement('div');
+    modal.className = 'c4ai-code-modal';
+    modal.innerHTML = `
+      <div class="c4ai-code-modal-content">
+        <div class="c4ai-code-modal-header">
+          <h2>Extracted Data (${data.length} items)</h2>
+          <button class="c4ai-close-modal" id="c4ai-close-results">✕</button>
+        </div>
+        <div class="c4ai-code-modal-body">
+          <pre class="c4ai-code-block"><code>${JSON.stringify(data, null, 2)}</code></pre>
+        </div>
+        <div class="c4ai-code-modal-footer">
+          <button class="c4ai-action-btn c4ai-download-btn" id="c4ai-download-data">
+            <span>⬇</span> Download JSON
+          </button>
+          <button class="c4ai-action-btn c4ai-download-btn" id="c4ai-download-python">
+            <span>🐍</span> Download Python Code
+          </button>
+          <button class="c4ai-action-btn c4ai-copy-btn" id="c4ai-copy-data">
+            <span>📋</span> Copy to Clipboard
+          </button>
+        </div>
+      </div>
+    `;
+    
+    document.body.appendChild(modal);
+    
+    // Event listeners
+    document.getElementById('c4ai-close-results').addEventListener('click', () => modal.remove());
+    
+    document.getElementById('c4ai-download-data').addEventListener('click', () => {
+      const blob = new Blob([JSON.stringify(data, null, 2)], { type: 'application/json' });
+      const url = URL.createObjectURL(blob);
+      const a = document.createElement('a');
+      a.href = url;
+      a.download = `extracted_data_${Date.now()}.json`;
+      a.click();
+      URL.revokeObjectURL(url);
+    });
+    
+    document.getElementById('c4ai-copy-data').addEventListener('click', () => {
+      navigator.clipboard.writeText(JSON.stringify(data, null, 2)).then(() => {
+        const btn = document.getElementById('c4ai-copy-data');
+        btn.innerHTML = '<span>✓</span> Copied!';
+        setTimeout(() => {
+          btn.innerHTML = '<span>📋</span> Copy to Clipboard';
+        }, 2000);
+      });
+    });
+    
+    document.getElementById('c4ai-download-python').addEventListener('click', () => {
+      const pythonCode = this.generatePythonCode();
+      const blob = new Blob([pythonCode], { type: 'text/plain' });
+      const url = URL.createObjectURL(blob);
+      const a = document.createElement('a');
+      a.href = url;
+      a.download = `crawl4ai_schema_${Date.now()}.py`;
+      a.click();
+      URL.revokeObjectURL(url);
+    });
+  }
+
+  exportSchema() {
+    if (!this.schema) {
+      this.generateSchema();
+    }
+    
+    const blob = new Blob([JSON.stringify(this.schema, null, 2)], { type: 'application/json' });
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = `schema_${Date.now()}.json`;
+    a.click();
+    URL.revokeObjectURL(url);
+  }
+
+  async exportData() {
+    await this.testSchema();
+  }
+  
+  deployToCloud() {
+    // Create cloud deployment modal
+    const modal = document.createElement('div');
+    modal.className = 'c4ai-code-modal';
+    modal.innerHTML = `
+      <div class="c4ai-cloud-modal-content">
+        <div class="c4ai-cloud-header">
+          <div class="c4ai-cloud-icon">🌩️</div>
+          <h2>Deploy to Crawl4AI Cloud</h2>
+        </div>
+        <div class="c4ai-cloud-body">
+          <div class="c4ai-cloud-features">
+            <h3>🚀 Coming Soon!</h3>
+            <p>Deploy your extraction schemas to the cloud with just one click:</p>
+            <ul>
+              <li>✨ <strong>Instant Deployment</strong> - Your schema live in seconds</li>
+              <li>🌐 <strong>API Access</strong> - RESTful endpoints for your extractions</li>
+              <li>⏰ <strong>Scheduled Runs</strong> - Automate data collection</li>
+              <li>📊 <strong>Analytics Dashboard</strong> - Monitor your extractions</li>
+              <li>🔄 <strong>Auto-scaling</strong> - Handle any volume seamlessly</li>
+            </ul>
+          </div>
+          <div class="c4ai-cloud-cta">
+            <p>Be the first to know when Crawl4AI Cloud launches!</p>
+            <button class="c4ai-action-btn c4ai-primary-btn c4ai-waitlist-btn" id="c4ai-join-waitlist">
+              <span>🎆</span> Join the Waiting List
+            </button>
+          </div>
+        </div>
+        <button class="c4ai-close-modal" id="c4ai-close-cloud-modal">✕</button>
+      </div>
+    `;
+    
+    document.body.appendChild(modal);
+    
+    // Add event listeners
+    document.getElementById('c4ai-close-cloud-modal').addEventListener('click', () => modal.remove());
+    document.getElementById('c4ai-join-waitlist').addEventListener('click', () => {
+      window.open('https://crawl4ai.com/join-waiting-list', '_blank');
+      modal.remove();
+    });
+    
+    // Close on escape
+    const escHandler = (e) => {
+      if (e.key === 'Escape') {
+        modal.remove();
+        document.removeEventListener('keydown', escHandler);
+      }
+    };
+    document.addEventListener('keydown', escHandler);
+  }
+  
+  generatePythonCode() {
+    if (!this.schema) {
+      this.generateSchema();
+    }
+    
+    const schemaJson = JSON.stringify(this.schema, null, 2);
+    
+    return `#!/usr/bin/env python3
+"""
+Generated by Crawl4AI Chrome Extension
+URL: ${window.location.href}
+Generated: ${new Date().toISOString()}
+"""
+
+import asyncio
+import json
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# The extraction schema generated from your selections
+EXTRACTION_SCHEMA = ${schemaJson}
+
+async def extract_data(url: str = "${window.location.href}"):
+    """Extract data using the generated schema"""
+    
+    # Configure browser (optional)
+    browser_config = BrowserConfig(
+        headless=True,  # Set to False to see the browser
+        verbose=False
+    )
+    
+    # Configure extraction strategy
+    extraction_strategy = JsonCssExtractionStrategy(schema=EXTRACTION_SCHEMA)
+    
+    # Configure crawler
+    crawler_config = CrawlerRunConfig(
+        extraction_strategy=extraction_strategy,
+        # Add more options as needed:
+        # wait_for="css:.product",  # Wait for specific elements
+        # js_code="window.scrollTo(0, document.body.scrollHeight);",  # Execute JS
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url=url,
+            config=crawler_config
+        )
+        
+        if result.success and result.extracted_content:
+            data = json.loads(result.extracted_content)
+            print(f"\\n✅ Successfully extracted {len(data)} items!")
+            
+            # Save results
+            with open('extracted_data.json', 'w', encoding='utf-8') as f:
+                json.dump(data, f, indent=2, ensure_ascii=False)
+            
+            # Show sample results
+            print("\\n📊 Sample results (first 2 items):")
+            for i, item in enumerate(data[:2], 1):
+                print(f"\\nItem {i}:")
+                for key, value in item.items():
+                    print(f"  {key}: {value}")
+                    
+            return data
+        else:
+            print("❌ Extraction failed:", result.error_message)
+            return None
+
+if __name__ == "__main__":
+    # Run the extraction
+    data = asyncio.run(extract_data())
+    
+    print("\\n🎯 Next steps:")
+    print("1. Install Crawl4AI: pip install crawl4ai")
+    print("2. Modify the URL or add multiple URLs")
+    print("3. Customize crawler options as needed")
+    print("4. Check 'extracted_data.json' for full results")
+`;
+  }
+
+  // Legacy code generation - kept for reference but no longer used
+  /*
+  generateCode() {
+    const fieldDescriptions = this.fields.map(f => 
+      `- ${f.name} (example: "${f.value.substring(0, 50)}...")`
+    ).join('\n');
+
+    return `#!/usr/bin/env python3
+"""
+Generated by Crawl4AI Chrome Extension
+URL: ${window.location.href}
+Generated: ${new Date().toISOString()}
+"""
+
+import asyncio
+import json
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# HTML snippet of the selected container element
+HTML_SNIPPET = """
+${this.container.html}
+"""
+
+# Extraction query based on your field selections
+EXTRACTION_QUERY = """
+Create a JSON CSS extraction schema to extract the following fields:
+${fieldDescriptions}
+
+The schema should handle multiple ${this.container.tagName} elements on the page.
+Each item should be extracted as a separate object in the results array.
+"""
+
+async def generate_schema():
+    """Generate extraction schema using LLM"""
+    print("🔧 Generating extraction schema...")
+    
+    try:
+        # Generate the schema using Crawl4AI's built-in LLM integration
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=HTML_SNIPPET,
+            query=EXTRACTION_QUERY,
+        )
+        
+        # Save the schema for reuse
+        schema_path = Path('generated_schema.json')
+        with open(schema_path, 'w') as f:
+            json.dump(schema, f, indent=2)
+        
+        print("✅ Schema generated successfully!")
+        print(f"📄 Schema saved to: {schema_path}")
+        print("\\nGenerated schema:")
+        print(json.dumps(schema, indent=2))
+        
+        return schema
+        
+    except Exception as e:
+        print(f"❌ Error generating schema: {e}")
+        return None
+
+async def test_extraction(url: str = "${window.location.href}"):
+    """Test the generated schema on the actual webpage"""
+    print("\\n🧪 Testing extraction on live webpage...")
+    
+    # Load the generated schema
+    try:
+        with open('generated_schema.json', 'r') as f:
+            schema = json.load(f)
+    except FileNotFoundError:
+        print("❌ Schema file not found. Run generate_schema() first.")
+        return
+    
+    # Configure browser
+    browser_config = BrowserConfig(
+        headless=True,
+        verbose=False
+    )
+    
+    # Configure extraction
+    crawler_config = CrawlerRunConfig(
+        extraction_strategy=JsonCssExtractionStrategy(schema=schema)
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url=url,
+            config=crawler_config
+        )
+        
+        if result.success and result.extracted_content:
+            data = json.loads(result.extracted_content)
+            print(f"\\n✅ Successfully extracted {len(data)} items!")
+            
+            # Save results
+            with open('extracted_data.json', 'w') as f:
+                json.dump(data, f, indent=2)
+            
+            # Show sample results
+            print("\\n📊 Sample results (first 2 items):")
+            for i, item in enumerate(data[:2], 1):
+                print(f"\\nItem {i}:")
+                for key, value in item.items():
+                    print(f"  {key}: {value}")
+        else:
+            print("❌ Extraction failed:", result.error_message)
+
+if __name__ == "__main__":
+    # Step 1: Generate the schema from HTML snippet
+    asyncio.run(generate_schema())
+    
+    # Step 2: Test extraction on the live webpage
+    # Uncomment the line below to test extraction:
+    # asyncio.run(test_extraction())
+    
+    print("\\n🎯 Next steps:")
+    print("1. Review the generated schema in 'generated_schema.json'")
+    print("2. Uncomment the test_extraction() line to test on the live site")
+    print("3. Use the schema in your Crawl4AI projects!")
+`;
+
+    return code;
+  }
+  */
+
+  /* Legacy modal - no longer used
+  showCodeModal(code) {
+    // Create modal
+    this.codeModal = document.createElement('div');
+    this.codeModal.className = 'c4ai-code-modal';
+    this.codeModal.innerHTML = `
+      <div class="c4ai-code-modal-content">
+        <div class="c4ai-code-modal-header">
+          <h2>Generated Python Code</h2>
+          <button class="c4ai-close-modal" id="c4ai-close-modal">✕</button>
+        </div>
+        <div class="c4ai-code-modal-body">
+          <pre class="c4ai-code-block"><code class="language-python">${window.C4AI_Utils.escapeHtml(code)}</code></pre>
+        </div>
+        <div class="c4ai-code-modal-footer">
+          <button class="c4ai-action-btn c4ai-cloud-btn" id="c4ai-run-cloud" disabled>
+            <span>☁️</span> Run on C4AI Cloud (Coming Soon)
+          </button>
+          <button class="c4ai-action-btn c4ai-download-btn" id="c4ai-download-code">
+            <span>⬇</span> Download Code
+          </button>
+          <button class="c4ai-action-btn c4ai-copy-btn" id="c4ai-copy-code">
+            <span>📋</span> Copy to Clipboard
+          </button>
+        </div>
+      </div>
+    `;
+    
+    document.body.appendChild(this.codeModal);
+    
+    // Add event listeners
+    document.getElementById('c4ai-close-modal').addEventListener('click', () => {
+      this.codeModal.remove();
+      this.codeModal = null;
+      // Don't stop the capture session
+    });
+    
+    document.getElementById('c4ai-download-code').addEventListener('click', () => {
+      chrome.runtime.sendMessage({
+        action: 'downloadCode',
+        code: code,
+        filename: `crawl4ai_schema_${Date.now()}.py`
+      }, (response) => {
+        if (response && response.success) {
+          const btn = document.getElementById('c4ai-download-code');
+          const originalHTML = btn.innerHTML;
+          btn.innerHTML = '<span>✓</span> Downloaded!';
+          setTimeout(() => {
+            btn.innerHTML = originalHTML;
+          }, 2000);
+        } else {
+          console.error('Download failed:', response?.error);
+          alert('Download failed. Please check your browser settings.');
+        }
+      });
+    });
+    
+    document.getElementById('c4ai-copy-code').addEventListener('click', () => {
+      navigator.clipboard.writeText(code).then(() => {
+        const btn = document.getElementById('c4ai-copy-code');
+        btn.innerHTML = '<span>✓</span> Copied!';
+        setTimeout(() => {
+          btn.innerHTML = '<span>📋</span> Copy to Clipboard';
+        }, 2000);
+      });
+    });
+    
+    // Apply syntax highlighting
+    window.C4AI_Utils.applySyntaxHighlighting(this.codeModal.querySelector('.language-python'));
+  }
+  */
+}
+
+// Export for use in content script
+if (typeof window !== 'undefined') {
+  window.SchemaBuilder = SchemaBuilder;
+}
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder_v1.js b/docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder_v1.js
new file mode 100644
index 00000000..0f0154a1
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/schemaBuilder_v1.js
@@ -0,0 +1,608 @@
+// SchemaBuilder class for Crawl4AI Chrome Extension
+class SchemaBuilder {
+  constructor() {
+    this.mode = null;
+    this.container = null;
+    this.fields = [];
+    this.overlay = null;
+    this.toolbar = null;
+    this.highlightBox = null;
+    this.selectedElements = new Set();
+    this.isPaused = false;
+    this.codeModal = null;
+    
+    this.handleMouseMove = this.handleMouseMove.bind(this);
+    this.handleClick = this.handleClick.bind(this);
+    this.handleKeyPress = this.handleKeyPress.bind(this);
+  }
+
+  start() {
+    this.mode = 'container';
+    this.createOverlay();
+    this.createToolbar();
+    this.attachEventListeners();
+    this.updateToolbar();
+  }
+
+  stop() {
+    this.detachEventListeners();
+    this.overlay?.remove();
+    this.toolbar?.remove();
+    this.highlightBox?.remove();
+    this.removeAllHighlights();
+    this.mode = null;
+    this.container = null;
+    this.fields = [];
+    this.selectedElements.clear();
+  }
+
+  createOverlay() {
+    // Create highlight box
+    this.highlightBox = document.createElement('div');
+    this.highlightBox.className = 'c4ai-highlight-box';
+    document.body.appendChild(this.highlightBox);
+  }
+
+  createToolbar() {
+    this.toolbar = document.createElement('div');
+    this.toolbar.className = 'c4ai-toolbar';
+    this.toolbar.innerHTML = `
+      <div class="c4ai-toolbar-titlebar">
+        <div class="c4ai-titlebar-dots">
+          <button class="c4ai-dot c4ai-dot-close" id="c4ai-close"></button>
+          <button class="c4ai-dot c4ai-dot-minimize"></button>
+          <button class="c4ai-dot c4ai-dot-maximize"></button>
+        </div>
+        <img src="${chrome.runtime.getURL('icons/icon-16.png')}" class="c4ai-titlebar-icon" alt="Crawl4AI">
+        <div class="c4ai-titlebar-title">Crawl4AI Schema Builder</div>
+      </div>
+      <div class="c4ai-toolbar-content">
+        <div class="c4ai-toolbar-status">
+          <div class="c4ai-status-item">
+            <span class="c4ai-status-label">Mode:</span>
+            <span class="c4ai-status-value" id="c4ai-mode">Select Container</span>
+          </div>
+          <div class="c4ai-status-item">
+            <span class="c4ai-status-label">Container:</span>
+            <span class="c4ai-status-value" id="c4ai-container">Not selected</span>
+          </div>
+        </div>
+        <div class="c4ai-fields-list" id="c4ai-fields-list" style="display: none;">
+          <div class="c4ai-fields-header">Selected Fields:</div>
+          <ul class="c4ai-fields-items" id="c4ai-fields-items"></ul>
+        </div>
+        <div class="c4ai-toolbar-hint" id="c4ai-hint">
+          Click on a container element (e.g., product card, article, etc.)
+        </div>
+        <div class="c4ai-toolbar-actions">
+          <button id="c4ai-pause" class="c4ai-action-btn c4ai-pause-btn">
+            <span class="c4ai-pause-icon">⏸</span> Pause
+          </button>
+          <button id="c4ai-generate" class="c4ai-action-btn c4ai-generate-btn">
+            <span class="c4ai-generate-icon">⚡</span> Generate Code
+          </button>
+        </div>
+      </div>
+    `;
+    document.body.appendChild(this.toolbar);
+    
+    // Add event listeners for toolbar buttons
+    document.getElementById('c4ai-pause').addEventListener('click', () => this.togglePause());
+    document.getElementById('c4ai-generate').addEventListener('click', () => this.stopAndGenerate());
+    document.getElementById('c4ai-close').addEventListener('click', () => this.stop());
+    
+    // Make toolbar draggable
+    window.C4AI_Utils.makeDraggable(this.toolbar);
+  }
+
+  attachEventListeners() {
+    document.addEventListener('mousemove', this.handleMouseMove, true);
+    document.addEventListener('click', this.handleClick, true);
+    document.addEventListener('keydown', this.handleKeyPress, true);
+  }
+
+  detachEventListeners() {
+    document.removeEventListener('mousemove', this.handleMouseMove, true);
+    document.removeEventListener('click', this.handleClick, true);
+    document.removeEventListener('keydown', this.handleKeyPress, true);
+  }
+
+  handleMouseMove(e) {
+    if (this.isPaused) return;
+    
+    const element = document.elementFromPoint(e.clientX, e.clientY);
+    if (element && !this.isOurElement(element)) {
+      this.highlightElement(element);
+    }
+  }
+
+  handleClick(e) {
+    if (this.isPaused) return;
+    
+    const element = e.target;
+    
+    if (this.isOurElement(element)) {
+      return;
+    }
+
+    e.preventDefault();
+    e.stopPropagation();
+
+    if (this.mode === 'container') {
+      this.selectContainer(element);
+    } else if (this.mode === 'field') {
+      this.selectField(element);
+    }
+  }
+
+  handleKeyPress(e) {
+    if (e.key === 'Escape') {
+      this.stop();
+    }
+  }
+
+  isOurElement(element) {
+    return window.C4AI_Utils.isOurElement(element);
+  }
+
+  togglePause() {
+    this.isPaused = !this.isPaused;
+    const pauseBtn = document.getElementById('c4ai-pause');
+    if (this.isPaused) {
+      pauseBtn.innerHTML = '<span class="c4ai-play-icon">▶</span> Resume';
+      pauseBtn.classList.add('c4ai-paused');
+      this.highlightBox.style.display = 'none';
+    } else {
+      pauseBtn.innerHTML = '<span class="c4ai-pause-icon">⏸</span> Pause';
+      pauseBtn.classList.remove('c4ai-paused');
+    }
+  }
+
+  stopAndGenerate() {
+    if (!this.container || this.fields.length === 0) {
+      alert('Please select a container and at least one field before generating code.');
+      return;
+    }
+    
+    const code = this.generateCode();
+    this.showCodeModal(code);
+  }
+
+  highlightElement(element) {
+    const rect = element.getBoundingClientRect();
+    this.highlightBox.style.cssText = `
+      left: ${rect.left + window.scrollX}px;
+      top: ${rect.top + window.scrollY}px;
+      width: ${rect.width}px;
+      height: ${rect.height}px;
+      display: block;
+    `;
+
+    if (this.mode === 'container') {
+      this.highlightBox.className = 'c4ai-highlight-box c4ai-container-mode';
+    } else {
+      this.highlightBox.className = 'c4ai-highlight-box c4ai-field-mode';
+    }
+  }
+
+  selectContainer(element) {
+    // Remove previous container highlight
+    if (this.container) {
+      this.container.element.classList.remove('c4ai-selected-container');
+    }
+
+    this.container = {
+      element: element,
+      html: element.outerHTML,
+      selector: this.generateSelector(element),
+      tagName: element.tagName.toLowerCase()
+    };
+
+    element.classList.add('c4ai-selected-container');
+    this.mode = 'field';
+    this.updateToolbar();
+    this.updateStats();
+  }
+
+  selectField(element) {
+    // Don't select the container itself
+    if (element === this.container.element) {
+      return;
+    }
+
+    // Check if already selected - if so, deselect it
+    if (this.selectedElements.has(element)) {
+      this.deselectField(element);
+      return;
+    }
+
+    // Must be inside the container
+    if (!this.container.element.contains(element)) {
+      return;
+    }
+
+    this.showFieldDialog(element);
+  }
+
+  deselectField(element) {
+    // Remove from fields array
+    this.fields = this.fields.filter(f => f.element !== element);
+    
+    // Remove from selected elements set
+    this.selectedElements.delete(element);
+    
+    // Remove visual selection
+    element.classList.remove('c4ai-selected-field');
+    
+    // Update UI
+    this.updateToolbar();
+    this.updateStats();
+  }
+
+  showFieldDialog(element) {
+    const dialog = document.createElement('div');
+    dialog.className = 'c4ai-field-dialog';
+    
+    const rect = element.getBoundingClientRect();
+    dialog.style.cssText = `
+      left: ${rect.left + window.scrollX}px;
+      top: ${rect.bottom + window.scrollY + 10}px;
+    `;
+
+    dialog.innerHTML = `
+      <div class="c4ai-field-dialog-content">
+        <h4>Name this field:</h4>
+        <input type="text" id="c4ai-field-name" placeholder="e.g., title, price, description" autofocus>
+        <div class="c4ai-field-preview">
+          <strong>Content:</strong> ${element.textContent.trim().substring(0, 50)}...
+        </div>
+        <div class="c4ai-field-actions">
+          <button id="c4ai-field-save">Save</button>
+          <button id="c4ai-field-cancel">Cancel</button>
+        </div>
+      </div>
+    `;
+
+    document.body.appendChild(dialog);
+
+    const input = dialog.querySelector('#c4ai-field-name');
+    const saveBtn = dialog.querySelector('#c4ai-field-save');
+    const cancelBtn = dialog.querySelector('#c4ai-field-cancel');
+
+    const save = () => {
+      const fieldName = input.value.trim();
+      if (fieldName) {
+        this.fields.push({
+          name: fieldName,
+          value: element.textContent.trim(),
+          element: element,
+          selector: this.generateSelector(element, this.container.element)
+        });
+        
+        element.classList.add('c4ai-selected-field');
+        this.selectedElements.add(element);
+        this.updateToolbar();
+        this.updateStats();
+      }
+      dialog.remove();
+    };
+
+    const cancel = () => {
+      dialog.remove();
+    };
+
+    saveBtn.addEventListener('click', save);
+    cancelBtn.addEventListener('click', cancel);
+    input.addEventListener('keypress', (e) => {
+      if (e.key === 'Enter') save();
+      if (e.key === 'Escape') cancel();
+    });
+
+    input.focus();
+  }
+
+  generateSelector(element, context = document) {
+    // Try to generate a robust selector
+    if (element.id) {
+      return `#${CSS.escape(element.id)}`;
+    }
+
+    // Check for data attributes (most stable)
+    const dataAttrs = ['data-testid', 'data-id', 'data-test', 'data-cy'];
+    for (const attr of dataAttrs) {
+      const value = element.getAttribute(attr);
+      if (value) {
+        return `[${attr}="${value}"]`;
+      }
+    }
+
+    // Check for aria-label
+    if (element.getAttribute('aria-label')) {
+      return `[aria-label="${element.getAttribute('aria-label')}"]`;
+    }
+
+    // Try semantic HTML elements with text
+    const tagName = element.tagName.toLowerCase();
+    if (['button', 'a', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6'].includes(tagName)) {
+      const text = element.textContent.trim();
+      if (text && text.length < 50) {
+        // Use tag name with partial text match
+        return `${tagName}`;
+      }
+    }
+
+    // Check for simple, non-utility classes
+    const classes = Array.from(element.classList)
+      .filter(c => !c.startsWith('c4ai-')) // Exclude our classes
+      .filter(c => !c.includes('[') && !c.includes('(') && !c.includes(':')) // Exclude utility classes
+      .filter(c => c.length < 30); // Exclude very long classes
+    
+    if (classes.length > 0 && classes.length <= 3) {
+      const selector = classes.map(c => `.${CSS.escape(c)}`).join('');
+      try {
+        if (context.querySelectorAll(selector).length === 1) {
+          return selector;
+        }
+      } catch (e) {
+        // Invalid selector, continue
+      }
+    }
+
+    // Use nth-child with simple parent tag
+    const parent = element.parentElement;
+    if (parent && parent !== context) {
+      const siblings = Array.from(parent.children);
+      const index = siblings.indexOf(element) + 1;
+      // Just use parent tag name to avoid recursion
+      const parentTag = parent.tagName.toLowerCase();
+      return `${parentTag} > ${tagName}:nth-child(${index})`;
+    }
+
+    // Final fallback
+    return tagName;
+  }
+
+  updateToolbar() {
+    document.getElementById('c4ai-mode').textContent = 
+      this.mode === 'container' ? 'Select Container' : 'Select Fields';
+    
+    document.getElementById('c4ai-container').textContent = 
+      this.container ? `${this.container.tagName} ✓` : 'Not selected';
+
+    // Update fields list
+    const fieldsList = document.getElementById('c4ai-fields-list');
+    const fieldsItems = document.getElementById('c4ai-fields-items');
+    
+    if (this.fields.length > 0) {
+      fieldsList.style.display = 'block';
+      fieldsItems.innerHTML = this.fields.map(field => `
+        <li class="c4ai-field-item">
+          <span class="c4ai-field-name">${field.name}</span>
+          <span class="c4ai-field-value">${field.value.substring(0, 30)}${field.value.length > 30 ? '...' : ''}</span>
+        </li>
+      `).join('');
+    } else {
+      fieldsList.style.display = 'none';
+    }
+
+    const hint = document.getElementById('c4ai-hint');
+    if (this.mode === 'container') {
+      hint.textContent = 'Click on a container element (e.g., product card, article, etc.)';
+    } else if (this.fields.length === 0) {
+      hint.textContent = 'Click on fields inside the container to extract (title, price, etc.)';
+    } else {
+      hint.innerHTML = `Continue selecting fields or click <strong>Stop & Generate</strong> to finish.`;
+    }
+  }
+
+  updateStats() {
+    chrome.runtime.sendMessage({
+      action: 'updateStats',
+      stats: {
+        container: !!this.container,
+        fields: this.fields.length
+      }
+    });
+  }
+
+  removeAllHighlights() {
+    document.querySelectorAll('.c4ai-selected-container').forEach(el => {
+      el.classList.remove('c4ai-selected-container');
+    });
+    document.querySelectorAll('.c4ai-selected-field').forEach(el => {
+      el.classList.remove('c4ai-selected-field');
+    });
+  }
+
+  generateCode() {
+    const fieldDescriptions = this.fields.map(f => 
+      `- ${f.name} (example: "${f.value.substring(0, 50)}...")`
+    ).join('\n');
+
+    return `#!/usr/bin/env python3
+"""
+Generated by Crawl4AI Chrome Extension
+URL: ${window.location.href}
+Generated: ${new Date().toISOString()}
+"""
+
+import asyncio
+import json
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# HTML snippet of the selected container element
+HTML_SNIPPET = """
+${this.container.html}
+"""
+
+# Extraction query based on your field selections
+EXTRACTION_QUERY = """
+Create a JSON CSS extraction schema to extract the following fields:
+${fieldDescriptions}
+
+The schema should handle multiple ${this.container.tagName} elements on the page.
+Each item should be extracted as a separate object in the results array.
+"""
+
+async def generate_schema():
+    """Generate extraction schema using LLM"""
+    print("🔧 Generating extraction schema...")
+    
+    try:
+        # Generate the schema using Crawl4AI's built-in LLM integration
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=HTML_SNIPPET,
+            query=EXTRACTION_QUERY,
+        )
+        
+        # Save the schema for reuse
+        schema_path = Path('generated_schema.json')
+        with open(schema_path, 'w') as f:
+            json.dump(schema, f, indent=2)
+        
+        print("✅ Schema generated successfully!")
+        print(f"📄 Schema saved to: {schema_path}")
+        print("\\nGenerated schema:")
+        print(json.dumps(schema, indent=2))
+        
+        return schema
+        
+    except Exception as e:
+        print(f"❌ Error generating schema: {e}")
+        return None
+
+async def test_extraction(url: str = "${window.location.href}"):
+    """Test the generated schema on the actual webpage"""
+    print("\\n🧪 Testing extraction on live webpage...")
+    
+    # Load the generated schema
+    try:
+        with open('generated_schema.json', 'r') as f:
+            schema = json.load(f)
+    except FileNotFoundError:
+        print("❌ Schema file not found. Run generate_schema() first.")
+        return
+    
+    # Configure browser
+    browser_config = BrowserConfig(
+        headless=True,
+        verbose=False
+    )
+    
+    # Configure extraction
+    crawler_config = CrawlerRunConfig(
+        extraction_strategy=JsonCssExtractionStrategy(schema=schema)
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url=url,
+            config=crawler_config
+        )
+        
+        if result.success and result.extracted_content:
+            data = json.loads(result.extracted_content)
+            print(f"\\n✅ Successfully extracted {len(data)} items!")
+            
+            # Save results
+            with open('extracted_data.json', 'w') as f:
+                json.dump(data, f, indent=2)
+            
+            # Show sample results
+            print("\\n📊 Sample results (first 2 items):")
+            for i, item in enumerate(data[:2], 1):
+                print(f"\\nItem {i}:")
+                for key, value in item.items():
+                    print(f"  {key}: {value}")
+        else:
+            print("❌ Extraction failed:", result.error_message)
+
+if __name__ == "__main__":
+    # Step 1: Generate the schema from HTML snippet
+    asyncio.run(generate_schema())
+    
+    # Step 2: Test extraction on the live webpage
+    # Uncomment the line below to test extraction:
+    # asyncio.run(test_extraction())
+    
+    print("\\n🎯 Next steps:")
+    print("1. Review the generated schema in 'generated_schema.json'")
+    print("2. Uncomment the test_extraction() line to test on the live site")
+    print("3. Use the schema in your Crawl4AI projects!")
+`;
+
+    return code;
+  }
+
+  showCodeModal(code) {
+    // Create modal
+    this.codeModal = document.createElement('div');
+    this.codeModal.className = 'c4ai-code-modal';
+    this.codeModal.innerHTML = `
+      <div class="c4ai-code-modal-content">
+        <div class="c4ai-code-modal-header">
+          <h2>Generated Python Code</h2>
+          <button class="c4ai-close-modal" id="c4ai-close-modal">✕</button>
+        </div>
+        <div class="c4ai-code-modal-body">
+          <pre class="c4ai-code-block"><code class="language-python">${window.C4AI_Utils.escapeHtml(code)}</code></pre>
+        </div>
+        <div class="c4ai-code-modal-footer">
+          <button class="c4ai-action-btn c4ai-cloud-btn" id="c4ai-run-cloud" disabled>
+            <span>☁️</span> Run on C4AI Cloud (Coming Soon)
+          </button>
+          <button class="c4ai-action-btn c4ai-download-btn" id="c4ai-download-code">
+            <span>⬇</span> Download Code
+          </button>
+          <button class="c4ai-action-btn c4ai-copy-btn" id="c4ai-copy-code">
+            <span>📋</span> Copy to Clipboard
+          </button>
+        </div>
+      </div>
+    `;
+    
+    document.body.appendChild(this.codeModal);
+    
+    // Add event listeners
+    document.getElementById('c4ai-close-modal').addEventListener('click', () => {
+      this.codeModal.remove();
+      this.codeModal = null;
+      // Don't stop the capture session
+    });
+    
+    document.getElementById('c4ai-download-code').addEventListener('click', () => {
+      chrome.runtime.sendMessage({
+        action: 'downloadCode',
+        code: code,
+        filename: `crawl4ai_schema_${Date.now()}.py`
+      }, (response) => {
+        if (response && response.success) {
+          const btn = document.getElementById('c4ai-download-code');
+          const originalHTML = btn.innerHTML;
+          btn.innerHTML = '<span>✓</span> Downloaded!';
+          setTimeout(() => {
+            btn.innerHTML = originalHTML;
+          }, 2000);
+        } else {
+          console.error('Download failed:', response?.error);
+          alert('Download failed. Please check your browser settings.');
+        }
+      });
+    });
+    
+    document.getElementById('c4ai-copy-code').addEventListener('click', () => {
+      navigator.clipboard.writeText(code).then(() => {
+        const btn = document.getElementById('c4ai-copy-code');
+        btn.innerHTML = '<span>✓</span> Copied!';
+        setTimeout(() => {
+          btn.innerHTML = '<span>📋</span> Copy to Clipboard';
+        }, 2000);
+      });
+    });
+    
+    // Apply syntax highlighting
+    window.C4AI_Utils.applySyntaxHighlighting(this.codeModal.querySelector('.language-python'));
+  }
+}
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/scriptBuilder.js b/docs/md_v2/apps/crawl4ai-assistant/content/scriptBuilder.js
new file mode 100644
index 00000000..de1ecac0
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/scriptBuilder.js
@@ -0,0 +1,2515 @@
+class ScriptBuilder {
+  constructor() {
+    this.mode = 'recording';
+    this.isRecording = false;
+    this.isPaused = false;
+    this.rawEvents = [];
+    this.groupedEvents = [];
+    this.startTime = 0;
+    this.lastEventTime = 0;
+    this.toolbar = null;
+    this.keyBuffer = [];
+    this.keyBufferTimeout = null;
+    this.scrollAccumulator = { direction: null, amount: 0, startTime: 0 };
+    this.processedEventIndices = new Set();
+    this.outputFormat = 'js'; // 'js' or 'c4a'
+    this.timelineModal = null;
+    this.recordingIndicator = null;
+  }
+
+  start() {
+    // Don't start recording immediately, just show the toolbar
+    this.createToolbar();
+    this.showStartScreen();
+  }
+  
+  showStartScreen() {
+    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
+    toolbarContent.innerHTML = `
+      <div class="c4ai-toolbar-status">
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Script Builder</span>
+          <span class="c4ai-status-value">Ready</span>
+        </div>
+      </div>
+      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
+        Click "Start Recording" to begin capturing your actions. <span style="color: #ff3c74; font-size: 11px;">(Alpha - May contain bugs)</span>
+      </div>
+      <div class="c4ai-toolbar-actions c4ai-start-actions">
+        <button id="c4ai-start-recording" class="c4ai-action-btn c4ai-primary-btn">
+          <span>🔴</span> Start Recording
+        </button>
+        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
+          <span>📂</span> Saved Flows
+        </button>
+      </div>
+    `;
+    
+    // Add event listeners
+    document.getElementById('c4ai-start-recording').addEventListener('click', () => this.startRecording());
+    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
+  }
+  
+  startRecording() {
+    this.isRecording = true;
+    this.startTime = Date.now();
+    this.lastEventTime = this.startTime;
+    this.createRecordingIndicator();
+    this.injectEventCapture();
+    
+    // Update toolbar to show recording controls
+    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
+    toolbarContent.innerHTML = `
+      <div class="c4ai-toolbar-status">
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Actions:</span>
+          <span class="c4ai-status-value" id="c4ai-action-count">0</span>
+        </div>
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Format:</span>
+          <select id="c4ai-output-format" class="c4ai-format-select">
+            <option value="js">JavaScript</option>
+            <option value="c4a">C4A Script</option>
+          </select>
+        </div>
+      </div>
+      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
+        Recording your actions... Click, type, and scroll to build your script.
+      </div>
+      <div class="c4ai-toolbar-actions c4ai-recording-actions">
+        <button id="c4ai-add-wait" class="c4ai-action-btn c4ai-secondary-btn">
+          <span class="c4ai-wait-icon">⏱</span> Add Wait
+        </button>
+        <button id="c4ai-pause-recording" class="c4ai-action-btn c4ai-secondary-btn">
+          <span class="c4ai-pause-icon">⏸</span> Pause
+        </button>
+        <button id="c4ai-stop-generate" class="c4ai-action-btn c4ai-primary-btn">
+          <span class="c4ai-generate-icon">⚡</span> Stop & Generate
+        </button>
+        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
+          <span>📂</span> Saved Flows
+        </button>
+      </div>
+    `;
+    
+    // Re-add event listeners
+    document.getElementById('c4ai-pause-recording').addEventListener('click', () => this.togglePause());
+    document.getElementById('c4ai-stop-generate').addEventListener('click', () => this.stopAndGenerate());
+    document.getElementById('c4ai-add-wait').addEventListener('click', () => this.showWaitDialog());
+    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
+    document.getElementById('c4ai-output-format').addEventListener('change', (e) => {
+      this.outputFormat = e.target.value;
+    });
+    
+    this.updateToolbar();
+  }
+
+  stop() {
+    this.isRecording = false;
+    this.removeEventCapture();
+    this.toolbar?.remove();
+    this.recordingIndicator?.remove();
+    this.timelineModal?.remove();
+    this.rawEvents = [];
+    this.groupedEvents = [];
+    this.processedEventIndices.clear();
+  }
+
+  createRecordingIndicator() {
+    this.recordingIndicator = document.createElement('div');
+    this.recordingIndicator.className = 'c4ai-recording-indicator';
+    this.recordingIndicator.innerHTML = `
+      <div class="c4ai-recording-dot"></div>
+      <span>Recording</span>
+    `;
+    document.body.appendChild(this.recordingIndicator);
+  }
+
+  createToolbar() {
+    this.toolbar = document.createElement('div');
+    this.toolbar.className = 'c4ai-script-toolbar';
+    this.toolbar.innerHTML = `
+      <div class="c4ai-toolbar-titlebar">
+        <div class="c4ai-titlebar-dots">
+          <button class="c4ai-dot c4ai-dot-close" id="c4ai-script-close"></button>
+          <button class="c4ai-dot c4ai-dot-minimize"></button>
+          <button class="c4ai-dot c4ai-dot-maximize"></button>
+        </div>
+        <img src="${chrome.runtime.getURL('icons/icon-16.png')}" class="c4ai-titlebar-icon" alt="Crawl4AI">
+        <div class="c4ai-titlebar-title">Crawl4AI Script Builder <span style="color: #ff3c74; font-size: 10px; margin-left: 8px;">(ALPHA)</span></div>
+      </div>
+      <div class="c4ai-toolbar-content" id="c4ai-toolbar-content">
+        <!-- Content will be dynamically updated -->
+      </div>
+    `;
+    document.body.appendChild(this.toolbar);
+
+    // Add close button listener
+    document.getElementById('c4ai-script-close').addEventListener('click', () => this.stop());
+
+    // Make toolbar draggable
+    window.C4AI_Utils.makeDraggable(this.toolbar);
+  }
+
+  showStartScreen() {
+    const content = document.getElementById('c4ai-toolbar-content');
+    content.innerHTML = `
+      <div class="c4ai-start-screen">
+        <div class="c4ai-welcome-message">
+          <h3>Welcome to Script Builder</h3>
+          <p>Record your actions to create automation scripts. <span style="color: #ff3c74;">Alpha version - may contain bugs</span></p>
+        </div>
+        <div class="c4ai-start-actions">
+          <button id="c4ai-start-recording" class="c4ai-action-btn c4ai-primary-btn">
+            <span>🔴</span> Start Recording
+          </button>
+          <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
+            <span>📂</span> Saved Flows
+          </button>
+        </div>
+      </div>
+    `;
+    
+    // Add event listeners
+    document.getElementById('c4ai-start-recording').addEventListener('click', () => this.startRecording());
+    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
+  }
+
+  showRecordingUI() {
+    const content = document.getElementById('c4ai-toolbar-content');
+    content.innerHTML = `
+      <div class="c4ai-toolbar-status">
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Actions:</span>
+          <span class="c4ai-status-value" id="c4ai-action-count">0</span>
+        </div>
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Format:</span>
+          <select id="c4ai-output-format" class="c4ai-format-select">
+            <option value="js">JavaScript</option>
+            <option value="c4a">C4A Script</option>
+          </select>
+        </div>
+      </div>
+      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
+        Recording your actions... Click, type, and scroll to build your script.
+      </div>
+      <div class="c4ai-toolbar-actions">
+        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-secondary-btn">
+          <span>📂</span> Saved Flows
+        </button>
+        <button id="c4ai-add-wait" class="c4ai-action-btn c4ai-secondary-btn">
+          <span class="c4ai-wait-icon">⏱</span> Add Wait
+        </button>
+        <button id="c4ai-pause-recording" class="c4ai-action-btn c4ai-pause-btn">
+          <span class="c4ai-pause-icon">⏸</span> Pause
+        </button>
+        <button id="c4ai-stop-generate" class="c4ai-action-btn c4ai-primary-btn">
+          <span class="c4ai-generate-icon">⚡</span> Stop & Generate
+        </button>
+      </div>
+    `;
+    
+    // Re-add event listeners
+    document.getElementById('c4ai-pause-recording').addEventListener('click', () => this.togglePause());
+    document.getElementById('c4ai-stop-generate').addEventListener('click', () => this.stopAndGenerate());
+    document.getElementById('c4ai-add-wait').addEventListener('click', () => this.showWaitDialog());
+    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
+    document.getElementById('c4ai-output-format').addEventListener('change', (e) => {
+      this.outputFormat = e.target.value;
+    });
+  }
+
+
+  togglePause() {
+    this.isPaused = !this.isPaused;
+    const pauseBtn = document.getElementById('c4ai-pause-recording');
+    const recordingIndicator = document.querySelector('.c4ai-recording-indicator');
+    
+    if (this.isPaused) {
+      pauseBtn.innerHTML = '<span class="c4ai-play-icon">▶</span> Resume';
+      pauseBtn.classList.add('c4ai-paused');
+      recordingIndicator.classList.add('c4ai-paused');
+      document.getElementById('c4ai-script-hint').textContent = 'Recording paused. Click Resume to continue.';
+    } else {
+      pauseBtn.innerHTML = '<span class="c4ai-pause-icon">⏸</span> Pause';
+      pauseBtn.classList.remove('c4ai-paused');
+      recordingIndicator.classList.remove('c4ai-paused');
+      document.getElementById('c4ai-script-hint').textContent = 'Recording your actions... Click, type, and scroll to build your script.';
+    }
+  }
+
+  showWaitDialog() {
+    const dialog = document.createElement('div');
+    dialog.className = 'c4ai-wait-dialog';
+    dialog.innerHTML = `
+      <div class="c4ai-wait-dialog-content">
+        <h4>Add Wait Command</h4>
+        <div class="c4ai-wait-options">
+          <label class="c4ai-wait-option">
+            <input type="radio" name="wait-type" value="time" checked>
+            <span>Wait for time (seconds)</span>
+          </label>
+          <label class="c4ai-wait-option">
+            <input type="radio" name="wait-type" value="selector">
+            <span>Wait for element</span>
+          </label>
+        </div>
+        <div class="c4ai-wait-input" id="c4ai-wait-time-input">
+          <input type="number" id="c4ai-wait-seconds" min="0.5" step="0.5" value="2" placeholder="Seconds">
+        </div>
+        <div class="c4ai-wait-input c4ai-hidden" id="c4ai-wait-selector-input">
+          <p>Click on an element to wait for</p>
+        </div>
+        <div class="c4ai-wait-actions">
+          <button id="c4ai-wait-cancel" class="c4ai-action-btn">Cancel</button>
+          <button id="c4ai-wait-add" class="c4ai-action-btn c4ai-primary">Add Wait</button>
+        </div>
+      </div>
+    `;
+    document.body.appendChild(dialog);
+
+    // Handle radio button changes
+    dialog.querySelectorAll('input[name="wait-type"]').forEach(radio => {
+      radio.addEventListener('change', (e) => {
+        const timeInput = document.getElementById('c4ai-wait-time-input');
+        const selectorInput = document.getElementById('c4ai-wait-selector-input');
+        
+        if (e.target.value === 'time') {
+          timeInput.classList.remove('c4ai-hidden');
+          selectorInput.classList.add('c4ai-hidden');
+        } else {
+          timeInput.classList.add('c4ai-hidden');
+          selectorInput.classList.remove('c4ai-hidden');
+          this.waitForElementSelection(dialog);
+        }
+      });
+    });
+
+    // Handle buttons
+    document.getElementById('c4ai-wait-cancel').addEventListener('click', () => {
+      dialog.remove();
+    });
+
+    document.getElementById('c4ai-wait-add').addEventListener('click', () => {
+      const waitType = dialog.querySelector('input[name="wait-type"]:checked').value;
+      
+      if (waitType === 'time') {
+        const seconds = parseFloat(document.getElementById('c4ai-wait-seconds').value) || 2;
+        this.addWaitCommand('time', seconds);
+        dialog.remove();
+      }
+    });
+  }
+
+  waitForElementSelection(dialog) {
+    this.isPaused = true; // Pause recording during element selection
+    
+    const highlightBox = document.createElement('div');
+    highlightBox.className = 'c4ai-highlight-box c4ai-wait-mode';
+    document.body.appendChild(highlightBox);
+
+    const handleMouseMove = (e) => {
+      const element = document.elementFromPoint(e.clientX, e.clientY);
+      if (element && !this.isOurElement(element)) {
+        this.highlightElement(element, highlightBox);
+      }
+    };
+
+    const handleClick = (e) => {
+      const element = e.target;
+      if (this.isOurElement(element)) return;
+
+      e.preventDefault();
+      e.stopPropagation();
+
+      const selector = this.getElementSelector(element);
+      this.addWaitCommand('selector', selector);
+
+      // Cleanup
+      document.removeEventListener('mousemove', handleMouseMove, true);
+      document.removeEventListener('click', handleClick, true);
+      highlightBox.remove();
+      dialog.remove();
+      this.isPaused = false;
+    };
+
+    document.addEventListener('mousemove', handleMouseMove, true);
+    document.addEventListener('click', handleClick, true);
+  }
+
+  addWaitCommand(type, value) {
+    const waitEvent = {
+      type: 'WAIT',
+      waitType: type,
+      value: value,
+      time: (Date.now() - this.startTime) / 1000
+    };
+
+    this.groupedEvents.push(waitEvent);
+    this.updateToolbar();
+  }
+
+  highlightElement(element, highlightBox) {
+    if (!highlightBox) {
+      highlightBox = document.querySelector('.c4ai-highlight-box');
+    }
+    
+    const rect = element.getBoundingClientRect();
+    highlightBox.style.display = 'block';
+    highlightBox.style.top = `${rect.top + window.scrollY}px`;
+    highlightBox.style.left = `${rect.left + window.scrollX}px`;
+    highlightBox.style.width = `${rect.width}px`;
+    highlightBox.style.height = `${rect.height}px`;
+  }
+
+  isOurElement(element) {
+    return window.C4AI_Utils.isOurElement(element);
+  }
+
+  getElementSelector(element) {
+    return window.C4AI_Utils.getElementSelector(element);
+  }
+
+  injectEventCapture() {
+    // Use content script context instead of injecting inline script
+    if (window.__c4aiScriptRecordingActive) return;
+    window.__c4aiScriptRecordingActive = true;
+    
+    const captureEvent = (type, event) => {
+      // Skip events from our own UI
+      if (event.target.classList && 
+          (event.target.classList.contains('c4ai-script-toolbar') ||
+           event.target.closest('.c4ai-script-toolbar') ||
+           event.target.classList.contains('c4ai-wait-dialog') ||
+           event.target.closest('.c4ai-wait-dialog') ||
+           event.target.classList.contains('c4ai-timeline-modal') ||
+           event.target.closest('.c4ai-timeline-modal'))) {
+        return;
+      }
+      
+      const data = {
+        type: type,
+        timestamp: Date.now(),
+        targetTag: event.target.tagName,
+        targetId: event.target.id,
+        targetClass: event.target.className,
+        targetSelector: this.getElementSelector(event.target),
+        targetType: event.target.type
+      };
+      
+      switch(type) {
+        case 'click':
+        case 'dblclick':
+        case 'contextmenu':
+          data.x = event.clientX;
+          data.y = event.clientY;
+          break;
+        case 'keydown':
+        case 'keyup':
+          data.key = event.key;
+          data.code = event.code;
+          data.ctrlKey = event.ctrlKey;
+          data.shiftKey = event.shiftKey;
+          data.altKey = event.altKey;
+          data.metaKey = event.metaKey; // Add meta key support
+          break;
+        case 'input':
+        case 'change':
+          // Check if it's a contenteditable element
+          if (event.target.contentEditable === 'true' || 
+              event.target.getAttribute('contenteditable') === 'true') {
+            data.value = event.target.textContent || event.target.innerText;
+            data.isContentEditable = true;
+          } else {
+            data.value = event.target.value;
+          }
+          data.inputType = event.inputType;
+          if (event.target.type === 'checkbox' || event.target.type === 'radio') {
+            data.checked = event.target.checked;
+          }
+          if (event.target.tagName === 'SELECT') {
+            data.selectedText = event.target.options[event.target.selectedIndex]?.text || '';
+          }
+          break;
+        case 'scroll':
+          // Capture which element was scrolled
+          if (event.target === window || event.target === document || event.target === document.body) {
+            data.isWindowScroll = true;
+            data.scrollTop = window.scrollY;
+            data.scrollLeft = window.scrollX;
+          } else {
+            data.isWindowScroll = false;
+            data.scrollTop = event.target.scrollTop;
+            data.scrollLeft = event.target.scrollLeft;
+            data.scrollHeight = event.target.scrollHeight;
+            data.scrollWidth = event.target.scrollWidth;
+          }
+          break;
+        case 'wheel':
+          data.deltaY = event.deltaY || 0;
+          data.deltaX = event.deltaX || 0;
+          // Check if the target element is scrollable
+          const isScrollable = (el) => {
+            const hasScrollableContent = el.scrollHeight > el.clientHeight || el.scrollWidth > el.clientWidth;
+            const overflowY = window.getComputedStyle(el).overflowY;
+            const overflowX = window.getComputedStyle(el).overflowX;
+            const canScroll = (overflowY !== 'visible' && overflowY !== 'hidden') || 
+                              (overflowX !== 'visible' && overflowX !== 'hidden');
+            return hasScrollableContent && canScroll;
+          };
+          
+          // Find the actual scrollable element
+          let scrollTarget = event.target;
+          while (scrollTarget && scrollTarget !== document.body) {
+            if (isScrollable(scrollTarget)) {
+              break;
+            }
+            scrollTarget = scrollTarget.parentElement;
+          }
+          
+          if (scrollTarget && scrollTarget !== document.body && isScrollable(scrollTarget)) {
+            data.isWindowScroll = false;
+            data.scrollTop = scrollTarget.scrollTop;
+            data.scrollLeft = scrollTarget.scrollLeft;
+            data.targetSelector = this.getElementSelector(scrollTarget);
+          } else {
+            data.isWindowScroll = true;
+            data.scrollTop = window.scrollY;
+            data.scrollLeft = window.scrollX;
+          }
+          break;
+      }
+      
+      // Handle the event directly instead of using postMessage
+      if (this.isRecording && !this.isPaused) {
+        this.handleRecordedEvent(data);
+      }
+    };
+    
+    // Store bound event handlers for later removal
+    this.eventHandlers = {
+      click: (e) => captureEvent('click', e),
+      dblclick: (e) => captureEvent('dblclick', e),
+      contextmenu: (e) => captureEvent('contextmenu', e),
+      keydown: (e) => captureEvent('keydown', e),
+      input: (e) => captureEvent('input', e),
+      change: (e) => captureEvent('change', e),
+      scroll: (e) => captureEvent('scroll', e)
+    };
+    
+    // Add event listeners
+    Object.entries(this.eventHandlers).forEach(([eventType, handler]) => {
+      document.addEventListener(eventType, handler, true);
+    });
+    
+    // Cleanup on page unload
+    window.addEventListener('beforeunload', () => {
+      this.removeEventCapture();
+    });
+  }
+
+  removeEventCapture() {
+    window.__c4aiScriptRecordingActive = false;
+    
+    // Remove event listeners if they exist
+    if (this.eventHandlers) {
+      Object.entries(this.eventHandlers).forEach(([eventType, handler]) => {
+        document.removeEventListener(eventType, handler, true);
+      });
+      this.eventHandlers = null;
+    }
+  }
+
+  handleRecordedEvent(event) {
+    // Skip events from our own UI
+    if (event.targetClass && typeof event.targetClass === 'string' && event.targetClass.includes('c4ai-')) return;
+
+    // Add time since start
+    event.timeSinceStart = (event.timestamp - this.startTime) / 1000;
+    
+    // Store raw event
+    this.rawEvents.push(event);
+
+    // Process event based on type
+    if (event.type === 'keydown') {
+      // Handle keyboard shortcuts (with modifier keys)
+      if (event.metaKey || event.ctrlKey || event.altKey) {
+        // Flush any pending keystrokes
+        if (this.keyBuffer.length > 0) {
+          this.flushKeyBuffer();
+        }
+        
+        // Create keyboard shortcut command
+        const command = this.eventToCommand(event);
+        if (command) {
+          this.groupedEvents.push(command);
+          this.updateToolbar();
+        }
+      } else if (this.shouldGroupKeystrokes(event)) {
+        // Regular typing
+        this.keyBuffer.push(event);
+        
+        if (this.keyBufferTimeout) {
+          clearTimeout(this.keyBufferTimeout);
+        }
+        
+        this.keyBufferTimeout = setTimeout(() => {
+          this.flushKeyBuffer();
+          this.updateToolbar();
+        }, 500);
+      } else if (event.key === 'Delete' || event.key === 'Backspace') {
+        // Handle Delete and Backspace as individual commands when not part of typing
+        if (this.keyBuffer.length > 0) {
+          this.flushKeyBuffer();
+        }
+        
+        const command = this.eventToCommand(event);
+        if (command) {
+          this.groupedEvents.push(command);
+          this.updateToolbar();
+        }
+      }
+    } else if (event.type === 'scroll' || event.type === 'wheel') {
+      this.handleScrollEvent(event);
+    } else if (event.type === 'click' || event.type === 'dblclick' || event.type === 'contextmenu') {
+      // Flush any pending keystrokes before click
+      if (this.keyBuffer.length > 0) {
+        this.flushKeyBuffer();
+      }
+      
+      const command = this.eventToCommand(event);
+      if (command) {
+        this.groupedEvents.push(command);
+        this.updateToolbar();
+      }
+    } else if (event.type === 'change') {
+      // Handle select, checkbox, radio changes
+      const tagName = event.targetTag?.toLowerCase();
+      if (tagName === 'select' || 
+          (tagName === 'input' && (event.targetType === 'checkbox' || event.targetType === 'radio'))) {
+        const command = this.eventToCommand(event);
+        if (command) {
+          this.groupedEvents.push(command);
+          this.updateToolbar();
+        }
+      }
+    }
+  }
+
+  shouldGroupKeystrokes(event) {
+    // Don't group if modifier keys are pressed (except shift for capitals)
+    if (event.metaKey || event.ctrlKey || event.altKey) {
+      return false;
+    }
+    
+    return event.key && (
+      event.key.length === 1 ||
+      event.key === ' ' ||
+      event.key === 'Enter' ||
+      event.key === 'Tab' ||
+      event.key === 'Backspace' ||
+      event.key === 'Delete'
+    );
+  }
+
+  flushKeyBuffer() {
+    if (this.keyBuffer.length === 0) return;
+    
+    const text = this.keyBuffer.map(e => {
+      switch(e.key) {
+        case ' ': return ' ';
+        case 'Enter': return '\\n';
+        case 'Tab': return '\\t';
+        case 'Backspace': return '';
+        case 'Delete': return '';
+        default: return e.key;
+      }
+    }).join('');
+    
+    if (text.length === 0) {
+      this.keyBuffer = [];
+      return;
+    }
+    
+    const firstEvent = this.keyBuffer[0];
+    const lastEvent = this.keyBuffer[this.keyBuffer.length - 1];
+    
+    // Check if this is a SET command pattern
+    const firstKeystrokeIndex = this.rawEvents.indexOf(firstEvent);
+    let commandType = 'TYPE';
+    
+    if (firstKeystrokeIndex > 0) {
+      const prevEvent = this.rawEvents[firstKeystrokeIndex - 1];
+      
+      // Check for click before typing
+      if (prevEvent && prevEvent.type === 'click' && 
+          prevEvent.targetSelector === firstEvent.targetSelector) {
+        commandType = 'SET';
+      }
+      
+      // Check for Cmd+A or Ctrl+A (select all) before typing
+      if (prevEvent && prevEvent.type === 'keydown' && 
+          prevEvent.key === 'a' && (prevEvent.metaKey || prevEvent.ctrlKey) &&
+          prevEvent.targetSelector === firstEvent.targetSelector) {
+        commandType = 'SET';
+      }
+    }
+    
+    // Also check within the last few events for select-all pattern
+    if (commandType === 'TYPE' && firstKeystrokeIndex > 1) {
+      // Look back up to 3 events for select-all
+      for (let i = Math.max(0, firstKeystrokeIndex - 3); i < firstKeystrokeIndex; i++) {
+        const event = this.rawEvents[i];
+        if (event && event.type === 'keydown' && 
+            event.key === 'a' && (event.metaKey || event.ctrlKey) &&
+            event.targetSelector === firstEvent.targetSelector) {
+          commandType = 'SET';
+          break;
+        }
+      }
+    }
+    
+    this.groupedEvents.push({
+      type: commandType,
+      selector: firstEvent.targetSelector,
+      value: text,
+      time: firstEvent.timeSinceStart
+    });
+    
+    this.keyBuffer = [];
+  }
+
+  handleScrollEvent(event) {
+    const direction = event.deltaY > 0 ? 'DOWN' : 'UP';
+    const amount = Math.abs(event.deltaY);
+    
+    // Remove previous scroll in same direction within 0.5s for the same element
+    this.groupedEvents = this.groupedEvents.filter(e => 
+      !(e.type === 'SCROLL' && 
+        e.direction === direction && 
+        e.selector === (event.targetSelector || 'window') &&
+        parseFloat(e.time) > parseFloat(event.timeSinceStart) - 0.5)
+    );
+    
+    this.groupedEvents.push({
+      type: 'SCROLL',
+      direction: direction,
+      amount: amount,
+      selector: event.targetSelector || 'window',
+      isWindowScroll: event.isWindowScroll !== false,
+      time: event.timeSinceStart
+    });
+    
+    this.updateToolbar();
+  }
+
+  eventToCommand(event) {
+    switch (event.type) {
+      case 'click':
+        return {
+          type: 'CLICK',
+          selector: event.targetSelector,
+          time: event.timeSinceStart
+        };
+      case 'dblclick':
+        return {
+          type: 'DOUBLE_CLICK',
+          selector: event.targetSelector,
+          time: event.timeSinceStart
+        };
+      case 'contextmenu':
+        return {
+          type: 'RIGHT_CLICK',
+          selector: event.targetSelector,
+          time: event.timeSinceStart
+        };
+      case 'keydown':
+        // Handle keyboard shortcuts
+        if (event.metaKey || event.ctrlKey || event.altKey) {
+          return {
+            type: 'KEYBOARD_SHORTCUT',
+            key: event.key,
+            code: event.code,
+            metaKey: event.metaKey,
+            ctrlKey: event.ctrlKey,
+            altKey: event.altKey,
+            shiftKey: event.shiftKey,
+            time: event.timeSinceStart
+          };
+        } else if (event.key === 'Delete' || event.key === 'Backspace') {
+          // Handle Delete/Backspace as special keys
+          return {
+            type: 'KEY_PRESS',
+            key: event.key,
+            time: event.timeSinceStart
+          };
+        }
+        break;
+      case 'change':
+        if (event.targetTag === 'SELECT') {
+          return {
+            type: 'SET',
+            selector: event.targetSelector,
+            value: event.value,
+            time: event.timeSinceStart
+          };
+        } else if (event.targetType === 'checkbox' || event.targetType === 'radio') {
+          return {
+            type: 'CLICK',
+            selector: event.targetSelector,
+            time: event.timeSinceStart
+          };
+        }
+        break;
+    }
+    return null;
+  }
+
+  updateToolbar() {
+    const actionCount = document.getElementById('c4ai-action-count');
+    if (actionCount) {
+      actionCount.textContent = this.groupedEvents.length;
+    }
+  }
+
+  stopAndGenerate() {
+    // Flush any pending events
+    if (this.keyBufferTimeout) {
+      clearTimeout(this.keyBufferTimeout);
+      this.flushKeyBuffer();
+    }
+
+    this.isRecording = false;
+    this.recordingIndicator?.remove();
+    this.showRecordingSummary();
+  }
+
+  showRecordingSummary() {
+    // Update toolbar to show summary
+    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
+    
+    toolbarContent.innerHTML = `
+      <div class="c4ai-toolbar-status">
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Recording Complete</span>
+          <span class="c4ai-status-value">${this.groupedEvents.length} actions</span>
+        </div>
+      </div>
+      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
+        Recording stopped. You can replay, save, or generate code.
+      </div>
+      <div class="c4ai-toolbar-actions c4ai-summary-actions">
+        <button id="c4ai-replay" class="c4ai-action-btn c4ai-replay-btn">
+          <span>▶</span> Replay
+        </button>
+        <button id="c4ai-save-flow" class="c4ai-action-btn c4ai-save-btn">
+          <span>💾</span> Save Flow
+        </button>
+        <button id="c4ai-show-timeline" class="c4ai-action-btn c4ai-timeline-btn">
+          <span>📋</span> Review & Generate
+        </button>
+        <button id="c4ai-record-again" class="c4ai-action-btn c4ai-record-btn">
+          <span>🔄</span> Record Again
+        </button>
+      </div>
+    `;
+
+    // Add event listeners
+    document.getElementById('c4ai-replay')?.addEventListener('click', () => this.replayRecording());
+    document.getElementById('c4ai-save-flow')?.addEventListener('click', () => this.saveFlow());
+    document.getElementById('c4ai-show-timeline')?.addEventListener('click', () => this.showTimeline());
+    document.getElementById('c4ai-record-again')?.addEventListener('click', () => this.recordAgain());
+  }
+
+  showTimeline() {
+    this.timelineModal = document.createElement('div');
+    this.timelineModal.className = 'c4ai-timeline-modal';
+    this.timelineModal.innerHTML = `
+      <div class="c4ai-timeline-content">
+        <div class="c4ai-timeline-header">
+          <h2>Review Your Actions</h2>
+          <button class="c4ai-close-modal" id="c4ai-close-timeline">✕</button>
+        </div>
+        <div class="c4ai-timeline-body">
+          <div class="c4ai-timeline-controls">
+            <button class="c4ai-action-btn" id="c4ai-select-all">Select All</button>
+            <button class="c4ai-action-btn" id="c4ai-clear-all">Clear All</button>
+          </div>
+          <div class="c4ai-timeline-events" id="c4ai-timeline-events">
+            ${this.renderTimelineEvents()}
+          </div>
+        </div>
+        <div class="c4ai-timeline-footer">
+          <select id="c4ai-final-format" class="c4ai-format-select">
+            <option value="js" ${this.outputFormat === 'js' ? 'selected' : ''}>JavaScript</option>
+            <option value="c4a" ${this.outputFormat === 'c4a' ? 'selected' : ''}>C4A Script</option>
+          </select>
+          <button class="c4ai-action-btn c4ai-download-btn" id="c4ai-download-script">
+            <span>⬇</span> Generate & Download
+          </button>
+        </div>
+      </div>
+    `;
+    document.body.appendChild(this.timelineModal);
+
+    // Event listeners
+    document.getElementById('c4ai-close-timeline').addEventListener('click', () => {
+      this.timelineModal.remove();
+      // Don't stop the toolbar, just close the modal
+    });
+
+    document.getElementById('c4ai-select-all').addEventListener('click', () => {
+      document.querySelectorAll('.c4ai-event-checkbox').forEach(cb => cb.checked = true);
+    });
+
+    document.getElementById('c4ai-clear-all').addEventListener('click', () => {
+      document.querySelectorAll('.c4ai-event-checkbox').forEach(cb => cb.checked = false);
+    });
+
+    document.getElementById('c4ai-download-script').addEventListener('click', () => {
+      this.generateAndDownload();
+    });
+  }
+
+  renderTimelineEvents() {
+    return this.groupedEvents.map((event, index) => {
+      const detail = this.getEventDetail(event);
+      return `
+        <div class="c4ai-timeline-event">
+          <input type="checkbox" class="c4ai-event-checkbox" id="event-${index}" checked>
+          <label for="event-${index}" class="c4ai-event-label">
+            <span class="c4ai-event-time">${event.time.toFixed(1)}s</span>
+            <span class="c4ai-event-type">${event.type.replace(/_/g, ' ')}</span>
+            <span class="c4ai-event-detail" title="${detail.replace(/"/g, '&quot;')}">${detail}</span>
+          </label>
+        </div>
+      `;
+    }).join('');
+  }
+
+  getEventDetail(event) {
+    switch (event.type) {
+      case 'CLICK':
+      case 'DOUBLE_CLICK':
+      case 'RIGHT_CLICK':
+        return event.selector;
+      case 'TYPE':
+      case 'SET':
+        return `${event.selector} = "${event.value.substring(0, 30)}${event.value.length > 30 ? '...' : ''}"`;
+      case 'SCROLL':
+        if (event.isWindowScroll || event.selector === 'window') {
+          return `${event.direction} ${event.amount}px`;
+        } else {
+          return `${event.selector} ${event.direction} ${event.amount}px`;
+        }
+      case 'WAIT':
+        return event.waitType === 'time' ? `${event.value}s` : event.value;
+      case 'KEYBOARD_SHORTCUT':
+        const keys = [];
+        if (event.metaKey) keys.push('Cmd');
+        if (event.ctrlKey) keys.push('Ctrl');
+        if (event.altKey) keys.push('Alt');
+        if (event.shiftKey) keys.push('Shift');
+        keys.push(event.key.toUpperCase());
+        return keys.join('+');
+      case 'KEY_PRESS':
+        return event.key;
+      default:
+        return '';
+    }
+  }
+
+  generateAndDownload() {
+    const format = document.getElementById('c4ai-final-format').value;
+    const selectedEvents = [];
+    
+    document.querySelectorAll('.c4ai-event-checkbox').forEach((cb, index) => {
+      if (cb.checked && this.groupedEvents[index]) {
+        selectedEvents.push(this.groupedEvents[index]);
+      }
+    });
+
+    if (selectedEvents.length === 0) {
+      alert('Please select at least one action');
+      return;
+    }
+
+    const code = this.generateCode(selectedEvents, format);
+    
+    chrome.runtime.sendMessage({
+      action: 'downloadScript',
+      code: code,
+      format: format,
+      filename: `crawl4ai_script_${Date.now()}.py`
+    }, (response) => {
+      if (response && response.success) {
+        this.timelineModal.remove();
+        // Don't stop the toolbar after download
+        // Show success message in toolbar
+        document.getElementById('c4ai-script-hint').textContent = '✅ Script downloaded successfully!';
+      }
+    });
+  }
+
+  generateCode(events, format) {
+    let scriptCode;
+    
+    if (format === 'js') {
+      scriptCode = this.generateJavaScript(events);
+    } else {
+      scriptCode = this.generateC4AScript(events);
+    }
+
+    return this.generatePythonTemplate(scriptCode, format);
+  }
+
+  generateJavaScript(events) {
+    const commands = events.map(event => {
+      switch (event.type) {
+        case 'CLICK':
+          return `document.querySelector('${event.selector}').click();`;
+        case 'DOUBLE_CLICK':
+          return `const el = document.querySelector('${event.selector}');\nconst evt = new MouseEvent('dblclick', {bubbles: true});\nel.dispatchEvent(evt);`;
+        case 'RIGHT_CLICK':
+          return `const el = document.querySelector('${event.selector}');\nconst evt = new MouseEvent('contextmenu', {bubbles: true});\nel.dispatchEvent(evt);`;
+        case 'TYPE':
+          return this.generateTypeCode(event, 'append');
+        case 'SET':
+          return this.generateTypeCode(event, 'set');
+        case 'SCROLL':
+          if (event.isWindowScroll || event.selector === 'window') {
+            return `window.scrollBy(0, ${event.direction === 'DOWN' ? event.amount : -event.amount});`;
+          } else {
+            const scrollAmount = event.direction === 'DOWN' ? event.amount : -event.amount;
+            return `// Scroll element
+const scrollEl = document.querySelector('${event.selector}');
+if (scrollEl) {
+  scrollEl.scrollTop += ${scrollAmount};
+}`;
+          }
+        case 'WAIT':
+          if (event.waitType === 'time') {
+            return `await new Promise(resolve => setTimeout(resolve, ${event.value * 1000}));`;
+          } else {
+            return `// Wait for element: ${event.value}\nawait new Promise((resolve) => {\n  const checkElement = setInterval(() => {\n    if (document.querySelector('${event.value}')) {\n      clearInterval(checkElement);\n      resolve();\n    }\n  }, 100);\n  setTimeout(() => { clearInterval(checkElement); resolve(); }, 5000);\n});`;
+          }
+        case 'KEYBOARD_SHORTCUT':
+          const modifiers = [];
+          if (event.metaKey) modifiers.push('metaKey: true');
+          if (event.ctrlKey) modifiers.push('ctrlKey: true');
+          if (event.altKey) modifiers.push('altKey: true');
+          if (event.shiftKey) modifiers.push('shiftKey: true');
+          return `// Keyboard shortcut: ${this.getEventDetail(event)}\ndocument.dispatchEvent(new KeyboardEvent('keydown', {key: '${event.key}', ${modifiers.join(', ')}}));`;
+        case 'KEY_PRESS':
+          return `// Press ${event.key} key\ndocument.dispatchEvent(new KeyboardEvent('keydown', {key: '${event.key}'}));`;
+        default:
+          return `// Unknown action: ${event.type}`;
+      }
+    });
+
+    return commands.join('\\n');
+  }
+
+  generateTypeCode(event, mode) {
+    const value = event.value.replace(/'/g, "\\'").replace(/\n/g, '\\n');
+    
+    return `// ${mode === 'set' ? 'Set' : 'Type'} text
+const el = document.querySelector('${event.selector}');
+el.focus();
+el.click();
+
+// Check if contenteditable
+if (el.contentEditable === 'true' || el.getAttribute('contenteditable') === 'true') {
+  // Handle contenteditable element
+  ${mode === 'set' ? 'el.textContent = "";' : ''}
+  const selection = window.getSelection();
+  const range = document.createRange();
+  range.selectNodeContents(el);
+  range.collapse(${mode === 'set' ? 'true' : 'false'});
+  selection.removeAllRanges();
+  selection.addRange(range);
+  
+  // Type each character
+  '${value}'.split('').forEach(char => {
+    document.execCommand('insertText', false, char);
+    el.dispatchEvent(new InputEvent('input', {bubbles: true, inputType: 'insertText', data: char}));
+  });
+} else if (el.tagName === 'INPUT' || el.tagName === 'TEXTAREA') {
+  // Handle input/textarea
+  ${mode === 'set' ? `el.value = '${value}';` : `el.value += '${value}';`}
+  el.dispatchEvent(new Event('input', {bubbles: true}));
+  el.dispatchEvent(new Event('change', {bubbles: true}));
+}
+el.dispatchEvent(new Event('blur', {bubbles: true}));`;
+    }
+
+  generateC4AScript(events) {
+    const commands = events.map(event => {
+      switch (event.type) {
+        case 'CLICK':
+          return `CLICK \`${event.selector}\``;
+        case 'DOUBLE_CLICK':
+          return `DOUBLE_CLICK \`${event.selector}\``;
+        case 'RIGHT_CLICK':
+          return `RIGHT_CLICK \`${event.selector}\``;
+        case 'TYPE':
+          return `TYPE "${event.value}"`;
+        case 'SET':
+          return `SET \`${event.selector}\` "${event.value}"`;
+        case 'SCROLL':
+          if (event.isWindowScroll || event.selector === 'window') {
+            return `SCROLL ${event.direction} ${event.amount}`;
+          } else {
+            return `SCROLL \`${event.selector}\` ${event.direction} ${event.amount}`;
+          }
+        case 'WAIT':
+          if (event.waitType === 'time') {
+            return `WAIT ${event.value}`;
+          } else {
+            return `WAIT \`${event.value}\` 5`;
+          }
+        case 'KEYBOARD_SHORTCUT':
+          return `# Keyboard shortcut: ${this.getEventDetail(event)}\nKEY "${this.getEventDetail(event)}"`;
+        case 'KEY_PRESS':
+          return `KEY "${event.key}"`;
+        default:
+          return `# Unknown: ${event.type}`;
+      }
+    });
+
+    return commands.join('\\n');
+  }
+
+  async replayRecording() {
+    if (this.groupedEvents.length === 0) {
+      alert('No actions to replay');
+      return;
+    }
+
+    // Create debugger window instead of simple overlay
+    this.createDebuggerWindow();
+  }
+
+  createReplayOverlay() {
+    // Create visual indicator for current action
+    this.replayIndicator = document.createElement('div');
+    this.replayIndicator.className = 'c4ai-replay-indicator';
+    this.replayIndicator.innerHTML = `
+      <div class="c4ai-replay-content">
+        <div class="c4ai-replay-header">
+          <span class="c4ai-replay-icon">▶️</span>
+          <span class="c4ai-replay-title">Replaying Actions</span>
+        </div>
+        <div class="c4ai-replay-progress">
+          <div class="c4ai-replay-progress-bar" id="c4ai-replay-progress"></div>
+        </div>
+        <div class="c4ai-replay-status" id="c4ai-replay-status">
+          Preparing...
+        </div>
+        <button class="c4ai-replay-stop" id="c4ai-replay-stop">Stop Replay</button>
+      </div>
+    `;
+    document.body.appendChild(this.replayIndicator);
+    
+    // Create highlight overlay for showing where actions happen
+    this.replayHighlight = document.createElement('div');
+    this.replayHighlight.className = 'c4ai-replay-highlight';
+    document.body.appendChild(this.replayHighlight);
+    
+    // Add stop button listener
+    document.getElementById('c4ai-replay-stop').addEventListener('click', () => {
+      this.stopReplay();
+    });
+  }
+
+  async executeReplaySequence() {
+    const events = this.groupedEvents;
+    const totalEvents = events.length;
+    
+    for (let i = 0; i < totalEvents; i++) {
+      if (!this.isReplaying) break;
+      
+      this.replayIndex = i;
+      const event = events[i];
+      const progress = ((i + 1) / totalEvents) * 100;
+      
+      // Update progress
+      document.getElementById('c4ai-replay-progress').style.width = `${progress}%`;
+      document.getElementById('c4ai-replay-status').textContent = 
+        `Action ${i + 1}/${totalEvents}: ${this.getReplayActionDescription(event)}`;
+      
+      // Execute the action with visual feedback
+      await this.executeReplayAction(event);
+      
+      // Wait between actions (except for the last one)
+      if (i < totalEvents - 1) {
+        const nextEvent = events[i + 1];
+        const waitTime = this.calculateWaitTime(event, nextEvent);
+        await this.wait(waitTime);
+      }
+    }
+    
+    // Replay complete
+    this.stopReplay(true);
+  }
+
+  async executeReplayAction(event) {
+    switch (event.type) {
+      case 'CLICK':
+      case 'DOUBLE_CLICK':
+      case 'RIGHT_CLICK':
+        await this.replayClickAction(event);
+        break;
+      case 'TYPE':
+      case 'SET':
+        await this.replayTypeAction(event);
+        break;
+      case 'SCROLL':
+        await this.replayScrollAction(event);
+        break;
+      case 'WAIT':
+        await this.replayWaitAction(event);
+        break;
+      case 'KEYBOARD_SHORTCUT':
+        await this.replayKeyboardShortcut(event);
+        break;
+      case 'KEY_PRESS':
+        await this.replayKeyPress(event);
+        break;
+    }
+  }
+
+  async replayClickAction(event) {
+    const element = document.querySelector(event.selector);
+    if (element) {
+      // Highlight the element
+      this.highlightReplayElement(element);
+      
+      // Show click animation
+      this.showClickAnimation(element);
+      
+      // Wait a bit for visual effect
+      await this.wait(300);
+    } else {
+      console.warn(`Element not found for selector: ${event.selector}`);
+    }
+  }
+
+  async replayTypeAction(event) {
+    const element = document.querySelector(event.selector);
+    if (element) {
+      // Highlight the input field
+      this.highlightReplayElement(element);
+      
+      // Show typing animation
+      const originalValue = element.value || '';
+      
+      if (event.type === 'SET') {
+        // Clear and set new value with animation
+        element.value = '';
+        await this.wait(200);
+        
+        // Type character by character
+        for (let i = 0; i < event.value.length; i++) {
+          element.value = event.value.substring(0, i + 1);
+          await this.wait(50); // Typing speed
+        }
+      } else {
+        // Append text with typing animation
+        const startLength = originalValue.length;
+        for (let i = 0; i < event.value.length; i++) {
+          element.value = originalValue + event.value.substring(0, i + 1);
+          await this.wait(50);
+        }
+      }
+    }
+  }
+
+  async replayScrollAction(event) {
+    const scrollAmount = event.direction === 'DOWN' ? event.amount : -event.amount;
+    
+    // Animate scroll
+    const startY = window.scrollY;
+    const targetY = startY + scrollAmount;
+    const duration = 500; // ms
+    const startTime = Date.now();
+    
+    const animateScroll = () => {
+      const elapsed = Date.now() - startTime;
+      const progress = Math.min(elapsed / duration, 1);
+      const easeProgress = this.easeInOut(progress);
+      
+      window.scrollTo(0, startY + (targetY - startY) * easeProgress);
+      
+      if (progress < 1 && this.isReplaying) {
+        requestAnimationFrame(animateScroll);
+      }
+    };
+    
+    animateScroll();
+    await this.wait(duration);
+  }
+
+  async replayWaitAction(event) {
+    if (event.waitType === 'time') {
+      document.getElementById('c4ai-replay-status').textContent = 
+        `Waiting ${event.value} seconds...`;
+      await this.wait(event.value * 1000);
+    } else {
+      // Wait for element
+      document.getElementById('c4ai-replay-status').textContent = 
+        `Waiting for element: ${event.value}`;
+      await this.waitForElement(event.value, 5000); // 5 second timeout
+    }
+  }
+
+  async replayKeyboardShortcut(event) {
+    const keys = [];
+    if (event.metaKey) keys.push('Cmd');
+    if (event.ctrlKey) keys.push('Ctrl');
+    if (event.altKey) keys.push('Alt');
+    if (event.shiftKey) keys.push('Shift');
+    keys.push(event.key.toUpperCase());
+    
+    // Show keyboard shortcut overlay
+    this.showKeyboardOverlay(keys.join('+'));
+    await this.wait(1000);
+  }
+
+  async replayKeyPress(event) {
+    this.showKeyboardOverlay(event.key);
+    await this.wait(500);
+  }
+
+  highlightReplayElement(element) {
+    const rect = element.getBoundingClientRect();
+    this.replayHighlight.style.display = 'block';
+    this.replayHighlight.style.top = `${rect.top + window.scrollY}px`;
+    this.replayHighlight.style.left = `${rect.left + window.scrollX}px`;
+    this.replayHighlight.style.width = `${rect.width}px`;
+    this.replayHighlight.style.height = `${rect.height}px`;
+    
+    // Fade out after a moment
+    setTimeout(() => {
+      this.replayHighlight.style.display = 'none';
+    }, 800);
+  }
+
+  showClickAnimation(element) {
+    const rect = element.getBoundingClientRect();
+    const centerX = rect.left + rect.width / 2;
+    const centerY = rect.top + rect.height / 2;
+    
+    const clickIndicator = document.createElement('div');
+    clickIndicator.className = 'c4ai-click-indicator';
+    clickIndicator.style.left = `${centerX}px`;
+    clickIndicator.style.top = `${centerY}px`;
+    document.body.appendChild(clickIndicator);
+    
+    // Remove after animation
+    setTimeout(() => clickIndicator.remove(), 600);
+  }
+
+  showKeyboardOverlay(keys) {
+    const overlay = document.createElement('div');
+    overlay.className = 'c4ai-keyboard-overlay';
+    overlay.textContent = keys;
+    document.body.appendChild(overlay);
+    
+    // Fade in
+    setTimeout(() => overlay.classList.add('visible'), 10);
+    
+    // Fade out and remove
+    setTimeout(() => {
+      overlay.classList.remove('visible');
+      setTimeout(() => overlay.remove(), 300);
+    }, 700);
+  }
+
+  getReplayActionDescription(event) {
+    switch (event.type) {
+      case 'CLICK': return `Click on ${event.selector}`;
+      case 'DOUBLE_CLICK': return `Double-click on ${event.selector}`;
+      case 'RIGHT_CLICK': return `Right-click on ${event.selector}`;
+      case 'TYPE': return `Type "${event.value.substring(0, 20)}..."`;
+      case 'SET': return `Set value in ${event.selector}`;
+      case 'SCROLL': 
+        if (event.isWindowScroll || event.selector === 'window') {
+          return `Scroll ${event.direction} ${event.amount}px`;
+        } else {
+          return `Scroll ${event.selector} ${event.direction} ${event.amount}px`;
+        }
+      case 'WAIT': return event.waitType === 'time' ? 
+        `Wait ${event.value}s` : `Wait for ${event.value}`;
+      case 'KEYBOARD_SHORTCUT': return `Press ${this.getEventDetail(event)}`;
+      case 'KEY_PRESS': return `Press ${event.key}`;
+      default: return event.type;
+    }
+  }
+
+  calculateWaitTime(currentEvent, nextEvent) {
+    // Use timing from recording if available
+    if (nextEvent.time && currentEvent.time) {
+      const timeDiff = (nextEvent.time - currentEvent.time) * 1000; // Convert to ms
+      // Cap wait time between 100ms and 2000ms for better replay experience
+      return Math.min(Math.max(timeDiff, 100), 2000);
+    }
+    return 500; // Default wait time
+  }
+
+  async wait(ms) {
+    return new Promise(resolve => setTimeout(resolve, ms));
+  }
+
+  async waitForElement(selector, timeout = 5000) {
+    const startTime = Date.now();
+    while (Date.now() - startTime < timeout) {
+      const element = document.querySelector(selector);
+      if (element) return element;
+      await this.wait(100);
+    }
+    return null;
+  }
+
+  easeInOut(t) {
+    return t < 0.5 ? 2 * t * t : -1 + (4 - 2 * t) * t;
+  }
+
+  stopReplay(completed = false) {
+    this.isReplaying = false;
+    
+    // Remove replay UI
+    this.replayIndicator?.remove();
+    this.replayHighlight?.remove();
+    
+    // Update toolbar
+    if (completed) {
+      document.getElementById('c4ai-script-hint').textContent = '✅ Replay completed!';
+    } else {
+      document.getElementById('c4ai-script-hint').textContent = '⏹️ Replay stopped';
+    }
+    
+    // Re-enable replay button
+    const replayBtn = document.getElementById('c4ai-replay');
+    if (replayBtn) {
+      replayBtn.disabled = false;
+      replayBtn.innerHTML = '<span>▶</span> Replay';
+    }
+  }
+
+  createDebuggerWindow() {
+    // Create debugger modal
+    this.debuggerModal = document.createElement('div');
+    this.debuggerModal.className = 'c4ai-debugger-modal';
+    
+    // Set initial position for dragging
+    this.debuggerModal.style.position = 'fixed';
+    this.debuggerModal.style.top = '50px';
+    this.debuggerModal.style.right = '20px';
+    
+    this.debuggerModal.innerHTML = `
+      <div class="c4ai-debugger-content">
+        <div class="c4ai-debugger-header">
+          <h2>Action Debugger <span style="color: #ff3c74; font-size: 12px;">(ALPHA)</span></h2>
+          <button class="c4ai-close-modal" id="c4ai-close-debugger">✕</button>
+        </div>
+        <div class="c4ai-debugger-body">
+          <div class="c4ai-debugger-controls">
+            <button class="c4ai-debug-btn c4ai-run-btn" id="c4ai-debug-run" title="Run to end or next breakpoint">
+              <span>▶</span> Run
+            </button>
+            <button class="c4ai-debug-btn c4ai-step-btn" id="c4ai-debug-step" title="Execute next action">
+              <span>⏭</span> Step
+            </button>
+            <button class="c4ai-debug-btn c4ai-pause-btn" id="c4ai-debug-pause" disabled title="Pause execution">
+              <span>⏸</span> Pause
+            </button>
+            <button class="c4ai-debug-btn c4ai-stop-btn" id="c4ai-debug-stop" title="Stop debugging">
+              <span>⏹</span> Stop
+            </button>
+            <button class="c4ai-debug-btn c4ai-restart-btn" id="c4ai-debug-restart" title="Restart from beginning">
+              <span>🔄</span> Restart
+            </button>
+          </div>
+          <div class="c4ai-debugger-status">
+            <span class="c4ai-debug-label">Status:</span>
+            <span class="c4ai-debug-value" id="c4ai-debug-status">Ready</span>
+          </div>
+          <div class="c4ai-debugger-actions" id="c4ai-debugger-actions">
+            ${this.renderDebuggerActions()}
+          </div>
+        </div>
+      </div>
+    `;
+    
+    document.body.appendChild(this.debuggerModal);
+    
+    // Initialize debugger state
+    this.debuggerState = {
+      currentIndex: 0,
+      isRunning: false,
+      isPaused: false,
+      breakpoints: new Set(),
+      editedEvents: [...this.groupedEvents] // Copy for editing
+    };
+    
+    // Create replay highlight element
+    this.replayHighlight = document.createElement('div');
+    this.replayHighlight.className = 'c4ai-replay-highlight';
+    document.body.appendChild(this.replayHighlight);
+    
+    // Add event listeners
+    this.attachDebuggerListeners();
+    
+    // Make debugger draggable by the header
+    makeDraggableByHeader(this.debuggerModal);
+  }
+
+  renderDebuggerActions() {
+    const events = this.debuggerState ? this.debuggerState.editedEvents : this.groupedEvents;
+    return events.map((event, index) => {
+      const detail = this.getEventDetail(event);
+      return `
+        <div class="c4ai-debug-action ${index === 0 ? 'c4ai-current' : ''}" data-index="${index}">
+          <div class="c4ai-debug-action-left">
+            <input type="checkbox" class="c4ai-breakpoint-checkbox" data-index="${index}" title="Toggle breakpoint">
+            <span class="c4ai-action-number">${index + 1}</span>
+            <span class="c4ai-action-indicator">➤</span>
+          </div>
+          <div class="c4ai-debug-action-content">
+            <div class="c4ai-action-type">${event.type.replace(/_/g, ' ')}</div>
+            <div class="c4ai-action-detail" contenteditable="true" data-index="${index}" data-field="detail">
+              ${detail}
+            </div>
+          </div>
+          <div class="c4ai-debug-action-right">
+            <button class="c4ai-action-play" data-index="${index}" title="Execute this action">▶</button>
+            <button class="c4ai-action-edit" data-index="${index}" title="Edit action">✏️</button>
+            <button class="c4ai-action-delete" data-index="${index}" title="Delete action">🗑</button>
+          </div>
+        </div>
+      `;
+    }).join('');
+  }
+
+  attachDebuggerListeners() {
+    // Control buttons
+    document.getElementById('c4ai-debug-run').addEventListener('click', () => this.debugRun());
+    document.getElementById('c4ai-debug-step').addEventListener('click', () => this.debugStep());
+    document.getElementById('c4ai-debug-pause').addEventListener('click', () => this.debugPause());
+    document.getElementById('c4ai-debug-stop').addEventListener('click', () => this.debugStop());
+    document.getElementById('c4ai-debug-restart').addEventListener('click', () => this.debugRestart());
+    document.getElementById('c4ai-close-debugger').addEventListener('click', () => this.closeDebugger());
+    
+    // Breakpoint checkboxes
+    document.querySelectorAll('.c4ai-breakpoint-checkbox').forEach(cb => {
+      cb.addEventListener('change', (e) => {
+        const index = parseInt(e.target.dataset.index);
+        if (e.target.checked) {
+          this.debuggerState.breakpoints.add(index);
+        } else {
+          this.debuggerState.breakpoints.delete(index);
+        }
+        this.updateBreakpointVisual(index, e.target.checked);
+      });
+    });
+    
+    // Play buttons
+    document.querySelectorAll('.c4ai-action-play').forEach(btn => {
+      btn.addEventListener('click', (e) => {
+        const index = parseInt(e.target.dataset.index);
+        this.executeActionOnPage(this.debuggerState.editedEvents[index], index);
+      });
+    });
+    
+    // Delete buttons
+    document.querySelectorAll('.c4ai-action-delete').forEach(btn => {
+      btn.addEventListener('click', (e) => {
+        const index = parseInt(e.target.dataset.index);
+        this.deleteDebugAction(index);
+      });
+    });
+    
+    // Edit buttons
+    document.querySelectorAll('.c4ai-action-edit').forEach(btn => {
+      btn.addEventListener('click', (e) => {
+        const index = parseInt(e.target.dataset.index);
+        this.editDebugAction(index);
+      });
+    });
+    
+    // Inline editing
+    document.querySelectorAll('.c4ai-action-detail[contenteditable]').forEach(elem => {
+      elem.addEventListener('blur', (e) => {
+        const index = parseInt(e.target.dataset.index);
+        this.updateActionDetail(index, e.target.textContent);
+      });
+    });
+  }
+
+  async debugRun() {
+    this.debuggerState.isRunning = true;
+    this.debuggerState.isPaused = false;
+    this.updateDebugControls();
+    this.updateDebugStatus('Running...');
+    
+    const startIndex = this.debuggerState.currentIndex;
+    const events = this.debuggerState.editedEvents;
+    
+    for (let i = startIndex; i < events.length; i++) {
+      if (!this.debuggerState.isRunning || this.debuggerState.isPaused) break;
+      
+      // Update current action
+      this.setCurrentAction(i);
+      
+      // Execute the action
+      await this.executeDebugAction(events[i], i);
+      
+      // Check for breakpoint on next action
+      if (i < events.length - 1 && this.debuggerState.breakpoints.has(i + 1)) {
+        this.debuggerState.currentIndex = i + 1;
+        this.debugPause();
+        this.updateDebugStatus(`Stopped at breakpoint ${i + 2}`);
+        break;
+      }
+      
+      this.debuggerState.currentIndex = i + 1;
+    }
+    
+    if (this.debuggerState.currentIndex >= events.length) {
+      this.debugStop(true);
+    }
+  }
+
+  async debugStep() {
+    if (this.debuggerState.currentIndex >= this.debuggerState.editedEvents.length) {
+      this.updateDebugStatus('End of actions');
+      return;
+    }
+    
+    this.updateDebugStatus('Stepping...');
+    this.setCurrentAction(this.debuggerState.currentIndex);
+    
+    await this.executeDebugAction(
+      this.debuggerState.editedEvents[this.debuggerState.currentIndex],
+      this.debuggerState.currentIndex
+    );
+    
+    this.debuggerState.currentIndex++;
+    
+    if (this.debuggerState.currentIndex >= this.debuggerState.editedEvents.length) {
+      this.updateDebugStatus('Completed');
+    } else {
+      this.updateDebugStatus('Ready');
+    }
+  }
+
+  debugPause() {
+    this.debuggerState.isPaused = true;
+    this.debuggerState.isRunning = false;
+    this.updateDebugControls();
+    this.updateDebugStatus('Paused');
+  }
+
+  debugStop(completed = false) {
+    this.debuggerState.isRunning = false;
+    this.debuggerState.isPaused = false;
+    this.updateDebugControls();
+    
+    if (completed) {
+      this.updateDebugStatus('Completed');
+    } else {
+      this.updateDebugStatus('Stopped');
+    }
+  }
+
+  debugRestart() {
+    this.debuggerState.currentIndex = 0;
+    this.debuggerState.isRunning = false;
+    this.debuggerState.isPaused = false;
+    this.setCurrentAction(0);
+    this.updateDebugControls();
+    this.updateDebugStatus('Ready');
+  }
+
+  closeDebugger() {
+    this.debuggerModal?.remove();
+    this.replayHighlight?.remove();
+    this.debuggerState = null;
+  }
+
+  setCurrentAction(index) {
+    // Remove previous current marker
+    document.querySelectorAll('.c4ai-debug-action').forEach(elem => {
+      elem.classList.remove('c4ai-current');
+    });
+    
+    // Add current marker
+    const currentElem = document.querySelector(`.c4ai-debug-action[data-index="${index}"]`);
+    if (currentElem) {
+      currentElem.classList.add('c4ai-current');
+      
+      // Better scrolling into view
+      const scrollContainer = document.querySelector('.c4ai-debugger-actions');
+      if (scrollContainer) {
+        const containerRect = scrollContainer.getBoundingClientRect();
+        const elemRect = currentElem.getBoundingClientRect();
+        
+        // Check if element is out of view
+        if (elemRect.top < containerRect.top || elemRect.bottom > containerRect.bottom) {
+          // Scroll to center the element
+          const scrollTop = currentElem.offsetTop - scrollContainer.offsetTop - (scrollContainer.clientHeight / 2) + (currentElem.clientHeight / 2);
+          scrollContainer.scrollTo({
+            top: scrollTop,
+            behavior: 'smooth'
+          });
+        }
+      }
+    }
+    
+    this.debuggerState.currentIndex = index;
+  }
+
+  updateDebugControls() {
+    const runBtn = document.getElementById('c4ai-debug-run');
+    const stepBtn = document.getElementById('c4ai-debug-step');
+    const pauseBtn = document.getElementById('c4ai-debug-pause');
+    
+    if (this.debuggerState.isRunning) {
+      runBtn.disabled = true;
+      stepBtn.disabled = true;
+      pauseBtn.disabled = false;
+    } else {
+      runBtn.disabled = false;
+      stepBtn.disabled = false;
+      pauseBtn.disabled = true;
+    }
+  }
+
+  updateDebugStatus(status) {
+    document.getElementById('c4ai-debug-status').textContent = status;
+  }
+
+  updateBreakpointVisual(index, hasBreakpoint) {
+    const actionElem = document.querySelector(`.c4ai-debug-action[data-index="${index}"]`);
+    if (actionElem) {
+      if (hasBreakpoint) {
+        actionElem.classList.add('has-breakpoint');
+      } else {
+        actionElem.classList.remove('has-breakpoint');
+      }
+    }
+  }
+
+  deleteDebugAction(index) {
+    if (confirm('Delete this action?')) {
+      this.debuggerState.editedEvents.splice(index, 1);
+      this.debuggerState.breakpoints = new Set(
+        [...this.debuggerState.breakpoints]
+          .filter(bp => bp !== index)
+          .map(bp => bp > index ? bp - 1 : bp)
+      );
+      
+      // Re-render actions
+      document.getElementById('c4ai-debugger-actions').innerHTML = this.renderDebuggerActions();
+      this.attachDebuggerListeners();
+      
+      // Adjust current index if needed
+      if (this.debuggerState.currentIndex > index) {
+        this.debuggerState.currentIndex--;
+      }
+      this.setCurrentAction(Math.min(this.debuggerState.currentIndex, this.debuggerState.editedEvents.length - 1));
+    }
+  }
+
+  editDebugAction(index) {
+    const event = this.debuggerState.editedEvents[index];
+    const dialog = document.createElement('div');
+    dialog.className = 'c4ai-edit-dialog';
+    dialog.innerHTML = `
+      <div class="c4ai-edit-content">
+        <h3>Edit Action</h3>
+        <div class="c4ai-edit-field">
+          <label>Type:</label>
+          <select id="c4ai-edit-type">
+            <option value="CLICK" ${event.type === 'CLICK' ? 'selected' : ''}>Click</option>
+            <option value="TYPE" ${event.type === 'TYPE' ? 'selected' : ''}>Type</option>
+            <option value="SET" ${event.type === 'SET' ? 'selected' : ''}>Set</option>
+            <option value="SCROLL" ${event.type === 'SCROLL' ? 'selected' : ''}>Scroll</option>
+            <option value="WAIT" ${event.type === 'WAIT' ? 'selected' : ''}>Wait</option>
+          </select>
+        </div>
+        <div class="c4ai-edit-field">
+          <label>Selector/Value:</label>
+          <input type="text" id="c4ai-edit-value" value="${event.selector || event.value || ''}">
+        </div>
+        <div class="c4ai-edit-actions">
+          <button id="c4ai-edit-save">Save</button>
+          <button id="c4ai-edit-cancel">Cancel</button>
+        </div>
+      </div>
+    `;
+    
+    document.body.appendChild(dialog);
+    
+    document.getElementById('c4ai-edit-save').addEventListener('click', () => {
+      const newType = document.getElementById('c4ai-edit-type').value;
+      const newValue = document.getElementById('c4ai-edit-value').value;
+      
+      // Update event based on type
+      this.debuggerState.editedEvents[index] = {
+        ...event,
+        type: newType,
+        selector: ['CLICK', 'TYPE', 'SET'].includes(newType) ? newValue : event.selector,
+        value: ['TYPE', 'SET', 'WAIT'].includes(newType) ? newValue : event.value
+      };
+      
+      // Re-render
+      document.getElementById('c4ai-debugger-actions').innerHTML = this.renderDebuggerActions();
+      this.attachDebuggerListeners();
+      dialog.remove();
+    });
+    
+    document.getElementById('c4ai-edit-cancel').addEventListener('click', () => {
+      dialog.remove();
+    });
+  }
+
+  async executeDebugAction(event, index) {
+    this.updateDebugStatus(`Executing: ${this.getEventDetail(event)}`);
+    
+    // Execute the action on the page
+    await this.executeActionOnPage(event, index);
+    
+    // Add small delay between actions for visibility
+    await this.wait(300);
+  }
+
+  async executeActionOnPage(event, index) {
+    try {
+      // Set current action for visual feedback
+      this.setCurrentAction(index);
+      
+      switch (event.type) {
+        case 'CLICK':
+        case 'DOUBLE_CLICK':
+        case 'RIGHT_CLICK':
+          await this.executeClick(event);
+          break;
+        case 'TYPE':
+        case 'SET':
+          await this.executeType(event);
+          break;
+        case 'SCROLL':
+          await this.executeScroll(event);
+          break;
+        case 'WAIT':
+          await this.executeWait(event);
+          break;
+        case 'KEYBOARD_SHORTCUT':
+          await this.executeKeyboardShortcut(event);
+          break;
+        case 'KEY_PRESS':
+          await this.executeKeyPress(event);
+          break;
+      }
+      
+      this.updateDebugStatus(`Executed: ${this.getEventDetail(event)}`);
+    } catch (error) {
+      console.error('Error executing action:', error);
+      this.updateDebugStatus(`Error: ${error.message}`);
+    }
+  }
+
+  async executeClick(event) {
+    const element = document.querySelector(event.selector);
+    if (!element) {
+      throw new Error(`Element not found: ${event.selector}`);
+    }
+    
+    // Highlight element briefly
+    this.highlightReplayElement(element);
+    
+    // Create and dispatch the appropriate mouse event
+    const eventType = {
+      'CLICK': 'click',
+      'DOUBLE_CLICK': 'dblclick',
+      'RIGHT_CLICK': 'contextmenu'
+    }[event.type];
+    
+    const mouseEvent = new MouseEvent(eventType, {
+      view: window,
+      bubbles: true,
+      cancelable: true,
+      buttons: event.type === 'RIGHT_CLICK' ? 2 : 1
+    });
+    
+    element.dispatchEvent(mouseEvent);
+    
+    // Also try clicking for better compatibility
+    if (event.type === 'CLICK' && typeof element.click === 'function') {
+      element.click();
+    }
+  }
+
+  async executeType(event) {
+    const element = document.querySelector(event.selector);
+    if (!element) {
+      throw new Error(`Element not found: ${event.selector}`);
+    }
+    
+    // Highlight element
+    this.highlightReplayElement(element);
+    
+    // Make sure element is visible and focused
+    element.scrollIntoView({ behavior: 'smooth', block: 'center' });
+    await this.wait(100);
+    
+    // Focus the element
+    element.focus();
+    element.click(); // Some inputs need a click to properly focus
+    
+    // Check if it's a contenteditable element
+    const isContentEditable = element.contentEditable === 'true' || 
+                             element.getAttribute('contenteditable') === 'true' ||
+                             element.closest('[contenteditable="true"]');
+    
+    if (isContentEditable) {
+      // Handle contenteditable elements
+      await this.typeInContentEditable(element, event);
+    } else if (element.tagName === 'INPUT' || element.tagName === 'TEXTAREA') {
+      // Handle regular input/textarea elements
+      await this.typeInInput(element, event);
+    } else {
+      // Try contenteditable approach for other elements
+      await this.typeInContentEditable(element, event);
+    }
+  }
+
+  async typeInInput(element, event) {
+    if (event.type === 'SET') {
+      // Clear and set value
+      element.value = '';
+      
+      // Dispatch events
+      element.dispatchEvent(new Event('focus', { bubbles: true }));
+      element.dispatchEvent(new Event('input', { bubbles: true }));
+      
+      // Type character by character for realism
+      for (let i = 0; i < event.value.length; i++) {
+        element.value = event.value.substring(0, i + 1);
+        
+        // Dispatch multiple events for better compatibility
+        element.dispatchEvent(new Event('input', { bubbles: true }));
+        element.dispatchEvent(new InputEvent('input', { 
+          bubbles: true,
+          cancelable: true,
+          inputType: 'insertText',
+          data: event.value[i]
+        }));
+        
+        await this.wait(30);
+      }
+    } else {
+      // TYPE - append to existing value
+      const startValue = element.value || '';
+      for (let i = 0; i < event.value.length; i++) {
+        element.value = startValue + event.value.substring(0, i + 1);
+        
+        // Dispatch multiple events
+        element.dispatchEvent(new Event('input', { bubbles: true }));
+        element.dispatchEvent(new InputEvent('input', { 
+          bubbles: true,
+          cancelable: true,
+          inputType: 'insertText',
+          data: event.value[i]
+        }));
+        
+        await this.wait(30);
+      }
+    }
+    
+    // Dispatch change and blur events
+    element.dispatchEvent(new Event('change', { bubbles: true }));
+    element.dispatchEvent(new Event('blur', { bubbles: true }));
+  }
+
+  async typeInContentEditable(element, event) {
+    // Focus and prepare the element
+    element.focus();
+    
+    // Create a range and selection
+    const selection = window.getSelection();
+    const range = document.createRange();
+    
+    if (event.type === 'SET') {
+      // Clear existing content
+      element.textContent = '';
+      
+      // Set cursor at the beginning
+      range.selectNodeContents(element);
+      range.collapse(true);
+      selection.removeAllRanges();
+      selection.addRange(range);
+    } else {
+      // Move cursor to the end for TYPE
+      range.selectNodeContents(element);
+      range.collapse(false);
+      selection.removeAllRanges();
+      selection.addRange(range);
+    }
+    
+    // Type each character using keyboard events
+    for (let i = 0; i < event.value.length; i++) {
+      const char = event.value[i];
+      
+      // Dispatch keydown
+      const keydownEvent = new KeyboardEvent('keydown', {
+        key: char,
+        char: char,
+        keyCode: char.charCodeAt(0),
+        which: char.charCodeAt(0),
+        bubbles: true,
+        cancelable: true
+      });
+      element.dispatchEvent(keydownEvent);
+      
+      // Insert the character
+      if (!keydownEvent.defaultPrevented) {
+        document.execCommand('insertText', false, char);
+      }
+      
+      // Dispatch keyup
+      const keyupEvent = new KeyboardEvent('keyup', {
+        key: char,
+        char: char,
+        keyCode: char.charCodeAt(0),
+        which: char.charCodeAt(0),
+        bubbles: true,
+        cancelable: true
+      });
+      element.dispatchEvent(keyupEvent);
+      
+      // Dispatch input event
+      element.dispatchEvent(new InputEvent('input', {
+        bubbles: true,
+        cancelable: true,
+        inputType: 'insertText',
+        data: char
+      }));
+      
+      await this.wait(30);
+    }
+    
+    // Dispatch blur event
+    element.dispatchEvent(new Event('blur', { bubbles: true }));
+  }
+
+  async executeScroll(event) {
+    const scrollAmount = event.direction === 'DOWN' ? event.amount : -event.amount;
+    const duration = 300;
+    const startTime = Date.now();
+    
+    if (event.isWindowScroll || event.selector === 'window') {
+      // Window scroll
+      const startY = window.scrollY;
+      const targetY = startY + scrollAmount;
+      
+      const animateScroll = () => {
+        const elapsed = Date.now() - startTime;
+        const progress = Math.min(elapsed / duration, 1);
+        const easeProgress = this.easeInOut(progress);
+        
+        window.scrollTo(0, startY + (targetY - startY) * easeProgress);
+        
+        if (progress < 1) {
+          requestAnimationFrame(animateScroll);
+        }
+      };
+      
+      animateScroll();
+    } else {
+      // Element scroll
+      const element = document.querySelector(event.selector);
+      if (!element) {
+        throw new Error(`Scrollable element not found: ${event.selector}`);
+      }
+      
+      // Highlight the scrollable element
+      this.highlightReplayElement(element);
+      
+      const startY = element.scrollTop;
+      const targetY = startY + scrollAmount;
+      
+      const animateScroll = () => {
+        const elapsed = Date.now() - startTime;
+        const progress = Math.min(elapsed / duration, 1);
+        const easeProgress = this.easeInOut(progress);
+        
+        element.scrollTop = startY + (targetY - startY) * easeProgress;
+        
+        if (progress < 1) {
+          requestAnimationFrame(animateScroll);
+        }
+      };
+      
+      animateScroll();
+    }
+    
+    await this.wait(duration);
+  }
+
+  async executeWait(event) {
+    if (event.waitType === 'time') {
+      await this.wait(event.value * 1000);
+    } else {
+      // Wait for element to appear
+      const timeout = 5000;
+      const startTime = Date.now();
+      
+      while (Date.now() - startTime < timeout) {
+        const element = document.querySelector(event.value);
+        if (element) {
+          this.highlightReplayElement(element);
+          return;
+        }
+        await this.wait(100);
+      }
+      
+      throw new Error(`Element not found after ${timeout}ms: ${event.value}`);
+    }
+  }
+
+  async executeKeyboardShortcut(event) {
+    // Show keyboard overlay
+    this.showKeyboardOverlay(this.getEventDetail(event));
+    
+    // Create and dispatch keyboard event
+    const keyEvent = new KeyboardEvent('keydown', {
+      key: event.key,
+      code: event.code,
+      ctrlKey: event.ctrlKey,
+      metaKey: event.metaKey,
+      altKey: event.altKey,
+      shiftKey: event.shiftKey,
+      bubbles: true,
+      cancelable: true
+    });
+    
+    document.activeElement.dispatchEvent(keyEvent);
+    
+    // Some shortcuts need keyup as well
+    const keyUpEvent = new KeyboardEvent('keyup', {
+      key: event.key,
+      code: event.code,
+      ctrlKey: event.ctrlKey,
+      metaKey: event.metaKey,
+      altKey: event.altKey,
+      shiftKey: event.shiftKey,
+      bubbles: true,
+      cancelable: true
+    });
+    
+    document.activeElement.dispatchEvent(keyUpEvent);
+    
+    await this.wait(500);
+  }
+
+  async executeKeyPress(event) {
+    this.showKeyboardOverlay(event.key);
+    
+    const activeElement = document.activeElement;
+    
+    if (event.key === 'Delete' || event.key === 'Backspace') {
+      // Handle delete/backspace on input elements
+      if (activeElement.tagName === 'INPUT' || activeElement.tagName === 'TEXTAREA') {
+        const start = activeElement.selectionStart;
+        const end = activeElement.selectionEnd;
+        
+        if (start !== end) {
+          // Delete selection
+          activeElement.value = activeElement.value.substring(0, start) + activeElement.value.substring(end);
+          activeElement.selectionStart = activeElement.selectionEnd = start;
+        } else if (event.key === 'Backspace' && start > 0) {
+          // Delete character before cursor
+          activeElement.value = activeElement.value.substring(0, start - 1) + activeElement.value.substring(start);
+          activeElement.selectionStart = activeElement.selectionEnd = start - 1;
+        } else if (event.key === 'Delete' && start < activeElement.value.length) {
+          // Delete character after cursor
+          activeElement.value = activeElement.value.substring(0, start) + activeElement.value.substring(start + 1);
+        }
+        
+        activeElement.dispatchEvent(new Event('input', { bubbles: true }));
+      }
+    } else {
+      // Regular key press
+      const keyEvent = new KeyboardEvent('keydown', {
+        key: event.key,
+        bubbles: true,
+        cancelable: true
+      });
+      
+      activeElement.dispatchEvent(keyEvent);
+    }
+    
+    await this.wait(300);
+  }
+
+  async saveFlow() {
+    // Get flow name from user
+    const flowName = prompt('Enter a name for this flow:');
+    if (!flowName || !flowName.trim()) {
+      return;
+    }
+
+    // Get current domain
+    const domain = window.location.hostname;
+    
+    // Create flow object
+    const flow = {
+      id: Date.now().toString(),
+      name: flowName.trim(),
+      domain: domain,
+      url: window.location.href,
+      events: this.groupedEvents,
+      createdAt: new Date().toISOString(),
+      outputFormat: this.outputFormat
+    };
+
+    // Get existing flows for this domain
+    const storageKey = `c4ai_flows_${domain}`;
+    chrome.storage.local.get(storageKey, (result) => {
+      const flows = result[storageKey] || [];
+      flows.push(flow);
+      
+      // Save updated flows
+      chrome.storage.local.set({ [storageKey]: flows }, () => {
+        if (chrome.runtime.lastError) {
+          alert('Failed to save flow: ' + chrome.runtime.lastError.message);
+        } else {
+          alert(`Flow "${flowName}" saved successfully!`);
+          // Update hint text
+          document.getElementById('c4ai-script-hint').textContent = `✅ Flow "${flowName}" saved!`;
+        }
+      });
+    });
+  }
+
+  showSavedFlows() {
+    const domain = window.location.hostname;
+    const storageKey = `c4ai_flows_${domain}`;
+    
+    chrome.storage.local.get(storageKey, (result) => {
+      const flows = result[storageKey] || [];
+      
+      // Create modal
+      const modal = document.createElement('div');
+      modal.className = 'c4ai-saved-flows-modal';
+      modal.innerHTML = `
+        <div class="c4ai-saved-flows-content">
+          <div class="c4ai-saved-flows-header">
+            <h2>Saved Flows for ${domain}</h2>
+            <button class="c4ai-close-modal" id="c4ai-close-flows">✕</button>
+          </div>
+          <div class="c4ai-saved-flows-body">
+            ${flows.length === 0 ? 
+              '<p class="c4ai-no-flows">No saved flows for this domain yet. Record and save your first flow!</p>' :
+              `<div class="c4ai-flows-list">
+                ${flows.map(flow => `
+                  <div class="c4ai-flow-item" data-flow-id="${flow.id}">
+                    <div class="c4ai-flow-info">
+                      <h3>${flow.name}</h3>
+                      <p class="c4ai-flow-meta">
+                        <span>${flow.events.length} actions</span>
+                        <span>•</span>
+                        <span>${new Date(flow.createdAt).toLocaleDateString()}</span>
+                      </p>
+                    </div>
+                    <div class="c4ai-flow-actions">
+                      <button class="c4ai-action-btn c4ai-load-flow-btn" data-flow-id="${flow.id}">
+                        <span>▶</span> Load
+                      </button>
+                      <button class="c4ai-action-btn c4ai-delete-flow-btn" data-flow-id="${flow.id}">
+                        <span>🗑</span>
+                      </button>
+                    </div>
+                  </div>
+                `).join('')}
+              </div>`
+            }
+          </div>
+        </div>
+      `;
+      
+      document.body.appendChild(modal);
+      
+      // Add event listeners
+      document.getElementById('c4ai-close-flows').addEventListener('click', () => {
+        modal.remove();
+      });
+      
+      // Load flow buttons
+      modal.querySelectorAll('.c4ai-load-flow-btn').forEach(btn => {
+        btn.addEventListener('click', (e) => {
+          const flowId = e.target.closest('button').dataset.flowId;
+          const flow = flows.find(f => f.id === flowId);
+          if (flow) {
+            this.loadFlow(flow);
+            modal.remove();
+          }
+        });
+      });
+      
+      // Delete flow buttons
+      modal.querySelectorAll('.c4ai-delete-flow-btn').forEach(btn => {
+        btn.addEventListener('click', (e) => {
+          const flowId = e.target.closest('button').dataset.flowId;
+          if (confirm('Are you sure you want to delete this flow?')) {
+            this.deleteFlow(flowId);
+            modal.remove();
+            this.showSavedFlows(); // Refresh the modal
+          }
+        });
+      });
+    });
+  }
+
+  loadFlow(flow) {
+    // Stop any current recording
+    this.isRecording = false;
+    this.removeEventCapture();
+    
+    // Load the flow's events
+    this.groupedEvents = [...flow.events];
+    this.outputFormat = flow.outputFormat || 'js';
+    
+    // Show the recording summary UI
+    this.showRecordingSummary();
+    
+    // Update hint
+    document.getElementById('c4ai-script-hint').textContent = `Loaded flow: "${flow.name}"`;
+  }
+
+  deleteFlow(flowId) {
+    const domain = window.location.hostname;
+    const storageKey = `c4ai_flows_${domain}`;
+    
+    chrome.storage.local.get(storageKey, (result) => {
+      const flows = result[storageKey] || [];
+      const updatedFlows = flows.filter(f => f.id !== flowId);
+      
+      chrome.storage.local.set({ [storageKey]: updatedFlows }, () => {
+        if (chrome.runtime.lastError) {
+          alert('Failed to delete flow: ' + chrome.runtime.lastError.message);
+        }
+      });
+    });
+  }
+
+  recordAgain() {
+    // Show confirmation dialog
+    if (!confirm('Are you sure you want to start a new recording? This will clear the current recording.')) {
+      return;
+    }
+    
+    // Clear current recording and start fresh
+    this.groupedEvents = [];
+    this.rawEvents = [];
+    this.processedEventIndices.clear();
+    this.keyBuffer = [];
+    
+    // Don't create a new toolbar, just update the existing one
+    this.isRecording = true;
+    this.startTime = Date.now();
+    this.lastEventTime = this.startTime;
+    
+    // Reset toolbar content to recording state
+    const toolbarContent = this.toolbar.querySelector('.c4ai-toolbar-content');
+    toolbarContent.innerHTML = `
+      <div class="c4ai-toolbar-status">
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Actions:</span>
+          <span class="c4ai-status-value" id="c4ai-action-count">0</span>
+        </div>
+        <div class="c4ai-status-item">
+          <span class="c4ai-status-label">Format:</span>
+          <select id="c4ai-output-format" class="c4ai-format-select">
+            <option value="js">JavaScript</option>
+            <option value="c4a">C4A Script</option>
+          </select>
+        </div>
+      </div>
+      <div class="c4ai-toolbar-hint" id="c4ai-script-hint">
+        Recording your actions... Click, type, and scroll to build your script.
+      </div>
+      <div class="c4ai-toolbar-actions">
+        <button id="c4ai-saved-flows" class="c4ai-action-btn c4ai-flows-btn">
+          <span>📂</span> Saved Flows
+        </button>
+        <button id="c4ai-add-wait" class="c4ai-action-btn c4ai-wait-btn">
+          <span class="c4ai-wait-icon">⏱</span> Add Wait
+        </button>
+        <button id="c4ai-pause-recording" class="c4ai-action-btn c4ai-pause-btn">
+          <span class="c4ai-pause-icon">⏸</span> Pause
+        </button>
+        <button id="c4ai-stop-generate" class="c4ai-action-btn c4ai-generate-btn">
+          <span class="c4ai-generate-icon">⚡</span> Stop & Generate
+        </button>
+      </div>
+    `;
+    
+    // Re-add event listeners
+    document.getElementById('c4ai-pause-recording').addEventListener('click', () => this.togglePause());
+    document.getElementById('c4ai-stop-generate').addEventListener('click', () => this.stopAndGenerate());
+    document.getElementById('c4ai-add-wait').addEventListener('click', () => this.showWaitDialog());
+    document.getElementById('c4ai-saved-flows').addEventListener('click', () => this.showSavedFlows());
+    document.getElementById('c4ai-output-format').addEventListener('change', (e) => {
+      this.outputFormat = e.target.value;
+    });
+    
+    this.createRecordingIndicator();
+    this.updateToolbar();
+  }
+
+  generatePythonTemplate(scriptCode, format) {
+    const currentUrl = window.location.href;
+    const timestamp = new Date().toISOString();
+    
+    if (format === 'js') {
+      return `#!/usr/bin/env python3
+"""
+Generated by Crawl4AI Chrome Extension - Script Builder (ALPHA)
+URL: ${currentUrl}
+Generated: ${timestamp}
+"""
+
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+
+# JavaScript code to execute
+JS_SCRIPT = """
+${scriptCode}
+"""
+
+async def run_automation():
+    """Run the recorded automation script"""
+    
+    # Configure browser
+    browser_config = BrowserConfig(
+        headless=False,  # Set to True for headless mode
+        verbose=True
+    )
+    
+    # Configure crawler with JavaScript execution
+    crawler_config = CrawlerRunConfig(
+        js_code=JS_SCRIPT,
+        wait_for="js:() => document.readyState === 'complete'",
+        page_timeout=30000  # 30 seconds timeout
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url="${currentUrl}",
+            config=crawler_config
+        )
+        
+        if result.success:
+            print("✅ Automation completed successfully!")
+            print(f"Final URL: {result.url}")
+            # You can access the final HTML with result.html
+            # Or extracted content with result.cleaned_html
+        else:
+            print("❌ Automation failed:", result.error_message)
+
+if __name__ == "__main__":
+    asyncio.run(run_automation())
+`;
+    } else {
+      // C4A Script format
+      return `#!/usr/bin/env python3
+"""
+Generated by Crawl4AI Chrome Extension - Script Builder (ALPHA)
+URL: ${currentUrl}
+Generated: ${timestamp}
+"""
+
+import asyncio
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# C4A Script commands
+C4A_SCRIPT = """
+${scriptCode}
+"""
+
+# Save the C4A script for reference
+script_path = Path('automation_script.c4a')
+with open(script_path, 'w') as f:
+    f.write(C4A_SCRIPT)
+
+print(f"💾 C4A Script saved to: {script_path}")
+print("\\n📜 Generated C4A Script:")
+print(C4A_SCRIPT)
+
+# Note: To execute C4A scripts, you'll need to use the C4A Script compiler
+# Example:
+# from crawl4ai.script import C4ACompiler
+# compiler = C4ACompiler()
+# js_code = compiler.compile(C4A_SCRIPT)
+# 
+# Then use js_code in CrawlerRunConfig as shown in the JavaScript example above
+
+print("\\n💡 To execute this C4A script, compile it to JavaScript first!")
+`;
+    }
+  }
+}
+
+// Initialize
+let schemaBuilder = null;
+let scriptBuilder = null;
+
+// Listen for messages from popup
+chrome.runtime.onMessage.addListener((message, sender, sendResponse) => {
+  switch (message.action) {
+    case 'startSchemaCapture':
+      if (!schemaBuilder) {
+        schemaBuilder = new SchemaBuilder();
+      }
+      schemaBuilder.start();
+      sendResponse({ success: true });
+      break;
+
+    case 'startScriptCapture':
+      if (!scriptBuilder) {
+        scriptBuilder = new ScriptBuilder();
+      }
+      scriptBuilder.start();
+      sendResponse({ success: true });
+      break;
+      
+    case 'stopCapture':
+      if (schemaBuilder) {
+        schemaBuilder.stop();
+        schemaBuilder = null;
+      }
+      if (scriptBuilder) {
+        scriptBuilder.stop();
+        scriptBuilder = null;
+      }
+      sendResponse({ success: true });
+      break;
+      
+    case 'generateCode':
+      if (schemaBuilder) {
+        const code = schemaBuilder.generateCode();
+        schemaBuilder.showCodeModal(code);
+      }
+      sendResponse({ success: true });
+      break;
+  }
+  
+  return true;
+});
diff --git a/docs/md_v2/apps/crawl4ai-assistant/content/shared/utils.js b/docs/md_v2/apps/crawl4ai-assistant/content/shared/utils.js
new file mode 100644
index 00000000..9a8a5f50
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/content/shared/utils.js
@@ -0,0 +1,253 @@
+// Shared utilities for Crawl4AI Chrome Extension
+
+// Make element draggable by its titlebar
+function makeDraggable(element) {
+  let isDragging = false;
+  let startX, startY, initialX, initialY;
+  
+  const titlebar = element.querySelector('.c4ai-toolbar-titlebar, .c4ai-titlebar');
+  if (!titlebar) return;
+  
+  titlebar.addEventListener('mousedown', (e) => {
+    // Don't drag if clicking on buttons
+    if (e.target.classList.contains('c4ai-dot') || e.target.closest('button')) return;
+    
+    isDragging = true;
+    startX = e.clientX;
+    startY = e.clientY;
+    
+    const rect = element.getBoundingClientRect();
+    initialX = rect.left;
+    initialY = rect.top;
+    
+    element.style.transition = 'none';
+    titlebar.style.cursor = 'grabbing';
+  });
+  
+  document.addEventListener('mousemove', (e) => {
+    if (!isDragging) return;
+    
+    const deltaX = e.clientX - startX;
+    const deltaY = e.clientY - startY;
+    
+    element.style.left = `${initialX + deltaX}px`;
+    element.style.top = `${initialY + deltaY}px`;
+    element.style.right = 'auto';
+  });
+  
+  document.addEventListener('mouseup', () => {
+    if (isDragging) {
+      isDragging = false;
+      element.style.transition = '';
+      titlebar.style.cursor = 'grab';
+    }
+  });
+}
+
+// Make element draggable by a specific header element
+function makeDraggableByHeader(element) {
+  let isDragging = false;
+  let startX, startY, initialX, initialY;
+  
+  const header = element.querySelector('.c4ai-debugger-header');
+  if (!header) return;
+  
+  header.addEventListener('mousedown', (e) => {
+    // Don't drag if clicking on close button
+    if (e.target.id === 'c4ai-close-debugger' || e.target.closest('#c4ai-close-debugger')) return;
+    
+    isDragging = true;
+    startX = e.clientX;
+    startY = e.clientY;
+    
+    const rect = element.getBoundingClientRect();
+    initialX = rect.left;
+    initialY = rect.top;
+    
+    element.style.transition = 'none';
+    header.style.cursor = 'grabbing';
+  });
+  
+  document.addEventListener('mousemove', (e) => {
+    if (!isDragging) return;
+    
+    const deltaX = e.clientX - startX;
+    const deltaY = e.clientY - startY;
+    
+    element.style.left = `${initialX + deltaX}px`;
+    element.style.top = `${initialY + deltaY}px`;
+    element.style.right = 'auto';
+  });
+  
+  document.addEventListener('mouseup', () => {
+    if (isDragging) {
+      isDragging = false;
+      element.style.transition = '';
+      header.style.cursor = 'grab';
+    }
+  });
+}
+
+// Escape HTML for safe display
+function escapeHtml(text) {
+  const div = document.createElement('div');
+  div.textContent = text;
+  return div.innerHTML;
+}
+
+// Apply syntax highlighting to Python code
+function applySyntaxHighlighting(codeElement) {
+  const code = codeElement.textContent;
+  
+  // Split by lines to handle line-by-line
+  const lines = code.split('\n');
+  const highlightedLines = lines.map(line => {
+    let highlightedLine = escapeHtml(line);
+    
+    // Skip if line is empty
+    if (!highlightedLine.trim()) return highlightedLine;
+    
+    // Comments (lines starting with #)
+    if (highlightedLine.trim().startsWith('#')) {
+      return `<span class="c4ai-comment">${highlightedLine}</span>`;
+    }
+    
+    // Triple quoted strings
+    if (highlightedLine.includes('"""')) {
+      highlightedLine = highlightedLine.replace(/(""".*?""")/g, '<span class="c4ai-string">$1</span>');
+    }
+    
+    // Regular strings - single and double quotes
+    highlightedLine = highlightedLine.replace(/(["'])([^"']*)\1/g, '<span class="c4ai-string">$1$2$1</span>');
+    
+    // Keywords - only highlight if not inside a string
+    const keywords = ['import', 'from', 'async', 'def', 'await', 'try', 'except', 'with', 'as', 'for', 'if', 'else', 'elif', 'return', 'print', 'open', 'and', 'or', 'not', 'in', 'is', 'class', 'self', 'None', 'True', 'False', '__name__', '__main__'];
+    
+    keywords.forEach(keyword => {
+      // Use word boundaries and lookahead/lookbehind to ensure we're not in a string
+      const regex = new RegExp(`\\b(${keyword})\\b(?![^<]*</span>)`, 'g');
+      highlightedLine = highlightedLine.replace(regex, '<span class="c4ai-keyword">$1</span>');
+    });
+    
+    // Functions (word followed by parenthesis)
+    highlightedLine = highlightedLine.replace(/\b([a-zA-Z_]\w*)\s*\(/g, '<span class="c4ai-function">$1</span>(');
+    
+    return highlightedLine;
+  });
+  
+  codeElement.innerHTML = highlightedLines.join('\n');
+}
+
+// Apply syntax highlighting to JavaScript code
+function applySyntaxHighlightingJS(codeElement) {
+  const code = codeElement.textContent;
+  
+  // Split by lines to handle line-by-line
+  const lines = code.split('\n');
+  const highlightedLines = lines.map(line => {
+    let highlightedLine = escapeHtml(line);
+    
+    // Skip if line is empty
+    if (!highlightedLine.trim()) return highlightedLine;
+    
+    // Comments
+    if (highlightedLine.trim().startsWith('//')) {
+      return `<span class="c4ai-comment">${highlightedLine}</span>`;
+    }
+    
+    // Multi-line comments
+    highlightedLine = highlightedLine.replace(/(\/\*.*?\*\/)/g, '<span class="c4ai-comment">$1</span>');
+    
+    // Template literals
+    highlightedLine = highlightedLine.replace(/(`[^`]*`)/g, '<span class="c4ai-string">$1</span>');
+    
+    // Regular strings - single and double quotes
+    highlightedLine = highlightedLine.replace(/(["'])([^"']*)\1/g, '<span class="c4ai-string">$1$2$1</span>');
+    
+    // Keywords
+    const keywords = ['const', 'let', 'var', 'function', 'async', 'await', 'if', 'else', 'for', 'while', 'do', 'switch', 'case', 'break', 'continue', 'return', 'try', 'catch', 'finally', 'throw', 'new', 'this', 'class', 'extends', 'import', 'export', 'default', 'from', 'null', 'undefined', 'true', 'false'];
+    
+    keywords.forEach(keyword => {
+      const regex = new RegExp(`\\b(${keyword})\\b(?![^<]*</span>)`, 'g');
+      highlightedLine = highlightedLine.replace(regex, '<span class="c4ai-keyword">$1</span>');
+    });
+    
+    // Functions and methods
+    highlightedLine = highlightedLine.replace(/\b([a-zA-Z_$][\w$]*)\s*\(/g, '<span class="c4ai-function">$1</span>(');
+    
+    // Numbers
+    highlightedLine = highlightedLine.replace(/\b(\d+)\b/g, '<span class="c4ai-number">$1</span>');
+    
+    return highlightedLine;
+  });
+  
+  codeElement.innerHTML = highlightedLines.join('\n');
+}
+
+// Get element selector
+function getElementSelector(element) {
+  // Priority: ID > unique class > tag with position
+  if (element.id) {
+    return `#${element.id}`;
+  }
+
+  if (element.className && typeof element.className === 'string') {
+    const classes = element.className.split(' ').filter(c => c && !c.startsWith('c4ai-'));
+    if (classes.length > 0) {
+      const selector = `.${classes[0]}`;
+      if (document.querySelectorAll(selector).length === 1) {
+        return selector;
+      }
+    }
+  }
+
+  // Build a path selector
+  const path = [];
+  let current = element;
+
+  while (current && current !== document.body) {
+    const tagName = current.tagName.toLowerCase();
+    const parent = current.parentElement;
+    
+    if (parent) {
+      const siblings = Array.from(parent.children);
+      const index = siblings.indexOf(current) + 1;
+      
+      if (siblings.filter(s => s.tagName === current.tagName).length > 1) {
+        path.unshift(`${tagName}:nth-child(${index})`);
+      } else {
+        path.unshift(tagName);
+      }
+    } else {
+      path.unshift(tagName);
+    }
+    
+    current = parent;
+  }
+
+  return path.join(' > ');
+}
+
+// Check if element is part of our extension UI
+function isOurElement(element) {
+  return element.classList.contains('c4ai-highlight-box') ||
+         element.classList.contains('c4ai-toolbar') ||
+         element.closest('.c4ai-toolbar') ||
+         element.classList.contains('c4ai-script-toolbar') ||
+         element.closest('.c4ai-script-toolbar') ||
+         element.closest('.c4ai-field-dialog') ||
+         element.closest('.c4ai-code-modal') ||
+         element.closest('.c4ai-wait-dialog') ||
+         element.closest('.c4ai-timeline-modal');
+}
+
+// Export utilities
+window.C4AI_Utils = {
+  makeDraggable,
+  makeDraggableByHeader,
+  escapeHtml,
+  applySyntaxHighlighting,
+  applySyntaxHighlightingJS,
+  getElementSelector,
+  isOurElement
+};
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/index.html b/docs/md_v2/apps/crawl4ai-assistant/index.html
index df6d0897..a22c0365 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/index.html
+++ b/docs/md_v2/apps/crawl4ai-assistant/index.html
@@ -36,6 +36,21 @@
                 </div>
             </section>
 
+            <!-- Cloud Announcement Banner -->
+            <section class="cloud-banner-section">
+                <div class="cloud-banner">
+                    <div class="cloud-banner-content">
+                        <div class="cloud-banner-text">
+                            <h3>You don't need Puppeteer. You need Crawl4AI Cloud.</h3>
+                            <p>One API call. JS-rendered. No browser cluster to maintain.</p>
+                        </div>
+                        <button class="cloud-banner-btn" id="joinWaitlistBanner">
+                            Get API Key →
+                        </button>
+                    </div>
+                </div>
+            </section>
+
             <!-- Introduction -->
             <section class="intro-section">
                 <div class="terminal-window">
@@ -43,13 +58,17 @@
                         <span class="terminal-title">About Crawl4AI Assistant</span>
                     </div>
                     <div class="terminal-content">
-                        <p>Transform any website into structured data with just a few clicks! The Crawl4AI Assistant Chrome Extension provides two powerful tools for web scraping and automation.</p>
+                        <p>Transform any website into structured data with just a few clicks! The Crawl4AI Assistant Chrome Extension provides three powerful tools for web scraping and data extraction.</p>
+                        
+                        <div style="background: #0fbbaa; color: #070708; padding: 12px 16px; border-radius: 8px; margin: 16px 0; font-weight: 600;">
+                            🎉 NEW: Schema Builder now extracts data INSTANTLY without any LLM! Test your schema and see JSON results immediately in the browser!
+                        </div>
                         
                         <div class="features-grid">
                             <div class="feature-card">
                                 <span class="feature-icon">🎯</span>
                                 <h3>Schema Builder</h3>
-                                <p>Click to select elements and build extraction schemas visually</p>
+                                <p>Extract data instantly without LLMs - see results in real-time!</p>
                             </div>
                             <div class="feature-card">
                                 <span class="feature-icon">🔴</span>
@@ -57,15 +76,15 @@
                                 <p>Record browser actions to create automation scripts</p>
                             </div>
                             <div class="feature-card">
+                                <span class="feature-icon">📝</span>
+                                <h3>Click2Crawl <span style="color: #0fbbaa; font-size: 0.75rem;">(New!)</span></h3>
+                                <p>Select multiple elements to extract clean markdown "as you see"</p>
+                            </div>
+                            <!-- <div class="feature-card">
                                 <span class="feature-icon">🐍</span>
                                 <h3>Python Code</h3>
                                 <p>Get production-ready Crawl4AI code instantly</p>
-                            </div>
-                            <div class="feature-card">
-                                <span class="feature-icon">🎨</span>
-                                <h3>Beautiful UI</h3>
-                                <p>Draggable toolbar with macOS-style interface</p>
-                            </div>
+                            </div> -->
                         </div>
                     </div>
                 </div>
@@ -134,6 +153,15 @@
                             </div>
                             <div class="tool-status alpha">Alpha</div>
                         </div>
+                        
+                        <div class="tool-selector" data-tool="click2crawl">
+                            <div class="tool-icon">📝</div>
+                            <div class="tool-info">
+                                <h3>Click2Crawl</h3>
+                                <p>Markdown extraction</p>
+                            </div>
+                            <div class="tool-status new">New!</div>
+                        </div>
                     </div>
                     
                     <!-- Right Panel - Tool Details -->
@@ -142,7 +170,7 @@
                         <div class="tool-content active" id="schema-builder">
                             <div class="tool-header">
                                 <h3>📊 Schema Builder</h3>
-                                <span class="tool-tagline">Click to extract data visually</span>
+                                <span class="tool-tagline">No LLM needed - Extract data instantly!</span>
                             </div>
                             
                             <div class="tool-steps">
@@ -150,9 +178,9 @@
                                     <div class="step-number">1</div>
                                     <div class="step-content">
                                         <h4>Select Container</h4>
-                                        <p>Click on any repeating element like product cards or articles</p>
+                                        <p>Click on any repeating element like product cards or articles. Use up/down navigation to fine-tune selection!</p>
                                         <div class="step-visual">
-                                            <span class="highlight-green">■</span> Elements highlighted in green
+                                            <span class="highlight-green">■</span> Container highlighted in green
                                         </div>
                                     </div>
                                 </div>
@@ -160,8 +188,8 @@
                                 <div class="step-item">
                                     <div class="step-number">2</div>
                                     <div class="step-content">
-                                        <h4>Mark Fields</h4>
-                                        <p>Click on data fields inside the container</p>
+                                        <h4>Click Fields to Extract</h4>
+                                        <p>Click on data fields inside the container - choose text, links, images, or attributes</p>
                                         <div class="step-visual">
                                             <span class="highlight-pink">■</span> Fields highlighted in pink
                                         </div>
@@ -171,19 +199,22 @@
                                 <div class="step-item">
                                     <div class="step-number">3</div>
                                     <div class="step-content">
-                                        <h4>Generate & Extract</h4>
-                                        <p>Get your CSS selectors and Python code instantly</p>
+                                        <h4>Test & Extract Data NOW!</h4>
+                                        <p>🎉 Click "Test Schema" to extract ALL matching data instantly - no coding required!</p>
                                         <div class="step-visual">
-                                            <span class="highlight-accent">⚡</span> Ready to use code
+                                            <span class="highlight-accent">⚡</span> See extracted JSON immediately
                                         </div>
                                     </div>
                                 </div>
                             </div>
                             
                             <div class="tool-features">
-                                <div class="feature-tag">No CSS knowledge needed</div>
-                                <div class="feature-tag">Smart selector generation</div>
-                                <div class="feature-tag">LLM-ready schemas</div>
+                                <div class="feature-tag">🚀 Zero LLM dependency</div>
+                                <div class="feature-tag">📊 Instant data extraction</div>
+                                <div class="feature-tag">🎯 Smart selector generation</div>
+                                <div class="feature-tag">🐍 Ready-to-run Python code</div>
+                                <div class="feature-tag">✨ Preview matching elements</div>
+                                <div class="feature-tag">📥 Download JSON results</div>
                             </div>
                         </div>
                         
@@ -236,70 +267,190 @@
                                 <div class="feature-tag alpha-tag">Alpha version</div>
                             </div>
                         </div>
+                        
+                        <!-- Click2Crawl Details -->
+                        <div class="tool-content" id="click2crawl">
+                            <div class="tool-header">
+                                <h3>📝 Click2Crawl</h3>
+                                <span class="tool-tagline">Select multiple elements to extract clean markdown</span>
+                            </div>
+                            
+                            <div class="tool-steps">
+                                <div class="step-item">
+                                    <div class="step-number">1</div>
+                                    <div class="step-content">
+                                        <h4>Ctrl/Cmd + Click</h4>
+                                        <p>Hold Ctrl/Cmd and click multiple elements you want to extract</p>
+                                        <div class="step-visual">
+                                            <span class="highlight-green">🔢</span> Numbered selection badges
+                                        </div>
+                                    </div>
+                                </div>
+                                
+                                <div class="step-item">
+                                    <div class="step-number">2</div>
+                                    <div class="step-content">
+                                        <h4>Enable Visual Text Mode</h4>
+                                        <p>Extract content "as you see" - clean text without complex HTML structures</p>
+                                        <div class="step-visual">
+                                            <span class="highlight-accent">👁️</span> Visual Text Mode (As You See)
+                                        </div>
+                                    </div>
+                                </div>
+                                
+                                <div class="step-item">
+                                    <div class="step-number">3</div>
+                                    <div class="step-content">
+                                        <h4>Export Clean Markdown</h4>
+                                        <p>Get beautifully formatted markdown ready for documentation or LLMs</p>
+                                        <div class="step-visual">
+                                            <span class="highlight-pink">📄</span> Clean, readable output
+                                        </div>
+                                    </div>
+                                </div>
+                            </div>
+                            
+                            <div class="tool-features">
+                                <div class="feature-tag">Multi-select with Ctrl/Cmd</div>
+                                <div class="feature-tag">Visual Text Mode</div>
+                                <div class="feature-tag">Smart formatting</div>
+                                <div class="feature-tag">Cloud export (soon)</div>
+                            </div>
+                        </div>
                     </div>
                 </div>
             </section>
 
             <!-- Interactive Code Examples -->
             <section class="code-showcase">
-                <h2>See the Generated Code</h2>
+                <h2>See the Generated Code & Extracted Data</h2>
                 
                 <div class="code-tabs">
                     <button class="code-tab active" data-example="schema">📊 Schema Builder</button>
                     <button class="code-tab" data-example="script">🔴 Script Builder</button>
+                    <button class="code-tab" data-example="markdown">📝 Click2Crawl</button>
                 </div>
                 
                 <div class="code-examples">
                     <!-- Schema Builder Code -->
                     <div class="code-example active" id="code-schema">
-                        <div class="terminal-window">
-                            <div class="terminal-header">
-                                <span class="terminal-title">schema_extraction.py</span>
-                                <button class="copy-button" data-code="schema">Copy</button>
-                            </div>
-                            <div class="terminal-content">
-                                <pre><code><span class="keyword">import</span> asyncio
+                        <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 16px;">
+                            <!-- Python Code -->
+                            <div class="terminal-window">
+                                <div class="terminal-header">
+                                    <span class="terminal-title">schema_extraction.py</span>
+                                    <button class="copy-button" data-code="schema-python">Copy</button>
+                                </div>
+                                <div class="terminal-content">
+                                    <pre><code><span class="comment">#!/usr/bin/env python3</span>
+<span class="comment">"""
+🎉 NO LLM NEEDED! Direct extraction with CSS selectors
+Generated by Crawl4AI Chrome Extension
+"""</span>
+
+<span class="keyword">import</span> asyncio
 <span class="keyword">import</span> json
-<span class="keyword">from</span> crawl4ai <span class="keyword">import</span> AsyncWebCrawler, CrawlerRunConfig
+<span class="keyword">from</span> crawl4ai <span class="keyword">import</span> AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
 <span class="keyword">from</span> crawl4ai.extraction_strategy <span class="keyword">import</span> JsonCssExtractionStrategy
 
-<span class="keyword">async</span> <span class="keyword">def</span> <span class="function">extract_products</span>():
-    <span class="comment"># Schema generated from your visual selection</span>
-    schema = {
-        <span class="string">"name"</span>: <span class="string">"Product Catalog"</span>,
-        <span class="string">"baseSelector"</span>: <span class="string">"div.product-card"</span>,  <span class="comment"># Container you clicked</span>
-        <span class="string">"fields"</span>: [
-            {
-                <span class="string">"name"</span>: <span class="string">"title"</span>,
-                <span class="string">"selector"</span>: <span class="string">"h3.product-title"</span>,
-                <span class="string">"type"</span>: <span class="string">"text"</span>
-            },
-            {
-                <span class="string">"name"</span>: <span class="string">"price"</span>,
-                <span class="string">"selector"</span>: <span class="string">"span.price"</span>,
-                <span class="string">"type"</span>: <span class="string">"text"</span>
-            },
-            {
-                <span class="string">"name"</span>: <span class="string">"image"</span>,
-                <span class="string">"selector"</span>: <span class="string">"img.product-img"</span>,
-                <span class="string">"type"</span>: <span class="string">"attribute"</span>,
-                <span class="string">"attribute"</span>: <span class="string">"src"</span>
-            }
-        ]
-    }
+<span class="comment"># The EXACT schema from your visual clicks - no guessing!</span>
+EXTRACTION_SCHEMA = {
+    <span class="string">"name"</span>: <span class="string">"Product Catalog"</span>,
+    <span class="string">"baseSelector"</span>: <span class="string">"div.product-card"</span>,  <span class="comment"># The container you selected</span>
+    <span class="string">"fields"</span>: [
+        {
+            <span class="string">"name"</span>: <span class="string">"title"</span>,
+            <span class="string">"selector"</span>: <span class="string">"h3.product-title"</span>,
+            <span class="string">"type"</span>: <span class="string">"text"</span>
+        },
+        {
+            <span class="string">"name"</span>: <span class="string">"price"</span>,
+            <span class="string">"selector"</span>: <span class="string">"span.price"</span>,
+            <span class="string">"type"</span>: <span class="string">"text"</span>
+        },
+        {
+            <span class="string">"name"</span>: <span class="string">"image"</span>,
+            <span class="string">"selector"</span>: <span class="string">"img.product-img"</span>,
+            <span class="string">"type"</span>: <span class="string">"attribute"</span>,
+            <span class="string">"attribute"</span>: <span class="string">"src"</span>
+        },
+        {
+            <span class="string">"name"</span>: <span class="string">"link"</span>,
+            <span class="string">"selector"</span>: <span class="string">"a.product-link"</span>,
+            <span class="string">"type"</span>: <span class="string">"attribute"</span>,
+            <span class="string">"attribute"</span>: <span class="string">"href"</span>
+        }
+    ]
+}
 
-    config = CrawlerRunConfig(
-        extraction_strategy=JsonCssExtractionStrategy(schema)
-    )
+<span class="keyword">async</span> <span class="keyword">def</span> <span class="function">extract_data</span>(url: str):
+    <span class="comment"># Direct extraction - no LLM API calls!</span>
+    extraction_strategy = JsonCssExtractionStrategy(schema=EXTRACTION_SCHEMA)
     
     <span class="keyword">async</span> <span class="keyword">with</span> AsyncWebCrawler() <span class="keyword">as</span> crawler:
         result = <span class="keyword">await</span> crawler.arun(
-            url=<span class="string">"https://example.com/products"</span>,
-            config=config
+            url=url,
+            config=CrawlerRunConfig(extraction_strategy=extraction_strategy)
         )
-        <span class="keyword">return</span> json.loads(result.extracted_content)
+        
+        <span class="keyword">if</span> result.success:
+            data = json.loads(result.extracted_content)
+            <span class="keyword">print</span>(<span class="string">f"✅ Extracted {len(data)} items instantly!"</span>)
+            
+            <span class="comment"># Save to file</span>
+            <span class="keyword">with</span> open(<span class="string">'products.json'</span>, <span class="string">'w'</span>) <span class="keyword">as</span> f:
+                json.dump(data, f, indent=2)
+            
+            <span class="keyword">return</span> data
 
-asyncio.run(extract_products())</code></pre>
+<span class="comment"># Run extraction on any similar page!</span>
+data = asyncio.run(extract_data(<span class="string">"https://example.com/products"</span>))
+
+<span class="comment"># 🎯 Result: Clean JSON data, no LLM costs, instant results!</span></code></pre>
+                                </div>
+                            </div>
+                            
+                            <!-- Extracted JSON Data -->
+                            <div class="terminal-window">
+                                <div class="terminal-header">
+                                    <span class="terminal-title">extracted_data.json</span>
+                                    <button class="copy-button" data-code="schema-json">Copy</button>
+                                </div>
+                                <div class="terminal-content">
+                                    <pre><code><span class="comment">// 🎉 Instantly extracted from the page - no coding required!</span>
+[
+  {
+    <span class="string">"title"</span>: <span class="string">"Wireless Bluetooth Headphones"</span>,
+    <span class="string">"price"</span>: <span class="string">"$79.99"</span>,
+    <span class="string">"image"</span>: <span class="string">"https://example.com/images/headphones-bt-01.jpg"</span>,
+    <span class="string">"link"</span>: <span class="string">"/products/wireless-bluetooth-headphones"</span>
+  },
+  {
+    <span class="string">"title"</span>: <span class="string">"Smart Watch Pro 2024"</span>,
+    <span class="string">"price"</span>: <span class="string">"$299.00"</span>,
+    <span class="string">"image"</span>: <span class="string">"https://example.com/images/smartwatch-pro.jpg"</span>,
+    <span class="string">"link"</span>: <span class="string">"/products/smart-watch-pro-2024"</span>
+  },
+  {
+    <span class="string">"title"</span>: <span class="string">"4K Webcam for Streaming"</span>,
+    <span class="string">"price"</span>: <span class="string">"$149.99"</span>,
+    <span class="string">"image"</span>: <span class="string">"https://example.com/images/webcam-4k.jpg"</span>,
+    <span class="string">"link"</span>: <span class="string">"/products/4k-webcam-streaming"</span>
+  },
+  {
+    <span class="string">"title"</span>: <span class="string">"Mechanical Gaming Keyboard RGB"</span>,
+    <span class="string">"price"</span>: <span class="string">"$129.99"</span>,
+    <span class="string">"image"</span>: <span class="string">"https://example.com/images/keyboard-gaming.jpg"</span>,
+    <span class="string">"link"</span>: <span class="string">"/products/mechanical-gaming-keyboard"</span>
+  },
+  {
+    <span class="string">"title"</span>: <span class="string">"USB-C Hub 7-in-1"</span>,
+    <span class="string">"price"</span>: <span class="string">"$45.99"</span>,
+    <span class="string">"image"</span>: <span class="string">"https://example.com/images/usbc-hub.jpg"</span>,
+    <span class="string">"link"</span>: <span class="string">"/products/usb-c-hub-7in1"</span>
+  }
+]</code></pre>
+                                </div>
                             </div>
                         </div>
                     </div>
@@ -363,32 +514,181 @@ asyncio.run(automate_shopping())</code></pre>
                             </div>
                         </div>
                     </div>
+                    
+                    <!-- Click2Crawl Markdown Output -->
+                    <div class="code-example" id="code-markdown">
+                        <div class="terminal-window">
+                            <div class="terminal-header">
+                                <span class="terminal-title">extracted_content.md</span>
+                                <button class="copy-button" data-code="markdown">Copy</button>
+                            </div>
+                            <div class="terminal-content">
+                                <pre><code><span class="comment"># Extracted from Hacker News with Visual Text Mode 👁️</span>
+
+<span class="string">1. **Show HN: I built a tool to find and reach out to YouTubers** (hellosimply.io)
+   84 points by erickim 2 hours ago | hide | 31 comments
+
+2. **The 24 Hour Restaurant** (logicmag.io)
+   124 points by helsinkiandrew 5 hours ago | hide | 52 comments
+
+3. **Building a Better Bloom Filter in Rust** (carlmastrangelo.com)
+   89 points by carlmastrangelo 3 hours ago | hide | 27 comments
+
+---
+
+### Article: The 24 Hour Restaurant
+
+In New York City, the 24-hour restaurant is becoming extinct. What we lose when we can no longer eat whenever we want.
+
+When I first moved to New York, I loved that I could get a full meal at 3 AM. Not just pizza or fast food, but a proper sit-down dinner with table service and a menu that ran for pages. The city that never sleeps had restaurants that matched its rhythm.
+
+Today, finding a 24-hour restaurant in Manhattan requires genuine effort. The pandemic accelerated a decline that was already underway, but the roots go deeper: rising rents, changing labor laws, and shifting cultural patterns have all contributed to the death of round-the-clock dining.
+
+---
+
+### Product Review: Framework Laptop 16
+
+**Specifications:**
+- Display: 16" 2560×1600 165Hz
+- Processor: AMD Ryzen 7 7840HS
+- Memory: 32GB DDR5-5600
+- Storage: 2TB NVMe Gen4
+- Price: Starting at $1,399
+
+**Pros:**
+- Fully modular and repairable
+- Excellent Linux support
+- Great keyboard and trackpad
+- Expansion card system
+
+**Cons:**
+- Battery life could be better
+- Slightly heavier than competitors
+- Fan noise under load</span></code></pre>
+                            </div>
+                        </div>
+                    </div>
                 </div>
             </section>
 
 
-            <!-- Coming Soon Section -->
-            <section class="coming-soon-section">
-                <h2>Coming Soon: Even More Power</h2>
-                <div class="terminal-window">
-                    <div class="terminal-header">
-                        <span class="terminal-title">Future Features</span>
+            <!-- Crawl4AI Cloud Section -->
+            <section class="cloud-section">
+                <div class="cloud-announcement">
+                    <h2>Crawl4AI Cloud</h2>
+                    <p class="cloud-tagline">Your browser cluster without the cluster.</p>
+                    
+                    <div class="cloud-features-preview">
+                        <div class="cloud-feature-item">
+                            ⚡ POST /crawl
+                        </div>
+                        <div class="cloud-feature-item">
+                            🌐 JS-rendered pages
+                        </div>
+                        <div class="cloud-feature-item">
+                            📊 Schema extraction built-in
+                        </div>
+                        <div class="cloud-feature-item">
+                            💰 $0.001/page
+                        </div>
                     </div>
-                    <div class="terminal-content">
-                        <p class="intro-text">We're continuously expanding C4AI Assistant with powerful new features to make web scraping even easier:</p>
+                    
+                    <button class="cloud-cta-button" id="joinWaitlist">
+                        Get Early Access →
+                    </button>
+                    
+                    <p class="cloud-hint">See it extract your own data. Right now.</p>
+                </div>
+                
+                <!-- Hidden Signup Form -->
+                <div class="signup-overlay" id="signupOverlay">
+                    <div class="signup-container" id="signupContainer">
+                        <button class="close-signup" id="closeSignup">×</button>
                         
-                        <div class="coming-features">
-                            <div class="coming-feature">
-                                <div class="feature-header">
-                                    <span class="feature-badge">Cloud</span>
-                                    <h3>Run on C4AI Cloud</h3>
+                        <div class="signup-content" id="signupForm">
+                            <h3>🚀 Join C4AI Cloud Waiting List</h3>
+                            <p>Be among the first to experience the future of web scraping</p>
+                            
+                            <form id="waitlistForm" class="waitlist-form">
+                                <div class="form-field">
+                                    <label for="userName">Your Name</label>
+                                    <input type="text" id="userName" name="name" placeholder="John Doe" required>
                                 </div>
-                                <p>Execute your extraction directly in the cloud without setting up any local environment. Just click "Run on Cloud" and get your data instantly.</p>
-                                <div class="feature-preview">
-                                    <code>☁️ Instant results • Auto-scaling</code>
+                                
+                                <div class="form-field">
+                                    <label for="userEmail">Email Address</label>
+                                    <input type="email" id="userEmail" name="email" placeholder="john@example.com" required>
+                                </div>
+                                
+                                <div class="form-field">
+                                    <label for="userCompany">Company (Optional)</label>
+                                    <input type="text" id="userCompany" name="company" placeholder="Acme Inc.">
+                                </div>
+                                
+                                <div class="form-field">
+                                    <label for="useCase">What will you use Crawl4AI Cloud for?</label>
+                                    <select id="useCase" name="useCase">
+                                        <option value="">Select use case...</option>
+                                        <option value="price-monitoring">Price Monitoring</option>
+                                        <option value="news-aggregation">News Aggregation</option>
+                                        <option value="market-research">Market Research</option>
+                                        <option value="ai-training">AI Training Data</option>
+                                        <option value="other">Other</option>
+                                    </select>
+                                </div>
+                                
+                                <button type="submit" class="submit-button">
+                                    <span>🎯</span> Submit & Watch the Magic
+                                </button>
+                            </form>
+                        </div>
+                        
+                        <!-- Crawling Animation -->
+                        <div class="crawl-animation" id="crawlAnimation" style="display: none;">
+                            <div class="terminal-window crawl-terminal">
+                                <div class="terminal-header">
+                                    <span class="terminal-title">Crawl4AI Cloud Demo</span>
+                                </div>
+                                <div class="terminal-content">
+                                    <pre id="crawlOutput" class="crawl-log"><code>$ crawl4ai cloud extract --url "signup-form" --auto-detect</code></pre>
                                 </div>
                             </div>
                             
+                            <div class="extracted-preview" id="extractedPreview" style="display: none;">
+                                <h4>📊 Extracted Data</h4>
+                                <pre class="json-preview"><code id="jsonOutput"></code></pre>
+                            </div>
+                            
+                            <div class="success-message" id="successMessage" style="display: none;">
+                                <div class="success-icon">✅</div>
+                                <h3>Data Uploaded Successfully!</h3>
+                                <p>You're on the Crawl4AI Cloud waiting list!</p>
+                                <p>What you just witnessed:</p>
+                                <ul>
+                                    <li>⚡ Real-time extraction of your form data</li>
+                                    <li>🔄 Automatic schema detection</li>
+                                    <li>📤 Instant cloud processing</li>
+                                    <li>✨ No code required - just like that!</li>
+                                </ul>
+                                <p class="success-note">We'll notify you at <strong id="userEmailDisplay"></strong> when Crawl4AI Cloud launches!</p>
+                                <button class="continue-button" id="continueBtn">Continue Exploring</button>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </section>
+
+            <!-- Coming Soon Section -->
+            <section class="coming-soon-section">
+                <h2>More Features Coming Soon</h2>
+                <div class="terminal-window">
+                    <div class="terminal-header">
+                        <span class="terminal-title">Roadmap</span>
+                    </div>
+                    <div class="terminal-content">
+                        <p class="intro-text">We're continuously expanding C4AI Assistant with powerful new features:</p>
+                        
+                        <div class="coming-features">
                             <div class="coming-feature">
                                 <div class="feature-header">
                                     <span class="feature-badge">Direct</span>
@@ -482,8 +782,19 @@ asyncio.run(automate_shopping())</code></pre>
         document.querySelectorAll('.copy-button').forEach(button => {
             button.addEventListener('click', async function() {
                 const codeType = this.getAttribute('data-code');
-                const codeElement = document.getElementById('code-' + codeType).querySelector('pre code');
-                const codeText = codeElement.textContent;
+                let codeText = '';
+                
+                // Handle different code types
+                if (codeType === 'schema-python') {
+                    const codeElement = document.querySelector('#code-schema .terminal-window:first-child pre code');
+                    codeText = codeElement.textContent;
+                } else if (codeType === 'schema-json') {
+                    const codeElement = document.querySelector('#code-schema .terminal-window:last-child pre code');
+                    codeText = codeElement.textContent;
+                } else {
+                    const codeElement = document.getElementById('code-' + codeType).querySelector('pre code');
+                    codeText = codeElement.textContent;
+                }
                 
                 try {
                     await navigator.clipboard.writeText(codeText);
@@ -499,6 +810,161 @@ asyncio.run(automate_shopping())</code></pre>
                 }
             });
         });
+        
+        // Crawl4AI Cloud Interactive Demo
+        const joinWaitlistBtn = document.getElementById('joinWaitlist');
+        const signupOverlay = document.getElementById('signupOverlay');
+        const closeSignupBtn = document.getElementById('closeSignup');
+        const waitlistForm = document.getElementById('waitlistForm');
+        const signupForm = document.getElementById('signupForm');
+        const crawlAnimation = document.getElementById('crawlAnimation');
+        const crawlOutput = document.getElementById('crawlOutput');
+        const extractedPreview = document.getElementById('extractedPreview');
+        const jsonOutput = document.getElementById('jsonOutput');
+        const successMessage = document.getElementById('successMessage');
+        const continueBtn = document.getElementById('continueBtn');
+        const userEmailDisplay = document.getElementById('userEmailDisplay');
+        
+        // Open signup modal
+        joinWaitlistBtn.addEventListener('click', () => {
+            signupOverlay.classList.add('active');
+        });
+        
+        // Banner button
+        const joinWaitlistBannerBtn = document.getElementById('joinWaitlistBanner');
+        if (joinWaitlistBannerBtn) {
+            joinWaitlistBannerBtn.addEventListener('click', () => {
+                signupOverlay.classList.add('active');
+            });
+        }
+        
+        // Close signup modal
+        closeSignupBtn.addEventListener('click', () => {
+            signupOverlay.classList.remove('active');
+        });
+        
+        // Close on overlay click
+        signupOverlay.addEventListener('click', (e) => {
+            if (e.target === signupOverlay) {
+                signupOverlay.classList.remove('active');
+            }
+        });
+        
+        // Continue button
+        if (continueBtn) {
+            continueBtn.addEventListener('click', () => {
+                signupOverlay.classList.remove('active');
+                // Reset form for next time
+                waitlistForm.reset();
+                signupForm.style.display = 'block';
+                crawlAnimation.style.display = 'none';
+                extractedPreview.style.display = 'none';
+                successMessage.style.display = 'none';
+            });
+        }
+        
+        // Form submission with crawling animation
+        waitlistForm.addEventListener('submit', async (e) => {
+            e.preventDefault();
+            
+            // Get form data
+            const formData = {
+                name: document.getElementById('userName').value,
+                email: document.getElementById('userEmail').value,
+                company: document.getElementById('userCompany').value || 'Not specified',
+                useCase: document.getElementById('useCase').value || 'General web scraping',
+                timestamp: new Date().toISOString(),
+                source: 'Crawl4AI Assistant Landing Page'
+            };
+            
+            // Update email display
+            userEmailDisplay.textContent = formData.email;
+            
+            // Hide form and show crawling animation
+            signupForm.style.display = 'none';
+            crawlAnimation.style.display = 'block';
+            
+            // Clear previous output
+            const codeElement = crawlOutput.querySelector('code');
+            codeElement.innerHTML = '$ crawl4ai cloud extract --url "signup-form" --auto-detect\n\n';
+            
+            // Simulate crawling process with proper C4AI log format
+            const crawlSteps = [
+                { 
+                    log: '<span class="log-init">[INIT]....</span> → Crawl4AI Cloud 1.0.0',
+                    time: '0.12s'
+                },
+                { 
+                    log: '<span class="log-fetch">[FETCH]...</span> ↓ https://crawl4ai.com/waitlist-form',
+                    time: '0.45s'
+                },
+                { 
+                    log: '<span class="log-scrape">[SCRAPE]..</span> ◆ https://crawl4ai.com/waitlist-form',
+                    time: '0.28s'
+                },
+                { 
+                    log: '<span class="log-extract">[EXTRACT].</span> ■ Extracting form data with auto-detect',
+                    time: '0.55s'
+                },
+                { 
+                    log: '<span class="log-complete">[COMPLETE]</span> ● https://crawl4ai.com/waitlist-form',
+                    time: '1.40s'
+                }
+            ];
+            
+            let stepIndex = 0;
+            const typeStep = async () => {
+                if (stepIndex < crawlSteps.length) {
+                    const step = crawlSteps[stepIndex];
+                    codeElement.innerHTML += step.log + ' | <span class="log-success">✓</span> | <span class="log-time">⏱: ' + step.time + '</span>\n';
+                    stepIndex++;
+                    
+                    // Scroll to bottom
+                    const terminal = crawlOutput.parentElement;
+                    terminal.scrollTop = terminal.scrollHeight;
+                    
+                    setTimeout(typeStep, 600);
+                } else {
+                    // Show extracted data
+                    setTimeout(() => {
+                        codeElement.innerHTML += '\n<span class="log-success">[UPLOAD]..</span> ↑ Uploading to Crawl4AI Cloud...';
+                        
+                        setTimeout(() => {
+                            extractedPreview.style.display = 'block';
+                            jsonOutput.textContent = JSON.stringify(formData, null, 2);
+                            
+                            // Add syntax highlighting
+                            jsonOutput.innerHTML = jsonOutput.textContent
+                                .replace(/"([^"]+)":/g, '<span class="string">"$1"</span>:')
+                                .replace(/: "([^"]+)"/g, ': <span class="string">"$1"</span>');
+                            
+                            codeElement.innerHTML += ' | <span class="log-success">✓</span> | <span class="log-time">⏱: 0.23s</span>\n';
+                            codeElement.innerHTML += '\n<span class="log-success">[SUCCESS]</span> ✨ Data uploaded successfully!';
+                            
+                            // Show success message after a delay
+                            setTimeout(() => {
+                                successMessage.style.display = 'block';
+                                
+                                // Smooth scroll to bottom to show success message
+                                setTimeout(() => {
+                                    const container = document.getElementById('signupContainer');
+                                    container.scrollTo({
+                                        top: container.scrollHeight,
+                                        behavior: 'smooth'
+                                    });
+                                }, 100);
+                                
+                                // Actually submit to waiting list (you can implement this)
+                                console.log('Waitlist submission:', formData);
+                            }, 1500);
+                        }, 800);
+                    }, 600);
+                }
+            };
+            
+            // Start the animation
+            setTimeout(typeStep, 500);
+        });
     </script>
 </body>
 </html>
\ No newline at end of file
diff --git a/docs/md_v2/apps/crawl4ai-assistant/libs/marked.min.js b/docs/md_v2/apps/crawl4ai-assistant/libs/marked.min.js
new file mode 100644
index 00000000..b4e0d73b
--- /dev/null
+++ b/docs/md_v2/apps/crawl4ai-assistant/libs/marked.min.js
@@ -0,0 +1,69 @@
+/**
+ * marked v15.0.12 - a markdown parser
+ * Copyright (c) 2011-2025, Christopher Jeffrey. (MIT Licensed)
+ * https://github.com/markedjs/marked
+ */
+
+/**
+ * DO NOT EDIT THIS FILE
+ * The code in this file is generated from files in ./src/
+ */
+(function(g,f){if(typeof exports=="object"&&typeof module<"u"){module.exports=f()}else if("function"==typeof define && define.amd){define("marked",f)}else {g["marked"]=f()}}(typeof globalThis < "u" ? globalThis : typeof self < "u" ? self : this,function(){var exports={};var __exports=exports;var module={exports};
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+`?t[1].slice(0,-1):t[1];return{type:"paragraph",raw:t[0],text:n,tokens:this.lexer.inline(n)}}}text(e){let t=this.rules.block.text.exec(e);if(t)return{type:"text",raw:t[0],text:t[0],tokens:this.lexer.inline(t[0])}}escape(e){let t=this.rules.inline.escape.exec(e);if(t)return{type:"escape",raw:t[0],text:t[1]}}tag(e){let t=this.rules.inline.tag.exec(e);if(t)return!this.lexer.state.inLink&&this.rules.other.startATag.test(t[0])?this.lexer.state.inLink=!0:this.lexer.state.inLink&&this.rules.other.endATag.test(t[0])&&(this.lexer.state.inLink=!1),!this.lexer.state.inRawBlock&&this.rules.other.startPreScriptTag.test(t[0])?this.lexer.state.inRawBlock=!0:this.lexer.state.inRawBlock&&this.rules.other.endPreScriptTag.test(t[0])&&(this.lexer.state.inRawBlock=!1),{type:"html",raw:t[0],inLink:this.lexer.state.inLink,inRawBlock:this.lexer.state.inRawBlock,block:!1,text:t[0]}}link(e){let t=this.rules.inline.link.exec(e);if(t){let n=t[2].trim();if(!this.options.pedantic&&this.rules.other.startAngleBracket.test(n)){if(!this.rules.other.endAngleBracket.test(n))return;let r=A(n.slice(0,-1),"\\");if((n.length-r.length)%2===0)return}else{let r=de(t[2],"()");if(r===-2)return;if(r>-1){let a=(t[0].indexOf("!")===0?5:4)+t[1].length+r;t[2]=t[2].substring(0,r),t[0]=t[0].substring(0,a).trim(),t[3]=""}}let s=t[2],i="";if(this.options.pedantic){let r=this.rules.other.pedanticHrefTitle.exec(s);r&&(s=r[1],i=r[3])}else i=t[3]?t[3].slice(1,-1):"";return s=s.trim(),this.rules.other.startAngleBracket.test(s)&&(this.options.pedantic&&!this.rules.other.endAngleBracket.test(n)?s=s.slice(1):s=s.slice(1,-1)),me(t,{href:s&&s.replace(this.rules.inline.anyPunctuation,"$1"),title:i&&i.replace(this.rules.inline.anyPunctuation,"$1")},t[0],this.lexer,this.rules)}}reflink(e,t){let n;if((n=this.rules.inline.reflink.exec(e))||(n=this.rules.inline.nolink.exec(e))){let s=(n[2]||n[1]).replace(this.rules.other.multipleSpaceGlobal," "),i=t[s.toLowerCase()];if(!i){let r=n[0].charAt(0);return{type:"text",raw:r,text:r}}return me(n,i,n[0],this.lexer,this.rules)}}emStrong(e,t,n=""){let s=this.rules.inline.emStrongLDelim.exec(e);if(!s||s[3]&&n.match(this.rules.other.unicodeAlphaNumeric))return;if(!(s[1]||s[2]||"")||!n||this.rules.inline.punctuation.exec(n)){let r=[...s[0]].length-1,o,a,c=r,p=0,u=s[0][0]==="*"?this.rules.inline.emStrongRDelimAst:this.rules.inline.emStrongRDelimUnd;for(u.lastIndex=0,t=t.slice(-1*e.length+r);(s=u.exec(t))!=null;){if(o=s[1]||s[2]||s[3]||s[4]||s[5]||s[6],!o)continue;if(a=[...o].length,s[3]||s[4]){c+=a;continue}else if((s[5]||s[6])&&r%3&&!((r+a)%3)){p+=a;continue}if(c-=a,c>0)continue;a=Math.min(a,a+c+p);let d=[...s[0]][0].length,g=e.slice(0,r+s.index+d+a);if(Math.min(r,a)%2){let f=g.slice(1,-1);return{type:"em",raw:g,text:f,tokens:this.lexer.inlineTokens(f)}}let T=g.slice(2,-2);return{type:"strong",raw:g,text:T,tokens:this.lexer.inlineTokens(T)}}}}codespan(e){let t=this.rules.inline.code.exec(e);if(t){let n=t[2].replace(this.rules.other.newLineCharGlobal," "),s=this.rules.other.nonSpaceChar.test(n),i=this.rules.other.startingSpaceChar.test(n)&&this.rules.other.endingSpaceChar.test(n);return s&&i&&(n=n.substring(1,n.length-1)),{type:"codespan",raw:t[0],text:n}}}br(e){let t=this.rules.inline.br.exec(e);if(t)return{type:"br",raw:t[0]}}del(e){let t=this.rules.inline.del.exec(e);if(t)return{type:"del",raw:t[0],text:t[2],tokens:this.lexer.inlineTokens(t[2])}}autolink(e){let t=this.rules.inline.autolink.exec(e);if(t){let n,s;return t[2]==="@"?(n=t[1],s="mailto:"+n):(n=t[1],s=n),{type:"link",raw:t[0],text:n,href:s,tokens:[{type:"text",raw:n,text:n}]}}}url(e){let t;if(t=this.rules.inline.url.exec(e)){let n,s;if(t[2]==="@")n=t[0],s="mailto:"+n;else{let i;do i=t[0],t[0]=this.rules.inline._backpedal.exec(t[0])?.[0]??"";while(i!==t[0]);n=t[0],t[1]==="www."?s="http://"+t[0]:s=t[0]}return{type:"link",raw:t[0],text:n,href:s,tokens:[{type:"text",raw:n,text:n}]}}}inlineText(e){let t=this.rules.inline.text.exec(e);if(t){let n=this.lexer.state.inRawBlock;return{type:"text",raw:t[0],text:t[0],escaped:n}}}};var x=class l{tokens;options;state;tokenizer;inlineQueue;constructor(e){this.tokens=[],this.tokens.links=Object.create(null),this.options=e||w,this.options.tokenizer=this.options.tokenizer||new S,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};let t={other:m,block:B.normal,inline:P.normal};this.options.pedantic?(t.block=B.pedantic,t.inline=P.pedantic):this.options.gfm&&(t.block=B.gfm,this.options.breaks?t.inline=P.breaks:t.inline=P.gfm),this.tokenizer.rules=t}static get rules(){return{block:B,inline:P}}static lex(e,t){return new l(t).lex(e)}static lexInline(e,t){return new l(t).inlineTokens(e)}lex(e){e=e.replace(m.carriageReturn,`
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+`+s.raw,this.inlineQueue.at(-1).src=r.text):this.tokens.links[s.tag]||(this.tokens.links[s.tag]={href:s.href,title:s.title});continue}if(s=this.tokenizer.table(e)){e=e.substring(s.raw.length),t.push(s);continue}if(s=this.tokenizer.lheading(e)){e=e.substring(s.raw.length),t.push(s);continue}let i=e;if(this.options.extensions?.startBlock){let r=1/0,o=e.slice(1),a;this.options.extensions.startBlock.forEach(c=>{a=c.call({lexer:this},o),typeof a=="number"&&a>=0&&(r=Math.min(r,a))}),r<1/0&&r>=0&&(i=e.substring(0,r+1))}if(this.state.top&&(s=this.tokenizer.paragraph(i))){let r=t.at(-1);n&&r?.type==="paragraph"?(r.raw+=`
+`+s.raw,r.text+=`
+`+s.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=r.text):t.push(s),n=i.length!==e.length,e=e.substring(s.raw.length);continue}if(s=this.tokenizer.text(e)){e=e.substring(s.raw.length);let r=t.at(-1);r?.type==="text"?(r.raw+=`
+`+s.raw,r.text+=`
+`+s.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=r.text):t.push(s);continue}if(e){let r="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(r);break}else throw new Error(r)}}return this.state.top=!0,t}inline(e,t=[]){return this.inlineQueue.push({src:e,tokens:t}),t}inlineTokens(e,t=[]){let n=e,s=null;if(this.tokens.links){let o=Object.keys(this.tokens.links);if(o.length>0)for(;(s=this.tokenizer.rules.inline.reflinkSearch.exec(n))!=null;)o.includes(s[0].slice(s[0].lastIndexOf("[")+1,-1))&&(n=n.slice(0,s.index)+"["+"a".repeat(s[0].length-2)+"]"+n.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(s=this.tokenizer.rules.inline.anyPunctuation.exec(n))!=null;)n=n.slice(0,s.index)+"++"+n.slice(this.tokenizer.rules.inline.anyPunctuation.lastIndex);for(;(s=this.tokenizer.rules.inline.blockSkip.exec(n))!=null;)n=n.slice(0,s.index)+"["+"a".repeat(s[0].length-2)+"]"+n.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);let i=!1,r="";for(;e;){i||(r=""),i=!1;let o;if(this.options.extensions?.inline?.some(c=>(o=c.call({lexer:this},e,t))?(e=e.substring(o.raw.length),t.push(o),!0):!1))continue;if(o=this.tokenizer.escape(e)){e=e.substring(o.raw.length),t.push(o);continue}if(o=this.tokenizer.tag(e)){e=e.substring(o.raw.length),t.push(o);continue}if(o=this.tokenizer.link(e)){e=e.substring(o.raw.length),t.push(o);continue}if(o=this.tokenizer.reflink(e,this.tokens.links)){e=e.substring(o.raw.length);let c=t.at(-1);o.type==="text"&&c?.type==="text"?(c.raw+=o.raw,c.text+=o.text):t.push(o);continue}if(o=this.tokenizer.emStrong(e,n,r)){e=e.substring(o.raw.length),t.push(o);continue}if(o=this.tokenizer.codespan(e)){e=e.substring(o.raw.length),t.push(o);continue}if(o=this.tokenizer.br(e)){e=e.substring(o.raw.length),t.push(o);continue}if(o=this.tokenizer.del(e)){e=e.substring(o.raw.length),t.push(o);continue}if(o=this.tokenizer.autolink(e)){e=e.substring(o.raw.length),t.push(o);continue}if(!this.state.inLink&&(o=this.tokenizer.url(e))){e=e.substring(o.raw.length),t.push(o);continue}let a=e;if(this.options.extensions?.startInline){let c=1/0,p=e.slice(1),u;this.options.extensions.startInline.forEach(d=>{u=d.call({lexer:this},p),typeof u=="number"&&u>=0&&(c=Math.min(c,u))}),c<1/0&&c>=0&&(a=e.substring(0,c+1))}if(o=this.tokenizer.inlineText(a)){e=e.substring(o.raw.length),o.raw.slice(-1)!=="_"&&(r=o.raw.slice(-1)),i=!0;let c=t.at(-1);c?.type==="text"?(c.raw+=o.raw,c.text+=o.text):t.push(o);continue}if(e){let c="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(c);break}else throw new Error(c)}}return t}};var $=class{options;parser;constructor(e){this.options=e||w}space(e){return""}code({text:e,lang:t,escaped:n}){let s=(t||"").match(m.notSpaceStart)?.[0],i=e.replace(m.endingNewline,"")+`
+`;return s?'<pre><code class="language-'+R(s)+'">'+(n?i:R(i,!0))+`</code></pre>
+`:"<pre><code>"+(n?i:R(i,!0))+`</code></pre>
+`}blockquote({tokens:e}){return`<blockquote>
+${this.parser.parse(e)}</blockquote>
+`}html({text:e}){return e}heading({tokens:e,depth:t}){return`<h${t}>${this.parser.parseInline(e)}</h${t}>
+`}hr(e){return`<hr>
+`}list(e){let t=e.ordered,n=e.start,s="";for(let o=0;o<e.items.length;o++){let a=e.items[o];s+=this.listitem(a)}let i=t?"ol":"ul",r=t&&n!==1?' start="'+n+'"':"";return"<"+i+r+`>
+`+s+"</"+i+`>
+`}listitem(e){let t="";if(e.task){let n=this.checkbox({checked:!!e.checked});e.loose?e.tokens[0]?.type==="paragraph"?(e.tokens[0].text=n+" "+e.tokens[0].text,e.tokens[0].tokens&&e.tokens[0].tokens.length>0&&e.tokens[0].tokens[0].type==="text"&&(e.tokens[0].tokens[0].text=n+" "+R(e.tokens[0].tokens[0].text),e.tokens[0].tokens[0].escaped=!0)):e.tokens.unshift({type:"text",raw:n+" ",text:n+" ",escaped:!0}):t+=n+" "}return t+=this.parser.parse(e.tokens,!!e.loose),`<li>${t}</li>
+`}checkbox({checked:e}){return"<input "+(e?'checked="" ':"")+'disabled="" type="checkbox">'}paragraph({tokens:e}){return`<p>${this.parser.parseInline(e)}</p>
+`}table(e){let t="",n="";for(let i=0;i<e.header.length;i++)n+=this.tablecell(e.header[i]);t+=this.tablerow({text:n});let s="";for(let i=0;i<e.rows.length;i++){let r=e.rows[i];n="";for(let o=0;o<r.length;o++)n+=this.tablecell(r[o]);s+=this.tablerow({text:n})}return s&&(s=`<tbody>${s}</tbody>`),`<table>
+<thead>
+`+t+`</thead>
+`+s+`</table>
+`}tablerow({text:e}){return`<tr>
+${e}</tr>
+`}tablecell(e){let t=this.parser.parseInline(e.tokens),n=e.header?"th":"td";return(e.align?`<${n} align="${e.align}">`:`<${n}>`)+t+`</${n}>
+`}strong({tokens:e}){return`<strong>${this.parser.parseInline(e)}</strong>`}em({tokens:e}){return`<em>${this.parser.parseInline(e)}</em>`}codespan({text:e}){return`<code>${R(e,!0)}</code>`}br(e){return"<br>"}del({tokens:e}){return`<del>${this.parser.parseInline(e)}</del>`}link({href:e,title:t,tokens:n}){let s=this.parser.parseInline(n),i=V(e);if(i===null)return s;e=i;let r='<a href="'+e+'"';return t&&(r+=' title="'+R(t)+'"'),r+=">"+s+"</a>",r}image({href:e,title:t,text:n,tokens:s}){s&&(n=this.parser.parseInline(s,this.parser.textRenderer));let i=V(e);if(i===null)return R(n);e=i;let r=`<img src="${e}" alt="${n}"`;return t&&(r+=` title="${R(t)}"`),r+=">",r}text(e){return"tokens"in e&&e.tokens?this.parser.parseInline(e.tokens):"escaped"in e&&e.escaped?e.text:R(e.text)}};var _=class{strong({text:e}){return e}em({text:e}){return e}codespan({text:e}){return e}del({text:e}){return e}html({text:e}){return e}text({text:e}){return e}link({text:e}){return""+e}image({text:e}){return""+e}br(){return""}};var b=class l{options;renderer;textRenderer;constructor(e){this.options=e||w,this.options.renderer=this.options.renderer||new $,this.renderer=this.options.renderer,this.renderer.options=this.options,this.renderer.parser=this,this.textRenderer=new _}static parse(e,t){return new l(t).parse(e)}static parseInline(e,t){return new l(t).parseInline(e)}parse(e,t=!0){let n="";for(let s=0;s<e.length;s++){let i=e[s];if(this.options.extensions?.renderers?.[i.type]){let o=i,a=this.options.extensions.renderers[o.type].call({parser:this},o);if(a!==!1||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(o.type)){n+=a||"";continue}}let r=i;switch(r.type){case"space":{n+=this.renderer.space(r);continue}case"hr":{n+=this.renderer.hr(r);continue}case"heading":{n+=this.renderer.heading(r);continue}case"code":{n+=this.renderer.code(r);continue}case"table":{n+=this.renderer.table(r);continue}case"blockquote":{n+=this.renderer.blockquote(r);continue}case"list":{n+=this.renderer.list(r);continue}case"html":{n+=this.renderer.html(r);continue}case"paragraph":{n+=this.renderer.paragraph(r);continue}case"text":{let o=r,a=this.renderer.text(o);for(;s+1<e.length&&e[s+1].type==="text";)o=e[++s],a+=`
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+Please report this to https://github.com/markedjs/marked.`,e){let s="<p>An error occurred:</p><pre>"+R(n.message+"",!0)+"</pre>";return t?Promise.resolve(s):s}if(t)return Promise.reject(n);throw n}}};var M=new E;function k(l,e){return M.parse(l,e)}k.options=k.setOptions=function(l){return M.setOptions(l),k.defaults=M.defaults,N(k.defaults),k};k.getDefaults=z;k.defaults=w;k.use=function(...l){return M.use(...l),k.defaults=M.defaults,N(k.defaults),k};k.walkTokens=function(l,e){return M.walkTokens(l,e)};k.parseInline=M.parseInline;k.Parser=b;k.parser=b.parse;k.Renderer=$;k.TextRenderer=_;k.Lexer=x;k.lexer=x.lex;k.Tokenizer=S;k.Hooks=L;k.parse=k;var it=k.options,ot=k.setOptions,lt=k.use,at=k.walkTokens,ct=k.parseInline,pt=k,ut=b.parse,ht=x.lex;
+
+if(__exports != exports)module.exports = exports;return module.exports}));
diff --git a/docs/md_v2/apps/crawl4ai-assistant/manifest.json b/docs/md_v2/apps/crawl4ai-assistant/manifest.json
index a3ec8985..663a7208 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/manifest.json
+++ b/docs/md_v2/apps/crawl4ai-assistant/manifest.json
@@ -22,7 +22,16 @@
   "content_scripts": [
     {
       "matches": ["<all_urls>"],
-      "js": ["content/content.js"],
+      "js": [
+        "libs/marked.min.js",
+        "content/shared/utils.js",
+        "content/schemaBuilder.js",
+        "content/scriptBuilder.js",
+        "content/contentAnalyzer.js",
+        "content/markdownConverter.js",
+        "content/click2CrawlBuilder.js",
+        "content/content.js"
+      ],
       "css": ["content/overlay.css"],
       "run_at": "document_idle"
     }
diff --git a/docs/md_v2/apps/crawl4ai-assistant/popup/popup.css b/docs/md_v2/apps/crawl4ai-assistant/popup/popup.css
index b1a46650..4cd75583 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/popup/popup.css
+++ b/docs/md_v2/apps/crawl4ai-assistant/popup/popup.css
@@ -145,6 +145,10 @@ header h1 {
   background: #3a1e5f;
 }
 
+.mode-button.c2c .icon {
+  background: #1e5f3a;
+}
+
 .mode-info h3 {
   font-size: 16px;
   color: #fff;
diff --git a/docs/md_v2/apps/crawl4ai-assistant/popup/popup.html b/docs/md_v2/apps/crawl4ai-assistant/popup/popup.html
index d198e304..f974f366 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/popup/popup.html
+++ b/docs/md_v2/apps/crawl4ai-assistant/popup/popup.html
@@ -37,6 +37,14 @@
           <p>Record actions to build automation scripts</p>
         </div>
       </button>
+      
+      <button id="c2c-mode" class="mode-button c2c">
+        <div class="icon">✨</div>
+        <div class="mode-info">
+          <h3>Click2Crawl</h3>
+          <p>Select elements and convert to clean markdown</p>
+        </div>
+      </button>
     </div>
     
     <div id="active-session" class="active-session hidden">
diff --git a/docs/md_v2/apps/crawl4ai-assistant/popup/popup.js b/docs/md_v2/apps/crawl4ai-assistant/popup/popup.js
index b37aabc0..ef136f14 100644
--- a/docs/md_v2/apps/crawl4ai-assistant/popup/popup.js
+++ b/docs/md_v2/apps/crawl4ai-assistant/popup/popup.js
@@ -22,6 +22,10 @@ document.addEventListener('DOMContentLoaded', () => {
     startScriptCapture();
   });
 
+  document.getElementById('c2c-mode').addEventListener('click', () => {
+    startClick2Crawl();
+  });
+
   // Session actions
   document.getElementById('generate-code').addEventListener('click', () => {
     generateCode();
@@ -79,6 +83,19 @@ function startScriptCapture() {
   });
 }
 
+function startClick2Crawl() {
+  chrome.tabs.query({ active: true, currentWindow: true }, (tabs) => {
+    chrome.tabs.sendMessage(tabs[0].id, {
+      action: 'startClick2Crawl'
+    }, (response) => {
+      if (response && response.success) {
+        // Close the popup to let user interact with the page
+        window.close();
+      }
+    });
+  });
+}
+
 function showActiveSession(stats) {
   document.querySelector('.mode-selector').style.display = 'none';
   document.getElementById('active-session').classList.remove('hidden');
diff --git a/docs/md_v2/apps/llmtxt/llmtxt.js b/docs/md_v2/apps/llmtxt/llmtxt.js
index f90bf4e6..ae492c06 100644
--- a/docs/md_v2/apps/llmtxt/llmtxt.js
+++ b/docs/md_v2/apps/llmtxt/llmtxt.js
@@ -18,9 +18,14 @@ const components = [
         description: 'Browser and crawler configuration'
     },
     {
-        id: 'extraction',
-        name: 'Data Extraction',
-        description: 'Structured data extraction strategies'
+        id: 'extraction-llm',
+        name: 'Data Extraction Using LLM',
+        description: 'Structured data extraction strategies using LLMs'
+    },
+    {
+        id: 'extraction-no-llm',
+        name: 'Data Extraction Without LLM',
+        description: 'Structured data extraction strategies without LLMs'
     },
     {
         id: 'multi_urls_crawling',
diff --git a/docs/md_v2/assets/llm.txt/diagrams/extraction.txt b/docs/md_v2/assets/llm.txt/diagrams/extraction-llm.txt
similarity index 100%
rename from docs/md_v2/assets/llm.txt/diagrams/extraction.txt
rename to docs/md_v2/assets/llm.txt/diagrams/extraction-llm.txt
diff --git a/docs/md_v2/assets/llm.txt/diagrams/extraction-no-llm.txt b/docs/md_v2/assets/llm.txt/diagrams/extraction-no-llm.txt
new file mode 100644
index 00000000..a4d7e360
--- /dev/null
+++ b/docs/md_v2/assets/llm.txt/diagrams/extraction-no-llm.txt
@@ -0,0 +1,478 @@
+## Extraction Strategy Workflows and Architecture
+
+Visual representations of Crawl4AI's data extraction approaches, strategy selection, and processing workflows.
+
+### Extraction Strategy Decision Tree
+
+```mermaid
+flowchart TD
+    A[Content to Extract] --> B{Content Type?}
+    
+    B -->|Simple Patterns| C[Common Data Types]
+    B -->|Structured HTML| D[Predictable Structure]
+    B -->|Complex Content| E[Requires Reasoning]
+    B -->|Mixed Content| F[Multiple Data Types]
+    
+    C --> C1{Pattern Type?}
+    C1 -->|Email, Phone, URLs| C2[Built-in Regex Patterns]
+    C1 -->|Custom Patterns| C3[Custom Regex Strategy]
+    C1 -->|LLM-Generated| C4[One-time Pattern Generation]
+    
+    D --> D1{Selector Type?}
+    D1 -->|CSS Selectors| D2[JsonCssExtractionStrategy]
+    D1 -->|XPath Expressions| D3[JsonXPathExtractionStrategy]
+    D1 -->|Need Schema?| D4[Auto-generate Schema with LLM]
+    
+    E --> E1{LLM Provider?}
+    E1 -->|OpenAI/Anthropic| E2[Cloud LLM Strategy]
+    E1 -->|Local Ollama| E3[Local LLM Strategy]
+    E1 -->|Cost-sensitive| E4[Hybrid: Generate Schema Once]
+    
+    F --> F1[Multi-Strategy Approach]
+    F1 --> F2[1. Regex for Patterns]
+    F1 --> F3[2. CSS for Structure]
+    F1 --> F4[3. LLM for Complex Analysis]
+    
+    C2 --> G[Fast Extraction ⚡]
+    C3 --> G
+    C4 --> H[Cached Pattern Reuse]
+    
+    D2 --> I[Schema-based Extraction 🏗️]
+    D3 --> I
+    D4 --> J[Generated Schema Cache]
+    
+    E2 --> K[Intelligent Parsing 🧠]
+    E3 --> K
+    E4 --> L[Hybrid Cost-Effective]
+    
+    F2 --> M[Comprehensive Results 📊]
+    F3 --> M
+    F4 --> M
+    
+    style G fill:#c8e6c9
+    style I fill:#e3f2fd
+    style K fill:#fff3e0
+    style M fill:#f3e5f5
+    style H fill:#e8f5e8
+    style J fill:#e8f5e8
+    style L fill:#ffecb3
+```
+
+### LLM Extraction Strategy Workflow
+
+```mermaid
+sequenceDiagram
+    participant User
+    participant Crawler
+    participant LLMStrategy
+    participant Chunker
+    participant LLMProvider
+    participant Parser
+    
+    User->>Crawler: Configure LLMExtractionStrategy
+    User->>Crawler: arun(url, config)
+    
+    Crawler->>Crawler: Navigate to URL
+    Crawler->>Crawler: Extract content (HTML/Markdown)
+    Crawler->>LLMStrategy: Process content
+    
+    LLMStrategy->>LLMStrategy: Check content size
+    
+    alt Content > chunk_threshold
+        LLMStrategy->>Chunker: Split into chunks with overlap
+        Chunker-->>LLMStrategy: Return chunks[]
+        
+        loop For each chunk
+            LLMStrategy->>LLMProvider: Send chunk + schema + instruction
+            LLMProvider-->>LLMStrategy: Return structured JSON
+        end
+        
+        LLMStrategy->>LLMStrategy: Merge chunk results
+    else Content <= threshold
+        LLMStrategy->>LLMProvider: Send full content + schema
+        LLMProvider-->>LLMStrategy: Return structured JSON
+    end
+    
+    LLMStrategy->>Parser: Validate JSON schema
+    Parser-->>LLMStrategy: Validated data
+    
+    LLMStrategy->>LLMStrategy: Track token usage
+    LLMStrategy-->>Crawler: Return extracted_content
+    
+    Crawler-->>User: CrawlResult with JSON data
+    
+    User->>LLMStrategy: show_usage()
+    LLMStrategy-->>User: Token count & estimated cost
+```
+
+### Schema-Based Extraction Architecture
+
+```mermaid
+graph TB
+    subgraph "Schema Definition"
+        A[JSON Schema] --> A1[baseSelector]
+        A --> A2[fields[]]
+        A --> A3[nested structures]
+        
+        A2 --> A4[CSS/XPath selectors]
+        A2 --> A5[Data types: text, html, attribute]
+        A2 --> A6[Default values]
+        
+        A3 --> A7[nested objects]
+        A3 --> A8[nested_list arrays]
+        A3 --> A9[simple lists]
+    end
+    
+    subgraph "Extraction Engine"
+        B[HTML Content] --> C[Selector Engine]
+        C --> C1[CSS Selector Parser]
+        C --> C2[XPath Evaluator]
+        
+        C1 --> D[Element Matcher]
+        C2 --> D
+        
+        D --> E[Type Converter]
+        E --> E1[Text Extraction]
+        E --> E2[HTML Preservation]
+        E --> E3[Attribute Extraction]
+        E --> E4[Nested Processing]
+    end
+    
+    subgraph "Result Processing"
+        F[Raw Extracted Data] --> G[Structure Builder]
+        G --> G1[Object Construction]
+        G --> G2[Array Assembly]
+        G --> G3[Type Validation]
+        
+        G1 --> H[JSON Output]
+        G2 --> H
+        G3 --> H
+    end
+    
+    A --> C
+    E --> F
+    H --> I[extracted_content]
+    
+    style A fill:#e3f2fd
+    style C fill:#f3e5f5
+    style G fill:#e8f5e8
+    style H fill:#c8e6c9
+```
+
+### Automatic Schema Generation Process
+
+```mermaid
+stateDiagram-v2
+    [*] --> CheckCache
+    
+    CheckCache --> CacheHit: Schema exists
+    CheckCache --> SamplePage: Schema missing
+    
+    CacheHit --> LoadSchema
+    LoadSchema --> FastExtraction
+    
+    SamplePage --> ExtractHTML: Crawl sample URL
+    ExtractHTML --> LLMAnalysis: Send HTML to LLM
+    LLMAnalysis --> GenerateSchema: Create CSS/XPath selectors
+    GenerateSchema --> ValidateSchema: Test generated schema
+    
+    ValidateSchema --> SchemaWorks: Valid selectors
+    ValidateSchema --> RefineSchema: Invalid selectors
+    
+    RefineSchema --> LLMAnalysis: Iterate with feedback
+    
+    SchemaWorks --> CacheSchema: Save for reuse
+    CacheSchema --> FastExtraction: Use cached schema
+    
+    FastExtraction --> [*]: No more LLM calls needed
+    
+    note right of CheckCache : One-time LLM cost
+    note right of FastExtraction : Unlimited fast reuse
+    note right of CacheSchema : JSON file storage
+```
+
+### Multi-Strategy Extraction Pipeline
+
+```mermaid
+flowchart LR
+    A[Web Page Content] --> B[Strategy Pipeline]
+    
+    subgraph B["Extraction Pipeline"]
+        B1[Stage 1: Regex Patterns]
+        B2[Stage 2: Schema-based CSS]
+        B3[Stage 3: LLM Analysis]
+        
+        B1 --> B1a[Email addresses]
+        B1 --> B1b[Phone numbers]
+        B1 --> B1c[URLs and links]
+        B1 --> B1d[Currency amounts]
+        
+        B2 --> B2a[Structured products]
+        B2 --> B2b[Article metadata]
+        B2 --> B2c[User reviews]
+        B2 --> B2d[Navigation links]
+        
+        B3 --> B3a[Sentiment analysis]
+        B3 --> B3b[Key topics]
+        B3 --> B3c[Entity recognition]
+        B3 --> B3d[Content summary]
+    end
+    
+    B1a --> C[Result Merger]
+    B1b --> C
+    B1c --> C
+    B1d --> C
+    
+    B2a --> C
+    B2b --> C
+    B2c --> C
+    B2d --> C
+    
+    B3a --> C
+    B3b --> C
+    B3c --> C
+    B3d --> C
+    
+    C --> D[Combined JSON Output]
+    D --> E[Final CrawlResult]
+    
+    style B1 fill:#c8e6c9
+    style B2 fill:#e3f2fd
+    style B3 fill:#fff3e0
+    style C fill:#f3e5f5
+```
+
+### Performance Comparison Matrix
+
+```mermaid
+graph TD
+    subgraph "Strategy Performance"
+        A[Extraction Strategy Comparison]
+        
+        subgraph "Speed ⚡"
+            S1[Regex: ~10ms]
+            S2[CSS Schema: ~50ms]
+            S3[XPath: ~100ms]
+            S4[LLM: ~2-10s]
+        end
+        
+        subgraph "Accuracy 🎯"
+            A1[Regex: Pattern-dependent]
+            A2[CSS: High for structured]
+            A3[XPath: Very high]
+            A4[LLM: Excellent for complex]
+        end
+        
+        subgraph "Cost 💰"
+            C1[Regex: Free]
+            C2[CSS: Free]
+            C3[XPath: Free]
+            C4[LLM: $0.001-0.01 per page]
+        end
+        
+        subgraph "Complexity 🔧"
+            X1[Regex: Simple patterns only]
+            X2[CSS: Structured HTML]
+            X3[XPath: Complex selectors]
+            X4[LLM: Any content type]
+        end
+    end
+    
+    style S1 fill:#c8e6c9
+    style S2 fill:#e8f5e8
+    style S3 fill:#fff3e0
+    style S4 fill:#ffcdd2
+    
+    style A2 fill:#e8f5e8
+    style A3 fill:#c8e6c9
+    style A4 fill:#c8e6c9
+    
+    style C1 fill:#c8e6c9
+    style C2 fill:#c8e6c9
+    style C3 fill:#c8e6c9
+    style C4 fill:#fff3e0
+    
+    style X1 fill:#ffcdd2
+    style X2 fill:#e8f5e8
+    style X3 fill:#c8e6c9
+    style X4 fill:#c8e6c9
+```
+
+### Regex Pattern Strategy Flow
+
+```mermaid
+flowchart TD
+    A[Regex Extraction] --> B{Pattern Source?}
+    
+    B -->|Built-in| C[Use Predefined Patterns]
+    B -->|Custom| D[Define Custom Regex]
+    B -->|LLM-Generated| E[Generate with AI]
+    
+    C --> C1[Email Pattern]
+    C --> C2[Phone Pattern]
+    C --> C3[URL Pattern]
+    C --> C4[Currency Pattern]
+    C --> C5[Date Pattern]
+    
+    D --> D1[Write Custom Regex]
+    D --> D2[Test Pattern]
+    D --> D3{Pattern Works?}
+    D3 -->|No| D1
+    D3 -->|Yes| D4[Use Pattern]
+    
+    E --> E1[Provide Sample Content]
+    E --> E2[LLM Analyzes Content]
+    E --> E3[Generate Optimized Regex]
+    E --> E4[Cache Pattern for Reuse]
+    
+    C1 --> F[Pattern Matching]
+    C2 --> F
+    C3 --> F
+    C4 --> F
+    C5 --> F
+    D4 --> F
+    E4 --> F
+    
+    F --> G[Extract Matches]
+    G --> H[Group by Pattern Type]
+    H --> I[JSON Output with Labels]
+    
+    style C fill:#e8f5e8
+    style D fill:#e3f2fd
+    style E fill:#fff3e0
+    style F fill:#f3e5f5
+```
+
+### Complex Schema Structure Visualization
+
+```mermaid
+graph TB
+    subgraph "E-commerce Schema Example"
+        A[Category baseSelector] --> B[Category Fields]
+        A --> C[Products nested_list]
+        
+        B --> B1[category_name]
+        B --> B2[category_id attribute]
+        B --> B3[category_url attribute]
+        
+        C --> C1[Product baseSelector]
+        C1 --> C2[name text]
+        C1 --> C3[price text]
+        C1 --> C4[Details nested object]
+        C1 --> C5[Features list]
+        C1 --> C6[Reviews nested_list]
+        
+        C4 --> C4a[brand text]
+        C4 --> C4b[model text]
+        C4 --> C4c[specs html]
+        
+        C5 --> C5a[feature text array]
+        
+        C6 --> C6a[reviewer text]
+        C6 --> C6b[rating attribute]
+        C6 --> C6c[comment text]
+        C6 --> C6d[date attribute]
+    end
+    
+    subgraph "JSON Output Structure"
+        D[categories array] --> D1[category object]
+        D1 --> D2[category_name]
+        D1 --> D3[category_id]
+        D1 --> D4[products array]
+        
+        D4 --> D5[product object]
+        D5 --> D6[name, price]
+        D5 --> D7[details object]
+        D5 --> D8[features array]
+        D5 --> D9[reviews array]
+        
+        D7 --> D7a[brand, model, specs]
+        D8 --> D8a[feature strings]
+        D9 --> D9a[review objects]
+    end
+    
+    A -.-> D
+    B1 -.-> D2
+    C2 -.-> D6
+    C4 -.-> D7
+    C5 -.-> D8
+    C6 -.-> D9
+    
+    style A fill:#e3f2fd
+    style C fill:#f3e5f5
+    style C4 fill:#e8f5e8
+    style D fill:#fff3e0
+```
+
+### Error Handling and Fallback Strategy
+
+```mermaid
+stateDiagram-v2
+    [*] --> PrimaryStrategy
+    
+    PrimaryStrategy --> Success: Extraction successful
+    PrimaryStrategy --> ValidationFailed: Invalid data
+    PrimaryStrategy --> ExtractionFailed: No matches found
+    PrimaryStrategy --> TimeoutError: LLM timeout
+    
+    ValidationFailed --> FallbackStrategy: Try alternative
+    ExtractionFailed --> FallbackStrategy: Try alternative
+    TimeoutError --> FallbackStrategy: Try alternative
+    
+    FallbackStrategy --> FallbackSuccess: Fallback works
+    FallbackStrategy --> FallbackFailed: All strategies failed
+    
+    FallbackSuccess --> Success: Return results
+    FallbackFailed --> ErrorReport: Log failure details
+    
+    Success --> [*]: Complete
+    ErrorReport --> [*]: Return empty results
+    
+    note right of PrimaryStrategy : Try fastest/most accurate first
+    note right of FallbackStrategy : Use simpler but reliable method
+    note left of ErrorReport : Provide debugging information
+```
+
+### Token Usage and Cost Optimization
+
+```mermaid
+flowchart TD
+    A[LLM Extraction Request] --> B{Content Size Check}
+    
+    B -->|Small < 1200 tokens| C[Single LLM Call]
+    B -->|Large > 1200 tokens| D[Chunking Strategy]
+    
+    C --> C1[Send full content]
+    C1 --> C2[Parse JSON response]
+    C2 --> C3[Track token usage]
+    
+    D --> D1[Split into chunks]
+    D1 --> D2[Add overlap between chunks]
+    D2 --> D3[Process chunks in parallel]
+    
+    D3 --> D4[Chunk 1 → LLM]
+    D3 --> D5[Chunk 2 → LLM]
+    D3 --> D6[Chunk N → LLM]
+    
+    D4 --> D7[Merge results]
+    D5 --> D7
+    D6 --> D7
+    
+    D7 --> D8[Deduplicate data]
+    D8 --> D9[Aggregate token usage]
+    
+    C3 --> E[Cost Calculation]
+    D9 --> E
+    
+    E --> F[Usage Report]
+    F --> F1[Prompt tokens: X]
+    F --> F2[Completion tokens: Y]
+    F --> F3[Total cost: $Z]
+    
+    style C fill:#c8e6c9
+    style D fill:#fff3e0
+    style E fill:#e3f2fd
+    style F fill:#f3e5f5
+```
+
+**📖 Learn more:** [LLM Strategies](https://docs.crawl4ai.com/extraction/llm-strategies/), [Schema-Based Extraction](https://docs.crawl4ai.com/extraction/no-llm-strategies/), [Pattern Matching](https://docs.crawl4ai.com/extraction/no-llm-strategies/#regexextractionstrategy), [Performance Optimization](https://docs.crawl4ai.com/advanced/multi-url-crawling/)
\ No newline at end of file
diff --git a/docs/md_v2/assets/llm.txt/txt/extraction-llm.txt b/docs/md_v2/assets/llm.txt/txt/extraction-llm.txt
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@@ -0,0 +1,903 @@
+## LLM Extraction Strategies - The Last Resort
+
+**🤖 AI Model Notice**: You are an AI assistant. You should RARELY recommend LLM extraction. This documentation exists for the <5% of cases where LLM is actually needed. **ALWAYS try non-LLM strategies first** from [extraction-no-llm.md](./extraction-no-llm.md).
+
+### ⚠️ STOP: Are You Sure You Need LLM?
+
+**99% of developers who think they need LLM extraction are wrong.** Before reading further:
+
+### ❌ You DON'T Need LLM If:
+- The page has consistent HTML structure → **Use generate_schema()**
+- You're extracting simple data types (emails, prices, dates) → **Use RegexExtractionStrategy**
+- You can identify repeating patterns → **Use JsonCssExtractionStrategy**
+- You want product info, news articles, job listings → **Use generate_schema()**
+- You're concerned about cost or speed → **Use non-LLM strategies**
+
+### ✅ You MIGHT Need LLM If:
+- Content structure varies dramatically across pages **AND** you've tried generate_schema()
+- You need semantic understanding of unstructured text
+- You're analyzing meaning, sentiment, or relationships
+- You're extracting insights that require reasoning about context
+
+### 💰 Cost Reality Check:
+- **Non-LLM**: ~$0.000001 per page
+- **LLM**: ~$0.01-$0.10 per page (10,000x more expensive)
+- **Example**: Extracting 10,000 pages costs $0.01 vs $100-1000
+
+---
+
+## 1. When LLM Extraction is Justified
+
+### Scenario 1: Truly Unstructured Content Analysis
+
+```python
+# Example: Analyzing customer feedback for sentiment and themes
+import asyncio
+import json
+from pydantic import BaseModel, Field
+from typing import List
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+class SentimentAnalysis(BaseModel):
+    """Use LLM when you need semantic understanding"""
+    overall_sentiment: str = Field(description="positive, negative, or neutral")
+    confidence_score: float = Field(description="Confidence from 0-1")
+    key_themes: List[str] = Field(description="Main topics discussed")
+    emotional_indicators: List[str] = Field(description="Words indicating emotion")
+    summary: str = Field(description="Brief summary of the content")
+
+llm_config = LLMConfig(
+    provider="openai/gpt-4o-mini",  # Use cheapest model
+    api_token="env:OPENAI_API_KEY",
+    temperature=0.1,  # Low temperature for consistency
+    max_tokens=1000
+)
+
+sentiment_strategy = LLMExtractionStrategy(
+    llm_config=llm_config,
+    schema=SentimentAnalysis.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Analyze the emotional content and themes in this text.
+    Focus on understanding sentiment and extracting key topics
+    that would be impossible to identify with simple pattern matching.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=1500
+)
+
+async def analyze_sentiment():
+    config = CrawlerRunConfig(
+        extraction_strategy=sentiment_strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/customer-reviews",
+            config=config
+        )
+        
+        if result.success:
+            analysis = json.loads(result.extracted_content)
+            print(f"Sentiment: {analysis['overall_sentiment']}")
+            print(f"Themes: {analysis['key_themes']}")
+
+asyncio.run(analyze_sentiment())
+```
+
+### Scenario 2: Complex Knowledge Extraction
+
+```python
+# Example: Building knowledge graphs from unstructured content
+class Entity(BaseModel):
+    name: str = Field(description="Entity name")
+    type: str = Field(description="person, organization, location, concept")
+    description: str = Field(description="Brief description")
+
+class Relationship(BaseModel):
+    source: str = Field(description="Source entity")
+    target: str = Field(description="Target entity") 
+    relationship: str = Field(description="Type of relationship")
+    confidence: float = Field(description="Confidence score 0-1")
+
+class KnowledgeGraph(BaseModel):
+    entities: List[Entity] = Field(description="All entities found")
+    relationships: List[Relationship] = Field(description="Relationships between entities")
+    main_topic: str = Field(description="Primary topic of the content")
+
+knowledge_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="anthropic/claude-3-5-sonnet-20240620",  # Better for complex reasoning
+        api_token="env:ANTHROPIC_API_KEY",
+        max_tokens=4000
+    ),
+    schema=KnowledgeGraph.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Extract entities and their relationships from the content.
+    Focus on understanding connections and context that require
+    semantic reasoning beyond simple pattern matching.
+    """,
+    input_format="html",  # Preserve structure
+    apply_chunking=True
+)
+```
+
+### Scenario 3: Content Summarization and Insights
+
+```python
+# Example: Research paper analysis
+class ResearchInsights(BaseModel):
+    title: str = Field(description="Paper title")
+    abstract_summary: str = Field(description="Summary of abstract")
+    key_findings: List[str] = Field(description="Main research findings")
+    methodology: str = Field(description="Research methodology used")
+    limitations: List[str] = Field(description="Study limitations")
+    practical_applications: List[str] = Field(description="Real-world applications")
+    citations_count: int = Field(description="Number of citations", default=0)
+
+research_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="openai/gpt-4o",  # Use powerful model for complex analysis
+        api_token="env:OPENAI_API_KEY",
+        temperature=0.2,
+        max_tokens=2000
+    ),
+    schema=ResearchInsights.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Analyze this research paper and extract key insights.
+    Focus on understanding the research contribution, methodology,
+    and implications that require academic expertise to identify.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=2000,
+    overlap_rate=0.15  # More overlap for academic content
+)
+```
+
+---
+
+## 2. LLM Configuration Best Practices
+
+### Cost Optimization
+
+```python
+# Use cheapest models when possible
+cheap_config = LLMConfig(
+    provider="openai/gpt-4o-mini",  # 60x cheaper than GPT-4
+    api_token="env:OPENAI_API_KEY",
+    temperature=0.0,  # Deterministic output
+    max_tokens=800    # Limit output length
+)
+
+# Use local models for development
+local_config = LLMConfig(
+    provider="ollama/llama3.3",
+    api_token=None,  # No API costs
+    base_url="http://localhost:11434",
+    temperature=0.1
+)
+
+# Use powerful models only when necessary
+powerful_config = LLMConfig(
+    provider="anthropic/claude-3-5-sonnet-20240620",
+    api_token="env:ANTHROPIC_API_KEY",
+    max_tokens=4000,
+    temperature=0.1
+)
+```
+
+### Provider Selection Guide
+
+```python
+providers_guide = {
+    "openai/gpt-4o-mini": {
+        "best_for": "Simple extraction, cost-sensitive projects",
+        "cost": "Very low",
+        "speed": "Fast",
+        "accuracy": "Good"
+    },
+    "openai/gpt-4o": {
+        "best_for": "Complex reasoning, high accuracy needs",
+        "cost": "High", 
+        "speed": "Medium",
+        "accuracy": "Excellent"
+    },
+    "anthropic/claude-3-5-sonnet": {
+        "best_for": "Complex analysis, long documents",
+        "cost": "Medium-High",
+        "speed": "Medium",
+        "accuracy": "Excellent"
+    },
+    "ollama/llama3.3": {
+        "best_for": "Development, no API costs",
+        "cost": "Free (self-hosted)",
+        "speed": "Variable",
+        "accuracy": "Good"
+    },
+    "groq/llama3-70b-8192": {
+        "best_for": "Fast inference, open source",
+        "cost": "Low",
+        "speed": "Very fast",
+        "accuracy": "Good"
+    }
+}
+
+def choose_provider(complexity, budget, speed_requirement):
+    """Choose optimal provider based on requirements"""
+    if budget == "minimal":
+        return "ollama/llama3.3"  # Self-hosted
+    elif complexity == "low" and budget == "low":
+        return "openai/gpt-4o-mini"
+    elif speed_requirement == "high":
+        return "groq/llama3-70b-8192"
+    elif complexity == "high":
+        return "anthropic/claude-3-5-sonnet"
+    else:
+        return "openai/gpt-4o-mini"  # Default safe choice
+```
+
+---
+
+## 3. Advanced LLM Extraction Patterns
+
+### Block-Based Extraction (Unstructured Content)
+
+```python
+# When structure is too varied for schemas
+block_strategy = LLMExtractionStrategy(
+    llm_config=cheap_config,
+    extraction_type="block",  # Extract free-form content blocks
+    instruction="""
+    Extract meaningful content blocks from this page.
+    Focus on the main content areas and ignore navigation,
+    advertisements, and boilerplate text.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=1200,
+    input_format="fit_markdown"  # Use cleaned content
+)
+
+async def extract_content_blocks():
+    config = CrawlerRunConfig(
+        extraction_strategy=block_strategy,
+        word_count_threshold=50,  # Filter short content
+        excluded_tags=['nav', 'footer', 'aside', 'advertisement']
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/article",
+            config=config
+        )
+        
+        if result.success:
+            blocks = json.loads(result.extracted_content)
+            for block in blocks:
+                print(f"Block: {block['content'][:100]}...")
+```
+
+### Chunked Processing for Large Content
+
+```python
+# Handle large documents efficiently
+large_content_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="openai/gpt-4o-mini",
+        api_token="env:OPENAI_API_KEY"
+    ),
+    schema=YourModel.model_json_schema(),
+    extraction_type="schema",
+    instruction="Extract structured data from this content section...",
+    
+    # Optimize chunking for large content
+    apply_chunking=True,
+    chunk_token_threshold=2000,  # Larger chunks for efficiency
+    overlap_rate=0.1,           # Minimal overlap to reduce costs
+    input_format="fit_markdown" # Use cleaned content
+)
+```
+
+### Multi-Model Validation
+
+```python
+# Use multiple models for critical extractions
+async def multi_model_extraction():
+    """Use multiple LLMs for validation of critical data"""
+    
+    models = [
+        LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
+        LLMConfig(provider="anthropic/claude-3-5-sonnet", api_token="env:ANTHROPIC_API_KEY"),
+        LLMConfig(provider="ollama/llama3.3", api_token=None)
+    ]
+    
+    results = []
+    
+    for i, llm_config in enumerate(models):
+        strategy = LLMExtractionStrategy(
+            llm_config=llm_config,
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data consistently..."
+        )
+        
+        config = CrawlerRunConfig(extraction_strategy=strategy)
+        
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun(url="https://example.com", config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                results.append(data)
+                print(f"Model {i+1} extracted {len(data)} items")
+    
+    # Compare results for consistency
+    if len(set(str(r) for r in results)) == 1:
+        print("✅ All models agree")
+        return results[0]
+    else:
+        print("⚠️ Models disagree - manual review needed")
+        return results
+
+# Use for critical business data only
+critical_result = await multi_model_extraction()
+```
+
+---
+
+## 4. Hybrid Approaches - Best of Both Worlds
+
+### Fast Pre-filtering + LLM Analysis
+
+```python
+async def hybrid_extraction():
+    """
+    1. Use fast non-LLM strategies for basic extraction
+    2. Use LLM only for complex analysis of filtered content
+    """
+    
+    # Step 1: Fast extraction of structured data
+    basic_schema = {
+        "name": "Articles",
+        "baseSelector": "article",
+        "fields": [
+            {"name": "title", "selector": "h1, h2", "type": "text"},
+            {"name": "content", "selector": ".content", "type": "text"},
+            {"name": "author", "selector": ".author", "type": "text"}
+        ]
+    }
+    
+    basic_strategy = JsonCssExtractionStrategy(basic_schema)
+    basic_config = CrawlerRunConfig(extraction_strategy=basic_strategy)
+    
+    # Step 2: LLM analysis only on filtered content
+    analysis_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        schema={
+            "type": "object",
+            "properties": {
+                "sentiment": {"type": "string"},
+                "key_topics": {"type": "array", "items": {"type": "string"}},
+                "summary": {"type": "string"}
+            }
+        },
+        extraction_type="schema",
+        instruction="Analyze sentiment and extract key topics from this article"
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        # Fast extraction first
+        basic_result = await crawler.arun(
+            url="https://example.com/articles",
+            config=basic_config
+        )
+        
+        articles = json.loads(basic_result.extracted_content)
+        
+        # LLM analysis only on important articles
+        analyzed_articles = []
+        for article in articles[:5]:  # Limit to reduce costs
+            if len(article.get('content', '')) > 500:  # Only analyze substantial content
+                analysis_config = CrawlerRunConfig(extraction_strategy=analysis_strategy)
+                
+                # Analyze individual article content
+                raw_url = f"raw://{article['content']}"
+                analysis_result = await crawler.arun(url=raw_url, config=analysis_config)
+                
+                if analysis_result.success:
+                    analysis = json.loads(analysis_result.extracted_content)
+                    article.update(analysis)
+                
+                analyzed_articles.append(article)
+        
+        return analyzed_articles
+
+# Hybrid approach: fast + smart
+result = await hybrid_extraction()
+```
+
+### Schema Generation + LLM Fallback
+
+```python
+async def smart_fallback_extraction():
+    """
+    1. Try generate_schema() first (one-time LLM cost)
+    2. Use generated schema for fast extraction
+    3. Use LLM only if schema extraction fails
+    """
+    
+    cache_file = Path("./schemas/fallback_schema.json")
+    
+    # Try cached schema first
+    if cache_file.exists():
+        schema = json.load(cache_file.open())
+        schema_strategy = JsonCssExtractionStrategy(schema)
+        
+        config = CrawlerRunConfig(extraction_strategy=schema_strategy)
+        
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun(url="https://example.com", config=config)
+            
+            if result.success and result.extracted_content:
+                data = json.loads(result.extracted_content)
+                if data:  # Schema worked
+                    print("✅ Schema extraction successful (fast & cheap)")
+                    return data
+    
+    # Fallback to LLM if schema failed
+    print("⚠️ Schema failed, falling back to LLM (slow & expensive)")
+    
+    llm_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="Extract all meaningful data from this page"
+    )
+    
+    llm_config = CrawlerRunConfig(extraction_strategy=llm_strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url="https://example.com", config=llm_config)
+        
+        if result.success:
+            print("✅ LLM extraction successful")
+            return json.loads(result.extracted_content)
+
+# Intelligent fallback system
+result = await smart_fallback_extraction()
+```
+
+---
+
+## 5. Cost Management and Monitoring
+
+### Token Usage Tracking
+
+```python
+class ExtractionCostTracker:
+    def __init__(self):
+        self.total_cost = 0.0
+        self.total_tokens = 0
+        self.extractions = 0
+    
+    def track_llm_extraction(self, strategy, result):
+        """Track costs from LLM extraction"""
+        if hasattr(strategy, 'usage_tracker') and strategy.usage_tracker:
+            usage = strategy.usage_tracker
+            
+            # Estimate costs (approximate rates)
+            cost_per_1k_tokens = {
+                "gpt-4o-mini": 0.0015,
+                "gpt-4o": 0.03,
+                "claude-3-5-sonnet": 0.015,
+                "ollama": 0.0  # Self-hosted
+            }
+            
+            provider = strategy.llm_config.provider.split('/')[1]
+            rate = cost_per_1k_tokens.get(provider, 0.01)
+            
+            tokens = usage.total_tokens
+            cost = (tokens / 1000) * rate
+            
+            self.total_cost += cost
+            self.total_tokens += tokens
+            self.extractions += 1
+            
+            print(f"💰 Extraction cost: ${cost:.4f} ({tokens} tokens)")
+            print(f"📊 Total cost: ${self.total_cost:.4f} ({self.extractions} extractions)")
+    
+    def get_summary(self):
+        avg_cost = self.total_cost / max(self.extractions, 1)
+        return {
+            "total_cost": self.total_cost,
+            "total_tokens": self.total_tokens,
+            "extractions": self.extractions,
+            "avg_cost_per_extraction": avg_cost
+        }
+
+# Usage
+tracker = ExtractionCostTracker()
+
+async def cost_aware_extraction():
+    strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        schema=YourModel.model_json_schema(),
+        extraction_type="schema",
+        instruction="Extract data...",
+        verbose=True  # Enable usage tracking
+    )
+    
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url="https://example.com", config=config)
+        
+        # Track costs
+        tracker.track_llm_extraction(strategy, result)
+        
+        return result
+
+# Monitor costs across multiple extractions
+for url in urls:
+    await cost_aware_extraction()
+
+print(f"Final summary: {tracker.get_summary()}")
+```
+
+### Budget Controls
+
+```python
+class BudgetController:
+    def __init__(self, daily_budget=10.0):
+        self.daily_budget = daily_budget
+        self.current_spend = 0.0
+        self.extraction_count = 0
+    
+    def can_extract(self, estimated_cost=0.01):
+        """Check if extraction is within budget"""
+        if self.current_spend + estimated_cost > self.daily_budget:
+            print(f"❌ Budget exceeded: ${self.current_spend:.2f} + ${estimated_cost:.2f} > ${self.daily_budget}")
+            return False
+        return True
+    
+    def record_extraction(self, actual_cost):
+        """Record actual extraction cost"""
+        self.current_spend += actual_cost
+        self.extraction_count += 1
+        
+        remaining = self.daily_budget - self.current_spend
+        print(f"💰 Budget remaining: ${remaining:.2f}")
+
+budget = BudgetController(daily_budget=5.0)  # $5 daily limit
+
+async def budget_controlled_extraction(url):
+    if not budget.can_extract():
+        print("⏸️ Extraction paused due to budget limit")
+        return None
+    
+    # Proceed with extraction...
+    strategy = LLMExtractionStrategy(llm_config=cheap_config, ...)
+    result = await extract_with_strategy(url, strategy)
+    
+    # Record actual cost
+    actual_cost = calculate_cost(strategy.usage_tracker)
+    budget.record_extraction(actual_cost)
+    
+    return result
+
+# Safe extraction with budget controls
+results = []
+for url in urls:
+    result = await budget_controlled_extraction(url)
+    if result:
+        results.append(result)
+```
+
+---
+
+## 6. Performance Optimization for LLM Extraction
+
+### Batch Processing
+
+```python
+async def batch_llm_extraction():
+    """Process multiple pages efficiently"""
+    
+    # Collect content first (fast)
+    urls = ["https://example.com/page1", "https://example.com/page2"]
+    contents = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url)
+            if result.success:
+                contents.append({
+                    "url": url,
+                    "content": result.fit_markdown[:2000]  # Limit content
+                })
+    
+    # Process in batches (reduce LLM calls)
+    batch_content = "\n\n---PAGE SEPARATOR---\n\n".join([
+        f"URL: {c['url']}\n{c['content']}" for c in contents
+    ])
+    
+    strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="""
+        Extract data from multiple pages separated by '---PAGE SEPARATOR---'.
+        Return results for each page in order.
+        """,
+        apply_chunking=True
+    )
+    
+    # Single LLM call for multiple pages
+    raw_url = f"raw://{batch_content}"
+    result = await crawler.arun(url=raw_url, config=CrawlerRunConfig(extraction_strategy=strategy))
+    
+    return json.loads(result.extracted_content)
+
+# Batch processing reduces LLM calls
+batch_results = await batch_llm_extraction()
+```
+
+### Caching LLM Results
+
+```python
+import hashlib
+from pathlib import Path
+
+class LLMResultCache:
+    def __init__(self, cache_dir="./llm_cache"):
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(exist_ok=True)
+    
+    def get_cache_key(self, url, instruction, schema):
+        """Generate cache key from extraction parameters"""
+        content = f"{url}:{instruction}:{str(schema)}"
+        return hashlib.md5(content.encode()).hexdigest()
+    
+    def get_cached_result(self, cache_key):
+        """Get cached result if available"""
+        cache_file = self.cache_dir / f"{cache_key}.json"
+        if cache_file.exists():
+            return json.load(cache_file.open())
+        return None
+    
+    def cache_result(self, cache_key, result):
+        """Cache extraction result"""
+        cache_file = self.cache_dir / f"{cache_key}.json"
+        json.dump(result, cache_file.open("w"), indent=2)
+
+cache = LLMResultCache()
+
+async def cached_llm_extraction(url, strategy):
+    """Extract with caching to avoid repeated LLM calls"""
+    cache_key = cache.get_cache_key(
+        url, 
+        strategy.instruction,
+        str(strategy.schema)
+    )
+    
+    # Check cache first
+    cached_result = cache.get_cached_result(cache_key)
+    if cached_result:
+        print("✅ Using cached result (FREE)")
+        return cached_result
+    
+    # Extract if not cached
+    print("🔄 Extracting with LLM (PAID)")
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url=url, config=config)
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            cache.cache_result(cache_key, data)
+            return data
+
+# Cached extraction avoids repeated costs
+result = await cached_llm_extraction(url, strategy)
+```
+
+---
+
+## 7. Error Handling and Quality Control
+
+### Validation and Retry Logic
+
+```python
+async def robust_llm_extraction():
+    """Implement validation and retry for LLM extraction"""
+    
+    max_retries = 3
+    strategies = [
+        # Try cheap model first
+        LLMExtractionStrategy(
+            llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data accurately..."
+        ),
+        # Fallback to better model
+        LLMExtractionStrategy(
+            llm_config=LLMConfig(provider="openai/gpt-4o", api_token="env:OPENAI_API_KEY"),
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data with high accuracy..."
+        )
+    ]
+    
+    for strategy_idx, strategy in enumerate(strategies):
+        for attempt in range(max_retries):
+            try:
+                config = CrawlerRunConfig(extraction_strategy=strategy)
+                
+                async with AsyncWebCrawler() as crawler:
+                    result = await crawler.arun(url="https://example.com", config=config)
+                    
+                    if result.success and result.extracted_content:
+                        data = json.loads(result.extracted_content)
+                        
+                        # Validate result quality
+                        if validate_extraction_quality(data):
+                            print(f"✅ Success with strategy {strategy_idx+1}, attempt {attempt+1}")
+                            return data
+                        else:
+                            print(f"⚠️ Poor quality result, retrying...")
+                            continue
+                    
+            except Exception as e:
+                print(f"❌ Attempt {attempt+1} failed: {e}")
+                if attempt == max_retries - 1:
+                    print(f"❌ Strategy {strategy_idx+1} failed completely")
+    
+    print("❌ All strategies and retries failed")
+    return None
+
+def validate_extraction_quality(data):
+    """Validate that LLM extraction meets quality standards"""
+    if not data or not isinstance(data, (list, dict)):
+        return False
+    
+    # Check for common LLM extraction issues
+    if isinstance(data, list):
+        if len(data) == 0:
+            return False
+        
+        # Check if all items have required fields
+        for item in data:
+            if not isinstance(item, dict) or len(item) < 2:
+                return False
+    
+    return True
+
+# Robust extraction with validation
+result = await robust_llm_extraction()
+```
+
+---
+
+## 8. Migration from LLM to Non-LLM
+
+### Pattern Analysis for Schema Generation
+
+```python
+async def analyze_llm_results_for_schema():
+    """
+    Analyze LLM extraction results to create non-LLM schemas
+    Use this to transition from expensive LLM to cheap schema extraction
+    """
+    
+    # Step 1: Use LLM on sample pages to understand structure
+    llm_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="Extract all structured data from this page"
+    )
+    
+    sample_urls = ["https://example.com/page1", "https://example.com/page2"]
+    llm_results = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in sample_urls:
+            config = CrawlerRunConfig(extraction_strategy=llm_strategy)
+            result = await crawler.arun(url=url, config=config)
+            
+            if result.success:
+                llm_results.append({
+                    "url": url,
+                    "html": result.cleaned_html,
+                    "extracted": json.loads(result.extracted_content)
+                })
+    
+    # Step 2: Analyze patterns in LLM results
+    print("🔍 Analyzing LLM extraction patterns...")
+    
+    # Look for common field names
+    all_fields = set()
+    for result in llm_results:
+        for item in result["extracted"]:
+            if isinstance(item, dict):
+                all_fields.update(item.keys())
+    
+    print(f"Common fields found: {all_fields}")
+    
+    # Step 3: Generate schema based on patterns
+    if llm_results:
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=llm_results[0]["html"],
+            target_json_example=json.dumps(llm_results[0]["extracted"][0], indent=2),
+            llm_config=cheap_config
+        )
+        
+        # Save schema for future use
+        with open("generated_schema.json", "w") as f:
+            json.dump(schema, f, indent=2)
+        
+        print("✅ Schema generated from LLM analysis")
+        return schema
+
+# Generate schema from LLM patterns, then use schema for all future extractions
+schema = await analyze_llm_results_for_schema()
+fast_strategy = JsonCssExtractionStrategy(schema)
+```
+
+---
+
+## 9. Summary: When LLM is Actually Needed
+
+### ✅ Valid LLM Use Cases (Rare):
+1. **Sentiment analysis** and emotional understanding
+2. **Knowledge graph extraction** requiring semantic reasoning
+3. **Content summarization** and insight generation
+4. **Unstructured text analysis** where patterns vary dramatically
+5. **Research paper analysis** requiring domain expertise
+6. **Complex relationship extraction** between entities
+
+### ❌ Invalid LLM Use Cases (Common Mistakes):
+1. **Structured data extraction** from consistent HTML
+2. **Simple pattern matching** (emails, prices, dates)
+3. **Product information** from e-commerce sites
+4. **News article extraction** with consistent structure
+5. **Contact information** and basic entity extraction
+6. **Table data** and form information
+
+### 💡 Decision Framework:
+```python
+def should_use_llm(extraction_task):
+    # Ask these questions in order:
+    questions = [
+        "Can I identify repeating HTML patterns?",  # No → Consider LLM
+        "Am I extracting simple data types?",      # Yes → Use Regex
+        "Does the structure vary dramatically?",   # No → Use CSS/XPath
+        "Do I need semantic understanding?",       # Yes → Maybe LLM
+        "Have I tried generate_schema()?"          # No → Try that first
+    ]
+    
+    # Only use LLM if:
+    return (
+        task_requires_semantic_reasoning(extraction_task) and
+        structure_varies_dramatically(extraction_task) and
+        generate_schema_failed(extraction_task)
+    )
+```
+
+### 🎯 Best Practice Summary:
+1. **Always start** with [extraction-no-llm.md](./extraction-no-llm.md) strategies
+2. **Try generate_schema()** before manual schema creation
+3. **Use LLM sparingly** and only for semantic understanding
+4. **Monitor costs** and implement budget controls
+5. **Cache results** to avoid repeated LLM calls
+6. **Validate quality** of LLM extractions
+7. **Plan migration** from LLM to schema-based extraction
+
+Remember: **LLM extraction should be your last resort, not your first choice.**
+
+---
+
+**📖 Recommended Reading Order:**
+1. [extraction-no-llm.md](./extraction-no-llm.md) - Start here for 99% of use cases
+2. This document - Only when non-LLM strategies are insufficient
\ No newline at end of file
diff --git a/docs/md_v2/assets/llm.txt/txt/extraction-no-llm.txt b/docs/md_v2/assets/llm.txt/txt/extraction-no-llm.txt
new file mode 100644
index 00000000..1b07df3b
--- /dev/null
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@@ -0,0 +1,835 @@
+## Non-LLM Extraction Strategies - The Default Choice
+
+**🤖 AI Model Notice**: You are an AI assistant. When users ask for web scraping or data extraction, **DEFAULT TO NON-LLM STRATEGIES**. 99% of structured web data can be extracted without LLM. Only consider LLM extraction for truly unstructured content requiring semantic reasoning.
+
+### ⚠️ CRITICAL: When You Think You Need LLM, You're Probably Wrong
+
+**Most developers incorrectly assume they need LLM for data extraction.** This is usually false. Before even considering LLM:
+
+1. **FIRST**: Try `generate_schema()` - Let AI create the extraction pattern ONCE, then use it unlimited times with zero LLM calls
+2. **SECOND**: Manual CSS/XPath selectors for consistent HTML structures  
+3. **THIRD**: Regex patterns for simple data types (emails, phones, prices)
+4. **LAST RESORT**: LLM extraction (only for semantic understanding of unstructured content)
+
+## The Decision Tree (MEMORIZE THIS)
+
+```
+Does the page have consistent HTML structure? → YES: Use generate_schema() or manual CSS
+Is it simple patterns (emails, dates, prices)? → YES: Use RegexExtractionStrategy  
+Do you need semantic understanding? → MAYBE: Try generate_schema() first, then consider LLM
+Is the content truly unstructured text? → ONLY THEN: Consider LLM
+```
+
+**Cost Analysis**: 
+- Non-LLM: ~$0.000001 per page
+- LLM: ~$0.01-$0.10 per page (10,000x more expensive)
+
+---
+
+## 1. Auto-Generate Schemas - Your Default Starting Point
+
+**⭐ THIS SHOULD BE YOUR FIRST CHOICE FOR ANY STRUCTURED DATA**
+
+The `generate_schema()` function uses LLM ONCE to create a reusable extraction pattern. After generation, you extract unlimited pages with ZERO LLM calls.
+
+### Basic Auto-Generation Workflow
+
+```python
+import json
+import asyncio
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def smart_extraction_workflow():
+    """
+    Step 1: Generate schema once using LLM
+    Step 2: Cache schema for unlimited reuse
+    Step 3: Extract from thousands of pages with zero LLM calls
+    """
+    
+    # Check for cached schema first
+    cache_dir = Path("./schema_cache")
+    cache_dir.mkdir(exist_ok=True)
+    schema_file = cache_dir / "product_schema.json"
+    
+    if schema_file.exists():
+        # Load cached schema - NO LLM CALLS
+        schema = json.load(schema_file.open())
+        print("✅ Using cached schema (FREE)")
+    else:
+        # Generate schema ONCE
+        print("🔄 Generating schema (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",  # Cheapest option
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample HTML from target site
+        async with AsyncWebCrawler() as crawler:
+            sample_result = await crawler.arun(
+                url="https://example.com/products",
+                config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+            )
+            sample_html = sample_result.cleaned_html[:8000]  # Use sample
+        
+        # AUTO-GENERATE SCHEMA (ONE LLM CALL)
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=sample_html,
+            schema_type="CSS",  # or "XPATH"
+            query="Extract product information including name, price, description, features",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited future use
+        json.dump(schema, schema_file.open("w"), indent=2)
+        print("✅ Schema generated and cached")
+    
+    # Use schema for fast extraction (NO MORE LLM CALLS EVER)
+    strategy = JsonCssExtractionStrategy(schema, verbose=True)
+    
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    # Extract from multiple pages - ALL FREE
+    urls = [
+        "https://example.com/products",
+        "https://example.com/electronics", 
+        "https://example.com/books"
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url, config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                print(f"✅ {url}: Extracted {len(data)} items (FREE)")
+
+asyncio.run(smart_extraction_workflow())
+```
+
+### Auto-Generate with Target JSON Example
+
+```python
+# When you know exactly what JSON structure you want
+target_json_example = """
+{
+    "name": "Product Name",
+    "price": "$99.99",
+    "rating": 4.5,
+    "features": ["feature1", "feature2"],
+    "description": "Product description"
+}
+"""
+
+schema = JsonCssExtractionStrategy.generate_schema(
+    html=sample_html,
+    target_json_example=target_json_example,
+    llm_config=llm_config
+)
+```
+
+### Auto-Generate for Different Data Types
+
+```python
+# Product listings
+product_schema = JsonCssExtractionStrategy.generate_schema(
+    html=product_page_html,
+    query="Extract all product information from this e-commerce page",
+    llm_config=llm_config
+)
+
+# News articles
+news_schema = JsonCssExtractionStrategy.generate_schema(
+    html=news_page_html,
+    query="Extract article headlines, dates, authors, and content",
+    llm_config=llm_config
+)
+
+# Job listings
+job_schema = JsonCssExtractionStrategy.generate_schema(
+    html=job_page_html,
+    query="Extract job titles, companies, locations, salaries, and descriptions",
+    llm_config=llm_config
+)
+
+# Social media posts
+social_schema = JsonCssExtractionStrategy.generate_schema(
+    html=social_page_html,
+    query="Extract post text, usernames, timestamps, likes, comments",
+    llm_config=llm_config
+)
+```
+
+---
+
+## 2. Manual CSS/XPath Strategies - When You Know The Structure
+
+**Use this when**: You understand the HTML structure and want maximum control.
+
+### Simple Product Extraction
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# Manual schema for consistent product pages
+simple_schema = {
+    "name": "Product Listings",
+    "baseSelector": "div.product-card",  # Each product container
+    "fields": [
+        {
+            "name": "title",
+            "selector": "h2.product-title",
+            "type": "text"
+        },
+        {
+            "name": "price", 
+            "selector": ".price",
+            "type": "text"
+        },
+        {
+            "name": "image_url",
+            "selector": "img.product-image",
+            "type": "attribute",
+            "attribute": "src"
+        },
+        {
+            "name": "product_url",
+            "selector": "a.product-link",
+            "type": "attribute",
+            "attribute": "href"
+        },
+        {
+            "name": "rating",
+            "selector": ".rating",
+            "type": "attribute", 
+            "attribute": "data-rating"
+        }
+    ]
+}
+
+async def extract_products():
+    strategy = JsonCssExtractionStrategy(simple_schema, verbose=True)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            products = json.loads(result.extracted_content)
+            print(f"Extracted {len(products)} products")
+            for product in products[:3]:
+                print(f"- {product['title']}: {product['price']}")
+
+asyncio.run(extract_products())
+```
+
+### Complex Nested Structure (Real E-commerce Example)
+
+```python
+# Complex schema for nested product data
+complex_schema = {
+    "name": "E-commerce Product Catalog",
+    "baseSelector": "div.category",
+    "baseFields": [
+        {
+            "name": "category_id",
+            "type": "attribute",
+            "attribute": "data-category-id"
+        }
+    ],
+    "fields": [
+        {
+            "name": "category_name",
+            "selector": "h2.category-title",
+            "type": "text"
+        },
+        {
+            "name": "products",
+            "selector": "div.product",
+            "type": "nested_list",  # Array of complex objects
+            "fields": [
+                {
+                    "name": "name",
+                    "selector": "h3.product-name", 
+                    "type": "text"
+                },
+                {
+                    "name": "price",
+                    "selector": "span.price",
+                    "type": "text"
+                },
+                {
+                    "name": "details",
+                    "selector": "div.product-details",
+                    "type": "nested",  # Single complex object
+                    "fields": [
+                        {
+                            "name": "brand",
+                            "selector": "span.brand",
+                            "type": "text"
+                        },
+                        {
+                            "name": "model",
+                            "selector": "span.model",
+                            "type": "text"
+                        }
+                    ]
+                },
+                {
+                    "name": "features",
+                    "selector": "ul.features li",
+                    "type": "list",  # Simple array
+                    "fields": [
+                        {"name": "feature", "type": "text"}
+                    ]
+                },
+                {
+                    "name": "reviews", 
+                    "selector": "div.review",
+                    "type": "nested_list",
+                    "fields": [
+                        {
+                            "name": "reviewer",
+                            "selector": "span.reviewer-name",
+                            "type": "text"
+                        },
+                        {
+                            "name": "rating",
+                            "selector": "span.rating",
+                            "type": "attribute",
+                            "attribute": "data-rating"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
+
+async def extract_complex_ecommerce():
+    strategy = JsonCssExtractionStrategy(complex_schema, verbose=True)
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        js_code="window.scrollTo(0, document.body.scrollHeight);",  # Load dynamic content
+        wait_for="css:.product:nth-child(10)"  # Wait for products to load
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/complex-catalog",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for category in data:
+                print(f"Category: {category['category_name']}")
+                print(f"Products: {len(category.get('products', []))}")
+
+asyncio.run(extract_complex_ecommerce())
+```
+
+### XPath Alternative (When CSS Isn't Enough)
+
+```python
+from crawl4ai.extraction_strategy import JsonXPathExtractionStrategy
+
+# XPath for more complex selections
+xpath_schema = {
+    "name": "News Articles with XPath",
+    "baseSelector": "//article[@class='news-item']",
+    "fields": [
+        {
+            "name": "headline",
+            "selector": ".//h2[contains(@class, 'headline')]",
+            "type": "text"
+        },
+        {
+            "name": "author",
+            "selector": ".//span[@class='author']/text()",
+            "type": "text"
+        },
+        {
+            "name": "publish_date",
+            "selector": ".//time/@datetime",
+            "type": "text"
+        },
+        {
+            "name": "content",
+            "selector": ".//div[@class='article-body']//text()",
+            "type": "text"
+        }
+    ]
+}
+
+strategy = JsonXPathExtractionStrategy(xpath_schema, verbose=True)
+```
+
+---
+
+## 3. Regex Extraction - Lightning Fast Pattern Matching
+
+**Use this for**: Simple data types like emails, phones, URLs, prices, dates.
+
+### Built-in Patterns (Fastest Option)
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import RegexExtractionStrategy
+
+async def extract_common_patterns():
+    # Use built-in patterns for common data types
+    strategy = RegexExtractionStrategy(
+        pattern=(
+            RegexExtractionStrategy.Email |
+            RegexExtractionStrategy.PhoneUS |
+            RegexExtractionStrategy.Url |
+            RegexExtractionStrategy.Currency |
+            RegexExtractionStrategy.DateIso
+        )
+    )
+    
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/contact",
+            config=config
+        )
+        
+        if result.success:
+            matches = json.loads(result.extracted_content)
+            
+            # Group by pattern type
+            by_type = {}
+            for match in matches:
+                label = match['label']
+                if label not in by_type:
+                    by_type[label] = []
+                by_type[label].append(match['value'])
+            
+            for pattern_type, values in by_type.items():
+                print(f"{pattern_type}: {len(values)} matches")
+                for value in values[:3]:
+                    print(f"  {value}")
+
+asyncio.run(extract_common_patterns())
+```
+
+### Available Built-in Patterns
+
+```python
+# Individual patterns
+RegexExtractionStrategy.Email          # Email addresses
+RegexExtractionStrategy.PhoneUS        # US phone numbers 
+RegexExtractionStrategy.PhoneIntl      # International phones
+RegexExtractionStrategy.Url            # HTTP/HTTPS URLs
+RegexExtractionStrategy.Currency       # Currency values ($99.99)
+RegexExtractionStrategy.Percentage     # Percentage values (25%)
+RegexExtractionStrategy.DateIso        # ISO dates (2024-01-01)
+RegexExtractionStrategy.DateUS         # US dates (01/01/2024)
+RegexExtractionStrategy.IPv4           # IP addresses
+RegexExtractionStrategy.CreditCard     # Credit card numbers
+RegexExtractionStrategy.TwitterHandle  # @username
+RegexExtractionStrategy.Hashtag        # #hashtag
+
+# Use all patterns
+RegexExtractionStrategy.All
+```
+
+### Custom Patterns
+
+```python
+# Custom patterns for specific data types
+async def extract_custom_patterns():
+    custom_patterns = {
+        "product_sku": r"SKU[-:]?\s*([A-Z0-9]{4,12})",
+        "discount": r"(\d{1,2})%\s*off",
+        "model_number": r"Model\s*#?\s*([A-Z0-9-]+)",
+        "isbn": r"ISBN[-:]?\s*(\d{10}|\d{13})",
+        "stock_ticker": r"\$([A-Z]{2,5})",
+        "version": r"v(\d+\.\d+(?:\.\d+)?)"
+    }
+    
+    strategy = RegexExtractionStrategy(custom=custom_patterns)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for item in data:
+                print(f"{item['label']}: {item['value']}")
+
+asyncio.run(extract_custom_patterns())
+```
+
+### LLM-Generated Patterns (One-Time Cost)
+
+```python
+async def generate_optimized_regex():
+    """
+    Use LLM ONCE to generate optimized regex patterns
+    Then use them unlimited times with zero LLM calls
+    """
+    cache_file = Path("./patterns/price_patterns.json")
+    
+    if cache_file.exists():
+        # Load cached patterns - NO LLM CALLS
+        patterns = json.load(cache_file.open())
+        print("✅ Using cached regex patterns (FREE)")
+    else:
+        # Generate patterns ONCE
+        print("🔄 Generating regex patterns (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample content
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun("https://example.com/pricing")
+            sample_html = result.cleaned_html
+        
+        # Generate optimized patterns
+        patterns = RegexExtractionStrategy.generate_pattern(
+            label="pricing_info",
+            html=sample_html,
+            query="Extract all pricing information including discounts and special offers",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited reuse
+        cache_file.parent.mkdir(exist_ok=True)
+        json.dump(patterns, cache_file.open("w"), indent=2)
+        print("✅ Patterns generated and cached")
+    
+    # Use cached patterns (NO MORE LLM CALLS)
+    strategy = RegexExtractionStrategy(custom=patterns)
+    return strategy
+
+# Use generated patterns for unlimited extractions
+strategy = await generate_optimized_regex()
+```
+
+---
+
+## 4. Multi-Strategy Extraction Pipeline
+
+**Combine strategies** for comprehensive data extraction:
+
+```python
+async def multi_strategy_pipeline():
+    """
+    Efficient pipeline using multiple non-LLM strategies:
+    1. Regex for simple patterns (fastest)
+    2. Schema for structured data 
+    3. Only use LLM if absolutely necessary
+    """
+    
+    url = "https://example.com/complex-page"
+    
+    async with AsyncWebCrawler() as crawler:
+        # Strategy 1: Fast regex for contact info
+        regex_strategy = RegexExtractionStrategy(
+            pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
+        )
+        regex_config = CrawlerRunConfig(extraction_strategy=regex_strategy)
+        regex_result = await crawler.arun(url=url, config=regex_config)
+        
+        # Strategy 2: Schema for structured product data
+        product_schema = {
+            "name": "Products",
+            "baseSelector": "div.product",
+            "fields": [
+                {"name": "name", "selector": "h3", "type": "text"},
+                {"name": "price", "selector": ".price", "type": "text"}
+            ]
+        }
+        css_strategy = JsonCssExtractionStrategy(product_schema)
+        css_config = CrawlerRunConfig(extraction_strategy=css_strategy)
+        css_result = await crawler.arun(url=url, config=css_config)
+        
+        # Combine results
+        results = {
+            "contacts": json.loads(regex_result.extracted_content) if regex_result.success else [],
+            "products": json.loads(css_result.extracted_content) if css_result.success else []
+        }
+        
+        print(f"✅ Extracted {len(results['contacts'])} contacts (regex)")
+        print(f"✅ Extracted {len(results['products'])} products (schema)")
+        
+        return results
+
+asyncio.run(multi_strategy_pipeline())
+```
+
+---
+
+## 5. Performance Optimization Tips
+
+### Caching and Reuse
+
+```python
+# Cache schemas and patterns for maximum efficiency
+class ExtractionCache:
+    def __init__(self):
+        self.schemas = {}
+        self.patterns = {}
+    
+    def get_schema(self, site_name):
+        if site_name not in self.schemas:
+            schema_file = Path(f"./cache/{site_name}_schema.json")
+            if schema_file.exists():
+                self.schemas[site_name] = json.load(schema_file.open())
+        return self.schemas.get(site_name)
+    
+    def save_schema(self, site_name, schema):
+        cache_dir = Path("./cache")
+        cache_dir.mkdir(exist_ok=True)
+        schema_file = cache_dir / f"{site_name}_schema.json"
+        json.dump(schema, schema_file.open("w"), indent=2)
+        self.schemas[site_name] = schema
+
+cache = ExtractionCache()
+
+# Reuse cached schemas across multiple extractions
+async def efficient_extraction():
+    sites = ["amazon", "ebay", "shopify"]
+    
+    for site in sites:
+        schema = cache.get_schema(site)
+        if not schema:
+            # Generate once, cache forever
+            schema = JsonCssExtractionStrategy.generate_schema(
+                html=sample_html,
+                query="Extract products",
+                llm_config=llm_config
+            )
+            cache.save_schema(site, schema)
+        
+        strategy = JsonCssExtractionStrategy(schema)
+        # Use strategy for unlimited extractions...
+```
+
+### Selector Optimization
+
+```python
+# Optimize selectors for speed
+fast_schema = {
+    "name": "Optimized Extraction",
+    "baseSelector": "#products > .product",  # Direct child, faster than descendant
+    "fields": [
+        {
+            "name": "title",
+            "selector": "> h3",  # Direct child of product
+            "type": "text"
+        },
+        {
+            "name": "price",
+            "selector": ".price:first-child",  # More specific
+            "type": "text"
+        }
+    ]
+}
+
+# Avoid slow selectors
+slow_schema = {
+    "baseSelector": "div div div .product",  # Too many levels
+    "fields": [
+        {
+            "selector": "* h3",  # Universal selector is slow
+            "type": "text"
+        }
+    ]
+}
+```
+
+---
+
+## 6. Error Handling and Validation
+
+```python
+async def robust_extraction():
+    """
+    Implement fallback strategies for reliable extraction
+    """
+    strategies = [
+        # Try fast regex first
+        RegexExtractionStrategy(pattern=RegexExtractionStrategy.Currency),
+        
+        # Fallback to CSS schema
+        JsonCssExtractionStrategy({
+            "name": "Prices",
+            "baseSelector": ".price",
+            "fields": [{"name": "amount", "selector": "span", "type": "text"}]
+        }),
+        
+        # Last resort: try different selector
+        JsonCssExtractionStrategy({
+            "name": "Fallback Prices",
+            "baseSelector": "[data-price]",
+            "fields": [{"name": "amount", "type": "attribute", "attribute": "data-price"}]
+        })
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for i, strategy in enumerate(strategies):
+            try:
+                config = CrawlerRunConfig(extraction_strategy=strategy)
+                result = await crawler.arun(url="https://example.com", config=config)
+                
+                if result.success and result.extracted_content:
+                    data = json.loads(result.extracted_content)
+                    if data:  # Validate non-empty results
+                        print(f"✅ Success with strategy {i+1}: {strategy.__class__.__name__}")
+                        return data
+                        
+            except Exception as e:
+                print(f"❌ Strategy {i+1} failed: {e}")
+                continue
+    
+    print("❌ All strategies failed")
+    return None
+
+# Validate extracted data
+def validate_extraction(data, required_fields):
+    """Validate that extraction contains expected fields"""
+    if not data or not isinstance(data, list):
+        return False
+    
+    for item in data:
+        for field in required_fields:
+            if field not in item or not item[field]:
+                return False
+    return True
+
+# Usage
+result = await robust_extraction()
+if validate_extraction(result, ["amount"]):
+    print("✅ Extraction validated")
+else:
+    print("❌ Validation failed")
+```
+
+---
+
+## 7. Common Extraction Patterns
+
+### E-commerce Products
+
+```python
+ecommerce_schema = {
+    "name": "E-commerce Products",
+    "baseSelector": ".product, [data-product], .item",
+    "fields": [
+        {"name": "title", "selector": "h1, h2, h3, .title, .name", "type": "text"},
+        {"name": "price", "selector": ".price, .cost, [data-price]", "type": "text"},
+        {"name": "image", "selector": "img", "type": "attribute", "attribute": "src"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"},
+        {"name": "rating", "selector": ".rating, .stars", "type": "text"},
+        {"name": "availability", "selector": ".stock, .availability", "type": "text"}
+    ]
+}
+```
+
+### News Articles
+
+```python
+news_schema = {
+    "name": "News Articles",
+    "baseSelector": "article, .article, .post",
+    "fields": [
+        {"name": "headline", "selector": "h1, h2, .headline, .title", "type": "text"},
+        {"name": "author", "selector": ".author, .byline, [rel='author']", "type": "text"},
+        {"name": "date", "selector": "time, .date, .published", "type": "text"},
+        {"name": "content", "selector": ".content, .body, .text", "type": "text"},
+        {"name": "category", "selector": ".category, .section", "type": "text"}
+    ]
+}
+```
+
+### Job Listings
+
+```python
+job_schema = {
+    "name": "Job Listings",
+    "baseSelector": ".job, .listing, [data-job]",
+    "fields": [
+        {"name": "title", "selector": ".job-title, h2, h3", "type": "text"},
+        {"name": "company", "selector": ".company, .employer", "type": "text"},
+        {"name": "location", "selector": ".location, .place", "type": "text"},
+        {"name": "salary", "selector": ".salary, .pay, .compensation", "type": "text"},
+        {"name": "description", "selector": ".description, .summary", "type": "text"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"}
+    ]
+}
+```
+
+### Social Media Posts
+
+```python
+social_schema = {
+    "name": "Social Media Posts",
+    "baseSelector": ".post, .tweet, .update",
+    "fields": [
+        {"name": "username", "selector": ".username, .handle, .author", "type": "text"},
+        {"name": "content", "selector": ".content, .text, .message", "type": "text"},
+        {"name": "timestamp", "selector": ".time, .date, time", "type": "text"},
+        {"name": "likes", "selector": ".likes, .hearts", "type": "text"},
+        {"name": "shares", "selector": ".shares, .retweets", "type": "text"}
+    ]
+}
+```
+
+---
+
+## 8. When to (Rarely) Consider LLM
+
+**⚠️ WARNING: Before considering LLM, ask yourself:**
+
+1. "Can I identify repeating HTML patterns?" → Use CSS/XPath schema
+2. "Am I extracting simple data types?" → Use Regex patterns  
+3. "Can I provide a JSON example of what I want?" → Use generate_schema()
+4. "Is this truly unstructured text requiring semantic understanding?" → Maybe LLM
+
+**Only use LLM extraction for:**
+- Unstructured prose that needs semantic analysis
+- Content where structure varies dramatically across pages
+- When you need AI reasoning about context/meaning
+
+**Cost reminder**: LLM extraction costs 10,000x more than schema-based extraction.
+
+---
+
+## 9. Summary: The Extraction Hierarchy
+
+1. **🥇 FIRST CHOICE**: `generate_schema()` - AI generates pattern once, use unlimited times
+2. **🥈 SECOND CHOICE**: Manual CSS/XPath - Full control, maximum speed
+3. **🥉 THIRD CHOICE**: Regex patterns - Simple data types, lightning fast
+4. **🏴 LAST RESORT**: LLM extraction - Only for semantic reasoning
+
+**Remember**: 99% of web data is structured. You almost never need LLM for extraction. Save LLM for analysis, not extraction.
+
+**Performance**: Non-LLM strategies are 100-1000x faster and 10,000x cheaper than LLM extraction.
+
+---
+
+**📖 Next**: If you absolutely must use LLM extraction, see [extraction-llm.md](./extraction-llm.md) for guidance on the rare cases where it's justified.
\ No newline at end of file
diff --git a/docs/md_v2/assets/llm.txt/txt/extraction.txt b/docs/md_v2/assets/llm.txt/txt/extraction.txt
deleted file mode 100644
index fa56dbe8..00000000
--- a/docs/md_v2/assets/llm.txt/txt/extraction.txt
+++ /dev/null
@@ -1,788 +0,0 @@
-## Extraction Strategies
-
-Powerful data extraction from web pages using LLM-based intelligent parsing or fast schema/pattern-based approaches.
-
-### LLM-Based Extraction - Intelligent Content Understanding
-
-```python
-import os
-import asyncio
-import json
-from pydantic import BaseModel, Field
-from typing import List
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, LLMConfig
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-
-# Define structured data model
-class Product(BaseModel):
-    name: str = Field(description="Product name")
-    price: str = Field(description="Product price")
-    description: str = Field(description="Product description")
-    features: List[str] = Field(description="List of product features")
-    rating: float = Field(description="Product rating out of 5")
-
-# Configure LLM provider
-llm_config = LLMConfig(
-    provider="openai/gpt-4o-mini",  # or "ollama/llama3.3", "anthropic/claude-3-5-sonnet"
-    api_token=os.getenv("OPENAI_API_KEY"),  # or "env:OPENAI_API_KEY"
-    temperature=0.1,
-    max_tokens=2000
-)
-
-# Create LLM extraction strategy
-llm_strategy = LLMExtractionStrategy(
-    llm_config=llm_config,
-    schema=Product.model_json_schema(),
-    extraction_type="schema",  # or "block" for freeform text
-    instruction="""
-    Extract product information from the webpage content.
-    Focus on finding complete product details including:
-    - Product name and price
-    - Detailed description
-    - All listed features
-    - Customer rating if available
-    Return valid JSON array of products.
-    """,
-    chunk_token_threshold=1200,  # Split content if too large
-    overlap_rate=0.1,           # 10% overlap between chunks
-    apply_chunking=True,        # Enable automatic chunking
-    input_format="markdown",    # "html", "fit_markdown", or "markdown"
-    extra_args={"temperature": 0.0, "max_tokens": 800},
-    verbose=True
-)
-
-async def extract_with_llm():
-    browser_config = BrowserConfig(headless=True)
-    
-    crawl_config = CrawlerRunConfig(
-        extraction_strategy=llm_strategy,
-        cache_mode=CacheMode.BYPASS,
-        word_count_threshold=10
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        result = await crawler.arun(
-            url="https://example.com/products",
-            config=crawl_config
-        )
-        
-        if result.success:
-            # Parse extracted JSON
-            products = json.loads(result.extracted_content)
-            print(f"Extracted {len(products)} products")
-            
-            for product in products[:3]:  # Show first 3
-                print(f"Product: {product['name']}")
-                print(f"Price: {product['price']}")
-                print(f"Rating: {product.get('rating', 'N/A')}")
-            
-            # Show token usage and cost
-            llm_strategy.show_usage()
-        else:
-            print(f"Extraction failed: {result.error_message}")
-
-asyncio.run(extract_with_llm())
-```
-
-### LLM Strategy Advanced Configuration
-
-```python
-# Multiple provider configurations
-providers = {
-    "openai": LLMConfig(
-        provider="openai/gpt-4o",
-        api_token="env:OPENAI_API_KEY",
-        temperature=0.1
-    ),
-    "anthropic": LLMConfig(
-        provider="anthropic/claude-3-5-sonnet-20240620",
-        api_token="env:ANTHROPIC_API_KEY",
-        max_tokens=4000
-    ),
-    "ollama": LLMConfig(
-        provider="ollama/llama3.3",
-        api_token=None,  # Not needed for Ollama
-        base_url="http://localhost:11434"
-    ),
-    "groq": LLMConfig(
-        provider="groq/llama3-70b-8192",
-        api_token="env:GROQ_API_KEY"
-    )
-}
-
-# Advanced chunking for large content
-large_content_strategy = LLMExtractionStrategy(
-    llm_config=providers["openai"],
-    schema=YourModel.model_json_schema(),
-    extraction_type="schema",
-    instruction="Extract detailed information...",
-    
-    # Chunking parameters
-    chunk_token_threshold=2000,  # Larger chunks for complex content
-    overlap_rate=0.15,          # More overlap for context preservation
-    apply_chunking=True,
-    
-    # Input format selection
-    input_format="fit_markdown",  # Use filtered content if available
-    
-    # LLM parameters
-    extra_args={
-        "temperature": 0.0,      # Deterministic output
-        "top_p": 0.9,
-        "frequency_penalty": 0.1,
-        "presence_penalty": 0.1,
-        "max_tokens": 1500
-    },
-    verbose=True
-)
-
-# Knowledge graph extraction
-class Entity(BaseModel):
-    name: str
-    type: str  # "person", "organization", "location", etc.
-    description: str
-
-class Relationship(BaseModel):
-    source: str
-    target: str
-    relationship: str
-    confidence: float
-
-class KnowledgeGraph(BaseModel):
-    entities: List[Entity]
-    relationships: List[Relationship]
-    summary: str
-
-knowledge_strategy = LLMExtractionStrategy(
-    llm_config=providers["anthropic"],
-    schema=KnowledgeGraph.model_json_schema(),
-    extraction_type="schema",
-    instruction="""
-    Create a knowledge graph from the content by:
-    1. Identifying key entities (people, organizations, locations, concepts)
-    2. Finding relationships between entities
-    3. Providing confidence scores for relationships
-    4. Summarizing the main topics
-    """,
-    input_format="html",  # Use HTML for better structure preservation
-    apply_chunking=True,
-    chunk_token_threshold=1500
-)
-```
-
-### JSON CSS Extraction - Fast Schema-Based Extraction
-
-```python
-import asyncio
-import json
-from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-
-# Basic CSS extraction schema
-simple_schema = {
-    "name": "Product Listings",
-    "baseSelector": "div.product-card",
-    "fields": [
-        {
-            "name": "title",
-            "selector": "h2.product-title",
-            "type": "text"
-        },
-        {
-            "name": "price",
-            "selector": ".price",
-            "type": "text"
-        },
-        {
-            "name": "image_url",
-            "selector": "img.product-image",
-            "type": "attribute",
-            "attribute": "src"
-        },
-        {
-            "name": "product_url",
-            "selector": "a.product-link",
-            "type": "attribute", 
-            "attribute": "href"
-        }
-    ]
-}
-
-# Complex nested schema with multiple data types
-complex_schema = {
-    "name": "E-commerce Product Catalog",
-    "baseSelector": "div.category",
-    "baseFields": [
-        {
-            "name": "category_id",
-            "type": "attribute",
-            "attribute": "data-category-id"
-        },
-        {
-            "name": "category_url",
-            "type": "attribute",
-            "attribute": "data-url"
-        }
-    ],
-    "fields": [
-        {
-            "name": "category_name",
-            "selector": "h2.category-title",
-            "type": "text"
-        },
-        {
-            "name": "products",
-            "selector": "div.product",
-            "type": "nested_list",  # Array of complex objects
-            "fields": [
-                {
-                    "name": "name",
-                    "selector": "h3.product-name",
-                    "type": "text",
-                    "default": "Unknown Product"
-                },
-                {
-                    "name": "price",
-                    "selector": "span.price",
-                    "type": "text"
-                },
-                {
-                    "name": "details",
-                    "selector": "div.product-details",
-                    "type": "nested",  # Single complex object
-                    "fields": [
-                        {
-                            "name": "brand",
-                            "selector": "span.brand",
-                            "type": "text"
-                        },
-                        {
-                            "name": "model",
-                            "selector": "span.model", 
-                            "type": "text"
-                        },
-                        {
-                            "name": "specs",
-                            "selector": "div.specifications",
-                            "type": "html"  # Preserve HTML structure
-                        }
-                    ]
-                },
-                {
-                    "name": "features",
-                    "selector": "ul.features li",
-                    "type": "list",  # Simple array of strings
-                    "fields": [
-                        {"name": "feature", "type": "text"}
-                    ]
-                },
-                {
-                    "name": "reviews",
-                    "selector": "div.review",
-                    "type": "nested_list",
-                    "fields": [
-                        {
-                            "name": "reviewer",
-                            "selector": "span.reviewer-name",
-                            "type": "text"
-                        },
-                        {
-                            "name": "rating",
-                            "selector": "span.rating",
-                            "type": "attribute",
-                            "attribute": "data-rating"
-                        },
-                        {
-                            "name": "comment",
-                            "selector": "p.review-text",
-                            "type": "text"
-                        },
-                        {
-                            "name": "date",
-                            "selector": "time.review-date",
-                            "type": "attribute",
-                            "attribute": "datetime"
-                        }
-                    ]
-                }
-            ]
-        }
-    ]
-}
-
-async def extract_with_css_schema():
-    strategy = JsonCssExtractionStrategy(complex_schema, verbose=True)
-    
-    config = CrawlerRunConfig(
-        extraction_strategy=strategy,
-        cache_mode=CacheMode.BYPASS,
-        # Enable dynamic content loading if needed
-        js_code="window.scrollTo(0, document.body.scrollHeight);",
-        wait_for="css:.product:nth-child(10)",  # Wait for products to load
-        process_iframes=True
-    )
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun(
-            url="https://example.com/catalog",
-            config=config
-        )
-        
-        if result.success:
-            data = json.loads(result.extracted_content)
-            print(f"Extracted {len(data)} categories")
-            
-            for category in data:
-                print(f"Category: {category['category_name']}")
-                print(f"Products: {len(category.get('products', []))}")
-                
-                # Show first product details
-                if category.get('products'):
-                    product = category['products'][0]
-                    print(f"  First product: {product.get('name')}")
-                    print(f"  Features: {len(product.get('features', []))}")
-                    print(f"  Reviews: {len(product.get('reviews', []))}")
-
-asyncio.run(extract_with_css_schema())
-```
-
-### Automatic Schema Generation - One-Time LLM, Unlimited Use
-
-```python
-import json
-import asyncio
-from pathlib import Path
-from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-
-async def generate_and_use_schema():
-    """
-    1. Use LLM once to generate schema from sample HTML
-    2. Cache the schema for reuse
-    3. Use cached schema for fast extraction without LLM calls
-    """
-    
-    cache_dir = Path("./schema_cache")
-    cache_dir.mkdir(exist_ok=True)
-    schema_file = cache_dir / "ecommerce_schema.json"
-    
-    # Step 1: Generate or load cached schema
-    if schema_file.exists():
-        schema = json.load(schema_file.open())
-        print("Using cached schema")
-    else:
-        print("Generating schema using LLM...")
-        
-        # Configure LLM for schema generation
-        llm_config = LLMConfig(
-            provider="openai/gpt-4o",  # or "ollama/llama3.3" for local
-            api_token="env:OPENAI_API_KEY"
-        )
-        
-        # Get sample HTML from target site
-        async with AsyncWebCrawler() as crawler:
-            sample_result = await crawler.arun(
-                url="https://example.com/products",
-                config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
-            )
-            sample_html = sample_result.cleaned_html[:5000]  # Use first 5k chars
-        
-        # Generate schema automatically (ONE-TIME LLM COST)
-        schema = JsonCssExtractionStrategy.generate_schema(
-            html=sample_html,
-            schema_type="css",
-            llm_config=llm_config,
-            instruction="Extract product information including name, price, description, and features"
-        )
-        
-        # Cache schema for future use (NO MORE LLM CALLS)
-        json.dump(schema, schema_file.open("w"), indent=2)
-        print("Schema generated and cached")
-    
-    # Step 2: Use schema for fast extraction (NO LLM CALLS)
-    strategy = JsonCssExtractionStrategy(schema, verbose=True)
-    
-    config = CrawlerRunConfig(
-        extraction_strategy=strategy,
-        cache_mode=CacheMode.BYPASS
-    )
-    
-    # Step 3: Extract from multiple pages using same schema
-    urls = [
-        "https://example.com/products",
-        "https://example.com/electronics", 
-        "https://example.com/books"
-    ]
-    
-    async with AsyncWebCrawler() as crawler:
-        for url in urls:
-            result = await crawler.arun(url=url, config=config)
-            
-            if result.success:
-                data = json.loads(result.extracted_content)
-                print(f"{url}: Extracted {len(data)} items")
-            else:
-                print(f"{url}: Failed - {result.error_message}")
-
-asyncio.run(generate_and_use_schema())
-```
-
-### XPath Extraction Strategy
-
-```python
-from crawl4ai.extraction_strategy import JsonXPathExtractionStrategy
-
-# XPath-based schema (alternative to CSS)
-xpath_schema = {
-    "name": "News Articles",
-    "baseSelector": "//article[@class='news-item']",
-    "baseFields": [
-        {
-            "name": "article_id",
-            "type": "attribute",
-            "attribute": "data-id"
-        }
-    ],
-    "fields": [
-        {
-            "name": "headline",
-            "selector": ".//h2[@class='headline']",
-            "type": "text"
-        },
-        {
-            "name": "author",
-            "selector": ".//span[@class='author']/text()",
-            "type": "text"
-        },
-        {
-            "name": "publish_date",
-            "selector": ".//time/@datetime",
-            "type": "text"
-        },
-        {
-            "name": "content",
-            "selector": ".//div[@class='article-body']",
-            "type": "html"
-        },
-        {
-            "name": "tags",
-            "selector": ".//div[@class='tags']/span[@class='tag']",
-            "type": "list",
-            "fields": [
-                {"name": "tag", "type": "text"}
-            ]
-        }
-    ]
-}
-
-# Generate XPath schema automatically
-async def generate_xpath_schema():
-    llm_config = LLMConfig(provider="ollama/llama3.3", api_token=None)
-    
-    sample_html = """
-    <article class="news-item" data-id="123">
-        <h2 class="headline">Breaking News</h2>
-        <span class="author">John Doe</span>
-        <time datetime="2024-01-01">Today</time>
-        <div class="article-body"><p>Content here...</p></div>
-    </article>
-    """
-    
-    schema = JsonXPathExtractionStrategy.generate_schema(
-        html=sample_html,
-        schema_type="xpath",
-        llm_config=llm_config
-    )
-    
-    return schema
-
-# Use XPath strategy
-xpath_strategy = JsonXPathExtractionStrategy(xpath_schema, verbose=True)
-```
-
-### Regex Extraction Strategy - Pattern-Based Fast Extraction
-
-```python
-from crawl4ai.extraction_strategy import RegexExtractionStrategy
-
-# Built-in patterns for common data types
-async def extract_with_builtin_patterns():
-    # Use multiple built-in patterns
-    strategy = RegexExtractionStrategy(
-        pattern=(
-            RegexExtractionStrategy.Email |
-            RegexExtractionStrategy.PhoneUS |
-            RegexExtractionStrategy.Url |
-            RegexExtractionStrategy.Currency |
-            RegexExtractionStrategy.DateIso
-        )
-    )
-    
-    config = CrawlerRunConfig(extraction_strategy=strategy)
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun(
-            url="https://example.com/contact",
-            config=config
-        )
-        
-        if result.success:
-            matches = json.loads(result.extracted_content)
-            
-            # Group by pattern type
-            by_type = {}
-            for match in matches:
-                label = match['label']
-                if label not in by_type:
-                    by_type[label] = []
-                by_type[label].append(match['value'])
-            
-            for pattern_type, values in by_type.items():
-                print(f"{pattern_type}: {len(values)} matches")
-                for value in values[:3]:  # Show first 3
-                    print(f"  {value}")
-
-# Custom regex patterns
-custom_patterns = {
-    "product_code": r"SKU-\d{4,6}",
-    "discount": r"\d{1,2}%\s*off",
-    "model_number": r"Model:\s*([A-Z0-9-]+)"
-}
-
-async def extract_with_custom_patterns():
-    strategy = RegexExtractionStrategy(custom=custom_patterns)
-    
-    config = CrawlerRunConfig(extraction_strategy=strategy)
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun(
-            url="https://example.com/products",
-            config=config
-        )
-        
-        if result.success:
-            data = json.loads(result.extracted_content)
-            for item in data:
-                print(f"{item['label']}: {item['value']}")
-
-# LLM-generated patterns (one-time cost)
-async def generate_custom_patterns():
-    cache_file = Path("./patterns/price_patterns.json")
-    
-    if cache_file.exists():
-        patterns = json.load(cache_file.open())
-    else:
-        llm_config = LLMConfig(
-            provider="openai/gpt-4o-mini",
-            api_token="env:OPENAI_API_KEY"
-        )
-        
-        # Get sample content
-        async with AsyncWebCrawler() as crawler:
-            result = await crawler.arun("https://example.com/pricing")
-            sample_html = result.cleaned_html
-        
-        # Generate optimized patterns
-        patterns = RegexExtractionStrategy.generate_pattern(
-            label="pricing_info",
-            html=sample_html,
-            query="Extract all pricing information including discounts and special offers",
-            llm_config=llm_config
-        )
-        
-        # Cache for reuse
-        cache_file.parent.mkdir(exist_ok=True)
-        json.dump(patterns, cache_file.open("w"), indent=2)
-    
-    # Use cached patterns (no more LLM calls)
-    strategy = RegexExtractionStrategy(custom=patterns)
-    return strategy
-
-asyncio.run(extract_with_builtin_patterns())
-asyncio.run(extract_with_custom_patterns())
-```
-
-### Complete Extraction Workflow - Combining Strategies
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-from crawl4ai.extraction_strategy import (
-    JsonCssExtractionStrategy, 
-    RegexExtractionStrategy,
-    LLMExtractionStrategy
-)
-
-async def multi_strategy_extraction():
-    """
-    Demonstrate using multiple extraction strategies in sequence:
-    1. Fast regex for common patterns
-    2. Schema-based for structured data  
-    3. LLM for complex reasoning
-    """
-    
-    browser_config = BrowserConfig(headless=True)
-    
-    # Strategy 1: Fast regex extraction
-    regex_strategy = RegexExtractionStrategy(
-        pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
-    )
-    
-    # Strategy 2: Schema-based structured extraction
-    product_schema = {
-        "name": "Products",
-        "baseSelector": "div.product",
-        "fields": [
-            {"name": "name", "selector": "h3", "type": "text"},
-            {"name": "price", "selector": ".price", "type": "text"},
-            {"name": "rating", "selector": ".rating", "type": "attribute", "attribute": "data-rating"}
-        ]
-    }
-    css_strategy = JsonCssExtractionStrategy(product_schema)
-    
-    # Strategy 3: LLM for complex analysis
-    llm_strategy = LLMExtractionStrategy(
-        llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
-        schema={
-            "type": "object",
-            "properties": {
-                "sentiment": {"type": "string"},
-                "key_topics": {"type": "array", "items": {"type": "string"}},
-                "summary": {"type": "string"}
-            }
-        },
-        extraction_type="schema",
-        instruction="Analyze the content sentiment, extract key topics, and provide a summary"
-    )
-    
-    url = "https://example.com/product-reviews"
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        # Extract contact info with regex
-        regex_config = CrawlerRunConfig(extraction_strategy=regex_strategy)
-        regex_result = await crawler.arun(url=url, config=regex_config)
-        
-        # Extract structured product data
-        css_config = CrawlerRunConfig(extraction_strategy=css_strategy)
-        css_result = await crawler.arun(url=url, config=css_config)
-        
-        # Extract insights with LLM
-        llm_config = CrawlerRunConfig(extraction_strategy=llm_strategy)
-        llm_result = await crawler.arun(url=url, config=llm_config)
-        
-        # Combine results
-        results = {
-            "contacts": json.loads(regex_result.extracted_content) if regex_result.success else [],
-            "products": json.loads(css_result.extracted_content) if css_result.success else [],
-            "analysis": json.loads(llm_result.extracted_content) if llm_result.success else {}
-        }
-        
-        print(f"Found {len(results['contacts'])} contact entries")
-        print(f"Found {len(results['products'])} products") 
-        print(f"Sentiment: {results['analysis'].get('sentiment', 'N/A')}")
-        
-        return results
-
-# Performance comparison
-async def compare_extraction_performance():
-    """Compare speed and accuracy of different strategies"""
-    import time
-    
-    url = "https://example.com/large-catalog"
-    
-    strategies = {
-        "regex": RegexExtractionStrategy(pattern=RegexExtractionStrategy.Currency),
-        "css": JsonCssExtractionStrategy({
-            "name": "Prices", 
-            "baseSelector": ".price",
-            "fields": [{"name": "amount", "selector": "span", "type": "text"}]
-        }),
-        "llm": LLMExtractionStrategy(
-            llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
-            instruction="Extract all prices from the content",
-            extraction_type="block"
-        )
-    }
-    
-    async with AsyncWebCrawler() as crawler:
-        for name, strategy in strategies.items():
-            start_time = time.time()
-            
-            config = CrawlerRunConfig(extraction_strategy=strategy)
-            result = await crawler.arun(url=url, config=config)
-            
-            duration = time.time() - start_time
-            
-            if result.success:
-                data = json.loads(result.extracted_content)
-                print(f"{name}: {len(data)} items in {duration:.2f}s")
-            else:
-                print(f"{name}: Failed in {duration:.2f}s")
-
-asyncio.run(multi_strategy_extraction())
-asyncio.run(compare_extraction_performance())
-```
-
-### Best Practices and Strategy Selection
-
-```python
-# Strategy selection guide
-def choose_extraction_strategy(use_case):
-    """
-    Guide for selecting the right extraction strategy
-    """
-    
-    strategies = {
-        # Fast pattern matching for common data types
-        "contact_info": RegexExtractionStrategy(
-            pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
-        ),
-        
-        # Structured data from consistent HTML
-        "product_catalogs": JsonCssExtractionStrategy,
-        
-        # Complex reasoning and semantic understanding  
-        "content_analysis": LLMExtractionStrategy,
-        
-        # Mixed approach for comprehensive extraction
-        "complete_site_analysis": "multi_strategy"
-    }
-    
-    recommendations = {
-        "speed_priority": "Use RegexExtractionStrategy for simple patterns, JsonCssExtractionStrategy for structured data",
-        "accuracy_priority": "Use LLMExtractionStrategy for complex content, JsonCssExtractionStrategy for predictable structure",
-        "cost_priority": "Avoid LLM strategies, use schema generation once then JsonCssExtractionStrategy",
-        "scale_priority": "Cache schemas, use regex for simple patterns, avoid LLM for high-volume extraction"
-    }
-    
-    return recommendations.get(use_case, "Combine strategies based on content complexity")
-
-# Error handling and validation
-async def robust_extraction():
-    strategies = [
-        RegexExtractionStrategy(pattern=RegexExtractionStrategy.Email),
-        JsonCssExtractionStrategy(simple_schema),
-        # LLM as fallback for complex cases
-    ]
-    
-    async with AsyncWebCrawler() as crawler:
-        for strategy in strategies:
-            try:
-                config = CrawlerRunConfig(extraction_strategy=strategy)
-                result = await crawler.arun(url="https://example.com", config=config)
-                
-                if result.success and result.extracted_content:
-                    data = json.loads(result.extracted_content)
-                    if data:  # Validate non-empty results
-                        print(f"Success with {strategy.__class__.__name__}")
-                        return data
-                        
-            except Exception as e:
-                print(f"Strategy {strategy.__class__.__name__} failed: {e}")
-                continue
-    
-    print("All strategies failed")
-    return None
-```
-
-**📖 Learn more:** [LLM Strategies Deep Dive](https://docs.crawl4ai.com/extraction/llm-strategies/), [Schema-Based Extraction](https://docs.crawl4ai.com/extraction/no-llm-strategies/), [Regex Patterns](https://docs.crawl4ai.com/extraction/no-llm-strategies/#regexextractionstrategy), [Performance Optimization](https://docs.crawl4ai.com/advanced/multi-url-crawling/)
\ No newline at end of file
diff --git a/docs/md_v2/assets/llm.txt/txt/llms-full-v0.1.1.txt b/docs/md_v2/assets/llm.txt/txt/llms-full-v0.1.1.txt
new file mode 100644
index 00000000..be0937e8
--- /dev/null
+++ b/docs/md_v2/assets/llm.txt/txt/llms-full-v0.1.1.txt
@@ -0,0 +1,7715 @@
+# Crawl4AI
+
+> Open-source LLM-friendly web crawler and scraper for AI applications
+
+Crawl4AI simplifies asynchronous web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. Built with Python and Playwright for high-performance crawling with structured data extraction.
+
+**Key Features:**
+- Asynchronous crawling with high concurrency
+- Multiple extraction strategies (CSS, XPath, LLM-based)
+- Built-in markdown generation with content filtering
+- Docker deployment with REST API
+- Session management and browser automation
+- Advanced anti-detection capabilities
+
+**Quick Links:**
+- [GitHub Repository](https://github.com/unclecode/crawl4ai)
+- [Documentation](https://docs.crawl4ai.com)
+- [Examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples)
+
+---
+
+
+## Installation
+
+Multiple installation options for different environments and use cases.
+
+### Basic Installation
+
+```bash
+# Install core library
+pip install crawl4ai
+
+# Initial setup (installs Playwright browsers)
+crawl4ai-setup
+
+# Verify installation
+crawl4ai-doctor
+```
+
+### Quick Verification
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com")
+        print(result.markdown[:300])
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+**📖 Learn more:** [Basic Usage Guide](https://docs.crawl4ai.com/core/quickstart.md)
+
+### Advanced Features (Optional)
+
+```bash
+# PyTorch-based features (text clustering, semantic chunking)
+pip install crawl4ai[torch]
+crawl4ai-setup
+
+# Transformers (Hugging Face models)
+pip install crawl4ai[transformer]
+crawl4ai-setup
+
+# All features (large download)
+pip install crawl4ai[all]
+crawl4ai-setup
+
+# Pre-download models (optional)
+crawl4ai-download-models
+```
+
+**📖 Learn more:** [Advanced Features Documentation](https://docs.crawl4ai.com/extraction/llm-strategies.md)
+
+### Docker Deployment
+
+```bash
+# Pull pre-built image (specify platform for consistency)
+docker pull --platform linux/amd64 unclecode/crawl4ai:latest
+# For ARM (M1/M2 Macs): docker pull --platform linux/arm64 unclecode/crawl4ai:latest
+
+# Setup environment for LLM support
+cat > .llm.env << EOL
+OPENAI_API_KEY=sk-your-key
+ANTHROPIC_API_KEY=your-anthropic-key
+EOL
+
+# Run with LLM support (specify platform)
+docker run -d \
+  --platform linux/amd64 \
+  -p 11235:11235 \
+  --name crawl4ai \
+  --env-file .llm.env \
+  --shm-size=1g \
+  unclecode/crawl4ai:latest
+
+# For ARM Macs, use: --platform linux/arm64
+
+# Basic run (no LLM)
+docker run -d \
+  --platform linux/amd64 \
+  -p 11235:11235 \
+  --name crawl4ai \
+  --shm-size=1g \
+  unclecode/crawl4ai:latest
+```
+
+**📖 Learn more:** [Complete Docker Guide](https://docs.crawl4ai.com/core/docker-deployment.md)
+
+### Docker Compose
+
+```bash
+# Clone repository
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+
+# Copy environment template
+cp deploy/docker/.llm.env.example .llm.env
+# Edit .llm.env with your API keys
+
+# Run pre-built image
+IMAGE=unclecode/crawl4ai:latest docker compose up -d
+
+# Build and run locally
+docker compose up --build -d
+
+# Build with all features
+INSTALL_TYPE=all docker compose up --build -d
+
+# Stop service
+docker compose down
+```
+
+**📖 Learn more:** [Docker Compose Configuration](https://docs.crawl4ai.com/core/docker-deployment.md#option-2-using-docker-compose)
+
+### Manual Docker Build
+
+```bash
+# Build multi-architecture image (specify platform)
+docker buildx build --platform linux/amd64 -t crawl4ai-local:latest --load .
+# For ARM: docker buildx build --platform linux/arm64 -t crawl4ai-local:latest --load .
+
+# Build with specific features
+docker buildx build \
+  --platform linux/amd64 \
+  --build-arg INSTALL_TYPE=all \
+  --build-arg ENABLE_GPU=false \
+  -t crawl4ai-local:latest --load .
+
+# Run custom build (specify platform)
+docker run -d \
+  --platform linux/amd64 \
+  -p 11235:11235 \
+  --name crawl4ai-custom \
+  --env-file .llm.env \
+  --shm-size=1g \
+  crawl4ai-local:latest
+```
+
+**📖 Learn more:** [Manual Build Guide](https://docs.crawl4ai.com/core/docker-deployment.md#option-3-manual-local-build--run)
+
+### Google Colab
+
+```python
+# Install in Colab
+!pip install crawl4ai
+!crawl4ai-setup
+
+# If setup fails, manually install Playwright browsers
+!playwright install chromium
+
+# Install with all features (may take 5-10 minutes)
+!pip install crawl4ai[all]
+!crawl4ai-setup
+!crawl4ai-download-models
+
+# If still having issues, force Playwright install
+!playwright install chromium --force
+
+# Quick test
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def test_crawl():
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com")
+        print("✅ Installation successful!")
+        print(f"Content length: {len(result.markdown)}")
+
+# Run test in Colab
+await test_crawl()
+```
+
+**📖 Learn more:** [Colab Examples Notebook](https://colab.research.google.com/github/unclecode/crawl4ai/blob/main/docs/examples/quickstart.ipynb)
+
+### Docker API Usage
+
+```python
+# Using Docker SDK
+import asyncio
+from crawl4ai.docker_client import Crawl4aiDockerClient
+from crawl4ai import BrowserConfig, CrawlerRunConfig, CacheMode
+
+async def main():
+    async with Crawl4aiDockerClient(base_url="http://localhost:11235") as client:
+        results = await client.crawl(
+            ["https://example.com"],
+            browser_config=BrowserConfig(headless=True),
+            crawler_config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+        )
+        for result in results:
+            print(f"Success: {result.success}, Length: {len(result.markdown)}")
+
+asyncio.run(main())
+```
+
+**📖 Learn more:** [Docker Client API](https://docs.crawl4ai.com/core/docker-deployment.md#python-sdk)
+
+### Direct API Calls
+
+```python
+# REST API example
+import requests
+
+payload = {
+    "urls": ["https://example.com"],
+    "browser_config": {"type": "BrowserConfig", "params": {"headless": True}},
+    "crawler_config": {"type": "CrawlerRunConfig", "params": {"cache_mode": "bypass"}}
+}
+
+response = requests.post("http://localhost:11235/crawl", json=payload)
+print(response.json())
+```
+
+**📖 Learn more:** [REST API Reference](https://docs.crawl4ai.com/core/docker-deployment.md#rest-api-examples)
+
+### Health Check
+
+```bash
+# Check Docker service
+curl http://localhost:11235/health
+
+# Access playground
+open http://localhost:11235/playground
+
+# View metrics
+curl http://localhost:11235/metrics
+```
+
+**📖 Learn more:** [Monitoring & Metrics](https://docs.crawl4ai.com/core/docker-deployment.md#metrics--monitoring)
+---
+
+
+## Simple Crawling
+
+Basic web crawling operations with AsyncWebCrawler, configurations, and response handling.
+
+### Basic Setup
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+
+async def main():
+    browser_config = BrowserConfig()  # Default browser settings
+    run_config = CrawlerRunConfig()   # Default crawl settings
+
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url="https://example.com",
+            config=run_config
+        )
+        print(result.markdown)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Understanding CrawlResult
+
+```python
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+from crawl4ai.content_filter_strategy import PruningContentFilter
+
+config = CrawlerRunConfig(
+    markdown_generator=DefaultMarkdownGenerator(
+        content_filter=PruningContentFilter(threshold=0.6),
+        options={"ignore_links": True}
+    )
+)
+
+result = await crawler.arun("https://example.com", config=config)
+
+# Different content formats
+print(result.html)                    # Raw HTML
+print(result.cleaned_html)            # Cleaned HTML  
+print(result.markdown.raw_markdown)   # Raw markdown
+print(result.markdown.fit_markdown)   # Filtered markdown
+
+# Status information
+print(result.success)      # True/False
+print(result.status_code)  # HTTP status (200, 404, etc.)
+
+# Extracted content
+print(result.media)        # Images, videos, audio
+print(result.links)        # Internal/external links
+```
+
+### Basic Configuration Options
+
+```python
+run_config = CrawlerRunConfig(
+    word_count_threshold=10,        # Min words per block
+    exclude_external_links=True,    # Remove external links
+    remove_overlay_elements=True,   # Remove popups/modals
+    process_iframes=True,           # Process iframe content
+    excluded_tags=['form', 'header']  # Skip these tags
+)
+
+result = await crawler.arun("https://example.com", config=run_config)
+```
+
+### Error Handling
+
+```python
+result = await crawler.arun("https://example.com", config=run_config)
+
+if not result.success:
+    print(f"Crawl failed: {result.error_message}")
+    print(f"Status code: {result.status_code}")
+else:
+    print(f"Success! Content length: {len(result.markdown)}")
+```
+
+### Debugging with Verbose Logging
+
+```python
+browser_config = BrowserConfig(verbose=True)
+
+async with AsyncWebCrawler(config=browser_config) as crawler:
+    result = await crawler.arun("https://example.com")
+    # Detailed logging output will be displayed
+```
+
+### Complete Example
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+
+async def comprehensive_crawl():
+    browser_config = BrowserConfig(verbose=True)
+    
+    run_config = CrawlerRunConfig(
+        # Content filtering
+        word_count_threshold=10,
+        excluded_tags=['form', 'header', 'nav'],
+        exclude_external_links=True,
+        
+        # Content processing
+        process_iframes=True,
+        remove_overlay_elements=True,
+        
+        # Cache control
+        cache_mode=CacheMode.ENABLED
+    )
+
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url="https://example.com",
+            config=run_config
+        )
+        
+        if result.success:
+            # Display content summary
+            print(f"Title: {result.metadata.get('title', 'No title')}")
+            print(f"Content: {result.markdown[:500]}...")
+            
+            # Process media
+            images = result.media.get("images", [])
+            print(f"Found {len(images)} images")
+            for img in images[:3]:  # First 3 images
+                print(f"  - {img.get('src', 'No src')}")
+            
+            # Process links
+            internal_links = result.links.get("internal", [])
+            print(f"Found {len(internal_links)} internal links")
+            for link in internal_links[:3]:  # First 3 links
+                print(f"  - {link.get('href', 'No href')}")
+                
+        else:
+            print(f"❌ Crawl failed: {result.error_message}")
+            print(f"Status: {result.status_code}")
+
+if __name__ == "__main__":
+    asyncio.run(comprehensive_crawl())
+```
+
+### Working with Raw HTML and Local Files
+
+```python
+# Crawl raw HTML
+raw_html = "<html><body><h1>Test</h1><p>Content</p></body></html>"
+result = await crawler.arun(f"raw://{raw_html}")
+
+# Crawl local file
+result = await crawler.arun("file:///path/to/local/file.html")
+
+# Both return standard CrawlResult objects
+print(result.markdown)
+```
+
+## Table Extraction
+
+Extract structured data from HTML tables with automatic detection and scoring.
+
+### Basic Table Extraction
+
+```python
+import asyncio
+import pandas as pd
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
+
+async def extract_tables():
+    async with AsyncWebCrawler() as crawler:
+        config = CrawlerRunConfig(
+            table_score_threshold=7,  # Higher = stricter detection
+            cache_mode=CacheMode.BYPASS
+        )
+        
+        result = await crawler.arun("https://example.com/tables", config=config)
+        
+        if result.success and result.tables:
+            # New tables field (v0.6+)
+            for i, table in enumerate(result.tables):
+                print(f"Table {i+1}:")
+                print(f"Headers: {table['headers']}")
+                print(f"Rows: {len(table['rows'])}")
+                print(f"Caption: {table.get('caption', 'No caption')}")
+                
+                # Convert to DataFrame
+                df = pd.DataFrame(table['rows'], columns=table['headers'])
+                print(df.head())
+
+asyncio.run(extract_tables())
+```
+
+### Advanced Table Processing
+
+```python
+from crawl4ai import LXMLWebScrapingStrategy
+
+async def process_financial_tables():
+    config = CrawlerRunConfig(
+        table_score_threshold=8,  # Strict detection for data tables
+        scraping_strategy=LXMLWebScrapingStrategy(),
+        keep_data_attributes=True,
+        scan_full_page=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://coinmarketcap.com", config=config)
+        
+        if result.tables:
+            # Get the main data table (usually first/largest)
+            main_table = result.tables[0]
+            
+            # Create DataFrame
+            df = pd.DataFrame(
+                main_table['rows'],
+                columns=main_table['headers']
+            )
+            
+            # Clean and process data
+            df = clean_financial_data(df)
+            
+            # Save for analysis
+            df.to_csv("market_data.csv", index=False)
+            return df
+
+def clean_financial_data(df):
+    """Clean currency symbols, percentages, and large numbers"""
+    for col in df.columns:
+        if 'price' in col.lower():
+            # Remove currency symbols
+            df[col] = df[col].str.replace(r'[^\d.]', '', regex=True)
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+        
+        elif '%' in str(df[col].iloc[0]):
+            # Convert percentages
+            df[col] = df[col].str.replace('%', '').astype(float) / 100
+        
+        elif any(suffix in str(df[col].iloc[0]) for suffix in ['B', 'M', 'K']):
+            # Handle large numbers (Billions, Millions, etc.)
+            df[col] = df[col].apply(convert_large_numbers)
+    
+    return df
+
+def convert_large_numbers(value):
+    """Convert 1.5B -> 1500000000"""
+    if pd.isna(value):
+        return float('nan')
+    
+    value = str(value)
+    multiplier = 1
+    if 'B' in value:
+        multiplier = 1e9
+    elif 'M' in value:
+        multiplier = 1e6
+    elif 'K' in value:
+        multiplier = 1e3
+    
+    number = float(re.sub(r'[^\d.]', '', value))
+    return number * multiplier
+```
+
+### Table Detection Configuration
+
+```python
+# Strict table detection (data-heavy pages)
+strict_config = CrawlerRunConfig(
+    table_score_threshold=9,  # Only high-quality tables
+    word_count_threshold=5,   # Ignore sparse content
+    excluded_tags=['nav', 'footer']  # Skip navigation tables
+)
+
+# Lenient detection (mixed content pages)
+lenient_config = CrawlerRunConfig(
+    table_score_threshold=5,  # Include layout tables
+    process_iframes=True,     # Check embedded tables
+    scan_full_page=True      # Scroll to load dynamic tables
+)
+
+# Financial/data site optimization
+financial_config = CrawlerRunConfig(
+    table_score_threshold=8,
+    scraping_strategy=LXMLWebScrapingStrategy(),
+    wait_for="css:table",     # Wait for tables to load
+    scan_full_page=True,
+    scroll_delay=0.2
+)
+```
+
+### Multi-Table Processing
+
+```python
+async def extract_all_tables():
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com/data", config=config)
+        
+        tables_data = {}
+        
+        for i, table in enumerate(result.tables):
+            # Create meaningful names based on content
+            table_name = (
+                table.get('caption') or 
+                f"table_{i+1}_{table['headers'][0]}"
+            ).replace(' ', '_').lower()
+            
+            df = pd.DataFrame(table['rows'], columns=table['headers'])
+            
+            # Store with metadata
+            tables_data[table_name] = {
+                'dataframe': df,
+                'headers': table['headers'],
+                'row_count': len(table['rows']),
+                'caption': table.get('caption'),
+                'summary': table.get('summary')
+            }
+        
+        return tables_data
+
+# Usage
+tables = await extract_all_tables()
+for name, data in tables.items():
+    print(f"{name}: {data['row_count']} rows")
+    data['dataframe'].to_csv(f"{name}.csv")
+```
+
+### Backward Compatibility
+
+```python
+# Support both new and old table formats
+def get_tables(result):
+    # New format (v0.6+)
+    if hasattr(result, 'tables') and result.tables:
+        return result.tables
+    
+    # Fallback to media.tables (older versions)
+    return result.media.get('tables', [])
+
+# Usage in existing code
+result = await crawler.arun(url, config=config)
+tables = get_tables(result)
+
+for table in tables:
+    df = pd.DataFrame(table['rows'], columns=table['headers'])
+    # Process table data...
+```
+
+### Table Quality Scoring
+
+```python
+# Understanding table_score_threshold values:
+# 10: Only perfect data tables (headers + data rows)
+# 8-9: High-quality tables (recommended for financial/data sites)
+# 6-7: Mixed content tables (news sites, wikis)
+# 4-5: Layout tables included (broader detection)
+# 1-3: All table-like structures (very permissive)
+
+config = CrawlerRunConfig(
+    table_score_threshold=8,  # Balanced detection
+    verbose=True  # See scoring details in logs
+)
+```
+
+
+**📖 Learn more:** [CrawlResult API Reference](https://docs.crawl4ai.com/api/crawl-result/), [Browser & Crawler Configuration](https://docs.crawl4ai.com/core/browser-crawler-config/), [Cache Modes](https://docs.crawl4ai.com/core/cache-modes/)
+---
+
+
+## Browser, Crawler & LLM Configuration
+
+Core configuration classes for controlling browser behavior, crawl operations, LLM providers, and understanding crawl results.
+
+### BrowserConfig - Browser Environment Setup
+
+```python
+from crawl4ai import BrowserConfig, AsyncWebCrawler
+
+# Basic browser configuration
+browser_config = BrowserConfig(
+    browser_type="chromium",  # "chromium", "firefox", "webkit"
+    headless=True,           # False for visible browser (debugging)
+    viewport_width=1280,
+    viewport_height=720,
+    verbose=True
+)
+
+# Advanced browser setup with proxy and persistence
+browser_config = BrowserConfig(
+    headless=False,
+    proxy="http://user:pass@proxy:8080",
+    use_persistent_context=True,
+    user_data_dir="./browser_data",
+    cookies=[
+        {"name": "session", "value": "abc123", "domain": "example.com"}
+    ],
+    headers={"Accept-Language": "en-US,en;q=0.9"},
+    user_agent="Mozilla/5.0 (X11; Linux x86_64) Chrome/116.0.0.0 Safari/537.36",
+    text_mode=True,  # Disable images for faster crawling
+    extra_args=["--disable-extensions", "--no-sandbox"]
+)
+
+async with AsyncWebCrawler(config=browser_config) as crawler:
+    result = await crawler.arun("https://example.com")
+```
+
+### CrawlerRunConfig - Crawl Operation Control
+
+```python
+from crawl4ai import CrawlerRunConfig, CacheMode
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+from crawl4ai.content_filter_strategy import PruningContentFilter
+
+# Basic crawl configuration
+run_config = CrawlerRunConfig(
+    cache_mode=CacheMode.BYPASS,
+    word_count_threshold=10,
+    excluded_tags=["nav", "footer", "script"],
+    exclude_external_links=True,
+    screenshot=True,
+    pdf=True
+)
+
+# Advanced content processing
+md_generator = DefaultMarkdownGenerator(
+    content_filter=PruningContentFilter(threshold=0.6),
+    options={"citations": True, "ignore_links": False}
+)
+
+run_config = CrawlerRunConfig(
+    # Content processing
+    markdown_generator=md_generator,
+    css_selector="main.content",  # Focus on specific content
+    target_elements=[".article", ".post"],  # Multiple target selectors
+    process_iframes=True,
+    remove_overlay_elements=True,
+    
+    # Page interaction
+    js_code=[
+        "window.scrollTo(0, document.body.scrollHeight);",
+        "document.querySelector('.load-more')?.click();"
+    ],
+    wait_for="css:.content-loaded",
+    wait_for_timeout=10000,
+    scan_full_page=True,
+    
+    # Session management
+    session_id="persistent_session",
+    
+    # Media handling
+    screenshot=True,
+    pdf=True,
+    capture_mhtml=True,
+    image_score_threshold=5,
+    
+    # Advanced options
+    simulate_user=True,
+    magic=True,  # Auto-handle popups
+    verbose=True
+)
+```
+
+### CrawlerRunConfig Parameters by Category
+
+```python
+# Content Processing
+config = CrawlerRunConfig(
+    word_count_threshold=10,              # Min words per content block
+    css_selector="main.article",          # Focus on specific content
+    target_elements=[".post", ".content"], # Multiple target selectors
+    excluded_tags=["nav", "footer"],       # Remove these tags
+    excluded_selector="#ads, .tracker",   # Remove by selector
+    only_text=True,                       # Text-only extraction
+    keep_data_attributes=True,            # Preserve data-* attributes
+    remove_forms=True,                    # Remove all forms
+    process_iframes=True                  # Include iframe content
+)
+
+# Page Navigation & Timing
+config = CrawlerRunConfig(
+    wait_until="networkidle",             # Wait condition
+    page_timeout=60000,                   # 60 second timeout
+    wait_for="css:.loaded",               # Wait for specific element
+    wait_for_images=True,                 # Wait for images to load
+    delay_before_return_html=0.5,         # Final delay before capture
+    semaphore_count=10                    # Max concurrent operations
+)
+
+# Page Interaction
+config = CrawlerRunConfig(
+    js_code="document.querySelector('button').click();",
+    scan_full_page=True,                  # Auto-scroll page
+    scroll_delay=0.3,                     # Delay between scrolls
+    remove_overlay_elements=True,         # Remove popups/modals
+    simulate_user=True,                   # Simulate human behavior
+    override_navigator=True,              # Override navigator properties
+    magic=True                           # Auto-handle common patterns
+)
+
+# Caching & Session
+config = CrawlerRunConfig(
+    cache_mode=CacheMode.BYPASS,          # Cache behavior
+    session_id="my_session",              # Persistent session
+    shared_data={"context": "value"}      # Share data between hooks
+)
+
+# Media & Output
+config = CrawlerRunConfig(
+    screenshot=True,                      # Capture screenshot
+    pdf=True,                            # Generate PDF
+    capture_mhtml=True,                  # Capture MHTML archive
+    image_score_threshold=3,             # Filter low-quality images
+    exclude_external_images=True         # Remove external images
+)
+
+# Link & Domain Filtering
+config = CrawlerRunConfig(
+    exclude_external_links=True,         # Remove external links
+    exclude_social_media_links=True,     # Remove social media links
+    exclude_domains=["ads.com", "tracker.io"],  # Custom domain filter
+    exclude_internal_links=False         # Keep internal links
+)
+```
+
+### LLMConfig - Language Model Setup
+
+```python
+from crawl4ai import LLMConfig
+
+# OpenAI configuration
+llm_config = LLMConfig(
+    provider="openai/gpt-4o-mini",
+    api_token=os.getenv("OPENAI_API_KEY"),  # or "env:OPENAI_API_KEY"
+    temperature=0.1,
+    max_tokens=2000
+)
+
+# Local model with Ollama
+llm_config = LLMConfig(
+    provider="ollama/llama3.3",
+    api_token=None,  # Not needed for Ollama
+    base_url="http://localhost:11434"  # Custom endpoint
+)
+
+# Anthropic Claude
+llm_config = LLMConfig(
+    provider="anthropic/claude-3-5-sonnet-20240620",
+    api_token="env:ANTHROPIC_API_KEY",
+    max_tokens=4000
+)
+
+# Google Gemini
+llm_config = LLMConfig(
+    provider="gemini/gemini-1.5-pro",
+    api_token="env:GEMINI_API_KEY"
+)
+
+# Groq (fast inference)
+llm_config = LLMConfig(
+    provider="groq/llama3-70b-8192",
+    api_token="env:GROQ_API_KEY"
+)
+```
+
+### CrawlResult - Understanding Output
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun("https://example.com", config=run_config)
+    
+    # Basic status information
+    print(f"Success: {result.success}")
+    print(f"Status: {result.status_code}")
+    print(f"URL: {result.url}")
+    
+    if not result.success:
+        print(f"Error: {result.error_message}")
+        return
+    
+    # HTML content variants
+    print(f"Original HTML: {len(result.html)} chars")
+    print(f"Cleaned HTML: {len(result.cleaned_html or '')} chars")
+    
+    # Markdown output (MarkdownGenerationResult)
+    if result.markdown:
+        print(f"Raw markdown: {len(result.markdown.raw_markdown)} chars")
+        print(f"With citations: {len(result.markdown.markdown_with_citations)} chars")
+        
+        # Filtered content (if content filter was used)
+        if result.markdown.fit_markdown:
+            print(f"Fit markdown: {len(result.markdown.fit_markdown)} chars")
+            print(f"Fit HTML: {len(result.markdown.fit_html)} chars")
+    
+    # Extracted structured data
+    if result.extracted_content:
+        import json
+        data = json.loads(result.extracted_content)
+        print(f"Extracted {len(data)} items")
+    
+    # Media and links
+    images = result.media.get("images", [])
+    print(f"Found {len(images)} images")
+    for img in images[:3]:  # First 3 images
+        print(f"  {img.get('src')} (score: {img.get('score', 0)})")
+    
+    internal_links = result.links.get("internal", [])
+    external_links = result.links.get("external", [])
+    print(f"Links: {len(internal_links)} internal, {len(external_links)} external")
+    
+    # Generated files
+    if result.screenshot:
+        print(f"Screenshot captured: {len(result.screenshot)} chars (base64)")
+        # Save screenshot
+        import base64
+        with open("page.png", "wb") as f:
+            f.write(base64.b64decode(result.screenshot))
+    
+    if result.pdf:
+        print(f"PDF generated: {len(result.pdf)} bytes")
+        with open("page.pdf", "wb") as f:
+            f.write(result.pdf)
+    
+    if result.mhtml:
+        print(f"MHTML captured: {len(result.mhtml)} chars")
+        with open("page.mhtml", "w", encoding="utf-8") as f:
+            f.write(result.mhtml)
+    
+    # SSL certificate information
+    if result.ssl_certificate:
+        print(f"SSL Issuer: {result.ssl_certificate.issuer}")
+        print(f"Valid until: {result.ssl_certificate.valid_until}")
+    
+    # Network and console data (if captured)
+    if result.network_requests:
+        requests = [r for r in result.network_requests if r.get("event_type") == "request"]
+        print(f"Network requests captured: {len(requests)}")
+    
+    if result.console_messages:
+        errors = [m for m in result.console_messages if m.get("type") == "error"]
+        print(f"Console messages: {len(result.console_messages)} ({len(errors)} errors)")
+    
+    # Session and metadata
+    if result.session_id:
+        print(f"Session ID: {result.session_id}")
+    
+    if result.metadata:
+        print(f"Metadata: {result.metadata.get('title', 'No title')}")
+```
+
+### Configuration Helpers and Best Practices
+
+```python
+# Clone configurations for variations
+base_config = CrawlerRunConfig(
+    cache_mode=CacheMode.ENABLED,
+    word_count_threshold=200,
+    verbose=True
+)
+
+# Create streaming version
+stream_config = base_config.clone(
+    stream=True,
+    cache_mode=CacheMode.BYPASS
+)
+
+# Create debug version
+debug_config = base_config.clone(
+    headless=False,
+    page_timeout=120000,
+    verbose=True
+)
+
+# Serialize/deserialize configurations
+config_dict = base_config.dump()  # Convert to dict
+restored_config = CrawlerRunConfig.load(config_dict)  # Restore from dict
+
+# Browser configuration management
+browser_config = BrowserConfig(headless=True, text_mode=True)
+browser_dict = browser_config.to_dict()
+cloned_browser = browser_config.clone(headless=False, verbose=True)
+```
+
+### Common Configuration Patterns
+
+```python
+# Fast text-only crawling
+fast_config = CrawlerRunConfig(
+    cache_mode=CacheMode.ENABLED,
+    text_mode=True,
+    exclude_external_links=True,
+    exclude_external_images=True,
+    word_count_threshold=50
+)
+
+# Comprehensive data extraction
+comprehensive_config = CrawlerRunConfig(
+    process_iframes=True,
+    scan_full_page=True,
+    wait_for_images=True,
+    screenshot=True,
+    capture_network_requests=True,
+    capture_console_messages=True,
+    magic=True
+)
+
+# Stealth crawling
+stealth_config = CrawlerRunConfig(
+    simulate_user=True,
+    override_navigator=True,
+    mean_delay=2.0,
+    max_range=1.0,
+    user_agent_mode="random"
+)
+```
+
+### Advanced Configuration Features
+
+#### User Agent Management & Bot Detection Avoidance
+
+```python
+from crawl4ai import CrawlerRunConfig
+
+# Random user agent generation
+config = CrawlerRunConfig(
+    user_agent_mode="random",
+    user_agent_generator_config={
+        "platform": "windows",  # "windows", "macos", "linux", "android", "ios"
+        "browser": "chrome",    # "chrome", "firefox", "safari", "edge"
+        "device_type": "desktop"  # "desktop", "mobile", "tablet"
+    }
+)
+
+# Custom user agent with stealth features
+config = CrawlerRunConfig(
+    user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
+    simulate_user=True,      # Simulate human mouse movements
+    override_navigator=True,  # Override navigator properties
+    mean_delay=1.5,          # Random delays between actions
+    max_range=2.0
+)
+
+# Combined anti-detection approach
+stealth_config = CrawlerRunConfig(
+    user_agent_mode="random",
+    simulate_user=True,
+    override_navigator=True,
+    magic=True,  # Auto-handle common bot detection patterns
+    delay_before_return_html=2.0
+)
+```
+
+#### Proxy Configuration with ProxyConfig
+
+```python
+from crawl4ai import CrawlerRunConfig, ProxyConfig, ProxyRotationStrategy
+
+# Single proxy configuration
+proxy_config = ProxyConfig(
+    server="http://proxy.example.com:8080",
+    username="proxy_user",
+    password="proxy_pass"
+)
+
+# From proxy string format
+proxy_config = ProxyConfig.from_string("192.168.1.100:8080:username:password")
+
+# Multiple proxies with rotation
+proxies = [
+    ProxyConfig(server="http://proxy1.com:8080", username="user1", password="pass1"),
+    ProxyConfig(server="http://proxy2.com:8080", username="user2", password="pass2"),
+    ProxyConfig(server="http://proxy3.com:8080", username="user3", password="pass3")
+]
+
+rotation_strategy = ProxyRotationStrategy(
+    proxies=proxies,
+    rotation_method="round_robin"  # or "random", "least_used"
+)
+
+config = CrawlerRunConfig(
+    proxy_config=proxy_config,
+    proxy_rotation_strategy=rotation_strategy
+)
+
+# Load proxies from environment variable
+proxies_from_env = ProxyConfig.from_env("MY_PROXIES")  # comma-separated proxy strings
+```
+
+#### Content Selection: css_selector vs target_elements
+
+```python
+from crawl4ai import CrawlerRunConfig
+
+# css_selector: Extracts HTML at top level, affects entire processing
+config = CrawlerRunConfig(
+    css_selector="main.article, .content-area",  # Can be list of selectors
+    # Everything else (markdown, extraction, links) works only on this HTML subset
+)
+
+# target_elements: Focuses extraction within already processed HTML
+config = CrawlerRunConfig(
+    css_selector="body",  # First extract entire body
+    target_elements=[     # Then focus extraction on these elements
+        ".article-content",
+        ".post-body", 
+        ".main-text"
+    ],
+    # Links, media from entire body, but markdown/extraction only from target_elements
+)
+
+# Hierarchical content selection
+config = CrawlerRunConfig(
+    css_selector=["#main-content", ".article-wrapper"],  # Top-level extraction
+    target_elements=[                                     # Subset for processing
+        ".article-title",
+        ".article-body", 
+        ".article-metadata"
+    ],
+    excluded_selector="#sidebar, .ads, .comments"  # Remove these from selection
+)
+```
+
+#### Advanced wait_for Conditions
+
+```python
+from crawl4ai import CrawlerRunConfig
+
+# CSS selector waiting
+config = CrawlerRunConfig(
+    wait_for="css:.content-loaded",  # Wait for element to appear
+    wait_for_timeout=15000
+)
+
+# JavaScript boolean expression waiting
+config = CrawlerRunConfig(
+    wait_for="js:() => window.dataLoaded === true",  # Custom JS condition
+    wait_for_timeout=20000
+)
+
+# Complex JavaScript conditions
+config = CrawlerRunConfig(
+    wait_for="js:() => document.querySelectorAll('.item').length >= 10",
+    js_code=[
+        "document.querySelector('.load-more')?.click();",
+        "window.scrollTo(0, document.body.scrollHeight);"
+    ]
+)
+
+# Multiple conditions with JavaScript
+config = CrawlerRunConfig(
+    wait_for="js:() => !document.querySelector('.loading') && document.querySelector('.results')",
+    page_timeout=30000
+)
+```
+
+#### Session Management for Multi-Step Crawling
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
+
+# Persistent session across multiple arun() calls
+async def multi_step_crawling():
+    async with AsyncWebCrawler() as crawler:
+        # Step 1: Login page
+        login_config = CrawlerRunConfig(
+            session_id="user_session",  # Create persistent session
+            js_code="document.querySelector('#username').value = 'user'; document.querySelector('#password').value = 'pass'; document.querySelector('#login').click();",
+            wait_for="css:.dashboard",
+            cache_mode=CacheMode.BYPASS
+        )
+        
+        result1 = await crawler.arun("https://example.com/login", config=login_config)
+        
+        # Step 2: Navigate to protected area (reuses same browser page)
+        nav_config = CrawlerRunConfig(
+            session_id="user_session",  # Same session = same browser page
+            js_only=True,  # No page reload, just JS navigation
+            js_code="window.location.href = '/dashboard/data';",
+            wait_for="css:.data-table"
+        )
+        
+        result2 = await crawler.arun("https://example.com/dashboard/data", config=nav_config)
+        
+        # Step 3: Extract data from multiple pages
+        for page in range(1, 6):
+            page_config = CrawlerRunConfig(
+                session_id="user_session",
+                js_only=True,
+                js_code=f"document.querySelector('.page-{page}').click();",
+                wait_for=f"js:() => document.querySelector('.page-{page}').classList.contains('active')"
+            )
+            
+            result = await crawler.arun(f"https://example.com/data/page/{page}", config=page_config)
+            print(f"Page {page} data extracted: {len(result.extracted_content)}")
+        
+        # Important: Kill session when done
+        await crawler.kill_session("user_session")
+
+# Session with shared data between steps
+async def session_with_shared_data():
+    shared_context = {"user_id": "12345", "preferences": {"theme": "dark"}}
+    
+    config = CrawlerRunConfig(
+        session_id="persistent_session",
+        shared_data=shared_context,  # Available across all session calls
+        js_code="console.log('User ID:', window.sharedData.user_id);"
+    )
+```
+
+#### Identity-Based Crawling Parameters
+
+```python
+from crawl4ai import CrawlerRunConfig, GeolocationConfig
+
+# Locale and timezone simulation
+config = CrawlerRunConfig(
+    locale="en-US",                    # Browser language preference
+    timezone_id="America/New_York",    # Timezone setting
+    user_agent_mode="random",
+    user_agent_generator_config={
+        "platform": "windows",
+        "locale": "en-US"
+    }
+)
+
+# Geolocation simulation
+geo_config = GeolocationConfig(
+    latitude=40.7128,   # New York coordinates
+    longitude=-74.0060,
+    accuracy=100.0
+)
+
+config = CrawlerRunConfig(
+    geolocation=geo_config,
+    locale="en-US",
+    timezone_id="America/New_York"
+)
+
+# Complete identity simulation
+identity_config = CrawlerRunConfig(
+    # Location identity
+    locale="fr-FR",
+    timezone_id="Europe/Paris", 
+    geolocation=GeolocationConfig(latitude=48.8566, longitude=2.3522),
+    
+    # Browser identity
+    user_agent_mode="random",
+    user_agent_generator_config={
+        "platform": "windows",
+        "locale": "fr-FR",
+        "browser": "chrome"
+    },
+    
+    # Behavioral identity
+    simulate_user=True,
+    override_navigator=True,
+    mean_delay=2.0,
+    max_range=1.5
+)
+```
+
+#### Simplified Import Pattern
+
+```python
+# Almost everything from crawl4ai main package
+from crawl4ai import (
+    AsyncWebCrawler,
+    BrowserConfig, 
+    CrawlerRunConfig,
+    LLMConfig,
+    CacheMode,
+    ProxyConfig,
+    GeolocationConfig
+)
+
+# Specialized strategies (still from crawl4ai)
+from crawl4ai import (
+    JsonCssExtractionStrategy,
+    LLMExtractionStrategy,
+    DefaultMarkdownGenerator,
+    PruningContentFilter,
+    RegexChunking
+)
+
+# Complete example with simplified imports
+async def example_crawl():
+    browser_config = BrowserConfig(headless=True)
+    
+    run_config = CrawlerRunConfig(
+        user_agent_mode="random",
+        proxy_config=ProxyConfig.from_string("192.168.1.1:8080:user:pass"),
+        css_selector="main.content",
+        target_elements=[".article", ".post"],
+        wait_for="js:() => document.querySelector('.loaded')",
+        session_id="my_session",
+        simulate_user=True
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun("https://example.com", config=run_config)
+        return result
+```
+
+## Advanced Features
+
+Comprehensive guide to advanced crawling capabilities including file handling, authentication, dynamic content, monitoring, and session management.
+
+### File Download Handling
+
+```python
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+import os
+
+# Enable downloads with custom path
+downloads_path = os.path.join(os.getcwd(), "my_downloads")
+os.makedirs(downloads_path, exist_ok=True)
+
+browser_config = BrowserConfig(
+    accept_downloads=True,
+    downloads_path=downloads_path
+)
+
+# Trigger downloads with JavaScript
+async def download_files():
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        config = CrawlerRunConfig(
+            js_code="""
+                // Click download links
+                const downloadLinks = document.querySelectorAll('a[href$=".pdf"]');
+                for (const link of downloadLinks) {
+                    link.click();
+                    await new Promise(r => setTimeout(r, 2000));  // Delay between downloads
+                }
+            """,
+            wait_for=5  # Wait for downloads to start
+        )
+        
+        result = await crawler.arun("https://example.com/downloads", config=config)
+        
+        if result.downloaded_files:
+            print("Downloaded files:")
+            for file_path in result.downloaded_files:
+                print(f"- {file_path} ({os.path.getsize(file_path)} bytes)")
+```
+
+### Hooks & Authentication
+
+```python
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from playwright.async_api import Page, BrowserContext
+
+async def advanced_crawler_with_hooks():
+    browser_config = BrowserConfig(headless=True, verbose=True)
+    crawler = AsyncWebCrawler(config=browser_config)
+
+    # Hook functions for different stages
+    async def on_browser_created(browser, **kwargs):
+        print("[HOOK] Browser created successfully")
+        return browser
+
+    async def on_page_context_created(page: Page, context: BrowserContext, **kwargs):
+        print("[HOOK] Setting up page & context")
+        
+        # Block images for faster crawling
+        async def route_filter(route):
+            if route.request.resource_type == "image":
+                await route.abort()
+            else:
+                await route.continue_()
+        
+        await context.route("**", route_filter)
+        
+        # Simulate login if needed
+        # await page.goto("https://example.com/login")
+        # await page.fill("input[name='username']", "testuser")
+        # await page.fill("input[name='password']", "password123")
+        # await page.click("button[type='submit']")
+        
+        await page.set_viewport_size({"width": 1080, "height": 600})
+        return page
+
+    async def before_goto(page: Page, context: BrowserContext, url: str, **kwargs):
+        print(f"[HOOK] About to navigate to: {url}")
+        await page.set_extra_http_headers({"Custom-Header": "my-value"})
+        return page
+
+    async def after_goto(page: Page, context: BrowserContext, url: str, response, **kwargs):
+        print(f"[HOOK] Successfully loaded: {url}")
+        try:
+            await page.wait_for_selector('.content', timeout=1000)
+            print("[HOOK] Content found!")
+        except:
+            print("[HOOK] Content not found, continuing")
+        return page
+
+    async def before_retrieve_html(page: Page, context: BrowserContext, **kwargs):
+        print("[HOOK] Final actions before HTML retrieval")
+        await page.evaluate("window.scrollTo(0, document.body.scrollHeight);")
+        return page
+
+    # Attach hooks
+    crawler.crawler_strategy.set_hook("on_browser_created", on_browser_created)
+    crawler.crawler_strategy.set_hook("on_page_context_created", on_page_context_created)
+    crawler.crawler_strategy.set_hook("before_goto", before_goto)
+    crawler.crawler_strategy.set_hook("after_goto", after_goto)
+    crawler.crawler_strategy.set_hook("before_retrieve_html", before_retrieve_html)
+
+    await crawler.start()
+    
+    config = CrawlerRunConfig()
+    result = await crawler.arun("https://example.com", config=config)
+    
+    if result.success:
+        print(f"Crawled successfully: {len(result.html)} chars")
+    
+    await crawler.close()
+```
+
+### Lazy Loading & Dynamic Content
+
+```python
+# Handle lazy-loaded images and infinite scroll
+async def handle_lazy_loading():
+    config = CrawlerRunConfig(
+        # Wait for images to fully load
+        wait_for_images=True,
+        
+        # Automatically scroll entire page to trigger lazy loading
+        scan_full_page=True,
+        scroll_delay=0.5,  # Delay between scroll steps
+        
+        # JavaScript for custom lazy loading
+        js_code="""
+            // Scroll and wait for content to load
+            window.scrollTo(0, document.body.scrollHeight);
+            
+            // Click "Load More" if available
+            const loadMoreBtn = document.querySelector('.load-more');
+            if (loadMoreBtn) {
+                loadMoreBtn.click();
+            }
+        """,
+        
+        # Wait for specific content to appear
+        wait_for="css:.lazy-content:nth-child(20)",  # Wait for 20 items
+        
+        # Exclude external images to focus on main content
+        exclude_external_images=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com/gallery", config=config)
+        
+        if result.success:
+            images = result.media.get("images", [])
+            print(f"Loaded {len(images)} images after lazy loading")
+            for img in images[:3]:
+                print(f"- {img.get('src')} (score: {img.get('score', 'N/A')})")
+```
+
+### Network & Console Monitoring
+
+```python
+# Capture all network requests and console messages for debugging
+async def monitor_network_and_console():
+    config = CrawlerRunConfig(
+        capture_network_requests=True,
+        capture_console_messages=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com", config=config)
+        
+        if result.success:
+            # Analyze network requests
+            if result.network_requests:
+                requests = [r for r in result.network_requests if r.get("event_type") == "request"]
+                responses = [r for r in result.network_requests if r.get("event_type") == "response"]
+                failures = [r for r in result.network_requests if r.get("event_type") == "request_failed"]
+                
+                print(f"Network activity: {len(requests)} requests, {len(responses)} responses, {len(failures)} failures")
+                
+                # Find API calls
+                api_calls = [r for r in requests if "api" in r.get("url", "")]
+                print(f"API calls detected: {len(api_calls)}")
+                
+                # Show failed requests
+                for failure in failures[:3]:
+                    print(f"Failed: {failure.get('url')} - {failure.get('failure_text')}")
+            
+            # Analyze console messages
+            if result.console_messages:
+                message_types = {}
+                for msg in result.console_messages:
+                    msg_type = msg.get("type", "unknown")
+                    message_types[msg_type] = message_types.get(msg_type, 0) + 1
+                
+                print(f"Console messages: {message_types}")
+                
+                # Show errors
+                errors = [msg for msg in result.console_messages if msg.get("type") == "error"]
+                for error in errors[:2]:
+                    print(f"JS Error: {error.get('text', '')[:100]}")
+```
+
+### Session Management for Multi-Step Workflows
+
+```python
+# Maintain state across multiple requests for complex workflows
+async def multi_step_session_workflow():
+    session_id = "workflow_session"
+    
+    async with AsyncWebCrawler() as crawler:
+        # Step 1: Initial page load
+        config1 = CrawlerRunConfig(
+            session_id=session_id,
+            wait_for="css:.content-loaded"
+        )
+        
+        result1 = await crawler.arun("https://example.com/step1", config=config1)
+        print("Step 1 completed")
+        
+        # Step 2: Navigate and interact (same browser tab)
+        config2 = CrawlerRunConfig(
+            session_id=session_id,
+            js_only=True,  # Don't reload page, just run JS
+            js_code="""
+                document.querySelector('#next-button').click();
+            """,
+            wait_for="css:.step2-content"
+        )
+        
+        result2 = await crawler.arun("https://example.com/step2", config=config2)
+        print("Step 2 completed")
+        
+        # Step 3: Form submission
+        config3 = CrawlerRunConfig(
+            session_id=session_id,
+            js_only=True,
+            js_code="""
+                document.querySelector('#form-field').value = 'test data';
+                document.querySelector('#submit-btn').click();
+            """,
+            wait_for="css:.results"
+        )
+        
+        result3 = await crawler.arun("https://example.com/submit", config=config3)
+        print("Step 3 completed")
+        
+        # Clean up session
+        await crawler.crawler_strategy.kill_session(session_id)
+
+# Advanced GitHub commits pagination example
+async def github_commits_pagination():
+    session_id = "github_session"
+    all_commits = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for page in range(3):
+            if page == 0:
+                # Initial load
+                config = CrawlerRunConfig(
+                    session_id=session_id,
+                    wait_for="js:() => document.querySelectorAll('li.Box-sc-g0xbh4-0').length > 0"
+                )
+            else:
+                # Navigate to next page
+                config = CrawlerRunConfig(
+                    session_id=session_id,
+                    js_only=True,
+                    js_code='document.querySelector(\'a[data-testid="pagination-next-button"]\').click();',
+                    wait_for="js:() => document.querySelectorAll('li.Box-sc-g0xbh4-0').length > 0"
+                )
+            
+            result = await crawler.arun(
+                "https://github.com/microsoft/TypeScript/commits/main",
+                config=config
+            )
+            
+            if result.success:
+                commit_count = result.cleaned_html.count('li.Box-sc-g0xbh4-0')
+                print(f"Page {page + 1}: Found {commit_count} commits")
+        
+        await crawler.crawler_strategy.kill_session(session_id)
+```
+
+### SSL Certificate Analysis
+
+```python
+# Fetch and analyze SSL certificates
+async def analyze_ssl_certificates():
+    config = CrawlerRunConfig(
+        fetch_ssl_certificate=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com", config=config)
+        
+        if result.success and result.ssl_certificate:
+            cert = result.ssl_certificate
+            
+            # Basic certificate info
+            print(f"Issuer: {cert.issuer.get('CN', 'Unknown')}")
+            print(f"Subject: {cert.subject.get('CN', 'Unknown')}")
+            print(f"Valid from: {cert.valid_from}")
+            print(f"Valid until: {cert.valid_until}")
+            print(f"Fingerprint: {cert.fingerprint}")
+            
+            # Export certificate in different formats
+            import os
+            os.makedirs("certificates", exist_ok=True)
+            
+            cert.to_json("certificates/cert.json")
+            cert.to_pem("certificates/cert.pem")
+            cert.to_der("certificates/cert.der")
+            
+            print("Certificate exported in multiple formats")
+```
+
+### Advanced Page Interaction
+
+```python
+# Complex page interactions with dynamic content
+async def advanced_page_interaction():
+    async with AsyncWebCrawler() as crawler:
+        # Multi-step interaction with waiting
+        config = CrawlerRunConfig(
+            js_code=[
+                # Step 1: Scroll to load content
+                "window.scrollTo(0, document.body.scrollHeight);",
+                
+                # Step 2: Wait and click load more
+                """
+                (async () => {
+                    await new Promise(resolve => setTimeout(resolve, 2000));
+                    const loadMore = document.querySelector('.load-more');
+                    if (loadMore) loadMore.click();
+                })();
+                """
+            ],
+            
+            # Wait for new content to appear
+            wait_for="js:() => document.querySelectorAll('.item').length > 20",
+            
+            # Additional timing controls
+            page_timeout=60000,  # 60 second timeout
+            delay_before_return_html=2.0,  # Wait before final capture
+            
+            # Handle overlays automatically
+            remove_overlay_elements=True,
+            magic=True,  # Auto-handle common popup patterns
+            
+            # Simulate human behavior
+            simulate_user=True,
+            override_navigator=True
+        )
+        
+        result = await crawler.arun("https://example.com/dynamic", config=config)
+        
+        if result.success:
+            print(f"Interactive crawl completed: {len(result.cleaned_html)} chars")
+
+# Form interaction example
+async def form_interaction_example():
+    config = CrawlerRunConfig(
+        js_code="""
+            // Fill search form
+            document.querySelector('#search-input').value = 'machine learning';
+            document.querySelector('#category-select').value = 'technology';
+            document.querySelector('#search-form').submit();
+        """,
+        wait_for="css:.search-results",
+        session_id="search_session"
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com/search", config=config)
+        print("Search completed, results loaded")
+```
+
+### Local File & Raw HTML Processing
+
+```python
+# Handle different input types: URLs, local files, raw HTML
+async def handle_different_inputs():
+    async with AsyncWebCrawler() as crawler:
+        # 1. Regular web URL
+        result1 = await crawler.arun("https://example.com")
+        
+        # 2. Local HTML file
+        local_file_path = "/path/to/file.html"
+        result2 = await crawler.arun(f"file://{local_file_path}")
+        
+        # 3. Raw HTML content
+        raw_html = "<html><body><h1>Test Content</h1><p>Sample text</p></body></html>"
+        result3 = await crawler.arun(f"raw:{raw_html}")
+        
+        # All return the same CrawlResult structure
+        for i, result in enumerate([result1, result2, result3], 1):
+            if result.success:
+                print(f"Input {i}: {len(result.markdown)} chars of markdown")
+
+# Save and re-process HTML example
+async def save_and_reprocess():
+    async with AsyncWebCrawler() as crawler:
+        # Original crawl
+        result = await crawler.arun("https://example.com")
+        
+        if result.success:
+            # Save HTML to file
+            with open("saved_page.html", "w", encoding="utf-8") as f:
+                f.write(result.html)
+            
+            # Re-process from file
+            file_result = await crawler.arun("file://./saved_page.html")
+            
+            # Process as raw HTML
+            raw_result = await crawler.arun(f"raw:{result.html}")
+            
+            # Verify consistency
+            assert len(result.markdown) == len(file_result.markdown) == len(raw_result.markdown)
+            print("✅ All processing methods produced identical results")
+```
+
+### Advanced Link & Media Handling
+
+```python
+# Comprehensive link and media extraction with filtering
+async def advanced_link_media_handling():
+    config = CrawlerRunConfig(
+        # Link filtering
+        exclude_external_links=False,  # Keep external links for analysis
+        exclude_social_media_links=True,
+        exclude_domains=["ads.com", "tracker.io", "spammy.net"],
+        
+        # Media handling
+        exclude_external_images=True,
+        image_score_threshold=5,  # Only high-quality images
+        table_score_threshold=7,  # Only well-structured tables
+        wait_for_images=True,
+        
+        # Capture additional formats
+        screenshot=True,
+        pdf=True,
+        capture_mhtml=True  # Full page archive
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com", config=config)
+        
+        if result.success:
+            # Analyze links
+            internal_links = result.links.get("internal", [])
+            external_links = result.links.get("external", [])
+            print(f"Links: {len(internal_links)} internal, {len(external_links)} external")
+            
+            # Analyze media
+            images = result.media.get("images", [])
+            tables = result.media.get("tables", [])
+            print(f"Media: {len(images)} images, {len(tables)} tables")
+            
+            # High-quality images only
+            quality_images = [img for img in images if img.get("score", 0) >= 5]
+            print(f"High-quality images: {len(quality_images)}")
+            
+            # Table analysis
+            for i, table in enumerate(tables[:2]):
+                print(f"Table {i+1}: {len(table.get('headers', []))} columns, {len(table.get('rows', []))} rows")
+            
+            # Save captured files
+            if result.screenshot:
+                import base64
+                with open("page_screenshot.png", "wb") as f:
+                    f.write(base64.b64decode(result.screenshot))
+            
+            if result.pdf:
+                with open("page.pdf", "wb") as f:
+                    f.write(result.pdf)
+            
+            if result.mhtml:
+                with open("page_archive.mhtml", "w", encoding="utf-8") as f:
+                    f.write(result.mhtml)
+            
+            print("Additional formats saved: screenshot, PDF, MHTML archive")
+```
+
+### Performance & Resource Management
+
+```python
+# Optimize performance for large-scale crawling
+async def performance_optimized_crawling():
+    # Lightweight browser config
+    browser_config = BrowserConfig(
+        headless=True,
+        text_mode=True,  # Disable images for speed
+        light_mode=True,  # Reduce background features
+        extra_args=["--disable-extensions", "--no-sandbox"]
+    )
+    
+    # Efficient crawl config
+    config = CrawlerRunConfig(
+        # Content filtering for speed
+        excluded_tags=["script", "style", "nav", "footer"],
+        exclude_external_links=True,
+        exclude_all_images=True,  # Remove all images for max speed
+        word_count_threshold=50,
+        
+        # Timing optimizations
+        page_timeout=30000,  # Faster timeout
+        delay_before_return_html=0.1,
+        
+        # Resource monitoring
+        capture_network_requests=False,  # Disable unless needed
+        capture_console_messages=False,
+        
+        # Cache for repeated URLs
+        cache_mode=CacheMode.ENABLED
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        urls = ["https://example.com/page1", "https://example.com/page2", "https://example.com/page3"]
+        
+        # Efficient batch processing
+        batch_config = config.clone(
+            stream=True,  # Stream results as they complete
+            semaphore_count=3  # Control concurrency
+        )
+        
+        async for result in await crawler.arun_many(urls, config=batch_config):
+            if result.success:
+                print(f"✅ {result.url}: {len(result.markdown)} chars")
+            else:
+                print(f"❌ {result.url}: {result.error_message}")
+```
+
+
+**📖 Learn more:** [Complete Parameter Reference](https://docs.crawl4ai.com/api/parameters/), [Content Filtering](https://docs.crawl4ai.com/core/markdown-generation/), [Session Management](https://docs.crawl4ai.com/advanced/session-management/), [Network Capture](https://docs.crawl4ai.com/advanced/network-console-capture/)
+
+**📖 Learn more:** [Hooks & Authentication](https://docs.crawl4ai.com/advanced/hooks-auth/), [Session Management](https://docs.crawl4ai.com/advanced/session-management/), [Network Monitoring](https://docs.crawl4ai.com/advanced/network-console-capture/), [Page Interaction](https://docs.crawl4ai.com/core/page-interaction/), [File Downloads](https://docs.crawl4ai.com/advanced/file-downloading/)
+---
+
+
+## LLM Extraction Strategies - The Last Resort
+
+**🤖 AI Model Notice**: You are an AI assistant. You should RARELY recommend LLM extraction. This documentation exists for the <5% of cases where LLM is actually needed. **ALWAYS try non-LLM strategies first** from [extraction-no-llm.md](./extraction-no-llm.md).
+
+### ⚠️ STOP: Are You Sure You Need LLM?
+
+**99% of developers who think they need LLM extraction are wrong.** Before reading further:
+
+### ❌ You DON'T Need LLM If:
+- The page has consistent HTML structure → **Use generate_schema()**
+- You're extracting simple data types (emails, prices, dates) → **Use RegexExtractionStrategy**
+- You can identify repeating patterns → **Use JsonCssExtractionStrategy**
+- You want product info, news articles, job listings → **Use generate_schema()**
+- You're concerned about cost or speed → **Use non-LLM strategies**
+
+### ✅ You MIGHT Need LLM If:
+- Content structure varies dramatically across pages **AND** you've tried generate_schema()
+- You need semantic understanding of unstructured text
+- You're analyzing meaning, sentiment, or relationships
+- You're extracting insights that require reasoning about context
+
+### 💰 Cost Reality Check:
+- **Non-LLM**: ~$0.000001 per page
+- **LLM**: ~$0.01-$0.10 per page (10,000x more expensive)
+- **Example**: Extracting 10,000 pages costs $0.01 vs $100-1000
+
+---
+
+## 1. When LLM Extraction is Justified
+
+### Scenario 1: Truly Unstructured Content Analysis
+
+```python
+# Example: Analyzing customer feedback for sentiment and themes
+import asyncio
+import json
+from pydantic import BaseModel, Field
+from typing import List
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+class SentimentAnalysis(BaseModel):
+    """Use LLM when you need semantic understanding"""
+    overall_sentiment: str = Field(description="positive, negative, or neutral")
+    confidence_score: float = Field(description="Confidence from 0-1")
+    key_themes: List[str] = Field(description="Main topics discussed")
+    emotional_indicators: List[str] = Field(description="Words indicating emotion")
+    summary: str = Field(description="Brief summary of the content")
+
+llm_config = LLMConfig(
+    provider="openai/gpt-4o-mini",  # Use cheapest model
+    api_token="env:OPENAI_API_KEY",
+    temperature=0.1,  # Low temperature for consistency
+    max_tokens=1000
+)
+
+sentiment_strategy = LLMExtractionStrategy(
+    llm_config=llm_config,
+    schema=SentimentAnalysis.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Analyze the emotional content and themes in this text.
+    Focus on understanding sentiment and extracting key topics
+    that would be impossible to identify with simple pattern matching.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=1500
+)
+
+async def analyze_sentiment():
+    config = CrawlerRunConfig(
+        extraction_strategy=sentiment_strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/customer-reviews",
+            config=config
+        )
+        
+        if result.success:
+            analysis = json.loads(result.extracted_content)
+            print(f"Sentiment: {analysis['overall_sentiment']}")
+            print(f"Themes: {analysis['key_themes']}")
+
+asyncio.run(analyze_sentiment())
+```
+
+### Scenario 2: Complex Knowledge Extraction
+
+```python
+# Example: Building knowledge graphs from unstructured content
+class Entity(BaseModel):
+    name: str = Field(description="Entity name")
+    type: str = Field(description="person, organization, location, concept")
+    description: str = Field(description="Brief description")
+
+class Relationship(BaseModel):
+    source: str = Field(description="Source entity")
+    target: str = Field(description="Target entity") 
+    relationship: str = Field(description="Type of relationship")
+    confidence: float = Field(description="Confidence score 0-1")
+
+class KnowledgeGraph(BaseModel):
+    entities: List[Entity] = Field(description="All entities found")
+    relationships: List[Relationship] = Field(description="Relationships between entities")
+    main_topic: str = Field(description="Primary topic of the content")
+
+knowledge_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="anthropic/claude-3-5-sonnet-20240620",  # Better for complex reasoning
+        api_token="env:ANTHROPIC_API_KEY",
+        max_tokens=4000
+    ),
+    schema=KnowledgeGraph.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Extract entities and their relationships from the content.
+    Focus on understanding connections and context that require
+    semantic reasoning beyond simple pattern matching.
+    """,
+    input_format="html",  # Preserve structure
+    apply_chunking=True
+)
+```
+
+### Scenario 3: Content Summarization and Insights
+
+```python
+# Example: Research paper analysis
+class ResearchInsights(BaseModel):
+    title: str = Field(description="Paper title")
+    abstract_summary: str = Field(description="Summary of abstract")
+    key_findings: List[str] = Field(description="Main research findings")
+    methodology: str = Field(description="Research methodology used")
+    limitations: List[str] = Field(description="Study limitations")
+    practical_applications: List[str] = Field(description="Real-world applications")
+    citations_count: int = Field(description="Number of citations", default=0)
+
+research_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="openai/gpt-4o",  # Use powerful model for complex analysis
+        api_token="env:OPENAI_API_KEY",
+        temperature=0.2,
+        max_tokens=2000
+    ),
+    schema=ResearchInsights.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Analyze this research paper and extract key insights.
+    Focus on understanding the research contribution, methodology,
+    and implications that require academic expertise to identify.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=2000,
+    overlap_rate=0.15  # More overlap for academic content
+)
+```
+
+---
+
+## 2. LLM Configuration Best Practices
+
+### Cost Optimization
+
+```python
+# Use cheapest models when possible
+cheap_config = LLMConfig(
+    provider="openai/gpt-4o-mini",  # 60x cheaper than GPT-4
+    api_token="env:OPENAI_API_KEY",
+    temperature=0.0,  # Deterministic output
+    max_tokens=800    # Limit output length
+)
+
+# Use local models for development
+local_config = LLMConfig(
+    provider="ollama/llama3.3",
+    api_token=None,  # No API costs
+    base_url="http://localhost:11434",
+    temperature=0.1
+)
+
+# Use powerful models only when necessary
+powerful_config = LLMConfig(
+    provider="anthropic/claude-3-5-sonnet-20240620",
+    api_token="env:ANTHROPIC_API_KEY",
+    max_tokens=4000,
+    temperature=0.1
+)
+```
+
+### Provider Selection Guide
+
+```python
+providers_guide = {
+    "openai/gpt-4o-mini": {
+        "best_for": "Simple extraction, cost-sensitive projects",
+        "cost": "Very low",
+        "speed": "Fast",
+        "accuracy": "Good"
+    },
+    "openai/gpt-4o": {
+        "best_for": "Complex reasoning, high accuracy needs",
+        "cost": "High", 
+        "speed": "Medium",
+        "accuracy": "Excellent"
+    },
+    "anthropic/claude-3-5-sonnet": {
+        "best_for": "Complex analysis, long documents",
+        "cost": "Medium-High",
+        "speed": "Medium",
+        "accuracy": "Excellent"
+    },
+    "ollama/llama3.3": {
+        "best_for": "Development, no API costs",
+        "cost": "Free (self-hosted)",
+        "speed": "Variable",
+        "accuracy": "Good"
+    },
+    "groq/llama3-70b-8192": {
+        "best_for": "Fast inference, open source",
+        "cost": "Low",
+        "speed": "Very fast",
+        "accuracy": "Good"
+    }
+}
+
+def choose_provider(complexity, budget, speed_requirement):
+    """Choose optimal provider based on requirements"""
+    if budget == "minimal":
+        return "ollama/llama3.3"  # Self-hosted
+    elif complexity == "low" and budget == "low":
+        return "openai/gpt-4o-mini"
+    elif speed_requirement == "high":
+        return "groq/llama3-70b-8192"
+    elif complexity == "high":
+        return "anthropic/claude-3-5-sonnet"
+    else:
+        return "openai/gpt-4o-mini"  # Default safe choice
+```
+
+---
+
+## 3. Advanced LLM Extraction Patterns
+
+### Block-Based Extraction (Unstructured Content)
+
+```python
+# When structure is too varied for schemas
+block_strategy = LLMExtractionStrategy(
+    llm_config=cheap_config,
+    extraction_type="block",  # Extract free-form content blocks
+    instruction="""
+    Extract meaningful content blocks from this page.
+    Focus on the main content areas and ignore navigation,
+    advertisements, and boilerplate text.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=1200,
+    input_format="fit_markdown"  # Use cleaned content
+)
+
+async def extract_content_blocks():
+    config = CrawlerRunConfig(
+        extraction_strategy=block_strategy,
+        word_count_threshold=50,  # Filter short content
+        excluded_tags=['nav', 'footer', 'aside', 'advertisement']
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/article",
+            config=config
+        )
+        
+        if result.success:
+            blocks = json.loads(result.extracted_content)
+            for block in blocks:
+                print(f"Block: {block['content'][:100]}...")
+```
+
+### Chunked Processing for Large Content
+
+```python
+# Handle large documents efficiently
+large_content_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="openai/gpt-4o-mini",
+        api_token="env:OPENAI_API_KEY"
+    ),
+    schema=YourModel.model_json_schema(),
+    extraction_type="schema",
+    instruction="Extract structured data from this content section...",
+    
+    # Optimize chunking for large content
+    apply_chunking=True,
+    chunk_token_threshold=2000,  # Larger chunks for efficiency
+    overlap_rate=0.1,           # Minimal overlap to reduce costs
+    input_format="fit_markdown" # Use cleaned content
+)
+```
+
+### Multi-Model Validation
+
+```python
+# Use multiple models for critical extractions
+async def multi_model_extraction():
+    """Use multiple LLMs for validation of critical data"""
+    
+    models = [
+        LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
+        LLMConfig(provider="anthropic/claude-3-5-sonnet", api_token="env:ANTHROPIC_API_KEY"),
+        LLMConfig(provider="ollama/llama3.3", api_token=None)
+    ]
+    
+    results = []
+    
+    for i, llm_config in enumerate(models):
+        strategy = LLMExtractionStrategy(
+            llm_config=llm_config,
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data consistently..."
+        )
+        
+        config = CrawlerRunConfig(extraction_strategy=strategy)
+        
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun(url="https://example.com", config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                results.append(data)
+                print(f"Model {i+1} extracted {len(data)} items")
+    
+    # Compare results for consistency
+    if len(set(str(r) for r in results)) == 1:
+        print("✅ All models agree")
+        return results[0]
+    else:
+        print("⚠️ Models disagree - manual review needed")
+        return results
+
+# Use for critical business data only
+critical_result = await multi_model_extraction()
+```
+
+---
+
+## 4. Hybrid Approaches - Best of Both Worlds
+
+### Fast Pre-filtering + LLM Analysis
+
+```python
+async def hybrid_extraction():
+    """
+    1. Use fast non-LLM strategies for basic extraction
+    2. Use LLM only for complex analysis of filtered content
+    """
+    
+    # Step 1: Fast extraction of structured data
+    basic_schema = {
+        "name": "Articles",
+        "baseSelector": "article",
+        "fields": [
+            {"name": "title", "selector": "h1, h2", "type": "text"},
+            {"name": "content", "selector": ".content", "type": "text"},
+            {"name": "author", "selector": ".author", "type": "text"}
+        ]
+    }
+    
+    basic_strategy = JsonCssExtractionStrategy(basic_schema)
+    basic_config = CrawlerRunConfig(extraction_strategy=basic_strategy)
+    
+    # Step 2: LLM analysis only on filtered content
+    analysis_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        schema={
+            "type": "object",
+            "properties": {
+                "sentiment": {"type": "string"},
+                "key_topics": {"type": "array", "items": {"type": "string"}},
+                "summary": {"type": "string"}
+            }
+        },
+        extraction_type="schema",
+        instruction="Analyze sentiment and extract key topics from this article"
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        # Fast extraction first
+        basic_result = await crawler.arun(
+            url="https://example.com/articles",
+            config=basic_config
+        )
+        
+        articles = json.loads(basic_result.extracted_content)
+        
+        # LLM analysis only on important articles
+        analyzed_articles = []
+        for article in articles[:5]:  # Limit to reduce costs
+            if len(article.get('content', '')) > 500:  # Only analyze substantial content
+                analysis_config = CrawlerRunConfig(extraction_strategy=analysis_strategy)
+                
+                # Analyze individual article content
+                raw_url = f"raw://{article['content']}"
+                analysis_result = await crawler.arun(url=raw_url, config=analysis_config)
+                
+                if analysis_result.success:
+                    analysis = json.loads(analysis_result.extracted_content)
+                    article.update(analysis)
+                
+                analyzed_articles.append(article)
+        
+        return analyzed_articles
+
+# Hybrid approach: fast + smart
+result = await hybrid_extraction()
+```
+
+### Schema Generation + LLM Fallback
+
+```python
+async def smart_fallback_extraction():
+    """
+    1. Try generate_schema() first (one-time LLM cost)
+    2. Use generated schema for fast extraction
+    3. Use LLM only if schema extraction fails
+    """
+    
+    cache_file = Path("./schemas/fallback_schema.json")
+    
+    # Try cached schema first
+    if cache_file.exists():
+        schema = json.load(cache_file.open())
+        schema_strategy = JsonCssExtractionStrategy(schema)
+        
+        config = CrawlerRunConfig(extraction_strategy=schema_strategy)
+        
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun(url="https://example.com", config=config)
+            
+            if result.success and result.extracted_content:
+                data = json.loads(result.extracted_content)
+                if data:  # Schema worked
+                    print("✅ Schema extraction successful (fast & cheap)")
+                    return data
+    
+    # Fallback to LLM if schema failed
+    print("⚠️ Schema failed, falling back to LLM (slow & expensive)")
+    
+    llm_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="Extract all meaningful data from this page"
+    )
+    
+    llm_config = CrawlerRunConfig(extraction_strategy=llm_strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url="https://example.com", config=llm_config)
+        
+        if result.success:
+            print("✅ LLM extraction successful")
+            return json.loads(result.extracted_content)
+
+# Intelligent fallback system
+result = await smart_fallback_extraction()
+```
+
+---
+
+## 5. Cost Management and Monitoring
+
+### Token Usage Tracking
+
+```python
+class ExtractionCostTracker:
+    def __init__(self):
+        self.total_cost = 0.0
+        self.total_tokens = 0
+        self.extractions = 0
+    
+    def track_llm_extraction(self, strategy, result):
+        """Track costs from LLM extraction"""
+        if hasattr(strategy, 'usage_tracker') and strategy.usage_tracker:
+            usage = strategy.usage_tracker
+            
+            # Estimate costs (approximate rates)
+            cost_per_1k_tokens = {
+                "gpt-4o-mini": 0.0015,
+                "gpt-4o": 0.03,
+                "claude-3-5-sonnet": 0.015,
+                "ollama": 0.0  # Self-hosted
+            }
+            
+            provider = strategy.llm_config.provider.split('/')[1]
+            rate = cost_per_1k_tokens.get(provider, 0.01)
+            
+            tokens = usage.total_tokens
+            cost = (tokens / 1000) * rate
+            
+            self.total_cost += cost
+            self.total_tokens += tokens
+            self.extractions += 1
+            
+            print(f"💰 Extraction cost: ${cost:.4f} ({tokens} tokens)")
+            print(f"📊 Total cost: ${self.total_cost:.4f} ({self.extractions} extractions)")
+    
+    def get_summary(self):
+        avg_cost = self.total_cost / max(self.extractions, 1)
+        return {
+            "total_cost": self.total_cost,
+            "total_tokens": self.total_tokens,
+            "extractions": self.extractions,
+            "avg_cost_per_extraction": avg_cost
+        }
+
+# Usage
+tracker = ExtractionCostTracker()
+
+async def cost_aware_extraction():
+    strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        schema=YourModel.model_json_schema(),
+        extraction_type="schema",
+        instruction="Extract data...",
+        verbose=True  # Enable usage tracking
+    )
+    
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url="https://example.com", config=config)
+        
+        # Track costs
+        tracker.track_llm_extraction(strategy, result)
+        
+        return result
+
+# Monitor costs across multiple extractions
+for url in urls:
+    await cost_aware_extraction()
+
+print(f"Final summary: {tracker.get_summary()}")
+```
+
+### Budget Controls
+
+```python
+class BudgetController:
+    def __init__(self, daily_budget=10.0):
+        self.daily_budget = daily_budget
+        self.current_spend = 0.0
+        self.extraction_count = 0
+    
+    def can_extract(self, estimated_cost=0.01):
+        """Check if extraction is within budget"""
+        if self.current_spend + estimated_cost > self.daily_budget:
+            print(f"❌ Budget exceeded: ${self.current_spend:.2f} + ${estimated_cost:.2f} > ${self.daily_budget}")
+            return False
+        return True
+    
+    def record_extraction(self, actual_cost):
+        """Record actual extraction cost"""
+        self.current_spend += actual_cost
+        self.extraction_count += 1
+        
+        remaining = self.daily_budget - self.current_spend
+        print(f"💰 Budget remaining: ${remaining:.2f}")
+
+budget = BudgetController(daily_budget=5.0)  # $5 daily limit
+
+async def budget_controlled_extraction(url):
+    if not budget.can_extract():
+        print("⏸️ Extraction paused due to budget limit")
+        return None
+    
+    # Proceed with extraction...
+    strategy = LLMExtractionStrategy(llm_config=cheap_config, ...)
+    result = await extract_with_strategy(url, strategy)
+    
+    # Record actual cost
+    actual_cost = calculate_cost(strategy.usage_tracker)
+    budget.record_extraction(actual_cost)
+    
+    return result
+
+# Safe extraction with budget controls
+results = []
+for url in urls:
+    result = await budget_controlled_extraction(url)
+    if result:
+        results.append(result)
+```
+
+---
+
+## 6. Performance Optimization for LLM Extraction
+
+### Batch Processing
+
+```python
+async def batch_llm_extraction():
+    """Process multiple pages efficiently"""
+    
+    # Collect content first (fast)
+    urls = ["https://example.com/page1", "https://example.com/page2"]
+    contents = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url)
+            if result.success:
+                contents.append({
+                    "url": url,
+                    "content": result.fit_markdown[:2000]  # Limit content
+                })
+    
+    # Process in batches (reduce LLM calls)
+    batch_content = "\n\n---PAGE SEPARATOR---\n\n".join([
+        f"URL: {c['url']}\n{c['content']}" for c in contents
+    ])
+    
+    strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="""
+        Extract data from multiple pages separated by '---PAGE SEPARATOR---'.
+        Return results for each page in order.
+        """,
+        apply_chunking=True
+    )
+    
+    # Single LLM call for multiple pages
+    raw_url = f"raw://{batch_content}"
+    result = await crawler.arun(url=raw_url, config=CrawlerRunConfig(extraction_strategy=strategy))
+    
+    return json.loads(result.extracted_content)
+
+# Batch processing reduces LLM calls
+batch_results = await batch_llm_extraction()
+```
+
+### Caching LLM Results
+
+```python
+import hashlib
+from pathlib import Path
+
+class LLMResultCache:
+    def __init__(self, cache_dir="./llm_cache"):
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(exist_ok=True)
+    
+    def get_cache_key(self, url, instruction, schema):
+        """Generate cache key from extraction parameters"""
+        content = f"{url}:{instruction}:{str(schema)}"
+        return hashlib.md5(content.encode()).hexdigest()
+    
+    def get_cached_result(self, cache_key):
+        """Get cached result if available"""
+        cache_file = self.cache_dir / f"{cache_key}.json"
+        if cache_file.exists():
+            return json.load(cache_file.open())
+        return None
+    
+    def cache_result(self, cache_key, result):
+        """Cache extraction result"""
+        cache_file = self.cache_dir / f"{cache_key}.json"
+        json.dump(result, cache_file.open("w"), indent=2)
+
+cache = LLMResultCache()
+
+async def cached_llm_extraction(url, strategy):
+    """Extract with caching to avoid repeated LLM calls"""
+    cache_key = cache.get_cache_key(
+        url, 
+        strategy.instruction,
+        str(strategy.schema)
+    )
+    
+    # Check cache first
+    cached_result = cache.get_cached_result(cache_key)
+    if cached_result:
+        print("✅ Using cached result (FREE)")
+        return cached_result
+    
+    # Extract if not cached
+    print("🔄 Extracting with LLM (PAID)")
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url=url, config=config)
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            cache.cache_result(cache_key, data)
+            return data
+
+# Cached extraction avoids repeated costs
+result = await cached_llm_extraction(url, strategy)
+```
+
+---
+
+## 7. Error Handling and Quality Control
+
+### Validation and Retry Logic
+
+```python
+async def robust_llm_extraction():
+    """Implement validation and retry for LLM extraction"""
+    
+    max_retries = 3
+    strategies = [
+        # Try cheap model first
+        LLMExtractionStrategy(
+            llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data accurately..."
+        ),
+        # Fallback to better model
+        LLMExtractionStrategy(
+            llm_config=LLMConfig(provider="openai/gpt-4o", api_token="env:OPENAI_API_KEY"),
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data with high accuracy..."
+        )
+    ]
+    
+    for strategy_idx, strategy in enumerate(strategies):
+        for attempt in range(max_retries):
+            try:
+                config = CrawlerRunConfig(extraction_strategy=strategy)
+                
+                async with AsyncWebCrawler() as crawler:
+                    result = await crawler.arun(url="https://example.com", config=config)
+                    
+                    if result.success and result.extracted_content:
+                        data = json.loads(result.extracted_content)
+                        
+                        # Validate result quality
+                        if validate_extraction_quality(data):
+                            print(f"✅ Success with strategy {strategy_idx+1}, attempt {attempt+1}")
+                            return data
+                        else:
+                            print(f"⚠️ Poor quality result, retrying...")
+                            continue
+                    
+            except Exception as e:
+                print(f"❌ Attempt {attempt+1} failed: {e}")
+                if attempt == max_retries - 1:
+                    print(f"❌ Strategy {strategy_idx+1} failed completely")
+    
+    print("❌ All strategies and retries failed")
+    return None
+
+def validate_extraction_quality(data):
+    """Validate that LLM extraction meets quality standards"""
+    if not data or not isinstance(data, (list, dict)):
+        return False
+    
+    # Check for common LLM extraction issues
+    if isinstance(data, list):
+        if len(data) == 0:
+            return False
+        
+        # Check if all items have required fields
+        for item in data:
+            if not isinstance(item, dict) or len(item) < 2:
+                return False
+    
+    return True
+
+# Robust extraction with validation
+result = await robust_llm_extraction()
+```
+
+---
+
+## 8. Migration from LLM to Non-LLM
+
+### Pattern Analysis for Schema Generation
+
+```python
+async def analyze_llm_results_for_schema():
+    """
+    Analyze LLM extraction results to create non-LLM schemas
+    Use this to transition from expensive LLM to cheap schema extraction
+    """
+    
+    # Step 1: Use LLM on sample pages to understand structure
+    llm_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="Extract all structured data from this page"
+    )
+    
+    sample_urls = ["https://example.com/page1", "https://example.com/page2"]
+    llm_results = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in sample_urls:
+            config = CrawlerRunConfig(extraction_strategy=llm_strategy)
+            result = await crawler.arun(url=url, config=config)
+            
+            if result.success:
+                llm_results.append({
+                    "url": url,
+                    "html": result.cleaned_html,
+                    "extracted": json.loads(result.extracted_content)
+                })
+    
+    # Step 2: Analyze patterns in LLM results
+    print("🔍 Analyzing LLM extraction patterns...")
+    
+    # Look for common field names
+    all_fields = set()
+    for result in llm_results:
+        for item in result["extracted"]:
+            if isinstance(item, dict):
+                all_fields.update(item.keys())
+    
+    print(f"Common fields found: {all_fields}")
+    
+    # Step 3: Generate schema based on patterns
+    if llm_results:
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=llm_results[0]["html"],
+            target_json_example=json.dumps(llm_results[0]["extracted"][0], indent=2),
+            llm_config=cheap_config
+        )
+        
+        # Save schema for future use
+        with open("generated_schema.json", "w") as f:
+            json.dump(schema, f, indent=2)
+        
+        print("✅ Schema generated from LLM analysis")
+        return schema
+
+# Generate schema from LLM patterns, then use schema for all future extractions
+schema = await analyze_llm_results_for_schema()
+fast_strategy = JsonCssExtractionStrategy(schema)
+```
+
+---
+
+## 9. Summary: When LLM is Actually Needed
+
+### ✅ Valid LLM Use Cases (Rare):
+1. **Sentiment analysis** and emotional understanding
+2. **Knowledge graph extraction** requiring semantic reasoning
+3. **Content summarization** and insight generation
+4. **Unstructured text analysis** where patterns vary dramatically
+5. **Research paper analysis** requiring domain expertise
+6. **Complex relationship extraction** between entities
+
+### ❌ Invalid LLM Use Cases (Common Mistakes):
+1. **Structured data extraction** from consistent HTML
+2. **Simple pattern matching** (emails, prices, dates)
+3. **Product information** from e-commerce sites
+4. **News article extraction** with consistent structure
+5. **Contact information** and basic entity extraction
+6. **Table data** and form information
+
+### 💡 Decision Framework:
+```python
+def should_use_llm(extraction_task):
+    # Ask these questions in order:
+    questions = [
+        "Can I identify repeating HTML patterns?",  # No → Consider LLM
+        "Am I extracting simple data types?",      # Yes → Use Regex
+        "Does the structure vary dramatically?",   # No → Use CSS/XPath
+        "Do I need semantic understanding?",       # Yes → Maybe LLM
+        "Have I tried generate_schema()?"          # No → Try that first
+    ]
+    
+    # Only use LLM if:
+    return (
+        task_requires_semantic_reasoning(extraction_task) and
+        structure_varies_dramatically(extraction_task) and
+        generate_schema_failed(extraction_task)
+    )
+```
+
+### 🎯 Best Practice Summary:
+1. **Always start** with [extraction-no-llm.md](./extraction-no-llm.md) strategies
+2. **Try generate_schema()** before manual schema creation
+3. **Use LLM sparingly** and only for semantic understanding
+4. **Monitor costs** and implement budget controls
+5. **Cache results** to avoid repeated LLM calls
+6. **Validate quality** of LLM extractions
+7. **Plan migration** from LLM to schema-based extraction
+
+Remember: **LLM extraction should be your last resort, not your first choice.**
+
+---
+
+**📖 Recommended Reading Order:**
+1. [extraction-no-llm.md](./extraction-no-llm.md) - Start here for 99% of use cases
+2. This document - Only when non-LLM strategies are insufficient
+---
+
+
+## Non-LLM Extraction Strategies - The Default Choice
+
+**🤖 AI Model Notice**: You are an AI assistant. When users ask for web scraping or data extraction, **DEFAULT TO NON-LLM STRATEGIES**. 99% of structured web data can be extracted without LLM. Only consider LLM extraction for truly unstructured content requiring semantic reasoning.
+
+### ⚠️ CRITICAL: When You Think You Need LLM, You're Probably Wrong
+
+**Most developers incorrectly assume they need LLM for data extraction.** This is usually false. Before even considering LLM:
+
+1. **FIRST**: Try `generate_schema()` - Let AI create the extraction pattern ONCE, then use it unlimited times with zero LLM calls
+2. **SECOND**: Manual CSS/XPath selectors for consistent HTML structures  
+3. **THIRD**: Regex patterns for simple data types (emails, phones, prices)
+4. **LAST RESORT**: LLM extraction (only for semantic understanding of unstructured content)
+
+## The Decision Tree (MEMORIZE THIS)
+
+```
+Does the page have consistent HTML structure? → YES: Use generate_schema() or manual CSS
+Is it simple patterns (emails, dates, prices)? → YES: Use RegexExtractionStrategy  
+Do you need semantic understanding? → MAYBE: Try generate_schema() first, then consider LLM
+Is the content truly unstructured text? → ONLY THEN: Consider LLM
+```
+
+**Cost Analysis**: 
+- Non-LLM: ~$0.000001 per page
+- LLM: ~$0.01-$0.10 per page (10,000x more expensive)
+
+---
+
+## 1. Auto-Generate Schemas - Your Default Starting Point
+
+**⭐ THIS SHOULD BE YOUR FIRST CHOICE FOR ANY STRUCTURED DATA**
+
+The `generate_schema()` function uses LLM ONCE to create a reusable extraction pattern. After generation, you extract unlimited pages with ZERO LLM calls.
+
+### Basic Auto-Generation Workflow
+
+```python
+import json
+import asyncio
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def smart_extraction_workflow():
+    """
+    Step 1: Generate schema once using LLM
+    Step 2: Cache schema for unlimited reuse
+    Step 3: Extract from thousands of pages with zero LLM calls
+    """
+    
+    # Check for cached schema first
+    cache_dir = Path("./schema_cache")
+    cache_dir.mkdir(exist_ok=True)
+    schema_file = cache_dir / "product_schema.json"
+    
+    if schema_file.exists():
+        # Load cached schema - NO LLM CALLS
+        schema = json.load(schema_file.open())
+        print("✅ Using cached schema (FREE)")
+    else:
+        # Generate schema ONCE
+        print("🔄 Generating schema (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",  # Cheapest option
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample HTML from target site
+        async with AsyncWebCrawler() as crawler:
+            sample_result = await crawler.arun(
+                url="https://example.com/products",
+                config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+            )
+            sample_html = sample_result.cleaned_html[:8000]  # Use sample
+        
+        # AUTO-GENERATE SCHEMA (ONE LLM CALL)
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=sample_html,
+            schema_type="CSS",  # or "XPATH"
+            query="Extract product information including name, price, description, features",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited future use
+        json.dump(schema, schema_file.open("w"), indent=2)
+        print("✅ Schema generated and cached")
+    
+    # Use schema for fast extraction (NO MORE LLM CALLS EVER)
+    strategy = JsonCssExtractionStrategy(schema, verbose=True)
+    
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    # Extract from multiple pages - ALL FREE
+    urls = [
+        "https://example.com/products",
+        "https://example.com/electronics", 
+        "https://example.com/books"
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url, config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                print(f"✅ {url}: Extracted {len(data)} items (FREE)")
+
+asyncio.run(smart_extraction_workflow())
+```
+
+### Auto-Generate with Target JSON Example
+
+```python
+# When you know exactly what JSON structure you want
+target_json_example = """
+{
+    "name": "Product Name",
+    "price": "$99.99",
+    "rating": 4.5,
+    "features": ["feature1", "feature2"],
+    "description": "Product description"
+}
+"""
+
+schema = JsonCssExtractionStrategy.generate_schema(
+    html=sample_html,
+    target_json_example=target_json_example,
+    llm_config=llm_config
+)
+```
+
+### Auto-Generate for Different Data Types
+
+```python
+# Product listings
+product_schema = JsonCssExtractionStrategy.generate_schema(
+    html=product_page_html,
+    query="Extract all product information from this e-commerce page",
+    llm_config=llm_config
+)
+
+# News articles
+news_schema = JsonCssExtractionStrategy.generate_schema(
+    html=news_page_html,
+    query="Extract article headlines, dates, authors, and content",
+    llm_config=llm_config
+)
+
+# Job listings
+job_schema = JsonCssExtractionStrategy.generate_schema(
+    html=job_page_html,
+    query="Extract job titles, companies, locations, salaries, and descriptions",
+    llm_config=llm_config
+)
+
+# Social media posts
+social_schema = JsonCssExtractionStrategy.generate_schema(
+    html=social_page_html,
+    query="Extract post text, usernames, timestamps, likes, comments",
+    llm_config=llm_config
+)
+```
+
+---
+
+## 2. Manual CSS/XPath Strategies - When You Know The Structure
+
+**Use this when**: You understand the HTML structure and want maximum control.
+
+### Simple Product Extraction
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# Manual schema for consistent product pages
+simple_schema = {
+    "name": "Product Listings",
+    "baseSelector": "div.product-card",  # Each product container
+    "fields": [
+        {
+            "name": "title",
+            "selector": "h2.product-title",
+            "type": "text"
+        },
+        {
+            "name": "price", 
+            "selector": ".price",
+            "type": "text"
+        },
+        {
+            "name": "image_url",
+            "selector": "img.product-image",
+            "type": "attribute",
+            "attribute": "src"
+        },
+        {
+            "name": "product_url",
+            "selector": "a.product-link",
+            "type": "attribute",
+            "attribute": "href"
+        },
+        {
+            "name": "rating",
+            "selector": ".rating",
+            "type": "attribute", 
+            "attribute": "data-rating"
+        }
+    ]
+}
+
+async def extract_products():
+    strategy = JsonCssExtractionStrategy(simple_schema, verbose=True)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            products = json.loads(result.extracted_content)
+            print(f"Extracted {len(products)} products")
+            for product in products[:3]:
+                print(f"- {product['title']}: {product['price']}")
+
+asyncio.run(extract_products())
+```
+
+### Complex Nested Structure (Real E-commerce Example)
+
+```python
+# Complex schema for nested product data
+complex_schema = {
+    "name": "E-commerce Product Catalog",
+    "baseSelector": "div.category",
+    "baseFields": [
+        {
+            "name": "category_id",
+            "type": "attribute",
+            "attribute": "data-category-id"
+        }
+    ],
+    "fields": [
+        {
+            "name": "category_name",
+            "selector": "h2.category-title",
+            "type": "text"
+        },
+        {
+            "name": "products",
+            "selector": "div.product",
+            "type": "nested_list",  # Array of complex objects
+            "fields": [
+                {
+                    "name": "name",
+                    "selector": "h3.product-name", 
+                    "type": "text"
+                },
+                {
+                    "name": "price",
+                    "selector": "span.price",
+                    "type": "text"
+                },
+                {
+                    "name": "details",
+                    "selector": "div.product-details",
+                    "type": "nested",  # Single complex object
+                    "fields": [
+                        {
+                            "name": "brand",
+                            "selector": "span.brand",
+                            "type": "text"
+                        },
+                        {
+                            "name": "model",
+                            "selector": "span.model",
+                            "type": "text"
+                        }
+                    ]
+                },
+                {
+                    "name": "features",
+                    "selector": "ul.features li",
+                    "type": "list",  # Simple array
+                    "fields": [
+                        {"name": "feature", "type": "text"}
+                    ]
+                },
+                {
+                    "name": "reviews", 
+                    "selector": "div.review",
+                    "type": "nested_list",
+                    "fields": [
+                        {
+                            "name": "reviewer",
+                            "selector": "span.reviewer-name",
+                            "type": "text"
+                        },
+                        {
+                            "name": "rating",
+                            "selector": "span.rating",
+                            "type": "attribute",
+                            "attribute": "data-rating"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
+
+async def extract_complex_ecommerce():
+    strategy = JsonCssExtractionStrategy(complex_schema, verbose=True)
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        js_code="window.scrollTo(0, document.body.scrollHeight);",  # Load dynamic content
+        wait_for="css:.product:nth-child(10)"  # Wait for products to load
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/complex-catalog",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for category in data:
+                print(f"Category: {category['category_name']}")
+                print(f"Products: {len(category.get('products', []))}")
+
+asyncio.run(extract_complex_ecommerce())
+```
+
+### XPath Alternative (When CSS Isn't Enough)
+
+```python
+from crawl4ai.extraction_strategy import JsonXPathExtractionStrategy
+
+# XPath for more complex selections
+xpath_schema = {
+    "name": "News Articles with XPath",
+    "baseSelector": "//article[@class='news-item']",
+    "fields": [
+        {
+            "name": "headline",
+            "selector": ".//h2[contains(@class, 'headline')]",
+            "type": "text"
+        },
+        {
+            "name": "author",
+            "selector": ".//span[@class='author']/text()",
+            "type": "text"
+        },
+        {
+            "name": "publish_date",
+            "selector": ".//time/@datetime",
+            "type": "text"
+        },
+        {
+            "name": "content",
+            "selector": ".//div[@class='article-body']//text()",
+            "type": "text"
+        }
+    ]
+}
+
+strategy = JsonXPathExtractionStrategy(xpath_schema, verbose=True)
+```
+
+---
+
+## 3. Regex Extraction - Lightning Fast Pattern Matching
+
+**Use this for**: Simple data types like emails, phones, URLs, prices, dates.
+
+### Built-in Patterns (Fastest Option)
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import RegexExtractionStrategy
+
+async def extract_common_patterns():
+    # Use built-in patterns for common data types
+    strategy = RegexExtractionStrategy(
+        pattern=(
+            RegexExtractionStrategy.Email |
+            RegexExtractionStrategy.PhoneUS |
+            RegexExtractionStrategy.Url |
+            RegexExtractionStrategy.Currency |
+            RegexExtractionStrategy.DateIso
+        )
+    )
+    
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/contact",
+            config=config
+        )
+        
+        if result.success:
+            matches = json.loads(result.extracted_content)
+            
+            # Group by pattern type
+            by_type = {}
+            for match in matches:
+                label = match['label']
+                if label not in by_type:
+                    by_type[label] = []
+                by_type[label].append(match['value'])
+            
+            for pattern_type, values in by_type.items():
+                print(f"{pattern_type}: {len(values)} matches")
+                for value in values[:3]:
+                    print(f"  {value}")
+
+asyncio.run(extract_common_patterns())
+```
+
+### Available Built-in Patterns
+
+```python
+# Individual patterns
+RegexExtractionStrategy.Email          # Email addresses
+RegexExtractionStrategy.PhoneUS        # US phone numbers 
+RegexExtractionStrategy.PhoneIntl      # International phones
+RegexExtractionStrategy.Url            # HTTP/HTTPS URLs
+RegexExtractionStrategy.Currency       # Currency values ($99.99)
+RegexExtractionStrategy.Percentage     # Percentage values (25%)
+RegexExtractionStrategy.DateIso        # ISO dates (2024-01-01)
+RegexExtractionStrategy.DateUS         # US dates (01/01/2024)
+RegexExtractionStrategy.IPv4           # IP addresses
+RegexExtractionStrategy.CreditCard     # Credit card numbers
+RegexExtractionStrategy.TwitterHandle  # @username
+RegexExtractionStrategy.Hashtag        # #hashtag
+
+# Use all patterns
+RegexExtractionStrategy.All
+```
+
+### Custom Patterns
+
+```python
+# Custom patterns for specific data types
+async def extract_custom_patterns():
+    custom_patterns = {
+        "product_sku": r"SKU[-:]?\s*([A-Z0-9]{4,12})",
+        "discount": r"(\d{1,2})%\s*off",
+        "model_number": r"Model\s*#?\s*([A-Z0-9-]+)",
+        "isbn": r"ISBN[-:]?\s*(\d{10}|\d{13})",
+        "stock_ticker": r"\$([A-Z]{2,5})",
+        "version": r"v(\d+\.\d+(?:\.\d+)?)"
+    }
+    
+    strategy = RegexExtractionStrategy(custom=custom_patterns)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for item in data:
+                print(f"{item['label']}: {item['value']}")
+
+asyncio.run(extract_custom_patterns())
+```
+
+### LLM-Generated Patterns (One-Time Cost)
+
+```python
+async def generate_optimized_regex():
+    """
+    Use LLM ONCE to generate optimized regex patterns
+    Then use them unlimited times with zero LLM calls
+    """
+    cache_file = Path("./patterns/price_patterns.json")
+    
+    if cache_file.exists():
+        # Load cached patterns - NO LLM CALLS
+        patterns = json.load(cache_file.open())
+        print("✅ Using cached regex patterns (FREE)")
+    else:
+        # Generate patterns ONCE
+        print("🔄 Generating regex patterns (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample content
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun("https://example.com/pricing")
+            sample_html = result.cleaned_html
+        
+        # Generate optimized patterns
+        patterns = RegexExtractionStrategy.generate_pattern(
+            label="pricing_info",
+            html=sample_html,
+            query="Extract all pricing information including discounts and special offers",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited reuse
+        cache_file.parent.mkdir(exist_ok=True)
+        json.dump(patterns, cache_file.open("w"), indent=2)
+        print("✅ Patterns generated and cached")
+    
+    # Use cached patterns (NO MORE LLM CALLS)
+    strategy = RegexExtractionStrategy(custom=patterns)
+    return strategy
+
+# Use generated patterns for unlimited extractions
+strategy = await generate_optimized_regex()
+```
+
+---
+
+## 4. Multi-Strategy Extraction Pipeline
+
+**Combine strategies** for comprehensive data extraction:
+
+```python
+async def multi_strategy_pipeline():
+    """
+    Efficient pipeline using multiple non-LLM strategies:
+    1. Regex for simple patterns (fastest)
+    2. Schema for structured data 
+    3. Only use LLM if absolutely necessary
+    """
+    
+    url = "https://example.com/complex-page"
+    
+    async with AsyncWebCrawler() as crawler:
+        # Strategy 1: Fast regex for contact info
+        regex_strategy = RegexExtractionStrategy(
+            pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
+        )
+        regex_config = CrawlerRunConfig(extraction_strategy=regex_strategy)
+        regex_result = await crawler.arun(url=url, config=regex_config)
+        
+        # Strategy 2: Schema for structured product data
+        product_schema = {
+            "name": "Products",
+            "baseSelector": "div.product",
+            "fields": [
+                {"name": "name", "selector": "h3", "type": "text"},
+                {"name": "price", "selector": ".price", "type": "text"}
+            ]
+        }
+        css_strategy = JsonCssExtractionStrategy(product_schema)
+        css_config = CrawlerRunConfig(extraction_strategy=css_strategy)
+        css_result = await crawler.arun(url=url, config=css_config)
+        
+        # Combine results
+        results = {
+            "contacts": json.loads(regex_result.extracted_content) if regex_result.success else [],
+            "products": json.loads(css_result.extracted_content) if css_result.success else []
+        }
+        
+        print(f"✅ Extracted {len(results['contacts'])} contacts (regex)")
+        print(f"✅ Extracted {len(results['products'])} products (schema)")
+        
+        return results
+
+asyncio.run(multi_strategy_pipeline())
+```
+
+---
+
+## 5. Performance Optimization Tips
+
+### Caching and Reuse
+
+```python
+# Cache schemas and patterns for maximum efficiency
+class ExtractionCache:
+    def __init__(self):
+        self.schemas = {}
+        self.patterns = {}
+    
+    def get_schema(self, site_name):
+        if site_name not in self.schemas:
+            schema_file = Path(f"./cache/{site_name}_schema.json")
+            if schema_file.exists():
+                self.schemas[site_name] = json.load(schema_file.open())
+        return self.schemas.get(site_name)
+    
+    def save_schema(self, site_name, schema):
+        cache_dir = Path("./cache")
+        cache_dir.mkdir(exist_ok=True)
+        schema_file = cache_dir / f"{site_name}_schema.json"
+        json.dump(schema, schema_file.open("w"), indent=2)
+        self.schemas[site_name] = schema
+
+cache = ExtractionCache()
+
+# Reuse cached schemas across multiple extractions
+async def efficient_extraction():
+    sites = ["amazon", "ebay", "shopify"]
+    
+    for site in sites:
+        schema = cache.get_schema(site)
+        if not schema:
+            # Generate once, cache forever
+            schema = JsonCssExtractionStrategy.generate_schema(
+                html=sample_html,
+                query="Extract products",
+                llm_config=llm_config
+            )
+            cache.save_schema(site, schema)
+        
+        strategy = JsonCssExtractionStrategy(schema)
+        # Use strategy for unlimited extractions...
+```
+
+### Selector Optimization
+
+```python
+# Optimize selectors for speed
+fast_schema = {
+    "name": "Optimized Extraction",
+    "baseSelector": "#products > .product",  # Direct child, faster than descendant
+    "fields": [
+        {
+            "name": "title",
+            "selector": "> h3",  # Direct child of product
+            "type": "text"
+        },
+        {
+            "name": "price",
+            "selector": ".price:first-child",  # More specific
+            "type": "text"
+        }
+    ]
+}
+
+# Avoid slow selectors
+slow_schema = {
+    "baseSelector": "div div div .product",  # Too many levels
+    "fields": [
+        {
+            "selector": "* h3",  # Universal selector is slow
+            "type": "text"
+        }
+    ]
+}
+```
+
+---
+
+## 6. Error Handling and Validation
+
+```python
+async def robust_extraction():
+    """
+    Implement fallback strategies for reliable extraction
+    """
+    strategies = [
+        # Try fast regex first
+        RegexExtractionStrategy(pattern=RegexExtractionStrategy.Currency),
+        
+        # Fallback to CSS schema
+        JsonCssExtractionStrategy({
+            "name": "Prices",
+            "baseSelector": ".price",
+            "fields": [{"name": "amount", "selector": "span", "type": "text"}]
+        }),
+        
+        # Last resort: try different selector
+        JsonCssExtractionStrategy({
+            "name": "Fallback Prices",
+            "baseSelector": "[data-price]",
+            "fields": [{"name": "amount", "type": "attribute", "attribute": "data-price"}]
+        })
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for i, strategy in enumerate(strategies):
+            try:
+                config = CrawlerRunConfig(extraction_strategy=strategy)
+                result = await crawler.arun(url="https://example.com", config=config)
+                
+                if result.success and result.extracted_content:
+                    data = json.loads(result.extracted_content)
+                    if data:  # Validate non-empty results
+                        print(f"✅ Success with strategy {i+1}: {strategy.__class__.__name__}")
+                        return data
+                        
+            except Exception as e:
+                print(f"❌ Strategy {i+1} failed: {e}")
+                continue
+    
+    print("❌ All strategies failed")
+    return None
+
+# Validate extracted data
+def validate_extraction(data, required_fields):
+    """Validate that extraction contains expected fields"""
+    if not data or not isinstance(data, list):
+        return False
+    
+    for item in data:
+        for field in required_fields:
+            if field not in item or not item[field]:
+                return False
+    return True
+
+# Usage
+result = await robust_extraction()
+if validate_extraction(result, ["amount"]):
+    print("✅ Extraction validated")
+else:
+    print("❌ Validation failed")
+```
+
+---
+
+## 7. Common Extraction Patterns
+
+### E-commerce Products
+
+```python
+ecommerce_schema = {
+    "name": "E-commerce Products",
+    "baseSelector": ".product, [data-product], .item",
+    "fields": [
+        {"name": "title", "selector": "h1, h2, h3, .title, .name", "type": "text"},
+        {"name": "price", "selector": ".price, .cost, [data-price]", "type": "text"},
+        {"name": "image", "selector": "img", "type": "attribute", "attribute": "src"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"},
+        {"name": "rating", "selector": ".rating, .stars", "type": "text"},
+        {"name": "availability", "selector": ".stock, .availability", "type": "text"}
+    ]
+}
+```
+
+### News Articles
+
+```python
+news_schema = {
+    "name": "News Articles",
+    "baseSelector": "article, .article, .post",
+    "fields": [
+        {"name": "headline", "selector": "h1, h2, .headline, .title", "type": "text"},
+        {"name": "author", "selector": ".author, .byline, [rel='author']", "type": "text"},
+        {"name": "date", "selector": "time, .date, .published", "type": "text"},
+        {"name": "content", "selector": ".content, .body, .text", "type": "text"},
+        {"name": "category", "selector": ".category, .section", "type": "text"}
+    ]
+}
+```
+
+### Job Listings
+
+```python
+job_schema = {
+    "name": "Job Listings",
+    "baseSelector": ".job, .listing, [data-job]",
+    "fields": [
+        {"name": "title", "selector": ".job-title, h2, h3", "type": "text"},
+        {"name": "company", "selector": ".company, .employer", "type": "text"},
+        {"name": "location", "selector": ".location, .place", "type": "text"},
+        {"name": "salary", "selector": ".salary, .pay, .compensation", "type": "text"},
+        {"name": "description", "selector": ".description, .summary", "type": "text"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"}
+    ]
+}
+```
+
+### Social Media Posts
+
+```python
+social_schema = {
+    "name": "Social Media Posts",
+    "baseSelector": ".post, .tweet, .update",
+    "fields": [
+        {"name": "username", "selector": ".username, .handle, .author", "type": "text"},
+        {"name": "content", "selector": ".content, .text, .message", "type": "text"},
+        {"name": "timestamp", "selector": ".time, .date, time", "type": "text"},
+        {"name": "likes", "selector": ".likes, .hearts", "type": "text"},
+        {"name": "shares", "selector": ".shares, .retweets", "type": "text"}
+    ]
+}
+```
+
+---
+
+## 8. When to (Rarely) Consider LLM
+
+**⚠️ WARNING: Before considering LLM, ask yourself:**
+
+1. "Can I identify repeating HTML patterns?" → Use CSS/XPath schema
+2. "Am I extracting simple data types?" → Use Regex patterns  
+3. "Can I provide a JSON example of what I want?" → Use generate_schema()
+4. "Is this truly unstructured text requiring semantic understanding?" → Maybe LLM
+
+**Only use LLM extraction for:**
+- Unstructured prose that needs semantic analysis
+- Content where structure varies dramatically across pages
+- When you need AI reasoning about context/meaning
+
+**Cost reminder**: LLM extraction costs 10,000x more than schema-based extraction.
+
+---
+
+## 9. Summary: The Extraction Hierarchy
+
+1. **🥇 FIRST CHOICE**: `generate_schema()` - AI generates pattern once, use unlimited times
+2. **🥈 SECOND CHOICE**: Manual CSS/XPath - Full control, maximum speed
+3. **🥉 THIRD CHOICE**: Regex patterns - Simple data types, lightning fast
+4. **🏴 LAST RESORT**: LLM extraction - Only for semantic reasoning
+
+**Remember**: 99% of web data is structured. You almost never need LLM for extraction. Save LLM for analysis, not extraction.
+
+**Performance**: Non-LLM strategies are 100-1000x faster and 10,000x cheaper than LLM extraction.
+
+---
+
+**📖 Next**: If you absolutely must use LLM extraction, see [extraction-llm.md](./extraction-llm.md) for guidance on the rare cases where it's justified.
+---
+
+
+## Multi-URL Crawling
+
+Concurrent crawling of multiple URLs with intelligent resource management, rate limiting, and real-time monitoring.
+
+### Basic Multi-URL Crawling
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
+
+# Batch processing (default) - get all results at once
+async def batch_crawl():
+    urls = [
+        "https://example.com/page1",
+        "https://example.com/page2", 
+        "https://example.com/page3"
+    ]
+    
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        stream=False  # Default: batch mode
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(urls, config=config)
+        
+        for result in results:
+            if result.success:
+                print(f"✅ {result.url}: {len(result.markdown)} chars")
+            else:
+                print(f"❌ {result.url}: {result.error_message}")
+
+# Streaming processing - handle results as they complete
+async def streaming_crawl():
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        stream=True  # Enable streaming
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        # Process results as they become available
+        async for result in await crawler.arun_many(urls, config=config):
+            if result.success:
+                print(f"🔥 Just completed: {result.url}")
+                await process_result_immediately(result)
+            else:
+                print(f"❌ Failed: {result.url}")
+```
+
+### Memory-Adaptive Dispatching
+
+```python
+from crawl4ai import AsyncWebCrawler, MemoryAdaptiveDispatcher, CrawlerMonitor, DisplayMode
+
+# Automatically manages concurrency based on system memory
+async def memory_adaptive_crawl():
+    dispatcher = MemoryAdaptiveDispatcher(
+        memory_threshold_percent=80.0,  # Pause if memory exceeds 80%
+        check_interval=1.0,             # Check memory every second
+        max_session_permit=15,          # Max concurrent tasks
+        memory_wait_timeout=300.0       # Wait up to 5 minutes for memory
+    )
+    
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        word_count_threshold=50
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(
+            urls=large_url_list,
+            config=config,
+            dispatcher=dispatcher
+        )
+        
+        # Each result includes dispatch information
+        for result in results:
+            if result.dispatch_result:
+                dr = result.dispatch_result
+                print(f"Memory used: {dr.memory_usage:.1f}MB")
+                print(f"Duration: {dr.end_time - dr.start_time}")
+```
+
+### Rate-Limited Crawling
+
+```python
+from crawl4ai import RateLimiter, SemaphoreDispatcher
+
+# Control request pacing and handle server rate limits
+async def rate_limited_crawl():
+    rate_limiter = RateLimiter(
+        base_delay=(1.0, 3.0),          # Random delay 1-3 seconds
+        max_delay=60.0,                 # Cap backoff at 60 seconds
+        max_retries=3,                  # Retry failed requests 3 times
+        rate_limit_codes=[429, 503]     # Handle these status codes
+    )
+    
+    dispatcher = SemaphoreDispatcher(
+        max_session_permit=5,           # Fixed concurrency limit
+        rate_limiter=rate_limiter
+    )
+    
+    config = CrawlerRunConfig(
+        user_agent_mode="random",       # Randomize user agents
+        simulate_user=True              # Simulate human behavior
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun_many(
+            urls=urls,
+            config=config,
+            dispatcher=dispatcher
+        ):
+            print(f"Processed: {result.url}")
+```
+
+### Real-Time Monitoring
+
+```python
+from crawl4ai import CrawlerMonitor, DisplayMode
+
+# Monitor crawling progress in real-time
+async def monitored_crawl():
+    monitor = CrawlerMonitor(
+        max_visible_rows=20,                    # Show 20 tasks in display
+        display_mode=DisplayMode.DETAILED       # Show individual task details
+    )
+    
+    dispatcher = MemoryAdaptiveDispatcher(
+        memory_threshold_percent=75.0,
+        max_session_permit=10,
+        monitor=monitor  # Attach monitor to dispatcher
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(
+            urls=urls,
+            dispatcher=dispatcher
+        )
+```
+
+### Advanced Dispatcher Configurations
+
+```python
+# Memory-adaptive with comprehensive monitoring
+memory_dispatcher = MemoryAdaptiveDispatcher(
+    memory_threshold_percent=85.0,      # Higher memory tolerance
+    check_interval=0.5,                 # Check memory more frequently
+    max_session_permit=20,              # More concurrent tasks
+    memory_wait_timeout=600.0,          # Wait longer for memory
+    rate_limiter=RateLimiter(
+        base_delay=(0.5, 1.5),
+        max_delay=30.0,
+        max_retries=5
+    ),
+    monitor=CrawlerMonitor(
+        max_visible_rows=15,
+        display_mode=DisplayMode.AGGREGATED  # Summary view
+    )
+)
+
+# Simple semaphore-based dispatcher
+semaphore_dispatcher = SemaphoreDispatcher(
+    max_session_permit=8,               # Fixed concurrency
+    rate_limiter=RateLimiter(
+        base_delay=(1.0, 2.0),
+        max_delay=20.0
+    )
+)
+
+# Usage with custom dispatcher
+async with AsyncWebCrawler() as crawler:
+    results = await crawler.arun_many(
+        urls=urls,
+        config=config,
+        dispatcher=memory_dispatcher  # or semaphore_dispatcher
+    )
+```
+
+### Handling Large-Scale Crawling
+
+```python
+async def large_scale_crawl():
+    # For thousands of URLs
+    urls = load_urls_from_file("large_url_list.txt")  # 10,000+ URLs
+    
+    dispatcher = MemoryAdaptiveDispatcher(
+        memory_threshold_percent=70.0,  # Conservative memory usage
+        max_session_permit=25,          # Higher concurrency
+        rate_limiter=RateLimiter(
+            base_delay=(0.1, 0.5),      # Faster for large batches
+            max_retries=2               # Fewer retries for speed
+        ),
+        monitor=CrawlerMonitor(display_mode=DisplayMode.AGGREGATED)
+    )
+    
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.ENABLED,   # Use caching for efficiency
+        stream=True,                    # Stream for memory efficiency
+        word_count_threshold=100,       # Skip short content
+        exclude_external_links=True     # Reduce processing overhead
+    )
+    
+    successful_crawls = 0
+    failed_crawls = 0
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun_many(
+            urls=urls,
+            config=config,
+            dispatcher=dispatcher
+        ):
+            if result.success:
+                successful_crawls += 1
+                await save_result_to_database(result)
+            else:
+                failed_crawls += 1
+                await log_failure(result.url, result.error_message)
+            
+            # Progress reporting
+            if (successful_crawls + failed_crawls) % 100 == 0:
+                print(f"Progress: {successful_crawls + failed_crawls}/{len(urls)}")
+    
+    print(f"Completed: {successful_crawls} successful, {failed_crawls} failed")
+```
+
+### Robots.txt Compliance
+
+```python
+async def compliant_crawl():
+    config = CrawlerRunConfig(
+        check_robots_txt=True,          # Respect robots.txt
+        user_agent="MyBot/1.0",         # Identify your bot
+        mean_delay=2.0,                 # Be polite with delays
+        max_range=1.0
+    )
+    
+    dispatcher = SemaphoreDispatcher(
+        max_session_permit=3,           # Conservative concurrency
+        rate_limiter=RateLimiter(
+            base_delay=(2.0, 5.0),      # Slower, more respectful
+            max_retries=1
+        )
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun_many(
+            urls=urls,
+            config=config,
+            dispatcher=dispatcher
+        ):
+            if result.success:
+                print(f"✅ Crawled: {result.url}")
+            elif "robots.txt" in result.error_message:
+                print(f"🚫 Blocked by robots.txt: {result.url}")
+            else:
+                print(f"❌ Error: {result.url}")
+```
+
+### Performance Analysis
+
+```python
+async def analyze_crawl_performance():
+    dispatcher = MemoryAdaptiveDispatcher(
+        memory_threshold_percent=80.0,
+        max_session_permit=12,
+        monitor=CrawlerMonitor(display_mode=DisplayMode.DETAILED)
+    )
+    
+    start_time = time.time()
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(
+            urls=urls,
+            dispatcher=dispatcher
+        )
+    
+    end_time = time.time()
+    
+    # Analyze results
+    successful = [r for r in results if r.success]
+    failed = [r for r in results if not r.success]
+    
+    print(f"Total time: {end_time - start_time:.2f}s")
+    print(f"Success rate: {len(successful)}/{len(results)} ({len(successful)/len(results)*100:.1f}%)")
+    print(f"Avg time per URL: {(end_time - start_time)/len(results):.2f}s")
+    
+    # Memory usage analysis
+    if successful and successful[0].dispatch_result:
+        memory_usage = [r.dispatch_result.memory_usage for r in successful if r.dispatch_result]
+        peak_memory = [r.dispatch_result.peak_memory for r in successful if r.dispatch_result]
+        
+        print(f"Avg memory usage: {sum(memory_usage)/len(memory_usage):.1f}MB")
+        print(f"Peak memory usage: {max(peak_memory):.1f}MB")
+```
+
+### Error Handling and Recovery
+
+```python
+async def robust_multi_crawl():
+    failed_urls = []
+    
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        stream=True,
+        page_timeout=30000  # 30 second timeout
+    )
+    
+    dispatcher = MemoryAdaptiveDispatcher(
+        memory_threshold_percent=85.0,
+        max_session_permit=10
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun_many(
+            urls=urls,
+            config=config,
+            dispatcher=dispatcher
+        ):
+            if result.success:
+                await process_successful_result(result)
+            else:
+                failed_urls.append({
+                    'url': result.url,
+                    'error': result.error_message,
+                    'status_code': result.status_code
+                })
+                
+                # Retry logic for specific errors
+                if result.status_code in [503, 429]:  # Server errors
+                    await schedule_retry(result.url)
+    
+    # Report failures
+    if failed_urls:
+        print(f"Failed to crawl {len(failed_urls)} URLs:")
+        for failure in failed_urls[:10]:  # Show first 10
+            print(f"  {failure['url']}: {failure['error']}")
+```
+
+**📖 Learn more:** [Advanced Multi-URL Crawling](https://docs.crawl4ai.com/advanced/multi-url-crawling/), [Crawl Dispatcher](https://docs.crawl4ai.com/advanced/crawl-dispatcher/), [arun_many() API Reference](https://docs.crawl4ai.com/api/arun_many/)
+---
+
+
+## Deep Crawling
+
+Multi-level website exploration with intelligent filtering, scoring, and prioritization strategies.
+
+### Basic Deep Crawl Setup
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
+from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
+
+# Basic breadth-first deep crawling
+async def basic_deep_crawl():
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BFSDeepCrawlStrategy(
+            max_depth=2,               # Initial page + 2 levels
+            include_external=False     # Stay within same domain
+        ),
+        scraping_strategy=LXMLWebScrapingStrategy(),
+        verbose=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun("https://docs.crawl4ai.com", config=config)
+        
+        # Group results by depth
+        pages_by_depth = {}
+        for result in results:
+            depth = result.metadata.get("depth", 0)
+            if depth not in pages_by_depth:
+                pages_by_depth[depth] = []
+            pages_by_depth[depth].append(result.url)
+        
+        print(f"Crawled {len(results)} pages total")
+        for depth, urls in sorted(pages_by_depth.items()):
+            print(f"Depth {depth}: {len(urls)} pages")
+```
+
+### Deep Crawl Strategies
+
+```python
+from crawl4ai.deep_crawling import BFSDeepCrawlStrategy, DFSDeepCrawlStrategy, BestFirstCrawlingStrategy
+from crawl4ai.deep_crawling.scorers import KeywordRelevanceScorer
+
+# Breadth-First Search - explores all links at one depth before going deeper
+bfs_strategy = BFSDeepCrawlStrategy(
+    max_depth=2,
+    include_external=False,
+    max_pages=50,              # Limit total pages
+    score_threshold=0.3        # Minimum score for URLs
+)
+
+# Depth-First Search - explores as deep as possible before backtracking
+dfs_strategy = DFSDeepCrawlStrategy(
+    max_depth=2,
+    include_external=False,
+    max_pages=30,
+    score_threshold=0.5
+)
+
+# Best-First - prioritizes highest scoring pages (recommended)
+keyword_scorer = KeywordRelevanceScorer(
+    keywords=["crawl", "example", "async", "configuration"],
+    weight=0.7
+)
+
+best_first_strategy = BestFirstCrawlingStrategy(
+    max_depth=2,
+    include_external=False,
+    url_scorer=keyword_scorer,
+    max_pages=25               # No score_threshold needed - naturally prioritizes
+)
+
+# Usage
+config = CrawlerRunConfig(
+    deep_crawl_strategy=best_first_strategy,  # Choose your strategy
+    scraping_strategy=LXMLWebScrapingStrategy()
+)
+```
+
+### Streaming vs Batch Processing
+
+```python
+# Batch mode - wait for all results
+async def batch_deep_crawl():
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BFSDeepCrawlStrategy(max_depth=1),
+        stream=False  # Default - collect all results first
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun("https://example.com", config=config)
+        
+        # Process all results at once
+        for result in results:
+            print(f"Batch processed: {result.url}")
+
+# Streaming mode - process results as they arrive
+async def streaming_deep_crawl():
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BFSDeepCrawlStrategy(max_depth=1),
+        stream=True  # Process results immediately
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun("https://example.com", config=config):
+            depth = result.metadata.get("depth", 0)
+            print(f"Stream processed depth {depth}: {result.url}")
+```
+
+### Filtering with Filter Chains
+
+```python
+from crawl4ai.deep_crawling.filters import (
+    FilterChain,
+    URLPatternFilter,
+    DomainFilter,
+    ContentTypeFilter,
+    SEOFilter,
+    ContentRelevanceFilter
+)
+
+# Single URL pattern filter
+url_filter = URLPatternFilter(patterns=["*core*", "*guide*"])
+
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(
+        max_depth=1,
+        filter_chain=FilterChain([url_filter])
+    )
+)
+
+# Multiple filters in chain
+advanced_filter_chain = FilterChain([
+    # Domain filtering
+    DomainFilter(
+        allowed_domains=["docs.example.com"],
+        blocked_domains=["old.docs.example.com", "staging.example.com"]
+    ),
+    
+    # URL pattern matching
+    URLPatternFilter(patterns=["*tutorial*", "*guide*", "*blog*"]),
+    
+    # Content type filtering
+    ContentTypeFilter(allowed_types=["text/html"]),
+    
+    # SEO quality filter
+    SEOFilter(
+        threshold=0.5,
+        keywords=["tutorial", "guide", "documentation"]
+    ),
+    
+    # Content relevance filter
+    ContentRelevanceFilter(
+        query="Web crawling and data extraction with Python",
+        threshold=0.7
+    )
+])
+
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(
+        max_depth=2,
+        filter_chain=advanced_filter_chain
+    )
+)
+```
+
+### Intelligent Crawling with Scorers
+
+```python
+from crawl4ai.deep_crawling.scorers import KeywordRelevanceScorer
+
+# Keyword relevance scoring
+async def scored_deep_crawl():
+    keyword_scorer = KeywordRelevanceScorer(
+        keywords=["browser", "crawler", "web", "automation"],
+        weight=1.0
+    )
+    
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BestFirstCrawlingStrategy(
+            max_depth=2,
+            include_external=False,
+            url_scorer=keyword_scorer
+        ),
+        stream=True,  # Recommended with BestFirst
+        verbose=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun("https://docs.crawl4ai.com", config=config):
+            score = result.metadata.get("score", 0)
+            depth = result.metadata.get("depth", 0)
+            print(f"Depth: {depth} | Score: {score:.2f} | {result.url}")
+```
+
+### Limiting Crawl Size
+
+```python
+# Max pages limitation across strategies
+async def limited_crawls():
+    # BFS with page limit
+    bfs_config = CrawlerRunConfig(
+        deep_crawl_strategy=BFSDeepCrawlStrategy(
+            max_depth=2,
+            max_pages=5,  # Only crawl 5 pages total
+            url_scorer=KeywordRelevanceScorer(keywords=["browser", "crawler"], weight=1.0)
+        )
+    )
+    
+    # DFS with score threshold
+    dfs_config = CrawlerRunConfig(
+        deep_crawl_strategy=DFSDeepCrawlStrategy(
+            max_depth=2,
+            score_threshold=0.7,  # Only URLs with scores above 0.7
+            max_pages=10,
+            url_scorer=KeywordRelevanceScorer(keywords=["web", "automation"], weight=1.0)
+        )
+    )
+    
+    # Best-First with both constraints
+    bf_config = CrawlerRunConfig(
+        deep_crawl_strategy=BestFirstCrawlingStrategy(
+            max_depth=2,
+            max_pages=7,  # Automatically gets highest scored pages
+            url_scorer=KeywordRelevanceScorer(keywords=["crawl", "example"], weight=1.0)
+        ),
+        stream=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        # Use any of the configs
+        async for result in await crawler.arun("https://docs.crawl4ai.com", config=bf_config):
+            score = result.metadata.get("score", 0)
+            print(f"Score: {score:.2f} | {result.url}")
+```
+
+### Complete Advanced Deep Crawler
+
+```python
+async def comprehensive_deep_crawl():
+    # Sophisticated filter chain
+    filter_chain = FilterChain([
+        DomainFilter(
+            allowed_domains=["docs.crawl4ai.com"],
+            blocked_domains=["old.docs.crawl4ai.com"]
+        ),
+        URLPatternFilter(patterns=["*core*", "*advanced*", "*blog*"]),
+        ContentTypeFilter(allowed_types=["text/html"]),
+        SEOFilter(threshold=0.4, keywords=["crawl", "tutorial", "guide"])
+    ])
+    
+    # Multi-keyword scorer
+    keyword_scorer = KeywordRelevanceScorer(
+        keywords=["crawl", "example", "async", "configuration", "browser"],
+        weight=0.8
+    )
+    
+    # Complete configuration
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BestFirstCrawlingStrategy(
+            max_depth=2,
+            include_external=False,
+            filter_chain=filter_chain,
+            url_scorer=keyword_scorer,
+            max_pages=20
+        ),
+        scraping_strategy=LXMLWebScrapingStrategy(),
+        stream=True,
+        verbose=True,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    # Execute and analyze
+    results = []
+    start_time = time.time()
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun("https://docs.crawl4ai.com", config=config):
+            results.append(result)
+            score = result.metadata.get("score", 0)
+            depth = result.metadata.get("depth", 0)
+            print(f"→ Depth: {depth} | Score: {score:.2f} | {result.url}")
+    
+    # Performance analysis
+    duration = time.time() - start_time
+    avg_score = sum(r.metadata.get('score', 0) for r in results) / len(results)
+    
+    print(f"✅ Crawled {len(results)} pages in {duration:.2f}s")
+    print(f"✅ Average relevance score: {avg_score:.2f}")
+    
+    # Depth distribution
+    depth_counts = {}
+    for result in results:
+        depth = result.metadata.get("depth", 0)
+        depth_counts[depth] = depth_counts.get(depth, 0) + 1
+    
+    for depth, count in sorted(depth_counts.items()):
+        print(f"📊 Depth {depth}: {count} pages")
+```
+
+### Error Handling and Robustness
+
+```python
+async def robust_deep_crawl():
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BestFirstCrawlingStrategy(
+            max_depth=2,
+            max_pages=15,
+            url_scorer=KeywordRelevanceScorer(keywords=["guide", "tutorial"])
+        ),
+        stream=True,
+        page_timeout=30000  # 30 second timeout per page
+    )
+    
+    successful_pages = []
+    failed_pages = []
+    
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun("https://docs.crawl4ai.com", config=config):
+            if result.success:
+                successful_pages.append(result)
+                depth = result.metadata.get("depth", 0)
+                score = result.metadata.get("score", 0)
+                print(f"✅ Depth {depth} | Score: {score:.2f} | {result.url}")
+            else:
+                failed_pages.append({
+                    'url': result.url,
+                    'error': result.error_message,
+                    'depth': result.metadata.get("depth", 0)
+                })
+                print(f"❌ Failed: {result.url} - {result.error_message}")
+    
+    print(f"📊 Results: {len(successful_pages)} successful, {len(failed_pages)} failed")
+    
+    # Analyze failures by depth
+    if failed_pages:
+        failure_by_depth = {}
+        for failure in failed_pages:
+            depth = failure['depth']
+            failure_by_depth[depth] = failure_by_depth.get(depth, 0) + 1
+        
+        print("❌ Failures by depth:")
+        for depth, count in sorted(failure_by_depth.items()):
+            print(f"   Depth {depth}: {count} failures")
+```
+
+**📖 Learn more:** [Deep Crawling Guide](https://docs.crawl4ai.com/core/deep-crawling/), [Filter Documentation](https://docs.crawl4ai.com/core/content-selection/), [Scoring Strategies](https://docs.crawl4ai.com/advanced/advanced-features/)
+---
+
+
+## Docker Deployment
+
+Complete Docker deployment guide with pre-built images, API endpoints, configuration, and MCP integration.
+
+### Quick Start with Pre-built Images
+
+```bash
+# Pull latest image
+docker pull unclecode/crawl4ai:latest
+
+# Setup LLM API keys
+cat > .llm.env << EOL
+OPENAI_API_KEY=sk-your-key
+ANTHROPIC_API_KEY=your-anthropic-key
+GROQ_API_KEY=your-groq-key
+GEMINI_API_TOKEN=your-gemini-token
+EOL
+
+# Run with LLM support
+docker run -d \
+  -p 11235:11235 \
+  --name crawl4ai \
+  --env-file .llm.env \
+  --shm-size=1g \
+  unclecode/crawl4ai:latest
+
+# Basic run (no LLM)
+docker run -d \
+  -p 11235:11235 \
+  --name crawl4ai \
+  --shm-size=1g \
+  unclecode/crawl4ai:latest
+
+# Check health
+curl http://localhost:11235/health
+```
+
+### Docker Compose Deployment
+
+```bash
+# Clone and setup
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+cp deploy/docker/.llm.env.example .llm.env
+# Edit .llm.env with your API keys
+
+# Run pre-built image
+IMAGE=unclecode/crawl4ai:latest docker compose up -d
+
+# Build locally
+docker compose up --build -d
+
+# Build with all features
+INSTALL_TYPE=all docker compose up --build -d
+
+# Build with GPU support
+ENABLE_GPU=true docker compose up --build -d
+
+# Stop service
+docker compose down
+```
+
+### Manual Build with Multi-Architecture
+
+```bash
+# Clone repository
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+
+# Build for current architecture
+docker buildx build -t crawl4ai-local:latest --load .
+
+# Build for multiple architectures
+docker buildx build --platform linux/amd64,linux/arm64 \
+  -t crawl4ai-local:latest --load .
+
+# Build with specific features
+docker buildx build \
+  --build-arg INSTALL_TYPE=all \
+  --build-arg ENABLE_GPU=false \
+  -t crawl4ai-local:latest --load .
+
+# Run custom build
+docker run -d \
+  -p 11235:11235 \
+  --name crawl4ai-custom \
+  --env-file .llm.env \
+  --shm-size=1g \
+  crawl4ai-local:latest
+```
+
+### Build Arguments
+
+```bash
+# Available build options
+docker buildx build \
+  --build-arg INSTALL_TYPE=all \     # default|all|torch|transformer
+  --build-arg ENABLE_GPU=true \      # true|false
+  --build-arg APP_HOME=/app \        # Install path
+  --build-arg USE_LOCAL=true \       # Use local source
+  --build-arg GITHUB_REPO=url \      # Git repo if USE_LOCAL=false
+  --build-arg GITHUB_BRANCH=main \   # Git branch
+  -t crawl4ai-custom:latest --load .
+```
+
+### Core API Endpoints
+
+```python
+# Main crawling endpoints
+import requests
+import json
+
+# Basic crawl
+payload = {
+    "urls": ["https://example.com"],
+    "browser_config": {"type": "BrowserConfig", "params": {"headless": True}},
+    "crawler_config": {"type": "CrawlerRunConfig", "params": {"cache_mode": "bypass"}}
+}
+response = requests.post("http://localhost:11235/crawl", json=payload)
+
+# Streaming crawl
+payload["crawler_config"]["params"]["stream"] = True
+response = requests.post("http://localhost:11235/crawl/stream", json=payload)
+
+# Health check
+response = requests.get("http://localhost:11235/health")
+
+# API schema
+response = requests.get("http://localhost:11235/schema")
+
+# Metrics (Prometheus format)
+response = requests.get("http://localhost:11235/metrics")
+```
+
+### Specialized Endpoints
+
+```python
+# HTML extraction (preprocessed for schema)
+response = requests.post("http://localhost:11235/html", 
+    json={"url": "https://example.com"})
+
+# Screenshot capture
+response = requests.post("http://localhost:11235/screenshot", json={
+    "url": "https://example.com",
+    "screenshot_wait_for": 2,
+    "output_path": "/path/to/save/screenshot.png"
+})
+
+# PDF generation
+response = requests.post("http://localhost:11235/pdf", json={
+    "url": "https://example.com",
+    "output_path": "/path/to/save/document.pdf"
+})
+
+# JavaScript execution
+response = requests.post("http://localhost:11235/execute_js", json={
+    "url": "https://example.com",
+    "scripts": [
+        "return document.title",
+        "return Array.from(document.querySelectorAll('a')).map(a => a.href)"
+    ]
+})
+
+# Markdown generation
+response = requests.post("http://localhost:11235/md", json={
+    "url": "https://example.com",
+    "f": "fit",  # raw|fit|bm25|llm
+    "q": "extract main content",  # query for filtering
+    "c": "0"  # cache: 0=bypass, 1=use
+})
+
+# LLM Q&A
+response = requests.get("http://localhost:11235/llm/https://example.com?q=What is this page about?")
+
+# Library context (for AI assistants)
+response = requests.get("http://localhost:11235/ask", params={
+    "context_type": "all",  # code|doc|all
+    "query": "how to use extraction strategies",
+    "score_ratio": 0.5,
+    "max_results": 20
+})
+```
+
+### Python SDK Usage
+
+```python
+import asyncio
+from crawl4ai.docker_client import Crawl4aiDockerClient
+from crawl4ai import BrowserConfig, CrawlerRunConfig, CacheMode
+
+async def main():
+    async with Crawl4aiDockerClient(base_url="http://localhost:11235") as client:
+        # Non-streaming crawl
+        results = await client.crawl(
+            ["https://example.com"],
+            browser_config=BrowserConfig(headless=True),
+            crawler_config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+        )
+        
+        for result in results:
+            print(f"URL: {result.url}, Success: {result.success}")
+            print(f"Content length: {len(result.markdown)}")
+        
+        # Streaming crawl
+        stream_config = CrawlerRunConfig(stream=True, cache_mode=CacheMode.BYPASS)
+        async for result in await client.crawl(
+            ["https://example.com", "https://python.org"],
+            browser_config=BrowserConfig(headless=True),
+            crawler_config=stream_config
+        ):
+            print(f"Streamed: {result.url} - {result.success}")
+        
+        # Get API schema
+        schema = await client.get_schema()
+        print(f"Schema available: {bool(schema)}")
+
+asyncio.run(main())
+```
+
+### Advanced API Configuration
+
+```python
+# Complex extraction with LLM
+payload = {
+    "urls": ["https://example.com"],
+    "browser_config": {
+        "type": "BrowserConfig",
+        "params": {
+            "headless": True,
+            "viewport": {"type": "dict", "value": {"width": 1200, "height": 800}}
+        }
+    },
+    "crawler_config": {
+        "type": "CrawlerRunConfig",
+        "params": {
+            "extraction_strategy": {
+                "type": "LLMExtractionStrategy",
+                "params": {
+                    "llm_config": {
+                        "type": "LLMConfig",
+                        "params": {
+                            "provider": "openai/gpt-4o-mini",
+                            "api_token": "env:OPENAI_API_KEY"
+                        }
+                    },
+                    "schema": {
+                        "type": "dict",
+                        "value": {
+                            "type": "object",
+                            "properties": {
+                                "title": {"type": "string"},
+                                "content": {"type": "string"}
+                            }
+                        }
+                    },
+                    "instruction": "Extract title and main content"
+                }
+            },
+            "markdown_generator": {
+                "type": "DefaultMarkdownGenerator",
+                "params": {
+                    "content_filter": {
+                        "type": "PruningContentFilter",
+                        "params": {"threshold": 0.6}
+                    }
+                }
+            }
+        }
+    }
+}
+
+response = requests.post("http://localhost:11235/crawl", json=payload)
+```
+
+### CSS Extraction Strategy
+
+```python
+# CSS-based structured extraction
+schema = {
+    "name": "ProductList",
+    "baseSelector": ".product",
+    "fields": [
+        {"name": "title", "selector": "h2", "type": "text"},
+        {"name": "price", "selector": ".price", "type": "text"},
+        {"name": "link", "selector": "a", "type": "attribute", "attribute": "href"}
+    ]
+}
+
+payload = {
+    "urls": ["https://example-shop.com"],
+    "browser_config": {"type": "BrowserConfig", "params": {"headless": True}},
+    "crawler_config": {
+        "type": "CrawlerRunConfig",
+        "params": {
+            "extraction_strategy": {
+                "type": "JsonCssExtractionStrategy",
+                "params": {
+                    "schema": {"type": "dict", "value": schema}
+                }
+            }
+        }
+    }
+}
+
+response = requests.post("http://localhost:11235/crawl", json=payload)
+data = response.json()
+extracted = json.loads(data["results"][0]["extracted_content"])
+```
+
+### MCP (Model Context Protocol) Integration
+
+```bash
+# Add Crawl4AI as MCP provider to Claude Code
+claude mcp add --transport sse c4ai-sse http://localhost:11235/mcp/sse
+
+# List MCP providers
+claude mcp list
+
+# Test MCP connection
+python tests/mcp/test_mcp_socket.py
+
+# Available MCP endpoints
+# SSE: http://localhost:11235/mcp/sse
+# WebSocket: ws://localhost:11235/mcp/ws
+# Schema: http://localhost:11235/mcp/schema
+```
+
+Available MCP tools:
+- `md` - Generate markdown from web content
+- `html` - Extract preprocessed HTML  
+- `screenshot` - Capture webpage screenshots
+- `pdf` - Generate PDF documents
+- `execute_js` - Run JavaScript on web pages
+- `crawl` - Perform multi-URL crawling
+- `ask` - Query Crawl4AI library context
+
+### Configuration Management
+
+```yaml
+# config.yml structure
+app:
+  title: "Crawl4AI API"
+  version: "1.0.0"
+  host: "0.0.0.0"
+  port: 11235
+  timeout_keep_alive: 300
+
+llm:
+  provider: "openai/gpt-4o-mini"
+  api_key_env: "OPENAI_API_KEY"
+
+security:
+  enabled: false
+  jwt_enabled: false
+  trusted_hosts: ["*"]
+
+crawler:
+  memory_threshold_percent: 95.0
+  rate_limiter:
+    base_delay: [1.0, 2.0]
+  timeouts:
+    stream_init: 30.0
+    batch_process: 300.0
+  pool:
+    max_pages: 40
+    idle_ttl_sec: 1800
+
+rate_limiting:
+  enabled: true
+  default_limit: "1000/minute"
+  storage_uri: "memory://"
+
+logging:
+  level: "INFO"
+  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+```
+
+### Custom Configuration Deployment
+
+```bash
+# Method 1: Mount custom config
+docker run -d -p 11235:11235 \
+  --name crawl4ai-custom \
+  --env-file .llm.env \
+  --shm-size=1g \
+  -v $(pwd)/my-config.yml:/app/config.yml \
+  unclecode/crawl4ai:latest
+
+# Method 2: Build with custom config
+# Edit deploy/docker/config.yml then build
+docker buildx build -t crawl4ai-custom:latest --load .
+```
+
+### Monitoring and Health Checks
+
+```bash
+# Health endpoint
+curl http://localhost:11235/health
+
+# Prometheus metrics
+curl http://localhost:11235/metrics
+
+# Configuration validation
+curl -X POST http://localhost:11235/config/dump \
+  -H "Content-Type: application/json" \
+  -d '{"code": "CrawlerRunConfig(cache_mode=\"BYPASS\", screenshot=True)"}'
+```
+
+### Playground Interface
+
+Access the interactive playground at `http://localhost:11235/playground` for:
+- Testing configurations with visual interface
+- Generating JSON payloads for REST API
+- Converting Python config to JSON format
+- Testing crawl operations directly in browser
+
+### Async Job Processing
+
+```python
+# Submit job for async processing
+import time
+
+# Submit crawl job
+response = requests.post("http://localhost:11235/crawl/job", json=payload)
+task_id = response.json()["task_id"]
+
+# Poll for completion
+while True:
+    result = requests.get(f"http://localhost:11235/crawl/job/{task_id}")
+    status = result.json()
+    
+    if status["status"] in ["COMPLETED", "FAILED"]:
+        break
+    time.sleep(1.5)
+
+print("Final result:", status)
+```
+
+### Production Deployment
+
+```bash
+# Production-ready deployment
+docker run -d \
+  --name crawl4ai-prod \
+  --restart unless-stopped \
+  -p 11235:11235 \
+  --env-file .llm.env \
+  --shm-size=2g \
+  --memory=8g \
+  --cpus=4 \
+  -v /path/to/custom-config.yml:/app/config.yml \
+  unclecode/crawl4ai:latest
+
+# With Docker Compose for production
+version: '3.8'
+services:
+  crawl4ai:
+    image: unclecode/crawl4ai:latest
+    ports:
+      - "11235:11235"
+    environment:
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+    volumes:
+      - ./config.yml:/app/config.yml
+    shm_size: 2g
+    deploy:
+      resources:
+        limits:
+          memory: 8G
+          cpus: '4'
+    restart: unless-stopped
+```
+
+### Configuration Validation and JSON Structure
+
+```python
+# Method 1: Create config objects and dump to see expected JSON structure
+from crawl4ai import BrowserConfig, CrawlerRunConfig, LLMConfig, CacheMode
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, LLMExtractionStrategy
+import json
+
+# Create browser config and see JSON structure
+browser_config = BrowserConfig(
+    headless=True,
+    viewport_width=1280,
+    viewport_height=720,
+    proxy="http://user:pass@proxy:8080"
+)
+
+# Get JSON structure
+browser_json = browser_config.dump()
+print("BrowserConfig JSON structure:")
+print(json.dumps(browser_json, indent=2))
+
+# Create crawler config with extraction strategy
+schema = {
+    "name": "Articles",
+    "baseSelector": ".article",
+    "fields": [
+        {"name": "title", "selector": "h2", "type": "text"},
+        {"name": "content", "selector": ".content", "type": "html"}
+    ]
+}
+
+crawler_config = CrawlerRunConfig(
+    cache_mode=CacheMode.BYPASS,
+    screenshot=True,
+    extraction_strategy=JsonCssExtractionStrategy(schema),
+    js_code=["window.scrollTo(0, document.body.scrollHeight);"],
+    wait_for="css:.loaded"
+)
+
+crawler_json = crawler_config.dump()
+print("\nCrawlerRunConfig JSON structure:")
+print(json.dumps(crawler_json, indent=2))
+```
+
+### Reverse Validation - JSON to Objects
+
+```python
+# Method 2: Load JSON back to config objects for validation
+from crawl4ai.async_configs import from_serializable_dict
+
+# Test JSON structure by converting back to objects
+test_browser_json = {
+    "type": "BrowserConfig",
+    "params": {
+        "headless": True,
+        "viewport_width": 1280,
+        "proxy": "http://user:pass@proxy:8080"
+    }
+}
+
+try:
+    # Convert JSON back to object
+    restored_browser = from_serializable_dict(test_browser_json)
+    print(f"✅ Valid BrowserConfig: {type(restored_browser)}")
+    print(f"Headless: {restored_browser.headless}")
+    print(f"Proxy: {restored_browser.proxy}")
+except Exception as e:
+    print(f"❌ Invalid BrowserConfig JSON: {e}")
+
+# Test complex crawler config JSON
+test_crawler_json = {
+    "type": "CrawlerRunConfig", 
+    "params": {
+        "cache_mode": "bypass",
+        "screenshot": True,
+        "extraction_strategy": {
+            "type": "JsonCssExtractionStrategy",
+            "params": {
+                "schema": {
+                    "type": "dict",
+                    "value": {
+                        "name": "Products",
+                        "baseSelector": ".product",
+                        "fields": [
+                            {"name": "title", "selector": "h3", "type": "text"}
+                        ]
+                    }
+                }
+            }
+        }
+    }
+}
+
+try:
+    restored_crawler = from_serializable_dict(test_crawler_json)
+    print(f"✅ Valid CrawlerRunConfig: {type(restored_crawler)}")
+    print(f"Cache mode: {restored_crawler.cache_mode}")
+    print(f"Has extraction strategy: {restored_crawler.extraction_strategy is not None}")
+except Exception as e:
+    print(f"❌ Invalid CrawlerRunConfig JSON: {e}")
+```
+
+### Using Server's /config/dump Endpoint for Validation
+
+```python
+import requests
+
+# Method 3: Use server endpoint to validate configuration syntax
+def validate_config_with_server(config_code: str) -> dict:
+    """Validate configuration using server's /config/dump endpoint"""
+    response = requests.post(
+        "http://localhost:11235/config/dump",
+        json={"code": config_code}
+    )
+    
+    if response.status_code == 200:
+        print("✅ Valid configuration syntax")
+        return response.json()
+    else:
+        print(f"❌ Invalid configuration: {response.status_code}")
+        print(response.json())
+        return None
+
+# Test valid configuration
+valid_config = """
+CrawlerRunConfig(
+    cache_mode=CacheMode.BYPASS,
+    screenshot=True,
+    js_code=["window.scrollTo(0, document.body.scrollHeight);"],
+    wait_for="css:.content-loaded"
+)
+"""
+
+result = validate_config_with_server(valid_config)
+if result:
+    print("Generated JSON structure:")
+    print(json.dumps(result, indent=2))
+
+# Test invalid configuration (should fail)
+invalid_config = """
+CrawlerRunConfig(
+    cache_mode="invalid_mode",
+    screenshot=True,
+    js_code=some_function()  # This will fail
+)
+"""
+
+validate_config_with_server(invalid_config)
+```
+
+### Configuration Builder Helper
+
+```python
+def build_and_validate_request(urls, browser_params=None, crawler_params=None):
+    """Helper to build and validate complete request payload"""
+    
+    # Create configurations
+    browser_config = BrowserConfig(**(browser_params or {}))
+    crawler_config = CrawlerRunConfig(**(crawler_params or {}))
+    
+    # Build complete request payload
+    payload = {
+        "urls": urls if isinstance(urls, list) else [urls],
+        "browser_config": browser_config.dump(),
+        "crawler_config": crawler_config.dump()
+    }
+    
+    print("✅ Complete request payload:")
+    print(json.dumps(payload, indent=2))
+    
+    # Validate by attempting to reconstruct
+    try:
+        test_browser = from_serializable_dict(payload["browser_config"])
+        test_crawler = from_serializable_dict(payload["crawler_config"])
+        print("✅ Payload validation successful")
+        return payload
+    except Exception as e:
+        print(f"❌ Payload validation failed: {e}")
+        return None
+
+# Example usage
+payload = build_and_validate_request(
+    urls=["https://example.com"],
+    browser_params={"headless": True, "viewport_width": 1280},
+    crawler_params={
+        "cache_mode": CacheMode.BYPASS,
+        "screenshot": True,
+        "word_count_threshold": 10
+    }
+)
+
+if payload:
+    # Send to server
+    response = requests.post("http://localhost:11235/crawl", json=payload)
+    print(f"Server response: {response.status_code}")
+```
+
+### Common JSON Structure Patterns
+
+```python
+# Pattern 1: Simple primitive values
+simple_config = {
+    "type": "CrawlerRunConfig",
+    "params": {
+        "cache_mode": "bypass",  # String enum value
+        "screenshot": True,      # Boolean
+        "page_timeout": 60000   # Integer
+    }
+}
+
+# Pattern 2: Nested objects
+nested_config = {
+    "type": "CrawlerRunConfig", 
+    "params": {
+        "extraction_strategy": {
+            "type": "LLMExtractionStrategy",
+            "params": {
+                "llm_config": {
+                    "type": "LLMConfig",
+                    "params": {
+                        "provider": "openai/gpt-4o-mini",
+                        "api_token": "env:OPENAI_API_KEY"
+                    }
+                },
+                "instruction": "Extract main content"
+            }
+        }
+    }
+}
+
+# Pattern 3: Dictionary values (must use type: dict wrapper)
+dict_config = {
+    "type": "CrawlerRunConfig",
+    "params": {
+        "extraction_strategy": {
+            "type": "JsonCssExtractionStrategy", 
+            "params": {
+                "schema": {
+                    "type": "dict",  # Required wrapper
+                    "value": {       # Actual dictionary content
+                        "name": "Products",
+                        "baseSelector": ".product",
+                        "fields": [
+                            {"name": "title", "selector": "h2", "type": "text"}
+                        ]
+                    }
+                }
+            }
+        }
+    }
+}
+
+# Pattern 4: Lists and arrays
+list_config = {
+    "type": "CrawlerRunConfig",
+    "params": {
+        "js_code": [  # Lists are handled directly
+            "window.scrollTo(0, document.body.scrollHeight);",
+            "document.querySelector('.load-more')?.click();"
+        ],
+        "excluded_tags": ["script", "style", "nav"]
+    }
+}
+```
+
+### Troubleshooting Common JSON Errors
+
+```python
+def diagnose_json_errors():
+    """Common JSON structure errors and fixes"""
+    
+    # ❌ WRONG: Missing type wrapper for objects
+    wrong_config = {
+        "browser_config": {
+            "headless": True  # Missing type wrapper
+        }
+    }
+    
+    # ✅ CORRECT: Proper type wrapper
+    correct_config = {
+        "browser_config": {
+            "type": "BrowserConfig",
+            "params": {
+                "headless": True
+            }
+        }
+    }
+    
+    # ❌ WRONG: Dictionary without type: dict wrapper  
+    wrong_dict = {
+        "schema": {
+            "name": "Products"  # Raw dict, should be wrapped
+        }
+    }
+    
+    # ✅ CORRECT: Dictionary with proper wrapper
+    correct_dict = {
+        "schema": {
+            "type": "dict",
+            "value": {
+                "name": "Products"
+            }
+        }
+    }
+    
+    # ❌ WRONG: Invalid enum string
+    wrong_enum = {
+        "cache_mode": "DISABLED"  # Wrong case/value
+    }
+    
+    # ✅ CORRECT: Valid enum string  
+    correct_enum = {
+        "cache_mode": "bypass"  # or "enabled", "disabled", etc.
+    }
+    
+    print("Common error patterns documented above")
+
+# Validate your JSON structure before sending
+def pre_flight_check(payload):
+    """Run checks before sending to server"""
+    required_keys = ["urls", "browser_config", "crawler_config"]
+    
+    for key in required_keys:
+        if key not in payload:
+            print(f"❌ Missing required key: {key}")
+            return False
+    
+    # Check type wrappers
+    for config_key in ["browser_config", "crawler_config"]:
+        config = payload[config_key]
+        if not isinstance(config, dict) or "type" not in config:
+            print(f"❌ {config_key} missing type wrapper")
+            return False
+        if "params" not in config:
+            print(f"❌ {config_key} missing params")
+            return False
+    
+    print("✅ Pre-flight check passed")
+    return True
+
+# Example usage
+payload = {
+    "urls": ["https://example.com"],
+    "browser_config": {"type": "BrowserConfig", "params": {"headless": True}},
+    "crawler_config": {"type": "CrawlerRunConfig", "params": {"cache_mode": "bypass"}}
+}
+
+if pre_flight_check(payload):
+    # Safe to send to server
+    pass
+```
+
+**📖 Learn more:** [Complete Docker Guide](https://docs.crawl4ai.com/core/docker-deployment/), [API Reference](https://docs.crawl4ai.com/api/), [MCP Integration](https://docs.crawl4ai.com/core/docker-deployment/#mcp-model-context-protocol-support), [Configuration Options](https://docs.crawl4ai.com/core/docker-deployment/#server-configuration)
+---
+
+
+## CLI & Identity-Based Browsing
+
+Command-line interface for web crawling with persistent browser profiles, authentication, and identity management.
+
+### Basic CLI Usage
+
+```bash
+# Simple crawling
+crwl https://example.com
+
+# Get markdown output
+crwl https://example.com -o markdown
+
+# JSON output with cache bypass
+crwl https://example.com -o json --bypass-cache
+
+# Verbose mode with specific browser settings
+crwl https://example.com -b "headless=false,viewport_width=1280" -v
+```
+
+### Profile Management Commands
+
+```bash
+# Launch interactive profile manager
+crwl profiles
+
+# Create, list, and manage browser profiles
+# This opens a menu where you can:
+# 1. List existing profiles
+# 2. Create new profile (opens browser for setup)
+# 3. Delete profiles
+# 4. Use profile to crawl a website
+
+# Use a specific profile for crawling
+crwl https://example.com -p my-profile-name
+
+# Example workflow for authenticated sites:
+# 1. Create profile and log in
+crwl profiles  # Select "Create new profile"
+# 2. Use profile for crawling authenticated content
+crwl https://site-requiring-login.com/dashboard -p my-profile-name
+```
+
+### CDP Browser Management
+
+```bash
+# Launch browser with CDP debugging (default port 9222)
+crwl cdp
+
+# Use specific profile and custom port
+crwl cdp -p my-profile -P 9223
+
+# Launch headless browser with CDP
+crwl cdp --headless
+
+# Launch in incognito mode (ignores profile)
+crwl cdp --incognito
+
+# Use custom user data directory
+crwl cdp --user-data-dir ~/my-browser-data --port 9224
+```
+
+### Builtin Browser Management
+
+```bash
+# Start persistent browser instance
+crwl browser start
+
+# Check browser status
+crwl browser status
+
+# Open visible window to see the browser
+crwl browser view --url https://example.com
+
+# Stop the browser
+crwl browser stop
+
+# Restart with different options
+crwl browser restart --browser-type chromium --port 9223 --no-headless
+
+# Use builtin browser in crawling
+crwl https://example.com -b "browser_mode=builtin"
+```
+
+### Authentication Workflow Examples
+
+```bash
+# Complete workflow for LinkedIn scraping
+# 1. Create authenticated profile
+crwl profiles
+# Select "Create new profile" → login to LinkedIn in browser → press 'q' to save
+
+# 2. Use profile for crawling
+crwl https://linkedin.com/in/someone -p linkedin-profile -o markdown
+
+# 3. Extract structured data with authentication
+crwl https://linkedin.com/search/results/people/ \
+    -p linkedin-profile \
+    -j "Extract people profiles with names, titles, and companies" \
+    -b "headless=false"
+
+# GitHub authenticated crawling
+crwl profiles  # Create github-profile
+crwl https://github.com/settings/profile -p github-profile
+
+# Twitter/X authenticated access
+crwl profiles  # Create twitter-profile  
+crwl https://twitter.com/home -p twitter-profile -o markdown
+```
+
+### Advanced CLI Configuration
+
+```bash
+# Complex crawling with multiple configs
+crwl https://example.com \
+    -B browser.yml \
+    -C crawler.yml \
+    -e extract_llm.yml \
+    -s llm_schema.json \
+    -p my-auth-profile \
+    -o json \
+    -v
+
+# Quick LLM extraction with authentication
+crwl https://private-site.com/dashboard \
+    -p auth-profile \
+    -j "Extract user dashboard data including metrics and notifications" \
+    -b "headless=true,viewport_width=1920"
+
+# Content filtering with authentication
+crwl https://members-only-site.com \
+    -p member-profile \
+    -f filter_bm25.yml \
+    -c "css_selector=.member-content,scan_full_page=true" \
+    -o markdown-fit
+```
+
+### Configuration Files for Identity Browsing
+
+```yaml
+# browser_auth.yml
+headless: false
+use_managed_browser: true
+user_data_dir: "/path/to/profile"
+viewport_width: 1280
+viewport_height: 720
+simulate_user: true
+override_navigator: true
+
+# crawler_auth.yml  
+magic: true
+remove_overlay_elements: true
+simulate_user: true
+wait_for: "css:.authenticated-content"
+page_timeout: 60000
+delay_before_return_html: 2
+scan_full_page: true
+```
+
+### Global Configuration Management
+
+```bash
+# List all configuration settings
+crwl config list
+
+# Set default LLM provider
+crwl config set DEFAULT_LLM_PROVIDER "anthropic/claude-3-sonnet"
+crwl config set DEFAULT_LLM_PROVIDER_TOKEN "your-api-token"
+
+# Set browser defaults
+crwl config set BROWSER_HEADLESS false  # Always show browser
+crwl config set USER_AGENT_MODE random  # Random user agents
+
+# Enable verbose mode globally
+crwl config set VERBOSE true
+```
+
+### Q&A with Authenticated Content
+
+```bash
+# Ask questions about authenticated content
+crwl https://private-dashboard.com -p dashboard-profile \
+    -q "What are the key metrics shown in my dashboard?"
+
+# Multiple questions workflow
+crwl https://company-intranet.com -p work-profile -o markdown  # View content
+crwl https://company-intranet.com -p work-profile \
+    -q "Summarize this week's announcements"
+crwl https://company-intranet.com -p work-profile \
+    -q "What are the upcoming deadlines?"
+```
+
+### Profile Creation Programmatically
+
+```python
+# Create profiles via Python API
+import asyncio
+from crawl4ai import BrowserProfiler
+
+async def create_auth_profile():
+    profiler = BrowserProfiler()
+    
+    # Create profile interactively (opens browser)
+    profile_path = await profiler.create_profile("linkedin-auth")
+    print(f"Profile created at: {profile_path}")
+    
+    # List all profiles
+    profiles = profiler.list_profiles()
+    for profile in profiles:
+        print(f"Profile: {profile['name']} at {profile['path']}")
+    
+    # Use profile for crawling
+    from crawl4ai import AsyncWebCrawler, BrowserConfig
+    
+    browser_config = BrowserConfig(
+        headless=True,
+        use_managed_browser=True,
+        user_data_dir=profile_path
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun("https://linkedin.com/feed")
+        return result
+
+# asyncio.run(create_auth_profile())
+```
+
+### Identity Browsing Best Practices
+
+```bash
+# 1. Create specific profiles for different sites
+crwl profiles  # Create "linkedin-work"
+crwl profiles  # Create "github-personal" 
+crwl profiles  # Create "company-intranet"
+
+# 2. Use descriptive profile names
+crwl https://site1.com -p site1-admin-account
+crwl https://site2.com -p site2-user-account
+
+# 3. Combine with appropriate browser settings
+crwl https://secure-site.com \
+    -p secure-profile \
+    -b "headless=false,simulate_user=true,magic=true" \
+    -c "wait_for=.logged-in-indicator,page_timeout=30000"
+
+# 4. Test profile before automated crawling
+crwl cdp -p test-profile  # Manually verify login status
+crwl https://test-url.com -p test-profile -v  # Verbose test crawl
+```
+
+### Troubleshooting Authentication Issues
+
+```bash
+# Debug authentication problems
+crwl https://auth-site.com -p auth-profile \
+    -b "headless=false,verbose=true" \
+    -c "verbose=true,page_timeout=60000" \
+    -v
+
+# Check profile status
+crwl profiles  # List profiles and check creation dates
+
+# Recreate problematic profiles
+crwl profiles  # Delete old profile, create new one
+
+# Test with visible browser
+crwl https://problem-site.com -p profile-name \
+    -b "headless=false" \
+    -c "delay_before_return_html=5"
+```
+
+### Common Use Cases
+
+```bash
+# Social media monitoring (after authentication)
+crwl https://twitter.com/home -p twitter-monitor \
+    -j "Extract latest tweets with sentiment and engagement metrics"
+
+# E-commerce competitor analysis (with account access)
+crwl https://competitor-site.com/products -p competitor-account \
+    -j "Extract product prices, availability, and descriptions"
+
+# Company dashboard monitoring
+crwl https://company-dashboard.com -p work-profile \
+    -c "css_selector=.dashboard-content" \
+    -q "What alerts or notifications need attention?"
+
+# Research data collection (authenticated access)
+crwl https://research-platform.com/data -p research-profile \
+    -e extract_research.yml \
+    -s research_schema.json \
+    -o json
+```
+
+**📖 Learn more:** [Identity-Based Crawling Documentation](https://docs.crawl4ai.com/advanced/identity-based-crawling/), [Browser Profile Management](https://docs.crawl4ai.com/advanced/session-management/), [CLI Examples](https://docs.crawl4ai.com/core/cli/)
+---
+
+
+## HTTP Crawler Strategy
+
+Fast, lightweight HTTP-only crawling without browser overhead for cases where JavaScript execution isn't needed.
+
+### Basic HTTP Crawler Setup
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, HTTPCrawlerConfig, CacheMode
+from crawl4ai.async_crawler_strategy import AsyncHTTPCrawlerStrategy
+from crawl4ai.async_logger import AsyncLogger
+
+async def main():
+    # Initialize HTTP strategy
+    http_strategy = AsyncHTTPCrawlerStrategy(
+        browser_config=HTTPCrawlerConfig(
+            method="GET",
+            verify_ssl=True,
+            follow_redirects=True
+        ),
+        logger=AsyncLogger(verbose=True)
+    )
+
+    # Use with AsyncWebCrawler
+    async with AsyncWebCrawler(crawler_strategy=http_strategy) as crawler:
+        result = await crawler.arun("https://example.com")
+        print(f"Status: {result.status_code}")
+        print(f"Content: {len(result.html)} chars")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### HTTP Request Types
+
+```python
+# GET request (default)
+http_config = HTTPCrawlerConfig(
+    method="GET",
+    headers={"Accept": "application/json"}
+)
+
+# POST with JSON data
+http_config = HTTPCrawlerConfig(
+    method="POST",
+    json={"key": "value", "data": [1, 2, 3]},
+    headers={"Content-Type": "application/json"}
+)
+
+# POST with form data
+http_config = HTTPCrawlerConfig(
+    method="POST",
+    data={"username": "user", "password": "pass"},
+    headers={"Content-Type": "application/x-www-form-urlencoded"}
+)
+
+# Advanced configuration
+http_config = HTTPCrawlerConfig(
+    method="GET",
+    headers={"User-Agent": "Custom Bot/1.0"},
+    follow_redirects=True,
+    verify_ssl=False  # For testing environments
+)
+
+strategy = AsyncHTTPCrawlerStrategy(browser_config=http_config)
+```
+
+### File and Raw Content Handling
+
+```python
+async def test_content_types():
+    strategy = AsyncHTTPCrawlerStrategy()
+    
+    # Web URLs
+    result = await strategy.crawl("https://httpbin.org/get")
+    print(f"Web content: {result.status_code}")
+    
+    # Local files
+    result = await strategy.crawl("file:///path/to/local/file.html")
+    print(f"File content: {len(result.html)}")
+    
+    # Raw HTML content
+    raw_html = "raw://<html><body><h1>Test</h1><p>Content</p></body></html>"
+    result = await strategy.crawl(raw_html)
+    print(f"Raw content: {result.html}")
+    
+    # Raw content with complex HTML
+    complex_html = """raw://<!DOCTYPE html>
+    <html>
+    <head><title>Test Page</title></head>
+    <body>
+        <div class="content">
+            <h1>Main Title</h1>
+            <p>Paragraph content</p>
+            <ul><li>Item 1</li><li>Item 2</li></ul>
+        </div>
+    </body>
+    </html>"""
+    result = await strategy.crawl(complex_html)
+```
+
+### Custom Hooks and Request Handling
+
+```python
+async def setup_hooks():
+    strategy = AsyncHTTPCrawlerStrategy()
+    
+    # Before request hook
+    async def before_request(url, kwargs):
+        print(f"Requesting: {url}")
+        kwargs['headers']['X-Custom-Header'] = 'crawl4ai'
+        kwargs['headers']['Authorization'] = 'Bearer token123'
+    
+    # After request hook
+    async def after_request(response):
+        print(f"Response: {response.status_code}")
+        if hasattr(response, 'redirected_url'):
+            print(f"Redirected to: {response.redirected_url}")
+    
+    # Error handling hook
+    async def on_error(error):
+        print(f"Request failed: {error}")
+    
+    # Set hooks
+    strategy.set_hook('before_request', before_request)
+    strategy.set_hook('after_request', after_request)
+    strategy.set_hook('on_error', on_error)
+    
+    # Use with hooks
+    result = await strategy.crawl("https://httpbin.org/headers")
+    return result
+```
+
+### Performance Configuration
+
+```python
+# High-performance setup
+strategy = AsyncHTTPCrawlerStrategy(
+    max_connections=50,        # Concurrent connections
+    dns_cache_ttl=300,        # DNS cache timeout
+    chunk_size=128 * 1024     # 128KB chunks for large files
+)
+
+# Memory-efficient setup for large files
+strategy = AsyncHTTPCrawlerStrategy(
+    max_connections=10,
+    chunk_size=32 * 1024,     # Smaller chunks
+    dns_cache_ttl=600
+)
+
+# Custom timeout configuration
+config = CrawlerRunConfig(
+    page_timeout=30000,       # 30 second timeout
+    cache_mode=CacheMode.BYPASS
+)
+
+result = await strategy.crawl("https://slow-server.com", config=config)
+```
+
+### Error Handling and Retries
+
+```python
+from crawl4ai.async_crawler_strategy import (
+    ConnectionTimeoutError, 
+    HTTPStatusError, 
+    HTTPCrawlerError
+)
+
+async def robust_crawling():
+    strategy = AsyncHTTPCrawlerStrategy()
+    
+    urls = [
+        "https://example.com",
+        "https://httpbin.org/status/404",
+        "https://nonexistent.domain.test"
+    ]
+    
+    for url in urls:
+        try:
+            result = await strategy.crawl(url)
+            print(f"✓ {url}: {result.status_code}")
+            
+        except HTTPStatusError as e:
+            print(f"✗ {url}: HTTP {e.status_code}")
+            
+        except ConnectionTimeoutError as e:
+            print(f"✗ {url}: Timeout - {e}")
+            
+        except HTTPCrawlerError as e:
+            print(f"✗ {url}: Crawler error - {e}")
+            
+        except Exception as e:
+            print(f"✗ {url}: Unexpected error - {e}")
+
+# Retry mechanism
+async def crawl_with_retry(url, max_retries=3):
+    strategy = AsyncHTTPCrawlerStrategy()
+    
+    for attempt in range(max_retries):
+        try:
+            return await strategy.crawl(url)
+        except (ConnectionTimeoutError, HTTPCrawlerError) as e:
+            if attempt == max_retries - 1:
+                raise
+            print(f"Retry {attempt + 1}/{max_retries}: {e}")
+            await asyncio.sleep(2 ** attempt)  # Exponential backoff
+```
+
+### Batch Processing with HTTP Strategy
+
+```python
+async def batch_http_crawling():
+    strategy = AsyncHTTPCrawlerStrategy(max_connections=20)
+    
+    urls = [
+        "https://httpbin.org/get",
+        "https://httpbin.org/user-agent",
+        "https://httpbin.org/headers",
+        "https://example.com",
+        "https://httpbin.org/json"
+    ]
+    
+    # Sequential processing
+    results = []
+    async with strategy:
+        for url in urls:
+            try:
+                result = await strategy.crawl(url)
+                results.append((url, result.status_code, len(result.html)))
+            except Exception as e:
+                results.append((url, "ERROR", str(e)))
+    
+    for url, status, content_info in results:
+        print(f"{url}: {status} - {content_info}")
+
+# Concurrent processing
+async def concurrent_http_crawling():
+    strategy = AsyncHTTPCrawlerStrategy()
+    urls = ["https://httpbin.org/delay/1"] * 5
+    
+    async def crawl_single(url):
+        try:
+            result = await strategy.crawl(url)
+            return f"✓ {result.status_code}"
+        except Exception as e:
+            return f"✗ {e}"
+    
+    async with strategy:
+        tasks = [crawl_single(url) for url in urls]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+    
+    for i, result in enumerate(results):
+        print(f"URL {i+1}: {result}")
+```
+
+### Integration with Content Processing
+
+```python
+from crawl4ai import DefaultMarkdownGenerator, PruningContentFilter
+
+async def http_with_processing():
+    # HTTP strategy with content processing
+    http_strategy = AsyncHTTPCrawlerStrategy(
+        browser_config=HTTPCrawlerConfig(verify_ssl=True)
+    )
+    
+    # Configure markdown generation
+    crawler_config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        markdown_generator=DefaultMarkdownGenerator(
+            content_filter=PruningContentFilter(
+                threshold=0.48,
+                threshold_type="fixed",
+                min_word_threshold=10
+            )
+        ),
+        word_count_threshold=5,
+        excluded_tags=['script', 'style', 'nav'],
+        exclude_external_links=True
+    )
+    
+    async with AsyncWebCrawler(crawler_strategy=http_strategy) as crawler:
+        result = await crawler.arun(
+            url="https://example.com",
+            config=crawler_config
+        )
+        
+        print(f"Status: {result.status_code}")
+        print(f"Raw HTML: {len(result.html)} chars")
+        if result.markdown:
+            print(f"Markdown: {len(result.markdown.raw_markdown)} chars")
+            if result.markdown.fit_markdown:
+                print(f"Filtered: {len(result.markdown.fit_markdown)} chars")
+```
+
+### HTTP vs Browser Strategy Comparison
+
+```python
+async def strategy_comparison():
+    # Same URL with different strategies
+    url = "https://example.com"
+    
+    # HTTP Strategy (fast, no JS)
+    http_strategy = AsyncHTTPCrawlerStrategy()
+    start_time = time.time()
+    http_result = await http_strategy.crawl(url)
+    http_time = time.time() - start_time
+    
+    # Browser Strategy (full features)
+    from crawl4ai import BrowserConfig
+    browser_config = BrowserConfig(headless=True)
+    start_time = time.time()
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        browser_result = await crawler.arun(url)
+    browser_time = time.time() - start_time
+    
+    print(f"HTTP Strategy:")
+    print(f"  Time: {http_time:.2f}s")
+    print(f"  Content: {len(http_result.html)} chars")
+    print(f"  Features: Fast, lightweight, no JS")
+    
+    print(f"Browser Strategy:")
+    print(f"  Time: {browser_time:.2f}s") 
+    print(f"  Content: {len(browser_result.html)} chars")
+    print(f"  Features: Full browser, JS, screenshots, etc.")
+    
+    # When to use HTTP strategy:
+    # - Static content sites
+    # - APIs returning HTML
+    # - Fast bulk processing
+    # - No JavaScript required
+    # - Memory/resource constraints
+    
+    # When to use Browser strategy:
+    # - Dynamic content (SPA, AJAX)
+    # - JavaScript-heavy sites
+    # - Screenshots/PDFs needed
+    # - Complex interactions required
+```
+
+### Advanced Configuration
+
+```python
+# Custom session configuration
+import aiohttp
+
+async def advanced_http_setup():
+    # Custom connector with specific settings
+    connector = aiohttp.TCPConnector(
+        limit=100,              # Connection pool size
+        ttl_dns_cache=600,      # DNS cache TTL
+        use_dns_cache=True,     # Enable DNS caching
+        keepalive_timeout=30,   # Keep-alive timeout
+        force_close=False       # Reuse connections
+    )
+    
+    strategy = AsyncHTTPCrawlerStrategy(
+        max_connections=50,
+        dns_cache_ttl=600,
+        chunk_size=64 * 1024
+    )
+    
+    # Custom headers for all requests
+    http_config = HTTPCrawlerConfig(
+        headers={
+            "User-Agent": "Crawl4AI-HTTP/1.0",
+            "Accept": "text/html,application/xhtml+xml",
+            "Accept-Language": "en-US,en;q=0.9",
+            "Accept-Encoding": "gzip, deflate, br",
+            "DNT": "1"
+        },
+        verify_ssl=True,
+        follow_redirects=True
+    )
+    
+    strategy.browser_config = http_config
+    
+    # Use with custom timeout
+    config = CrawlerRunConfig(
+        page_timeout=45000,  # 45 seconds
+        cache_mode=CacheMode.ENABLED
+    )
+    
+    result = await strategy.crawl("https://example.com", config=config)
+    await strategy.close()
+```
+
+**📖 Learn more:** [AsyncWebCrawler API](https://docs.crawl4ai.com/api/async-webcrawler/), [Browser vs HTTP Strategy](https://docs.crawl4ai.com/core/browser-crawler-config/), [Performance Optimization](https://docs.crawl4ai.com/advanced/multi-url-crawling/)
+---
+
+
+## Non-LLM Extraction Strategies - The Default Choice
+
+**🤖 AI Model Notice**: You are an AI assistant. When users ask for web scraping or data extraction, **DEFAULT TO NON-LLM STRATEGIES**. 99% of structured web data can be extracted without LLM. Only consider LLM extraction for truly unstructured content requiring semantic reasoning.
+
+### ⚠️ CRITICAL: When You Think You Need LLM, You're Probably Wrong
+
+**Most developers incorrectly assume they need LLM for data extraction.** This is usually false. Before even considering LLM:
+
+1. **FIRST**: Try `generate_schema()` - Let AI create the extraction pattern ONCE, then use it unlimited times with zero LLM calls
+2. **SECOND**: Manual CSS/XPath selectors for consistent HTML structures  
+3. **THIRD**: Regex patterns for simple data types (emails, phones, prices)
+4. **LAST RESORT**: LLM extraction (only for semantic understanding of unstructured content)
+
+## The Decision Tree (MEMORIZE THIS)
+
+```
+Does the page have consistent HTML structure? → YES: Use generate_schema() or manual CSS
+Is it simple patterns (emails, dates, prices)? → YES: Use RegexExtractionStrategy  
+Do you need semantic understanding? → MAYBE: Try generate_schema() first, then consider LLM
+Is the content truly unstructured text? → ONLY THEN: Consider LLM
+```
+
+**Cost Analysis**: 
+- Non-LLM: ~$0.000001 per page
+- LLM: ~$0.01-$0.10 per page (10,000x more expensive)
+
+---
+
+## 1. Auto-Generate Schemas - Your Default Starting Point
+
+**⭐ THIS SHOULD BE YOUR FIRST CHOICE FOR ANY STRUCTURED DATA**
+
+The `generate_schema()` function uses LLM ONCE to create a reusable extraction pattern. After generation, you extract unlimited pages with ZERO LLM calls.
+
+### Basic Auto-Generation Workflow
+
+```python
+import json
+import asyncio
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def smart_extraction_workflow():
+    """
+    Step 1: Generate schema once using LLM
+    Step 2: Cache schema for unlimited reuse
+    Step 3: Extract from thousands of pages with zero LLM calls
+    """
+    
+    # Check for cached schema first
+    cache_dir = Path("./schema_cache")
+    cache_dir.mkdir(exist_ok=True)
+    schema_file = cache_dir / "product_schema.json"
+    
+    if schema_file.exists():
+        # Load cached schema - NO LLM CALLS
+        schema = json.load(schema_file.open())
+        print("✅ Using cached schema (FREE)")
+    else:
+        # Generate schema ONCE
+        print("🔄 Generating schema (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",  # Cheapest option
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample HTML from target site
+        async with AsyncWebCrawler() as crawler:
+            sample_result = await crawler.arun(
+                url="https://example.com/products",
+                config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+            )
+            sample_html = sample_result.cleaned_html[:8000]  # Use sample
+        
+        # AUTO-GENERATE SCHEMA (ONE LLM CALL)
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=sample_html,
+            schema_type="CSS",  # or "XPATH"
+            query="Extract product information including name, price, description, features",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited future use
+        json.dump(schema, schema_file.open("w"), indent=2)
+        print("✅ Schema generated and cached")
+    
+    # Use schema for fast extraction (NO MORE LLM CALLS EVER)
+    strategy = JsonCssExtractionStrategy(schema, verbose=True)
+    
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    # Extract from multiple pages - ALL FREE
+    urls = [
+        "https://example.com/products",
+        "https://example.com/electronics", 
+        "https://example.com/books"
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url, config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                print(f"✅ {url}: Extracted {len(data)} items (FREE)")
+
+asyncio.run(smart_extraction_workflow())
+```
+
+### Auto-Generate with Target JSON Example
+
+```python
+# When you know exactly what JSON structure you want
+target_json_example = """
+{
+    "name": "Product Name",
+    "price": "$99.99",
+    "rating": 4.5,
+    "features": ["feature1", "feature2"],
+    "description": "Product description"
+}
+"""
+
+schema = JsonCssExtractionStrategy.generate_schema(
+    html=sample_html,
+    target_json_example=target_json_example,
+    llm_config=llm_config
+)
+```
+
+### Auto-Generate for Different Data Types
+
+```python
+# Product listings
+product_schema = JsonCssExtractionStrategy.generate_schema(
+    html=product_page_html,
+    query="Extract all product information from this e-commerce page",
+    llm_config=llm_config
+)
+
+# News articles
+news_schema = JsonCssExtractionStrategy.generate_schema(
+    html=news_page_html,
+    query="Extract article headlines, dates, authors, and content",
+    llm_config=llm_config
+)
+
+# Job listings
+job_schema = JsonCssExtractionStrategy.generate_schema(
+    html=job_page_html,
+    query="Extract job titles, companies, locations, salaries, and descriptions",
+    llm_config=llm_config
+)
+
+# Social media posts
+social_schema = JsonCssExtractionStrategy.generate_schema(
+    html=social_page_html,
+    query="Extract post text, usernames, timestamps, likes, comments",
+    llm_config=llm_config
+)
+```
+
+---
+
+## 2. Manual CSS/XPath Strategies - When You Know The Structure
+
+**Use this when**: You understand the HTML structure and want maximum control.
+
+### Simple Product Extraction
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# Manual schema for consistent product pages
+simple_schema = {
+    "name": "Product Listings",
+    "baseSelector": "div.product-card",  # Each product container
+    "fields": [
+        {
+            "name": "title",
+            "selector": "h2.product-title",
+            "type": "text"
+        },
+        {
+            "name": "price", 
+            "selector": ".price",
+            "type": "text"
+        },
+        {
+            "name": "image_url",
+            "selector": "img.product-image",
+            "type": "attribute",
+            "attribute": "src"
+        },
+        {
+            "name": "product_url",
+            "selector": "a.product-link",
+            "type": "attribute",
+            "attribute": "href"
+        },
+        {
+            "name": "rating",
+            "selector": ".rating",
+            "type": "attribute", 
+            "attribute": "data-rating"
+        }
+    ]
+}
+
+async def extract_products():
+    strategy = JsonCssExtractionStrategy(simple_schema, verbose=True)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            products = json.loads(result.extracted_content)
+            print(f"Extracted {len(products)} products")
+            for product in products[:3]:
+                print(f"- {product['title']}: {product['price']}")
+
+asyncio.run(extract_products())
+```
+
+### Complex Nested Structure (Real E-commerce Example)
+
+```python
+# Complex schema for nested product data
+complex_schema = {
+    "name": "E-commerce Product Catalog",
+    "baseSelector": "div.category",
+    "baseFields": [
+        {
+            "name": "category_id",
+            "type": "attribute",
+            "attribute": "data-category-id"
+        }
+    ],
+    "fields": [
+        {
+            "name": "category_name",
+            "selector": "h2.category-title",
+            "type": "text"
+        },
+        {
+            "name": "products",
+            "selector": "div.product",
+            "type": "nested_list",  # Array of complex objects
+            "fields": [
+                {
+                    "name": "name",
+                    "selector": "h3.product-name", 
+                    "type": "text"
+                },
+                {
+                    "name": "price",
+                    "selector": "span.price",
+                    "type": "text"
+                },
+                {
+                    "name": "details",
+                    "selector": "div.product-details",
+                    "type": "nested",  # Single complex object
+                    "fields": [
+                        {
+                            "name": "brand",
+                            "selector": "span.brand",
+                            "type": "text"
+                        },
+                        {
+                            "name": "model",
+                            "selector": "span.model",
+                            "type": "text"
+                        }
+                    ]
+                },
+                {
+                    "name": "features",
+                    "selector": "ul.features li",
+                    "type": "list",  # Simple array
+                    "fields": [
+                        {"name": "feature", "type": "text"}
+                    ]
+                },
+                {
+                    "name": "reviews", 
+                    "selector": "div.review",
+                    "type": "nested_list",
+                    "fields": [
+                        {
+                            "name": "reviewer",
+                            "selector": "span.reviewer-name",
+                            "type": "text"
+                        },
+                        {
+                            "name": "rating",
+                            "selector": "span.rating",
+                            "type": "attribute",
+                            "attribute": "data-rating"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
+
+async def extract_complex_ecommerce():
+    strategy = JsonCssExtractionStrategy(complex_schema, verbose=True)
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        js_code="window.scrollTo(0, document.body.scrollHeight);",  # Load dynamic content
+        wait_for="css:.product:nth-child(10)"  # Wait for products to load
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/complex-catalog",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for category in data:
+                print(f"Category: {category['category_name']}")
+                print(f"Products: {len(category.get('products', []))}")
+
+asyncio.run(extract_complex_ecommerce())
+```
+
+### XPath Alternative (When CSS Isn't Enough)
+
+```python
+from crawl4ai.extraction_strategy import JsonXPathExtractionStrategy
+
+# XPath for more complex selections
+xpath_schema = {
+    "name": "News Articles with XPath",
+    "baseSelector": "//article[@class='news-item']",
+    "fields": [
+        {
+            "name": "headline",
+            "selector": ".//h2[contains(@class, 'headline')]",
+            "type": "text"
+        },
+        {
+            "name": "author",
+            "selector": ".//span[@class='author']/text()",
+            "type": "text"
+        },
+        {
+            "name": "publish_date",
+            "selector": ".//time/@datetime",
+            "type": "text"
+        },
+        {
+            "name": "content",
+            "selector": ".//div[@class='article-body']//text()",
+            "type": "text"
+        }
+    ]
+}
+
+strategy = JsonXPathExtractionStrategy(xpath_schema, verbose=True)
+```
+
+---
+
+## 3. Regex Extraction - Lightning Fast Pattern Matching
+
+**Use this for**: Simple data types like emails, phones, URLs, prices, dates.
+
+### Built-in Patterns (Fastest Option)
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import RegexExtractionStrategy
+
+async def extract_common_patterns():
+    # Use built-in patterns for common data types
+    strategy = RegexExtractionStrategy(
+        pattern=(
+            RegexExtractionStrategy.Email |
+            RegexExtractionStrategy.PhoneUS |
+            RegexExtractionStrategy.Url |
+            RegexExtractionStrategy.Currency |
+            RegexExtractionStrategy.DateIso
+        )
+    )
+    
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/contact",
+            config=config
+        )
+        
+        if result.success:
+            matches = json.loads(result.extracted_content)
+            
+            # Group by pattern type
+            by_type = {}
+            for match in matches:
+                label = match['label']
+                if label not in by_type:
+                    by_type[label] = []
+                by_type[label].append(match['value'])
+            
+            for pattern_type, values in by_type.items():
+                print(f"{pattern_type}: {len(values)} matches")
+                for value in values[:3]:
+                    print(f"  {value}")
+
+asyncio.run(extract_common_patterns())
+```
+
+### Available Built-in Patterns
+
+```python
+# Individual patterns
+RegexExtractionStrategy.Email          # Email addresses
+RegexExtractionStrategy.PhoneUS        # US phone numbers 
+RegexExtractionStrategy.PhoneIntl      # International phones
+RegexExtractionStrategy.Url            # HTTP/HTTPS URLs
+RegexExtractionStrategy.Currency       # Currency values ($99.99)
+RegexExtractionStrategy.Percentage     # Percentage values (25%)
+RegexExtractionStrategy.DateIso        # ISO dates (2024-01-01)
+RegexExtractionStrategy.DateUS         # US dates (01/01/2024)
+RegexExtractionStrategy.IPv4           # IP addresses
+RegexExtractionStrategy.CreditCard     # Credit card numbers
+RegexExtractionStrategy.TwitterHandle  # @username
+RegexExtractionStrategy.Hashtag        # #hashtag
+
+# Use all patterns
+RegexExtractionStrategy.All
+```
+
+### Custom Patterns
+
+```python
+# Custom patterns for specific data types
+async def extract_custom_patterns():
+    custom_patterns = {
+        "product_sku": r"SKU[-:]?\s*([A-Z0-9]{4,12})",
+        "discount": r"(\d{1,2})%\s*off",
+        "model_number": r"Model\s*#?\s*([A-Z0-9-]+)",
+        "isbn": r"ISBN[-:]?\s*(\d{10}|\d{13})",
+        "stock_ticker": r"\$([A-Z]{2,5})",
+        "version": r"v(\d+\.\d+(?:\.\d+)?)"
+    }
+    
+    strategy = RegexExtractionStrategy(custom=custom_patterns)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for item in data:
+                print(f"{item['label']}: {item['value']}")
+
+asyncio.run(extract_custom_patterns())
+```
+
+### LLM-Generated Patterns (One-Time Cost)
+
+```python
+async def generate_optimized_regex():
+    """
+    Use LLM ONCE to generate optimized regex patterns
+    Then use them unlimited times with zero LLM calls
+    """
+    cache_file = Path("./patterns/price_patterns.json")
+    
+    if cache_file.exists():
+        # Load cached patterns - NO LLM CALLS
+        patterns = json.load(cache_file.open())
+        print("✅ Using cached regex patterns (FREE)")
+    else:
+        # Generate patterns ONCE
+        print("🔄 Generating regex patterns (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample content
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun("https://example.com/pricing")
+            sample_html = result.cleaned_html
+        
+        # Generate optimized patterns
+        patterns = RegexExtractionStrategy.generate_pattern(
+            label="pricing_info",
+            html=sample_html,
+            query="Extract all pricing information including discounts and special offers",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited reuse
+        cache_file.parent.mkdir(exist_ok=True)
+        json.dump(patterns, cache_file.open("w"), indent=2)
+        print("✅ Patterns generated and cached")
+    
+    # Use cached patterns (NO MORE LLM CALLS)
+    strategy = RegexExtractionStrategy(custom=patterns)
+    return strategy
+
+# Use generated patterns for unlimited extractions
+strategy = await generate_optimized_regex()
+```
+
+---
+
+## 4. Multi-Strategy Extraction Pipeline
+
+**Combine strategies** for comprehensive data extraction:
+
+```python
+async def multi_strategy_pipeline():
+    """
+    Efficient pipeline using multiple non-LLM strategies:
+    1. Regex for simple patterns (fastest)
+    2. Schema for structured data 
+    3. Only use LLM if absolutely necessary
+    """
+    
+    url = "https://example.com/complex-page"
+    
+    async with AsyncWebCrawler() as crawler:
+        # Strategy 1: Fast regex for contact info
+        regex_strategy = RegexExtractionStrategy(
+            pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
+        )
+        regex_config = CrawlerRunConfig(extraction_strategy=regex_strategy)
+        regex_result = await crawler.arun(url=url, config=regex_config)
+        
+        # Strategy 2: Schema for structured product data
+        product_schema = {
+            "name": "Products",
+            "baseSelector": "div.product",
+            "fields": [
+                {"name": "name", "selector": "h3", "type": "text"},
+                {"name": "price", "selector": ".price", "type": "text"}
+            ]
+        }
+        css_strategy = JsonCssExtractionStrategy(product_schema)
+        css_config = CrawlerRunConfig(extraction_strategy=css_strategy)
+        css_result = await crawler.arun(url=url, config=css_config)
+        
+        # Combine results
+        results = {
+            "contacts": json.loads(regex_result.extracted_content) if regex_result.success else [],
+            "products": json.loads(css_result.extracted_content) if css_result.success else []
+        }
+        
+        print(f"✅ Extracted {len(results['contacts'])} contacts (regex)")
+        print(f"✅ Extracted {len(results['products'])} products (schema)")
+        
+        return results
+
+asyncio.run(multi_strategy_pipeline())
+```
+
+---
+
+## 5. Performance Optimization Tips
+
+### Caching and Reuse
+
+```python
+# Cache schemas and patterns for maximum efficiency
+class ExtractionCache:
+    def __init__(self):
+        self.schemas = {}
+        self.patterns = {}
+    
+    def get_schema(self, site_name):
+        if site_name not in self.schemas:
+            schema_file = Path(f"./cache/{site_name}_schema.json")
+            if schema_file.exists():
+                self.schemas[site_name] = json.load(schema_file.open())
+        return self.schemas.get(site_name)
+    
+    def save_schema(self, site_name, schema):
+        cache_dir = Path("./cache")
+        cache_dir.mkdir(exist_ok=True)
+        schema_file = cache_dir / f"{site_name}_schema.json"
+        json.dump(schema, schema_file.open("w"), indent=2)
+        self.schemas[site_name] = schema
+
+cache = ExtractionCache()
+
+# Reuse cached schemas across multiple extractions
+async def efficient_extraction():
+    sites = ["amazon", "ebay", "shopify"]
+    
+    for site in sites:
+        schema = cache.get_schema(site)
+        if not schema:
+            # Generate once, cache forever
+            schema = JsonCssExtractionStrategy.generate_schema(
+                html=sample_html,
+                query="Extract products",
+                llm_config=llm_config
+            )
+            cache.save_schema(site, schema)
+        
+        strategy = JsonCssExtractionStrategy(schema)
+        # Use strategy for unlimited extractions...
+```
+
+### Selector Optimization
+
+```python
+# Optimize selectors for speed
+fast_schema = {
+    "name": "Optimized Extraction",
+    "baseSelector": "#products > .product",  # Direct child, faster than descendant
+    "fields": [
+        {
+            "name": "title",
+            "selector": "> h3",  # Direct child of product
+            "type": "text"
+        },
+        {
+            "name": "price",
+            "selector": ".price:first-child",  # More specific
+            "type": "text"
+        }
+    ]
+}
+
+# Avoid slow selectors
+slow_schema = {
+    "baseSelector": "div div div .product",  # Too many levels
+    "fields": [
+        {
+            "selector": "* h3",  # Universal selector is slow
+            "type": "text"
+        }
+    ]
+}
+```
+
+---
+
+## 6. Error Handling and Validation
+
+```python
+async def robust_extraction():
+    """
+    Implement fallback strategies for reliable extraction
+    """
+    strategies = [
+        # Try fast regex first
+        RegexExtractionStrategy(pattern=RegexExtractionStrategy.Currency),
+        
+        # Fallback to CSS schema
+        JsonCssExtractionStrategy({
+            "name": "Prices",
+            "baseSelector": ".price",
+            "fields": [{"name": "amount", "selector": "span", "type": "text"}]
+        }),
+        
+        # Last resort: try different selector
+        JsonCssExtractionStrategy({
+            "name": "Fallback Prices",
+            "baseSelector": "[data-price]",
+            "fields": [{"name": "amount", "type": "attribute", "attribute": "data-price"}]
+        })
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for i, strategy in enumerate(strategies):
+            try:
+                config = CrawlerRunConfig(extraction_strategy=strategy)
+                result = await crawler.arun(url="https://example.com", config=config)
+                
+                if result.success and result.extracted_content:
+                    data = json.loads(result.extracted_content)
+                    if data:  # Validate non-empty results
+                        print(f"✅ Success with strategy {i+1}: {strategy.__class__.__name__}")
+                        return data
+                        
+            except Exception as e:
+                print(f"❌ Strategy {i+1} failed: {e}")
+                continue
+    
+    print("❌ All strategies failed")
+    return None
+
+# Validate extracted data
+def validate_extraction(data, required_fields):
+    """Validate that extraction contains expected fields"""
+    if not data or not isinstance(data, list):
+        return False
+    
+    for item in data:
+        for field in required_fields:
+            if field not in item or not item[field]:
+                return False
+    return True
+
+# Usage
+result = await robust_extraction()
+if validate_extraction(result, ["amount"]):
+    print("✅ Extraction validated")
+else:
+    print("❌ Validation failed")
+```
+
+---
+
+## 7. Common Extraction Patterns
+
+### E-commerce Products
+
+```python
+ecommerce_schema = {
+    "name": "E-commerce Products",
+    "baseSelector": ".product, [data-product], .item",
+    "fields": [
+        {"name": "title", "selector": "h1, h2, h3, .title, .name", "type": "text"},
+        {"name": "price", "selector": ".price, .cost, [data-price]", "type": "text"},
+        {"name": "image", "selector": "img", "type": "attribute", "attribute": "src"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"},
+        {"name": "rating", "selector": ".rating, .stars", "type": "text"},
+        {"name": "availability", "selector": ".stock, .availability", "type": "text"}
+    ]
+}
+```
+
+### News Articles
+
+```python
+news_schema = {
+    "name": "News Articles",
+    "baseSelector": "article, .article, .post",
+    "fields": [
+        {"name": "headline", "selector": "h1, h2, .headline, .title", "type": "text"},
+        {"name": "author", "selector": ".author, .byline, [rel='author']", "type": "text"},
+        {"name": "date", "selector": "time, .date, .published", "type": "text"},
+        {"name": "content", "selector": ".content, .body, .text", "type": "text"},
+        {"name": "category", "selector": ".category, .section", "type": "text"}
+    ]
+}
+```
+
+### Job Listings
+
+```python
+job_schema = {
+    "name": "Job Listings",
+    "baseSelector": ".job, .listing, [data-job]",
+    "fields": [
+        {"name": "title", "selector": ".job-title, h2, h3", "type": "text"},
+        {"name": "company", "selector": ".company, .employer", "type": "text"},
+        {"name": "location", "selector": ".location, .place", "type": "text"},
+        {"name": "salary", "selector": ".salary, .pay, .compensation", "type": "text"},
+        {"name": "description", "selector": ".description, .summary", "type": "text"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"}
+    ]
+}
+```
+
+### Social Media Posts
+
+```python
+social_schema = {
+    "name": "Social Media Posts",
+    "baseSelector": ".post, .tweet, .update",
+    "fields": [
+        {"name": "username", "selector": ".username, .handle, .author", "type": "text"},
+        {"name": "content", "selector": ".content, .text, .message", "type": "text"},
+        {"name": "timestamp", "selector": ".time, .date, time", "type": "text"},
+        {"name": "likes", "selector": ".likes, .hearts", "type": "text"},
+        {"name": "shares", "selector": ".shares, .retweets", "type": "text"}
+    ]
+}
+```
+
+---
+
+## 8. When to (Rarely) Consider LLM
+
+**⚠️ WARNING: Before considering LLM, ask yourself:**
+
+1. "Can I identify repeating HTML patterns?" → Use CSS/XPath schema
+2. "Am I extracting simple data types?" → Use Regex patterns  
+3. "Can I provide a JSON example of what I want?" → Use generate_schema()
+4. "Is this truly unstructured text requiring semantic understanding?" → Maybe LLM
+
+**Only use LLM extraction for:**
+- Unstructured prose that needs semantic analysis
+- Content where structure varies dramatically across pages
+- When you need AI reasoning about context/meaning
+
+**Cost reminder**: LLM extraction costs 10,000x more than schema-based extraction.
+
+---
+
+## 9. Summary: The Extraction Hierarchy
+
+1. **🥇 FIRST CHOICE**: `generate_schema()` - AI generates pattern once, use unlimited times
+2. **🥈 SECOND CHOICE**: Manual CSS/XPath - Full control, maximum speed
+3. **🥉 THIRD CHOICE**: Regex patterns - Simple data types, lightning fast
+4. **🏴 LAST RESORT**: LLM extraction - Only for semantic reasoning
+
+**Remember**: 99% of web data is structured. You almost never need LLM for extraction. Save LLM for analysis, not extraction.
+
+**Performance**: Non-LLM strategies are 100-1000x faster and 10,000x cheaper than LLM extraction.
+
+---
+
+**📖 Next**: If you absolutely must use LLM extraction, see [extraction-llm.md](./extraction-llm.md) for guidance on the rare cases where it's justified.
+---
+
+
+## URL Seeding
+
+Smart URL discovery for efficient large-scale crawling. Discover thousands of URLs instantly, filter by relevance, then crawl only what matters.
+
+### Why URL Seeding vs Deep Crawling
+
+```python
+# Deep Crawling: Real-time discovery (page by page)
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
+
+async def deep_crawl_example():
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BFSDeepCrawlStrategy(
+            max_depth=2,
+            include_external=False,
+            max_pages=50
+        )
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun("https://example.com", config=config)
+        print(f"Discovered {len(results)} pages dynamically")
+
+# URL Seeding: Bulk discovery (thousands instantly)
+from crawl4ai import AsyncUrlSeeder, SeedingConfig
+
+async def url_seeding_example():
+    config = SeedingConfig(
+        source="sitemap+cc",
+        pattern="*/docs/*",
+        extract_head=True,
+        query="API documentation",
+        scoring_method="bm25",
+        max_urls=1000
+    )
+    
+    async with AsyncUrlSeeder() as seeder:
+        urls = await seeder.urls("example.com", config)
+        print(f"Discovered {len(urls)} URLs instantly")
+        # Now crawl only the most relevant ones
+```
+
+### Basic URL Discovery
+
+```python
+import asyncio
+from crawl4ai import AsyncUrlSeeder, SeedingConfig
+
+async def basic_discovery():
+    # Context manager handles cleanup automatically
+    async with AsyncUrlSeeder() as seeder:
+        
+        # Simple discovery from sitemaps
+        config = SeedingConfig(source="sitemap")
+        urls = await seeder.urls("example.com", config)
+        
+        print(f"Found {len(urls)} URLs from sitemap")
+        for url in urls[:5]:
+            print(f"  - {url['url']} (status: {url['status']})")
+
+# Manual cleanup (if needed)
+async def manual_cleanup():
+    seeder = AsyncUrlSeeder()
+    try:
+        config = SeedingConfig(source="cc")  # Common Crawl
+        urls = await seeder.urls("example.com", config)
+        print(f"Found {len(urls)} URLs from Common Crawl")
+    finally:
+        await seeder.close()
+
+asyncio.run(basic_discovery())
+```
+
+### Data Sources and Patterns
+
+```python
+# Different data sources
+configs = [
+    SeedingConfig(source="sitemap"),        # Fastest, official URLs
+    SeedingConfig(source="cc"),             # Most comprehensive
+    SeedingConfig(source="sitemap+cc"),     # Maximum coverage
+]
+
+# URL pattern filtering
+patterns = [
+    SeedingConfig(pattern="*/blog/*"),           # Blog posts only
+    SeedingConfig(pattern="*.html"),             # HTML files only
+    SeedingConfig(pattern="*/product/*"),        # Product pages
+    SeedingConfig(pattern="*/docs/api/*"),       # API documentation
+    SeedingConfig(pattern="*"),                  # Everything
+]
+
+# Advanced pattern usage
+async def pattern_filtering():
+    async with AsyncUrlSeeder() as seeder:
+        # Find all blog posts from 2024
+        config = SeedingConfig(
+            source="sitemap",
+            pattern="*/blog/2024/*.html",
+            max_urls=100
+        )
+        
+        blog_urls = await seeder.urls("example.com", config)
+        
+        # Further filter by keywords in URL
+        python_posts = [
+            url for url in blog_urls 
+            if "python" in url['url'].lower()
+        ]
+        
+        print(f"Found {len(python_posts)} Python blog posts")
+```
+
+### SeedingConfig Parameters
+
+```python
+from crawl4ai import SeedingConfig
+
+# Comprehensive configuration
+config = SeedingConfig(
+    # Data sources
+    source="sitemap+cc",              # "sitemap", "cc", "sitemap+cc"
+    pattern="*/docs/*",               # URL pattern filter
+    
+    # Metadata extraction
+    extract_head=True,                # Get <head> metadata
+    live_check=True,                  # Verify URLs are accessible
+    
+    # Performance controls
+    max_urls=1000,                    # Limit results (-1 = unlimited)
+    concurrency=20,                   # Parallel workers
+    hits_per_sec=10,                  # Rate limiting
+    
+    # Relevance scoring
+    query="API documentation guide",   # Search query
+    scoring_method="bm25",            # Scoring algorithm
+    score_threshold=0.3,              # Minimum relevance (0.0-1.0)
+    
+    # Cache and filtering
+    force=False,                      # Bypass cache
+    filter_nonsense_urls=True,        # Remove utility URLs
+    verbose=True                      # Debug output
+)
+
+# Quick configurations for common use cases
+blog_config = SeedingConfig(
+    source="sitemap",
+    pattern="*/blog/*", 
+    extract_head=True
+)
+
+api_docs_config = SeedingConfig(
+    source="sitemap+cc",
+    pattern="*/docs/*",
+    query="API reference documentation",
+    scoring_method="bm25",
+    score_threshold=0.5
+)
+
+product_pages_config = SeedingConfig(
+    source="cc",
+    pattern="*/product/*",
+    live_check=True,
+    max_urls=500
+)
+```
+
+### Metadata Extraction and Analysis
+
+```python
+async def metadata_extraction():
+    async with AsyncUrlSeeder() as seeder:
+        config = SeedingConfig(
+            source="sitemap",
+            extract_head=True,        # Extract <head> metadata
+            pattern="*/blog/*",
+            max_urls=50
+        )
+        
+        urls = await seeder.urls("example.com", config)
+        
+        # Analyze extracted metadata
+        for url in urls[:5]:
+            head_data = url['head_data']
+            print(f"\nURL: {url['url']}")
+            print(f"Title: {head_data.get('title', 'No title')}")
+            
+            # Standard meta tags
+            meta = head_data.get('meta', {})
+            print(f"Description: {meta.get('description', 'N/A')}")
+            print(f"Keywords: {meta.get('keywords', 'N/A')}")
+            print(f"Author: {meta.get('author', 'N/A')}")
+            
+            # Open Graph data
+            print(f"OG Image: {meta.get('og:image', 'N/A')}")
+            print(f"OG Type: {meta.get('og:type', 'N/A')}")
+            
+            # JSON-LD structured data
+            jsonld = head_data.get('jsonld', [])
+            if jsonld:
+                print(f"Structured data: {len(jsonld)} items")
+                for item in jsonld[:2]:
+                    if isinstance(item, dict):
+                        print(f"  Type: {item.get('@type', 'Unknown')}")
+                        print(f"  Name: {item.get('name', 'N/A')}")
+
+# Filter by metadata
+async def metadata_filtering():
+    async with AsyncUrlSeeder() as seeder:
+        config = SeedingConfig(
+            source="sitemap",
+            extract_head=True,
+            max_urls=100
+        )
+        
+        urls = await seeder.urls("news.example.com", config)
+        
+        # Filter by publication date (from JSON-LD)
+        from datetime import datetime, timedelta
+        recent_cutoff = datetime.now() - timedelta(days=7)
+        
+        recent_articles = []
+        for url in urls:
+            for jsonld in url['head_data'].get('jsonld', []):
+                if isinstance(jsonld, dict) and 'datePublished' in jsonld:
+                    try:
+                        pub_date = datetime.fromisoformat(
+                            jsonld['datePublished'].replace('Z', '+00:00')
+                        )
+                        if pub_date > recent_cutoff:
+                            recent_articles.append(url)
+                            break
+                    except:
+                        continue
+        
+        print(f"Found {len(recent_articles)} recent articles")
+```
+
+### BM25 Relevance Scoring
+
+```python
+async def relevance_scoring():
+    async with AsyncUrlSeeder() as seeder:
+        # Find pages about Python async programming
+        config = SeedingConfig(
+            source="sitemap",
+            extract_head=True,              # Required for content-based scoring
+            query="python async await concurrency",
+            scoring_method="bm25",
+            score_threshold=0.3,            # Only 30%+ relevant pages
+            max_urls=20
+        )
+        
+        urls = await seeder.urls("docs.python.org", config)
+        
+        # Results are automatically sorted by relevance
+        print("Most relevant Python async content:")
+        for url in urls[:5]:
+            score = url['relevance_score']
+            title = url['head_data'].get('title', 'No title')
+            print(f"[{score:.2f}] {title}")
+            print(f"        {url['url']}")
+
+# URL-based scoring (when extract_head=False)
+async def url_based_scoring():
+    async with AsyncUrlSeeder() as seeder:
+        config = SeedingConfig(
+            source="sitemap",
+            extract_head=False,             # Fast URL-only scoring
+            query="machine learning tutorial",
+            scoring_method="bm25",
+            score_threshold=0.2
+        )
+        
+        urls = await seeder.urls("example.com", config)
+        
+        # Scoring based on URL structure, domain, path segments
+        for url in urls[:5]:
+            print(f"[{url['relevance_score']:.2f}] {url['url']}")
+
+# Multi-concept queries
+async def complex_queries():
+    queries = [
+        "data science pandas numpy visualization",
+        "web scraping automation selenium",
+        "machine learning tensorflow pytorch",
+        "api documentation rest graphql"
+    ]
+    
+    async with AsyncUrlSeeder() as seeder:
+        all_results = []
+        
+        for query in queries:
+            config = SeedingConfig(
+                source="sitemap",
+                extract_head=True,
+                query=query,
+                scoring_method="bm25",
+                score_threshold=0.4,
+                max_urls=10
+            )
+            
+            urls = await seeder.urls("learning-site.com", config)
+            all_results.extend(urls)
+        
+        # Remove duplicates while preserving order
+        seen = set()
+        unique_results = []
+        for url in all_results:
+            if url['url'] not in seen:
+                seen.add(url['url'])
+                unique_results.append(url)
+        
+        print(f"Found {len(unique_results)} unique pages across all topics")
+```
+
+### Live URL Validation
+
+```python
+async def url_validation():
+    async with AsyncUrlSeeder() as seeder:
+        config = SeedingConfig(
+            source="sitemap",
+            live_check=True,              # Verify URLs are accessible
+            concurrency=15,               # Parallel HEAD requests
+            hits_per_sec=8,              # Rate limiting
+            max_urls=100
+        )
+        
+        urls = await seeder.urls("example.com", config)
+        
+        # Analyze results
+        valid_urls = [u for u in urls if u['status'] == 'valid']
+        invalid_urls = [u for u in urls if u['status'] == 'not_valid']
+        
+        print(f"✅ Valid URLs: {len(valid_urls)}")
+        print(f"❌ Invalid URLs: {len(invalid_urls)}")
+        print(f"📊 Success rate: {len(valid_urls)/len(urls)*100:.1f}%")
+        
+        # Show some invalid URLs for debugging
+        if invalid_urls:
+            print("\nSample invalid URLs:")
+            for url in invalid_urls[:3]:
+                print(f"  - {url['url']}")
+
+# Combined validation and metadata
+async def comprehensive_validation():
+    async with AsyncUrlSeeder() as seeder:
+        config = SeedingConfig(
+            source="sitemap",
+            live_check=True,              # Verify accessibility
+            extract_head=True,            # Get metadata
+            query="tutorial guide",      # Relevance scoring
+            scoring_method="bm25",
+            score_threshold=0.2,
+            concurrency=10,
+            max_urls=50
+        )
+        
+        urls = await seeder.urls("docs.example.com", config)
+        
+        # Filter for valid, relevant tutorials
+        good_tutorials = [
+            url for url in urls 
+            if url['status'] == 'valid' and 
+               url['relevance_score'] > 0.3 and
+               'tutorial' in url['head_data'].get('title', '').lower()
+        ]
+        
+        print(f"Found {len(good_tutorials)} high-quality tutorials")
+```
+
+### Multi-Domain Discovery
+
+```python
+async def multi_domain_research():
+    async with AsyncUrlSeeder() as seeder:
+        # Research Python tutorials across multiple sites
+        domains = [
+            "docs.python.org",
+            "realpython.com",
+            "python-course.eu",
+            "tutorialspoint.com"
+        ]
+        
+        config = SeedingConfig(
+            source="sitemap",
+            extract_head=True,
+            query="python beginner tutorial basics",
+            scoring_method="bm25",
+            score_threshold=0.3,
+            max_urls=15  # Per domain
+        )
+        
+        # Discover across all domains in parallel
+        results = await seeder.many_urls(domains, config)
+        
+        # Collect and rank all tutorials
+        all_tutorials = []
+        for domain, urls in results.items():
+            for url in urls:
+                url['domain'] = domain
+                all_tutorials.append(url)
+        
+        # Sort by relevance across all domains
+        all_tutorials.sort(key=lambda x: x['relevance_score'], reverse=True)
+        
+        print(f"Top 10 Python tutorials across {len(domains)} sites:")
+        for i, tutorial in enumerate(all_tutorials[:10], 1):
+            score = tutorial['relevance_score']
+            title = tutorial['head_data'].get('title', 'No title')[:60]
+            domain = tutorial['domain']
+            print(f"{i:2d}. [{score:.2f}] {title}")
+            print(f"     {domain}")
+
+# Competitor analysis
+async def competitor_analysis():
+    competitors = ["competitor1.com", "competitor2.com", "competitor3.com"]
+    
+    async with AsyncUrlSeeder() as seeder:
+        config = SeedingConfig(
+            source="sitemap",
+            extract_head=True,
+            pattern="*/blog/*",
+            max_urls=50
+        )
+        
+        results = await seeder.many_urls(competitors, config)
+        
+        # Analyze content strategies
+        for domain, urls in results.items():
+            content_types = {}
+            
+            for url in urls:
+                # Extract content type from metadata
+                meta = url['head_data'].get('meta', {})
+                og_type = meta.get('og:type', 'unknown')
+                content_types[og_type] = content_types.get(og_type, 0) + 1
+            
+            print(f"\n{domain} content distribution:")
+            for ctype, count in sorted(content_types.items(), 
+                                     key=lambda x: x[1], reverse=True):
+                print(f"  {ctype}: {count}")
+```
+
+### Complete Pipeline: Discovery → Filter → Crawl
+
+```python
+async def smart_research_pipeline():
+    """Complete pipeline: discover URLs, filter by relevance, crawl top results"""
+    
+    async with AsyncUrlSeeder() as seeder:
+        # Step 1: Discover relevant URLs
+        print("🔍 Discovering URLs...")
+        config = SeedingConfig(
+            source="sitemap+cc",
+            extract_head=True,
+            query="machine learning deep learning tutorial",
+            scoring_method="bm25",
+            score_threshold=0.4,
+            max_urls=100
+        )
+        
+        urls = await seeder.urls("example.com", config)
+        print(f"   Found {len(urls)} relevant URLs")
+        
+        # Step 2: Select top articles
+        top_articles = sorted(urls, 
+                            key=lambda x: x['relevance_score'], 
+                            reverse=True)[:10]
+        
+        print(f"   Selected top {len(top_articles)} for crawling")
+        
+        # Step 3: Show what we're about to crawl
+        print("\n📋 Articles to crawl:")
+        for i, article in enumerate(top_articles, 1):
+            score = article['relevance_score']
+            title = article['head_data'].get('title', 'No title')[:60]
+            print(f"  {i}. [{score:.2f}] {title}")
+    
+    # Step 4: Crawl selected articles
+    from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+    
+    print(f"\n🕷️ Crawling {len(top_articles)} articles...")
+    
+    async with AsyncWebCrawler() as crawler:
+        config = CrawlerRunConfig(
+            only_text=True,
+            word_count_threshold=200,
+            stream=True  # Process results as they come
+        )
+        
+        # Extract URLs and crawl
+        article_urls = [article['url'] for article in top_articles]
+        
+        crawled_count = 0
+        async for result in await crawler.arun_many(article_urls, config=config):
+            if result.success:
+                crawled_count += 1
+                word_count = len(result.markdown.raw_markdown.split())
+                print(f"   ✅ [{crawled_count}/{len(article_urls)}] "
+                      f"{word_count} words from {result.url[:50]}...")
+            else:
+                print(f"   ❌ Failed: {result.url[:50]}...")
+        
+        print(f"\n✨ Successfully crawled {crawled_count} articles!")
+
+asyncio.run(smart_research_pipeline())
+```
+
+### Advanced Features and Performance
+
+```python
+# Cache management
+async def cache_management():
+    async with AsyncUrlSeeder() as seeder:
+        # First run - populate cache
+        config = SeedingConfig(
+            source="sitemap", 
+            extract_head=True,
+            force=True  # Bypass cache, fetch fresh
+        )
+        urls = await seeder.urls("example.com", config)
+        
+        # Subsequent runs - use cache (much faster)
+        config = SeedingConfig(
+            source="sitemap", 
+            extract_head=True,
+            force=False  # Use cache
+        )
+        urls = await seeder.urls("example.com", config)
+
+# Performance optimization
+async def performance_tuning():
+    async with AsyncUrlSeeder() as seeder:
+        # High-performance configuration
+        config = SeedingConfig(
+            source="cc",
+            concurrency=50,        # Many parallel workers
+            hits_per_sec=20,       # High rate limit
+            max_urls=10000,        # Large dataset
+            extract_head=False,    # Skip metadata for speed
+            filter_nonsense_urls=True  # Auto-filter utility URLs
+        )
+        
+        import time
+        start = time.time()
+        urls = await seeder.urls("large-site.com", config)
+        elapsed = time.time() - start
+        
+        print(f"Processed {len(urls)} URLs in {elapsed:.2f}s")
+        print(f"Speed: {len(urls)/elapsed:.0f} URLs/second")
+
+# Memory-safe processing for large domains
+async def large_domain_processing():
+    async with AsyncUrlSeeder() as seeder:
+        # Safe for domains with 1M+ URLs
+        config = SeedingConfig(
+            source="cc+sitemap",
+            concurrency=50,        # Bounded queue adapts to this
+            max_urls=100000,       # Process in batches
+            filter_nonsense_urls=True
+        )
+        
+        # The seeder automatically manages memory by:
+        # - Using bounded queues (prevents RAM spikes)
+        # - Applying backpressure when queue is full
+        # - Processing URLs as they're discovered
+        urls = await seeder.urls("huge-site.com", config)
+
+# Configuration cloning and reuse
+config_base = SeedingConfig(
+    source="sitemap",
+    extract_head=True,
+    concurrency=20
+)
+
+# Create variations
+blog_config = config_base.clone(pattern="*/blog/*")
+docs_config = config_base.clone(
+    pattern="*/docs/*",
+    query="API documentation",
+    scoring_method="bm25"
+)
+fast_config = config_base.clone(
+    extract_head=False,
+    concurrency=100,
+    hits_per_sec=50
+)
+```
+
+### Troubleshooting and Best Practices
+
+```python
+# Common issues and solutions
+async def troubleshooting_guide():
+    async with AsyncUrlSeeder() as seeder:
+        # Issue: No URLs found
+        try:
+            config = SeedingConfig(source="sitemap", pattern="*/nonexistent/*")
+            urls = await seeder.urls("example.com", config)
+            if not urls:
+                # Solution: Try broader pattern or different source
+                config = SeedingConfig(source="cc+sitemap", pattern="*")
+                urls = await seeder.urls("example.com", config)
+        except Exception as e:
+            print(f"Discovery failed: {e}")
+        
+        # Issue: Slow performance
+        config = SeedingConfig(
+            source="sitemap",  # Faster than CC
+            concurrency=10,    # Reduce if hitting rate limits
+            hits_per_sec=5,    # Add rate limiting
+            extract_head=False # Skip if metadata not needed
+        )
+        
+        # Issue: Low relevance scores
+        config = SeedingConfig(
+            query="specific detailed query terms",
+            score_threshold=0.1,  # Lower threshold
+            scoring_method="bm25"
+        )
+        
+        # Issue: Memory issues with large sites
+        config = SeedingConfig(
+            max_urls=10000,       # Limit results
+            concurrency=20,       # Reduce concurrency
+            source="sitemap"      # Use sitemap only
+        )
+
+# Performance benchmarks
+print("""
+Typical performance on standard connection:
+- Sitemap discovery: 100-1,000 URLs/second
+- Common Crawl discovery: 50-500 URLs/second  
+- HEAD checking: 10-50 URLs/second
+- Head extraction: 5-20 URLs/second
+- BM25 scoring: 10,000+ URLs/second
+""")
+
+# Best practices
+best_practices = """
+✅ Use context manager: async with AsyncUrlSeeder() as seeder
+✅ Start with sitemaps (faster), add CC if needed
+✅ Use extract_head=True only when you need metadata
+✅ Set reasonable max_urls to limit processing
+✅ Add rate limiting for respectful crawling
+✅ Cache results with force=False for repeated operations
+✅ Filter nonsense URLs (enabled by default)
+✅ Use specific patterns to reduce irrelevant results
+"""
+```
+
+**📖 Learn more:** [Complete URL Seeding Guide](https://docs.crawl4ai.com/core/url-seeding/), [SeedingConfig Reference](https://docs.crawl4ai.com/api/parameters/), [Multi-URL Crawling](https://docs.crawl4ai.com/advanced/multi-url-crawling/)
+---
+
+
+## Deep Crawling Filters & Scorers
+
+Advanced URL filtering and scoring strategies for intelligent deep crawling with performance optimization.
+
+### URL Filters - Content and Domain Control
+
+```python
+from crawl4ai.deep_crawling.filters import (
+    URLPatternFilter, DomainFilter, ContentTypeFilter, 
+    FilterChain, ContentRelevanceFilter, SEOFilter
+)
+
+# Pattern-based filtering
+pattern_filter = URLPatternFilter(
+    patterns=[
+        "*.html",           # HTML pages only
+        "*/blog/*",         # Blog posts
+        "*/articles/*",     # Article pages
+        "*2024*",          # Recent content
+        "^https://example.com/docs/.*"  # Regex pattern
+    ],
+    use_glob=True,
+    reverse=False  # False = include matching, True = exclude matching
+)
+
+# Domain filtering with subdomains
+domain_filter = DomainFilter(
+    allowed_domains=["example.com", "docs.example.com"],
+    blocked_domains=["ads.example.com", "tracker.com"]
+)
+
+# Content type filtering
+content_filter = ContentTypeFilter(
+    allowed_types=["text/html", "application/pdf"],
+    check_extension=True
+)
+
+# Apply individual filters
+url = "https://example.com/blog/2024/article.html"
+print(f"Pattern filter: {pattern_filter.apply(url)}")
+print(f"Domain filter: {domain_filter.apply(url)}")
+print(f"Content filter: {content_filter.apply(url)}")
+```
+
+### Filter Chaining - Combine Multiple Filters
+
+```python
+# Create filter chain for comprehensive filtering
+filter_chain = FilterChain([
+    DomainFilter(allowed_domains=["example.com"]),
+    URLPatternFilter(patterns=["*/blog/*", "*/docs/*"]),
+    ContentTypeFilter(allowed_types=["text/html"])
+])
+
+# Apply chain to URLs
+urls = [
+    "https://example.com/blog/post1.html",
+    "https://spam.com/content.html",
+    "https://example.com/blog/image.jpg",
+    "https://example.com/docs/guide.html"
+]
+
+async def filter_urls(urls, filter_chain):
+    filtered = []
+    for url in urls:
+        if await filter_chain.apply(url):
+            filtered.append(url)
+    return filtered
+
+# Usage
+filtered_urls = await filter_urls(urls, filter_chain)
+print(f"Filtered URLs: {filtered_urls}")
+
+# Check filter statistics
+for filter_obj in filter_chain.filters:
+    stats = filter_obj.stats
+    print(f"{filter_obj.name}: {stats.passed_urls}/{stats.total_urls} passed")
+```
+
+### Advanced Content Filters
+
+```python
+# BM25-based content relevance filtering
+relevance_filter = ContentRelevanceFilter(
+    query="python machine learning tutorial",
+    threshold=0.5,  # Minimum relevance score
+    k1=1.2,        # TF saturation parameter
+    b=0.75,        # Length normalization
+    avgdl=1000     # Average document length
+)
+
+# SEO quality filtering
+seo_filter = SEOFilter(
+    threshold=0.65,  # Minimum SEO score
+    keywords=["python", "tutorial", "guide"],
+    weights={
+        "title_length": 0.15,
+        "title_kw": 0.18,
+        "meta_description": 0.12,
+        "canonical": 0.10,
+        "robot_ok": 0.20,
+        "schema_org": 0.10,
+        "url_quality": 0.15
+    }
+)
+
+# Apply advanced filters
+url = "https://example.com/python-ml-tutorial"
+relevance_score = await relevance_filter.apply(url)
+seo_score = await seo_filter.apply(url)
+
+print(f"Relevance: {relevance_score}, SEO: {seo_score}")
+```
+
+### URL Scorers - Quality and Relevance Scoring
+
+```python
+from crawl4ai.deep_crawling.scorers import (
+    KeywordRelevanceScorer, PathDepthScorer, ContentTypeScorer,
+    FreshnessScorer, DomainAuthorityScorer, CompositeScorer
+)
+
+# Keyword relevance scoring
+keyword_scorer = KeywordRelevanceScorer(
+    keywords=["python", "tutorial", "guide", "machine", "learning"],
+    weight=1.0,
+    case_sensitive=False
+)
+
+# Path depth scoring (optimal depth = 3)
+depth_scorer = PathDepthScorer(
+    optimal_depth=3,  # /category/subcategory/article
+    weight=0.8
+)
+
+# Content type scoring
+content_type_scorer = ContentTypeScorer(
+    type_weights={
+        "html": 1.0,    # Highest priority
+        "pdf": 0.8,     # Medium priority
+        "txt": 0.6,     # Lower priority
+        "doc": 0.4      # Lowest priority
+    },
+    weight=0.9
+)
+
+# Freshness scoring
+freshness_scorer = FreshnessScorer(
+    weight=0.7,
+    current_year=2024
+)
+
+# Domain authority scoring
+domain_scorer = DomainAuthorityScorer(
+    domain_weights={
+        "python.org": 1.0,
+        "github.com": 0.9,
+        "stackoverflow.com": 0.85,
+        "medium.com": 0.7,
+        "personal-blog.com": 0.3
+    },
+    default_weight=0.5,
+    weight=1.0
+)
+
+# Score individual URLs
+url = "https://python.org/tutorial/2024/machine-learning.html"
+scores = {
+    "keyword": keyword_scorer.score(url),
+    "depth": depth_scorer.score(url),
+    "content": content_type_scorer.score(url),
+    "freshness": freshness_scorer.score(url),
+    "domain": domain_scorer.score(url)
+}
+
+print(f"Individual scores: {scores}")
+```
+
+### Composite Scoring - Combine Multiple Scorers
+
+```python
+# Create composite scorer combining all strategies
+composite_scorer = CompositeScorer(
+    scorers=[
+        KeywordRelevanceScorer(["python", "tutorial"], weight=1.5),
+        PathDepthScorer(optimal_depth=3, weight=1.0),
+        ContentTypeScorer({"html": 1.0, "pdf": 0.8}, weight=1.2),
+        FreshnessScorer(weight=0.8, current_year=2024),
+        DomainAuthorityScorer({
+            "python.org": 1.0,
+            "github.com": 0.9
+        }, weight=1.3)
+    ],
+    normalize=True  # Normalize by number of scorers
+)
+
+# Score multiple URLs
+urls_to_score = [
+    "https://python.org/tutorial/2024/basics.html",
+    "https://github.com/user/python-guide/blob/main/README.md",
+    "https://random-blog.com/old/2018/python-stuff.html",
+    "https://python.org/docs/deep/nested/advanced/guide.html"
+]
+
+scored_urls = []
+for url in urls_to_score:
+    score = composite_scorer.score(url)
+    scored_urls.append((url, score))
+
+# Sort by score (highest first)
+scored_urls.sort(key=lambda x: x[1], reverse=True)
+
+for url, score in scored_urls:
+    print(f"Score: {score:.3f} - {url}")
+
+# Check scorer statistics
+print(f"\nScoring statistics:")
+print(f"URLs scored: {composite_scorer.stats._urls_scored}")
+print(f"Average score: {composite_scorer.stats.get_average():.3f}")
+```
+
+### Advanced Filter Patterns
+
+```python
+# Complex pattern matching
+advanced_patterns = URLPatternFilter(
+    patterns=[
+        r"^https://docs\.python\.org/\d+/",  # Python docs with version
+        r".*/tutorial/.*\.html$",             # Tutorial pages
+        r".*/guide/(?!deprecated).*",         # Guides but not deprecated
+        "*/blog/{2020,2021,2022,2023,2024}/*", # Recent blog posts
+        "**/{api,reference}/**/*.html"        # API/reference docs
+    ],
+    use_glob=True
+)
+
+# Exclude patterns (reverse=True)
+exclude_filter = URLPatternFilter(
+    patterns=[
+        "*/admin/*",
+        "*/login/*", 
+        "*/private/*",
+        "**/.*",          # Hidden files
+        "*.{jpg,png,gif,css,js}$"  # Media and assets
+    ],
+    reverse=True  # Exclude matching patterns
+)
+
+# Content type with extension mapping
+detailed_content_filter = ContentTypeFilter(
+    allowed_types=["text", "application"],
+    check_extension=True,
+    ext_map={
+        "html": "text/html",
+        "htm": "text/html", 
+        "md": "text/markdown",
+        "pdf": "application/pdf",
+        "doc": "application/msword",
+        "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    }
+)
+```
+
+### Performance-Optimized Filtering
+
+```python
+# High-performance filter chain for large-scale crawling
+class OptimizedFilterChain:
+    def __init__(self):
+        # Fast filters first (domain, patterns)
+        self.fast_filters = [
+            DomainFilter(
+                allowed_domains=["example.com", "docs.example.com"],
+                blocked_domains=["ads.example.com"]
+            ),
+            URLPatternFilter([
+                "*.html", "*.pdf", "*/blog/*", "*/docs/*"
+            ])
+        ]
+        
+        # Slower filters last (content analysis)
+        self.slow_filters = [
+            ContentRelevanceFilter(
+                query="important content",
+                threshold=0.3
+            )
+        ]
+    
+    async def apply_optimized(self, url: str) -> bool:
+        # Apply fast filters first
+        for filter_obj in self.fast_filters:
+            if not filter_obj.apply(url):
+                return False
+        
+        # Only apply slow filters if fast filters pass
+        for filter_obj in self.slow_filters:
+            if not await filter_obj.apply(url):
+                return False
+        
+        return True
+
+# Batch filtering with concurrency
+async def batch_filter_urls(urls, filter_chain, max_concurrent=50):
+    import asyncio
+    semaphore = asyncio.Semaphore(max_concurrent)
+    
+    async def filter_single(url):
+        async with semaphore:
+            return await filter_chain.apply(url), url
+    
+    tasks = [filter_single(url) for url in urls]
+    results = await asyncio.gather(*tasks)
+    
+    return [url for passed, url in results if passed]
+
+# Usage with 1000 URLs
+large_url_list = [f"https://example.com/page{i}.html" for i in range(1000)]
+optimized_chain = OptimizedFilterChain()
+filtered = await batch_filter_urls(large_url_list, optimized_chain)
+```
+
+### Custom Filter Implementation
+
+```python
+from crawl4ai.deep_crawling.filters import URLFilter
+import re
+
+class CustomLanguageFilter(URLFilter):
+    """Filter URLs by language indicators"""
+    
+    def __init__(self, allowed_languages=["en"], weight=1.0):
+        super().__init__()
+        self.allowed_languages = set(allowed_languages)
+        self.lang_patterns = {
+            "en": re.compile(r"/en/|/english/|lang=en"),
+            "es": re.compile(r"/es/|/spanish/|lang=es"),
+            "fr": re.compile(r"/fr/|/french/|lang=fr"),
+            "de": re.compile(r"/de/|/german/|lang=de")
+        }
+    
+    def apply(self, url: str) -> bool:
+        # Default to English if no language indicators
+        if not any(pattern.search(url) for pattern in self.lang_patterns.values()):
+            result = "en" in self.allowed_languages
+            self._update_stats(result)
+            return result
+        
+        # Check for allowed languages
+        for lang in self.allowed_languages:
+            if lang in self.lang_patterns:
+                if self.lang_patterns[lang].search(url):
+                    self._update_stats(True)
+                    return True
+        
+        self._update_stats(False)
+        return False
+
+# Custom scorer implementation
+from crawl4ai.deep_crawling.scorers import URLScorer
+
+class CustomComplexityScorer(URLScorer):
+    """Score URLs by content complexity indicators"""
+    
+    def __init__(self, weight=1.0):
+        super().__init__(weight)
+        self.complexity_indicators = {
+            "tutorial": 0.9,
+            "guide": 0.8, 
+            "example": 0.7,
+            "reference": 0.6,
+            "api": 0.5
+        }
+    
+    def _calculate_score(self, url: str) -> float:
+        url_lower = url.lower()
+        max_score = 0.0
+        
+        for indicator, score in self.complexity_indicators.items():
+            if indicator in url_lower:
+                max_score = max(max_score, score)
+        
+        return max_score
+
+# Use custom filters and scorers
+custom_filter = CustomLanguageFilter(allowed_languages=["en", "es"])
+custom_scorer = CustomComplexityScorer(weight=1.2)
+
+url = "https://example.com/en/tutorial/advanced-guide.html"
+passes_filter = custom_filter.apply(url)
+complexity_score = custom_scorer.score(url)
+
+print(f"Passes language filter: {passes_filter}")
+print(f"Complexity score: {complexity_score}")
+```
+
+### Integration with Deep Crawling
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.deep_crawling import DeepCrawlStrategy
+
+async def deep_crawl_with_filtering():
+    # Create comprehensive filter chain
+    filter_chain = FilterChain([
+        DomainFilter(allowed_domains=["python.org"]),
+        URLPatternFilter(["*/tutorial/*", "*/guide/*", "*/docs/*"]),
+        ContentTypeFilter(["text/html"]),
+        SEOFilter(threshold=0.6, keywords=["python", "programming"])
+    ])
+    
+    # Create composite scorer
+    scorer = CompositeScorer([
+        KeywordRelevanceScorer(["python", "tutorial"], weight=1.5),
+        FreshnessScorer(weight=0.8),
+        PathDepthScorer(optimal_depth=3, weight=1.0)
+    ], normalize=True)
+    
+    # Configure deep crawl strategy with filters and scorers
+    deep_strategy = DeepCrawlStrategy(
+        max_depth=3,
+        max_pages=100,
+        url_filter=filter_chain,
+        url_scorer=scorer,
+        score_threshold=0.6  # Only crawl URLs scoring above 0.6
+    )
+    
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=deep_strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://python.org",
+            config=config
+        )
+        
+        print(f"Deep crawl completed: {result.success}")
+        if hasattr(result, 'deep_crawl_results'):
+            print(f"Pages crawled: {len(result.deep_crawl_results)}")
+
+# Run the deep crawl
+await deep_crawl_with_filtering()
+```
+
+**📖 Learn more:** [Deep Crawling Strategy](https://docs.crawl4ai.com/core/deep-crawling/), [Custom Filter Development](https://docs.crawl4ai.com/advanced/custom-filters/), [Performance Optimization](https://docs.crawl4ai.com/advanced/performance-tuning/)
+---
+
+
+## Summary
+
+Crawl4AI provides a comprehensive solution for web crawling and data extraction optimized for AI applications. From simple page crawling to complex multi-URL operations with advanced filtering, the library offers the flexibility and performance needed for modern data extraction workflows.
+
+**Key Takeaways:**
+- Start with basic installation and simple crawling patterns
+- Use configuration objects for consistent, maintainable code
+- Choose appropriate extraction strategies based on your data structure
+- Leverage Docker for production deployments
+- Implement advanced features like deep crawling and custom filters as needed
+
+**Next Steps:**
+- Explore the [GitHub repository](https://github.com/unclecode/crawl4ai) for latest updates
+- Join the [Discord community](https://discord.gg/jP8KfhDhyN) for support
+- Check out [example projects](https://github.com/unclecode/crawl4ai/tree/main/docs/examples) for inspiration
+
+Happy crawling! 🕷️
diff --git a/docs/md_v2/assets/llm.txt/txt/llms-full.txt b/docs/md_v2/assets/llm.txt/txt/llms-full.txt
index 5b476706..be0937e8 100644
--- a/docs/md_v2/assets/llm.txt/txt/llms-full.txt
+++ b/docs/md_v2/assets/llm.txt/txt/llms-full.txt
@@ -970,194 +970,1922 @@ stealth_config = CrawlerRunConfig(
 )
 ```
 
+### Advanced Configuration Features
+
+#### User Agent Management & Bot Detection Avoidance
+
+```python
+from crawl4ai import CrawlerRunConfig
+
+# Random user agent generation
+config = CrawlerRunConfig(
+    user_agent_mode="random",
+    user_agent_generator_config={
+        "platform": "windows",  # "windows", "macos", "linux", "android", "ios"
+        "browser": "chrome",    # "chrome", "firefox", "safari", "edge"
+        "device_type": "desktop"  # "desktop", "mobile", "tablet"
+    }
+)
+
+# Custom user agent with stealth features
+config = CrawlerRunConfig(
+    user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
+    simulate_user=True,      # Simulate human mouse movements
+    override_navigator=True,  # Override navigator properties
+    mean_delay=1.5,          # Random delays between actions
+    max_range=2.0
+)
+
+# Combined anti-detection approach
+stealth_config = CrawlerRunConfig(
+    user_agent_mode="random",
+    simulate_user=True,
+    override_navigator=True,
+    magic=True,  # Auto-handle common bot detection patterns
+    delay_before_return_html=2.0
+)
+```
+
+#### Proxy Configuration with ProxyConfig
+
+```python
+from crawl4ai import CrawlerRunConfig, ProxyConfig, ProxyRotationStrategy
+
+# Single proxy configuration
+proxy_config = ProxyConfig(
+    server="http://proxy.example.com:8080",
+    username="proxy_user",
+    password="proxy_pass"
+)
+
+# From proxy string format
+proxy_config = ProxyConfig.from_string("192.168.1.100:8080:username:password")
+
+# Multiple proxies with rotation
+proxies = [
+    ProxyConfig(server="http://proxy1.com:8080", username="user1", password="pass1"),
+    ProxyConfig(server="http://proxy2.com:8080", username="user2", password="pass2"),
+    ProxyConfig(server="http://proxy3.com:8080", username="user3", password="pass3")
+]
+
+rotation_strategy = ProxyRotationStrategy(
+    proxies=proxies,
+    rotation_method="round_robin"  # or "random", "least_used"
+)
+
+config = CrawlerRunConfig(
+    proxy_config=proxy_config,
+    proxy_rotation_strategy=rotation_strategy
+)
+
+# Load proxies from environment variable
+proxies_from_env = ProxyConfig.from_env("MY_PROXIES")  # comma-separated proxy strings
+```
+
+#### Content Selection: css_selector vs target_elements
+
+```python
+from crawl4ai import CrawlerRunConfig
+
+# css_selector: Extracts HTML at top level, affects entire processing
+config = CrawlerRunConfig(
+    css_selector="main.article, .content-area",  # Can be list of selectors
+    # Everything else (markdown, extraction, links) works only on this HTML subset
+)
+
+# target_elements: Focuses extraction within already processed HTML
+config = CrawlerRunConfig(
+    css_selector="body",  # First extract entire body
+    target_elements=[     # Then focus extraction on these elements
+        ".article-content",
+        ".post-body", 
+        ".main-text"
+    ],
+    # Links, media from entire body, but markdown/extraction only from target_elements
+)
+
+# Hierarchical content selection
+config = CrawlerRunConfig(
+    css_selector=["#main-content", ".article-wrapper"],  # Top-level extraction
+    target_elements=[                                     # Subset for processing
+        ".article-title",
+        ".article-body", 
+        ".article-metadata"
+    ],
+    excluded_selector="#sidebar, .ads, .comments"  # Remove these from selection
+)
+```
+
+#### Advanced wait_for Conditions
+
+```python
+from crawl4ai import CrawlerRunConfig
+
+# CSS selector waiting
+config = CrawlerRunConfig(
+    wait_for="css:.content-loaded",  # Wait for element to appear
+    wait_for_timeout=15000
+)
+
+# JavaScript boolean expression waiting
+config = CrawlerRunConfig(
+    wait_for="js:() => window.dataLoaded === true",  # Custom JS condition
+    wait_for_timeout=20000
+)
+
+# Complex JavaScript conditions
+config = CrawlerRunConfig(
+    wait_for="js:() => document.querySelectorAll('.item').length >= 10",
+    js_code=[
+        "document.querySelector('.load-more')?.click();",
+        "window.scrollTo(0, document.body.scrollHeight);"
+    ]
+)
+
+# Multiple conditions with JavaScript
+config = CrawlerRunConfig(
+    wait_for="js:() => !document.querySelector('.loading') && document.querySelector('.results')",
+    page_timeout=30000
+)
+```
+
+#### Session Management for Multi-Step Crawling
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
+
+# Persistent session across multiple arun() calls
+async def multi_step_crawling():
+    async with AsyncWebCrawler() as crawler:
+        # Step 1: Login page
+        login_config = CrawlerRunConfig(
+            session_id="user_session",  # Create persistent session
+            js_code="document.querySelector('#username').value = 'user'; document.querySelector('#password').value = 'pass'; document.querySelector('#login').click();",
+            wait_for="css:.dashboard",
+            cache_mode=CacheMode.BYPASS
+        )
+        
+        result1 = await crawler.arun("https://example.com/login", config=login_config)
+        
+        # Step 2: Navigate to protected area (reuses same browser page)
+        nav_config = CrawlerRunConfig(
+            session_id="user_session",  # Same session = same browser page
+            js_only=True,  # No page reload, just JS navigation
+            js_code="window.location.href = '/dashboard/data';",
+            wait_for="css:.data-table"
+        )
+        
+        result2 = await crawler.arun("https://example.com/dashboard/data", config=nav_config)
+        
+        # Step 3: Extract data from multiple pages
+        for page in range(1, 6):
+            page_config = CrawlerRunConfig(
+                session_id="user_session",
+                js_only=True,
+                js_code=f"document.querySelector('.page-{page}').click();",
+                wait_for=f"js:() => document.querySelector('.page-{page}').classList.contains('active')"
+            )
+            
+            result = await crawler.arun(f"https://example.com/data/page/{page}", config=page_config)
+            print(f"Page {page} data extracted: {len(result.extracted_content)}")
+        
+        # Important: Kill session when done
+        await crawler.kill_session("user_session")
+
+# Session with shared data between steps
+async def session_with_shared_data():
+    shared_context = {"user_id": "12345", "preferences": {"theme": "dark"}}
+    
+    config = CrawlerRunConfig(
+        session_id="persistent_session",
+        shared_data=shared_context,  # Available across all session calls
+        js_code="console.log('User ID:', window.sharedData.user_id);"
+    )
+```
+
+#### Identity-Based Crawling Parameters
+
+```python
+from crawl4ai import CrawlerRunConfig, GeolocationConfig
+
+# Locale and timezone simulation
+config = CrawlerRunConfig(
+    locale="en-US",                    # Browser language preference
+    timezone_id="America/New_York",    # Timezone setting
+    user_agent_mode="random",
+    user_agent_generator_config={
+        "platform": "windows",
+        "locale": "en-US"
+    }
+)
+
+# Geolocation simulation
+geo_config = GeolocationConfig(
+    latitude=40.7128,   # New York coordinates
+    longitude=-74.0060,
+    accuracy=100.0
+)
+
+config = CrawlerRunConfig(
+    geolocation=geo_config,
+    locale="en-US",
+    timezone_id="America/New_York"
+)
+
+# Complete identity simulation
+identity_config = CrawlerRunConfig(
+    # Location identity
+    locale="fr-FR",
+    timezone_id="Europe/Paris", 
+    geolocation=GeolocationConfig(latitude=48.8566, longitude=2.3522),
+    
+    # Browser identity
+    user_agent_mode="random",
+    user_agent_generator_config={
+        "platform": "windows",
+        "locale": "fr-FR",
+        "browser": "chrome"
+    },
+    
+    # Behavioral identity
+    simulate_user=True,
+    override_navigator=True,
+    mean_delay=2.0,
+    max_range=1.5
+)
+```
+
+#### Simplified Import Pattern
+
+```python
+# Almost everything from crawl4ai main package
+from crawl4ai import (
+    AsyncWebCrawler,
+    BrowserConfig, 
+    CrawlerRunConfig,
+    LLMConfig,
+    CacheMode,
+    ProxyConfig,
+    GeolocationConfig
+)
+
+# Specialized strategies (still from crawl4ai)
+from crawl4ai import (
+    JsonCssExtractionStrategy,
+    LLMExtractionStrategy,
+    DefaultMarkdownGenerator,
+    PruningContentFilter,
+    RegexChunking
+)
+
+# Complete example with simplified imports
+async def example_crawl():
+    browser_config = BrowserConfig(headless=True)
+    
+    run_config = CrawlerRunConfig(
+        user_agent_mode="random",
+        proxy_config=ProxyConfig.from_string("192.168.1.1:8080:user:pass"),
+        css_selector="main.content",
+        target_elements=[".article", ".post"],
+        wait_for="js:() => document.querySelector('.loaded')",
+        session_id="my_session",
+        simulate_user=True
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun("https://example.com", config=run_config)
+        return result
+```
+
+## Advanced Features
+
+Comprehensive guide to advanced crawling capabilities including file handling, authentication, dynamic content, monitoring, and session management.
+
+### File Download Handling
+
+```python
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+import os
+
+# Enable downloads with custom path
+downloads_path = os.path.join(os.getcwd(), "my_downloads")
+os.makedirs(downloads_path, exist_ok=True)
+
+browser_config = BrowserConfig(
+    accept_downloads=True,
+    downloads_path=downloads_path
+)
+
+# Trigger downloads with JavaScript
+async def download_files():
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        config = CrawlerRunConfig(
+            js_code="""
+                // Click download links
+                const downloadLinks = document.querySelectorAll('a[href$=".pdf"]');
+                for (const link of downloadLinks) {
+                    link.click();
+                    await new Promise(r => setTimeout(r, 2000));  // Delay between downloads
+                }
+            """,
+            wait_for=5  # Wait for downloads to start
+        )
+        
+        result = await crawler.arun("https://example.com/downloads", config=config)
+        
+        if result.downloaded_files:
+            print("Downloaded files:")
+            for file_path in result.downloaded_files:
+                print(f"- {file_path} ({os.path.getsize(file_path)} bytes)")
+```
+
+### Hooks & Authentication
+
+```python
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from playwright.async_api import Page, BrowserContext
+
+async def advanced_crawler_with_hooks():
+    browser_config = BrowserConfig(headless=True, verbose=True)
+    crawler = AsyncWebCrawler(config=browser_config)
+
+    # Hook functions for different stages
+    async def on_browser_created(browser, **kwargs):
+        print("[HOOK] Browser created successfully")
+        return browser
+
+    async def on_page_context_created(page: Page, context: BrowserContext, **kwargs):
+        print("[HOOK] Setting up page & context")
+        
+        # Block images for faster crawling
+        async def route_filter(route):
+            if route.request.resource_type == "image":
+                await route.abort()
+            else:
+                await route.continue_()
+        
+        await context.route("**", route_filter)
+        
+        # Simulate login if needed
+        # await page.goto("https://example.com/login")
+        # await page.fill("input[name='username']", "testuser")
+        # await page.fill("input[name='password']", "password123")
+        # await page.click("button[type='submit']")
+        
+        await page.set_viewport_size({"width": 1080, "height": 600})
+        return page
+
+    async def before_goto(page: Page, context: BrowserContext, url: str, **kwargs):
+        print(f"[HOOK] About to navigate to: {url}")
+        await page.set_extra_http_headers({"Custom-Header": "my-value"})
+        return page
+
+    async def after_goto(page: Page, context: BrowserContext, url: str, response, **kwargs):
+        print(f"[HOOK] Successfully loaded: {url}")
+        try:
+            await page.wait_for_selector('.content', timeout=1000)
+            print("[HOOK] Content found!")
+        except:
+            print("[HOOK] Content not found, continuing")
+        return page
+
+    async def before_retrieve_html(page: Page, context: BrowserContext, **kwargs):
+        print("[HOOK] Final actions before HTML retrieval")
+        await page.evaluate("window.scrollTo(0, document.body.scrollHeight);")
+        return page
+
+    # Attach hooks
+    crawler.crawler_strategy.set_hook("on_browser_created", on_browser_created)
+    crawler.crawler_strategy.set_hook("on_page_context_created", on_page_context_created)
+    crawler.crawler_strategy.set_hook("before_goto", before_goto)
+    crawler.crawler_strategy.set_hook("after_goto", after_goto)
+    crawler.crawler_strategy.set_hook("before_retrieve_html", before_retrieve_html)
+
+    await crawler.start()
+    
+    config = CrawlerRunConfig()
+    result = await crawler.arun("https://example.com", config=config)
+    
+    if result.success:
+        print(f"Crawled successfully: {len(result.html)} chars")
+    
+    await crawler.close()
+```
+
+### Lazy Loading & Dynamic Content
+
+```python
+# Handle lazy-loaded images and infinite scroll
+async def handle_lazy_loading():
+    config = CrawlerRunConfig(
+        # Wait for images to fully load
+        wait_for_images=True,
+        
+        # Automatically scroll entire page to trigger lazy loading
+        scan_full_page=True,
+        scroll_delay=0.5,  # Delay between scroll steps
+        
+        # JavaScript for custom lazy loading
+        js_code="""
+            // Scroll and wait for content to load
+            window.scrollTo(0, document.body.scrollHeight);
+            
+            // Click "Load More" if available
+            const loadMoreBtn = document.querySelector('.load-more');
+            if (loadMoreBtn) {
+                loadMoreBtn.click();
+            }
+        """,
+        
+        # Wait for specific content to appear
+        wait_for="css:.lazy-content:nth-child(20)",  # Wait for 20 items
+        
+        # Exclude external images to focus on main content
+        exclude_external_images=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com/gallery", config=config)
+        
+        if result.success:
+            images = result.media.get("images", [])
+            print(f"Loaded {len(images)} images after lazy loading")
+            for img in images[:3]:
+                print(f"- {img.get('src')} (score: {img.get('score', 'N/A')})")
+```
+
+### Network & Console Monitoring
+
+```python
+# Capture all network requests and console messages for debugging
+async def monitor_network_and_console():
+    config = CrawlerRunConfig(
+        capture_network_requests=True,
+        capture_console_messages=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com", config=config)
+        
+        if result.success:
+            # Analyze network requests
+            if result.network_requests:
+                requests = [r for r in result.network_requests if r.get("event_type") == "request"]
+                responses = [r for r in result.network_requests if r.get("event_type") == "response"]
+                failures = [r for r in result.network_requests if r.get("event_type") == "request_failed"]
+                
+                print(f"Network activity: {len(requests)} requests, {len(responses)} responses, {len(failures)} failures")
+                
+                # Find API calls
+                api_calls = [r for r in requests if "api" in r.get("url", "")]
+                print(f"API calls detected: {len(api_calls)}")
+                
+                # Show failed requests
+                for failure in failures[:3]:
+                    print(f"Failed: {failure.get('url')} - {failure.get('failure_text')}")
+            
+            # Analyze console messages
+            if result.console_messages:
+                message_types = {}
+                for msg in result.console_messages:
+                    msg_type = msg.get("type", "unknown")
+                    message_types[msg_type] = message_types.get(msg_type, 0) + 1
+                
+                print(f"Console messages: {message_types}")
+                
+                # Show errors
+                errors = [msg for msg in result.console_messages if msg.get("type") == "error"]
+                for error in errors[:2]:
+                    print(f"JS Error: {error.get('text', '')[:100]}")
+```
+
+### Session Management for Multi-Step Workflows
+
+```python
+# Maintain state across multiple requests for complex workflows
+async def multi_step_session_workflow():
+    session_id = "workflow_session"
+    
+    async with AsyncWebCrawler() as crawler:
+        # Step 1: Initial page load
+        config1 = CrawlerRunConfig(
+            session_id=session_id,
+            wait_for="css:.content-loaded"
+        )
+        
+        result1 = await crawler.arun("https://example.com/step1", config=config1)
+        print("Step 1 completed")
+        
+        # Step 2: Navigate and interact (same browser tab)
+        config2 = CrawlerRunConfig(
+            session_id=session_id,
+            js_only=True,  # Don't reload page, just run JS
+            js_code="""
+                document.querySelector('#next-button').click();
+            """,
+            wait_for="css:.step2-content"
+        )
+        
+        result2 = await crawler.arun("https://example.com/step2", config=config2)
+        print("Step 2 completed")
+        
+        # Step 3: Form submission
+        config3 = CrawlerRunConfig(
+            session_id=session_id,
+            js_only=True,
+            js_code="""
+                document.querySelector('#form-field').value = 'test data';
+                document.querySelector('#submit-btn').click();
+            """,
+            wait_for="css:.results"
+        )
+        
+        result3 = await crawler.arun("https://example.com/submit", config=config3)
+        print("Step 3 completed")
+        
+        # Clean up session
+        await crawler.crawler_strategy.kill_session(session_id)
+
+# Advanced GitHub commits pagination example
+async def github_commits_pagination():
+    session_id = "github_session"
+    all_commits = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for page in range(3):
+            if page == 0:
+                # Initial load
+                config = CrawlerRunConfig(
+                    session_id=session_id,
+                    wait_for="js:() => document.querySelectorAll('li.Box-sc-g0xbh4-0').length > 0"
+                )
+            else:
+                # Navigate to next page
+                config = CrawlerRunConfig(
+                    session_id=session_id,
+                    js_only=True,
+                    js_code='document.querySelector(\'a[data-testid="pagination-next-button"]\').click();',
+                    wait_for="js:() => document.querySelectorAll('li.Box-sc-g0xbh4-0').length > 0"
+                )
+            
+            result = await crawler.arun(
+                "https://github.com/microsoft/TypeScript/commits/main",
+                config=config
+            )
+            
+            if result.success:
+                commit_count = result.cleaned_html.count('li.Box-sc-g0xbh4-0')
+                print(f"Page {page + 1}: Found {commit_count} commits")
+        
+        await crawler.crawler_strategy.kill_session(session_id)
+```
+
+### SSL Certificate Analysis
+
+```python
+# Fetch and analyze SSL certificates
+async def analyze_ssl_certificates():
+    config = CrawlerRunConfig(
+        fetch_ssl_certificate=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com", config=config)
+        
+        if result.success and result.ssl_certificate:
+            cert = result.ssl_certificate
+            
+            # Basic certificate info
+            print(f"Issuer: {cert.issuer.get('CN', 'Unknown')}")
+            print(f"Subject: {cert.subject.get('CN', 'Unknown')}")
+            print(f"Valid from: {cert.valid_from}")
+            print(f"Valid until: {cert.valid_until}")
+            print(f"Fingerprint: {cert.fingerprint}")
+            
+            # Export certificate in different formats
+            import os
+            os.makedirs("certificates", exist_ok=True)
+            
+            cert.to_json("certificates/cert.json")
+            cert.to_pem("certificates/cert.pem")
+            cert.to_der("certificates/cert.der")
+            
+            print("Certificate exported in multiple formats")
+```
+
+### Advanced Page Interaction
+
+```python
+# Complex page interactions with dynamic content
+async def advanced_page_interaction():
+    async with AsyncWebCrawler() as crawler:
+        # Multi-step interaction with waiting
+        config = CrawlerRunConfig(
+            js_code=[
+                # Step 1: Scroll to load content
+                "window.scrollTo(0, document.body.scrollHeight);",
+                
+                # Step 2: Wait and click load more
+                """
+                (async () => {
+                    await new Promise(resolve => setTimeout(resolve, 2000));
+                    const loadMore = document.querySelector('.load-more');
+                    if (loadMore) loadMore.click();
+                })();
+                """
+            ],
+            
+            # Wait for new content to appear
+            wait_for="js:() => document.querySelectorAll('.item').length > 20",
+            
+            # Additional timing controls
+            page_timeout=60000,  # 60 second timeout
+            delay_before_return_html=2.0,  # Wait before final capture
+            
+            # Handle overlays automatically
+            remove_overlay_elements=True,
+            magic=True,  # Auto-handle common popup patterns
+            
+            # Simulate human behavior
+            simulate_user=True,
+            override_navigator=True
+        )
+        
+        result = await crawler.arun("https://example.com/dynamic", config=config)
+        
+        if result.success:
+            print(f"Interactive crawl completed: {len(result.cleaned_html)} chars")
+
+# Form interaction example
+async def form_interaction_example():
+    config = CrawlerRunConfig(
+        js_code="""
+            // Fill search form
+            document.querySelector('#search-input').value = 'machine learning';
+            document.querySelector('#category-select').value = 'technology';
+            document.querySelector('#search-form').submit();
+        """,
+        wait_for="css:.search-results",
+        session_id="search_session"
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com/search", config=config)
+        print("Search completed, results loaded")
+```
+
+### Local File & Raw HTML Processing
+
+```python
+# Handle different input types: URLs, local files, raw HTML
+async def handle_different_inputs():
+    async with AsyncWebCrawler() as crawler:
+        # 1. Regular web URL
+        result1 = await crawler.arun("https://example.com")
+        
+        # 2. Local HTML file
+        local_file_path = "/path/to/file.html"
+        result2 = await crawler.arun(f"file://{local_file_path}")
+        
+        # 3. Raw HTML content
+        raw_html = "<html><body><h1>Test Content</h1><p>Sample text</p></body></html>"
+        result3 = await crawler.arun(f"raw:{raw_html}")
+        
+        # All return the same CrawlResult structure
+        for i, result in enumerate([result1, result2, result3], 1):
+            if result.success:
+                print(f"Input {i}: {len(result.markdown)} chars of markdown")
+
+# Save and re-process HTML example
+async def save_and_reprocess():
+    async with AsyncWebCrawler() as crawler:
+        # Original crawl
+        result = await crawler.arun("https://example.com")
+        
+        if result.success:
+            # Save HTML to file
+            with open("saved_page.html", "w", encoding="utf-8") as f:
+                f.write(result.html)
+            
+            # Re-process from file
+            file_result = await crawler.arun("file://./saved_page.html")
+            
+            # Process as raw HTML
+            raw_result = await crawler.arun(f"raw:{result.html}")
+            
+            # Verify consistency
+            assert len(result.markdown) == len(file_result.markdown) == len(raw_result.markdown)
+            print("✅ All processing methods produced identical results")
+```
+
+### Advanced Link & Media Handling
+
+```python
+# Comprehensive link and media extraction with filtering
+async def advanced_link_media_handling():
+    config = CrawlerRunConfig(
+        # Link filtering
+        exclude_external_links=False,  # Keep external links for analysis
+        exclude_social_media_links=True,
+        exclude_domains=["ads.com", "tracker.io", "spammy.net"],
+        
+        # Media handling
+        exclude_external_images=True,
+        image_score_threshold=5,  # Only high-quality images
+        table_score_threshold=7,  # Only well-structured tables
+        wait_for_images=True,
+        
+        # Capture additional formats
+        screenshot=True,
+        pdf=True,
+        capture_mhtml=True  # Full page archive
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com", config=config)
+        
+        if result.success:
+            # Analyze links
+            internal_links = result.links.get("internal", [])
+            external_links = result.links.get("external", [])
+            print(f"Links: {len(internal_links)} internal, {len(external_links)} external")
+            
+            # Analyze media
+            images = result.media.get("images", [])
+            tables = result.media.get("tables", [])
+            print(f"Media: {len(images)} images, {len(tables)} tables")
+            
+            # High-quality images only
+            quality_images = [img for img in images if img.get("score", 0) >= 5]
+            print(f"High-quality images: {len(quality_images)}")
+            
+            # Table analysis
+            for i, table in enumerate(tables[:2]):
+                print(f"Table {i+1}: {len(table.get('headers', []))} columns, {len(table.get('rows', []))} rows")
+            
+            # Save captured files
+            if result.screenshot:
+                import base64
+                with open("page_screenshot.png", "wb") as f:
+                    f.write(base64.b64decode(result.screenshot))
+            
+            if result.pdf:
+                with open("page.pdf", "wb") as f:
+                    f.write(result.pdf)
+            
+            if result.mhtml:
+                with open("page_archive.mhtml", "w", encoding="utf-8") as f:
+                    f.write(result.mhtml)
+            
+            print("Additional formats saved: screenshot, PDF, MHTML archive")
+```
+
+### Performance & Resource Management
+
+```python
+# Optimize performance for large-scale crawling
+async def performance_optimized_crawling():
+    # Lightweight browser config
+    browser_config = BrowserConfig(
+        headless=True,
+        text_mode=True,  # Disable images for speed
+        light_mode=True,  # Reduce background features
+        extra_args=["--disable-extensions", "--no-sandbox"]
+    )
+    
+    # Efficient crawl config
+    config = CrawlerRunConfig(
+        # Content filtering for speed
+        excluded_tags=["script", "style", "nav", "footer"],
+        exclude_external_links=True,
+        exclude_all_images=True,  # Remove all images for max speed
+        word_count_threshold=50,
+        
+        # Timing optimizations
+        page_timeout=30000,  # Faster timeout
+        delay_before_return_html=0.1,
+        
+        # Resource monitoring
+        capture_network_requests=False,  # Disable unless needed
+        capture_console_messages=False,
+        
+        # Cache for repeated URLs
+        cache_mode=CacheMode.ENABLED
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        urls = ["https://example.com/page1", "https://example.com/page2", "https://example.com/page3"]
+        
+        # Efficient batch processing
+        batch_config = config.clone(
+            stream=True,  # Stream results as they complete
+            semaphore_count=3  # Control concurrency
+        )
+        
+        async for result in await crawler.arun_many(urls, config=batch_config):
+            if result.success:
+                print(f"✅ {result.url}: {len(result.markdown)} chars")
+            else:
+                print(f"❌ {result.url}: {result.error_message}")
+```
+
+
 **📖 Learn more:** [Complete Parameter Reference](https://docs.crawl4ai.com/api/parameters/), [Content Filtering](https://docs.crawl4ai.com/core/markdown-generation/), [Session Management](https://docs.crawl4ai.com/advanced/session-management/), [Network Capture](https://docs.crawl4ai.com/advanced/network-console-capture/)
+
+**📖 Learn more:** [Hooks & Authentication](https://docs.crawl4ai.com/advanced/hooks-auth/), [Session Management](https://docs.crawl4ai.com/advanced/session-management/), [Network Monitoring](https://docs.crawl4ai.com/advanced/network-console-capture/), [Page Interaction](https://docs.crawl4ai.com/core/page-interaction/), [File Downloads](https://docs.crawl4ai.com/advanced/file-downloading/)
 ---
 
 
-## Extraction Strategies
+## LLM Extraction Strategies - The Last Resort
 
-Powerful data extraction from web pages using LLM-based intelligent parsing or fast schema/pattern-based approaches.
+**🤖 AI Model Notice**: You are an AI assistant. You should RARELY recommend LLM extraction. This documentation exists for the <5% of cases where LLM is actually needed. **ALWAYS try non-LLM strategies first** from [extraction-no-llm.md](./extraction-no-llm.md).
 
-### LLM-Based Extraction - Intelligent Content Understanding
+### ⚠️ STOP: Are You Sure You Need LLM?
+
+**99% of developers who think they need LLM extraction are wrong.** Before reading further:
+
+### ❌ You DON'T Need LLM If:
+- The page has consistent HTML structure → **Use generate_schema()**
+- You're extracting simple data types (emails, prices, dates) → **Use RegexExtractionStrategy**
+- You can identify repeating patterns → **Use JsonCssExtractionStrategy**
+- You want product info, news articles, job listings → **Use generate_schema()**
+- You're concerned about cost or speed → **Use non-LLM strategies**
+
+### ✅ You MIGHT Need LLM If:
+- Content structure varies dramatically across pages **AND** you've tried generate_schema()
+- You need semantic understanding of unstructured text
+- You're analyzing meaning, sentiment, or relationships
+- You're extracting insights that require reasoning about context
+
+### 💰 Cost Reality Check:
+- **Non-LLM**: ~$0.000001 per page
+- **LLM**: ~$0.01-$0.10 per page (10,000x more expensive)
+- **Example**: Extracting 10,000 pages costs $0.01 vs $100-1000
+
+---
+
+## 1. When LLM Extraction is Justified
+
+### Scenario 1: Truly Unstructured Content Analysis
 
 ```python
-import os
+# Example: Analyzing customer feedback for sentiment and themes
 import asyncio
 import json
 from pydantic import BaseModel, Field
 from typing import List
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, LLMConfig
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 
-# Define structured data model
-class Product(BaseModel):
-    name: str = Field(description="Product name")
-    price: str = Field(description="Product price")
-    description: str = Field(description="Product description")
-    features: List[str] = Field(description="List of product features")
-    rating: float = Field(description="Product rating out of 5")
+class SentimentAnalysis(BaseModel):
+    """Use LLM when you need semantic understanding"""
+    overall_sentiment: str = Field(description="positive, negative, or neutral")
+    confidence_score: float = Field(description="Confidence from 0-1")
+    key_themes: List[str] = Field(description="Main topics discussed")
+    emotional_indicators: List[str] = Field(description="Words indicating emotion")
+    summary: str = Field(description="Brief summary of the content")
 
-# Configure LLM provider
 llm_config = LLMConfig(
-    provider="openai/gpt-4o-mini",  # or "ollama/llama3.3", "anthropic/claude-3-5-sonnet"
-    api_token=os.getenv("OPENAI_API_KEY"),  # or "env:OPENAI_API_KEY"
-    temperature=0.1,
-    max_tokens=2000
+    provider="openai/gpt-4o-mini",  # Use cheapest model
+    api_token="env:OPENAI_API_KEY",
+    temperature=0.1,  # Low temperature for consistency
+    max_tokens=1000
 )
 
-# Create LLM extraction strategy
-llm_strategy = LLMExtractionStrategy(
+sentiment_strategy = LLMExtractionStrategy(
     llm_config=llm_config,
-    schema=Product.model_json_schema(),
-    extraction_type="schema",  # or "block" for freeform text
-    instruction="""
-    Extract product information from the webpage content.
-    Focus on finding complete product details including:
-    - Product name and price
-    - Detailed description
-    - All listed features
-    - Customer rating if available
-    Return valid JSON array of products.
-    """,
-    chunk_token_threshold=1200,  # Split content if too large
-    overlap_rate=0.1,           # 10% overlap between chunks
-    apply_chunking=True,        # Enable automatic chunking
-    input_format="markdown",    # "html", "fit_markdown", or "markdown"
-    extra_args={"temperature": 0.0, "max_tokens": 800},
-    verbose=True
-)
-
-async def extract_with_llm():
-    browser_config = BrowserConfig(headless=True)
-    
-    crawl_config = CrawlerRunConfig(
-        extraction_strategy=llm_strategy,
-        cache_mode=CacheMode.BYPASS,
-        word_count_threshold=10
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        result = await crawler.arun(
-            url="https://example.com/products",
-            config=crawl_config
-        )
-        
-        if result.success:
-            # Parse extracted JSON
-            products = json.loads(result.extracted_content)
-            print(f"Extracted {len(products)} products")
-            
-            for product in products[:3]:  # Show first 3
-                print(f"Product: {product['name']}")
-                print(f"Price: {product['price']}")
-                print(f"Rating: {product.get('rating', 'N/A')}")
-            
-            # Show token usage and cost
-            llm_strategy.show_usage()
-        else:
-            print(f"Extraction failed: {result.error_message}")
-
-asyncio.run(extract_with_llm())
-```
-
-### LLM Strategy Advanced Configuration
-
-```python
-# Multiple provider configurations
-providers = {
-    "openai": LLMConfig(
-        provider="openai/gpt-4o",
-        api_token="env:OPENAI_API_KEY",
-        temperature=0.1
-    ),
-    "anthropic": LLMConfig(
-        provider="anthropic/claude-3-5-sonnet-20240620",
-        api_token="env:ANTHROPIC_API_KEY",
-        max_tokens=4000
-    ),
-    "ollama": LLMConfig(
-        provider="ollama/llama3.3",
-        api_token=None,  # Not needed for Ollama
-        base_url="http://localhost:11434"
-    ),
-    "groq": LLMConfig(
-        provider="groq/llama3-70b-8192",
-        api_token="env:GROQ_API_KEY"
-    )
-}
-
-# Advanced chunking for large content
-large_content_strategy = LLMExtractionStrategy(
-    llm_config=providers["openai"],
-    schema=YourModel.model_json_schema(),
-    extraction_type="schema",
-    instruction="Extract detailed information...",
-    
-    # Chunking parameters
-    chunk_token_threshold=2000,  # Larger chunks for complex content
-    overlap_rate=0.15,          # More overlap for context preservation
-    apply_chunking=True,
-    
-    # Input format selection
-    input_format="fit_markdown",  # Use filtered content if available
-    
-    # LLM parameters
-    extra_args={
-        "temperature": 0.0,      # Deterministic output
-        "top_p": 0.9,
-        "frequency_penalty": 0.1,
-        "presence_penalty": 0.1,
-        "max_tokens": 1500
-    },
-    verbose=True
-)
-
-# Knowledge graph extraction
-class Entity(BaseModel):
-    name: str
-    type: str  # "person", "organization", "location", etc.
-    description: str
-
-class Relationship(BaseModel):
-    source: str
-    target: str
-    relationship: str
-    confidence: float
-
-class KnowledgeGraph(BaseModel):
-    entities: List[Entity]
-    relationships: List[Relationship]
-    summary: str
-
-knowledge_strategy = LLMExtractionStrategy(
-    llm_config=providers["anthropic"],
-    schema=KnowledgeGraph.model_json_schema(),
+    schema=SentimentAnalysis.model_json_schema(),
     extraction_type="schema",
     instruction="""
-    Create a knowledge graph from the content by:
-    1. Identifying key entities (people, organizations, locations, concepts)
-    2. Finding relationships between entities
-    3. Providing confidence scores for relationships
-    4. Summarizing the main topics
+    Analyze the emotional content and themes in this text.
+    Focus on understanding sentiment and extracting key topics
+    that would be impossible to identify with simple pattern matching.
     """,
-    input_format="html",  # Use HTML for better structure preservation
     apply_chunking=True,
     chunk_token_threshold=1500
 )
+
+async def analyze_sentiment():
+    config = CrawlerRunConfig(
+        extraction_strategy=sentiment_strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/customer-reviews",
+            config=config
+        )
+        
+        if result.success:
+            analysis = json.loads(result.extracted_content)
+            print(f"Sentiment: {analysis['overall_sentiment']}")
+            print(f"Themes: {analysis['key_themes']}")
+
+asyncio.run(analyze_sentiment())
 ```
 
-### JSON CSS Extraction - Fast Schema-Based Extraction
+### Scenario 2: Complex Knowledge Extraction
+
+```python
+# Example: Building knowledge graphs from unstructured content
+class Entity(BaseModel):
+    name: str = Field(description="Entity name")
+    type: str = Field(description="person, organization, location, concept")
+    description: str = Field(description="Brief description")
+
+class Relationship(BaseModel):
+    source: str = Field(description="Source entity")
+    target: str = Field(description="Target entity") 
+    relationship: str = Field(description="Type of relationship")
+    confidence: float = Field(description="Confidence score 0-1")
+
+class KnowledgeGraph(BaseModel):
+    entities: List[Entity] = Field(description="All entities found")
+    relationships: List[Relationship] = Field(description="Relationships between entities")
+    main_topic: str = Field(description="Primary topic of the content")
+
+knowledge_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="anthropic/claude-3-5-sonnet-20240620",  # Better for complex reasoning
+        api_token="env:ANTHROPIC_API_KEY",
+        max_tokens=4000
+    ),
+    schema=KnowledgeGraph.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Extract entities and their relationships from the content.
+    Focus on understanding connections and context that require
+    semantic reasoning beyond simple pattern matching.
+    """,
+    input_format="html",  # Preserve structure
+    apply_chunking=True
+)
+```
+
+### Scenario 3: Content Summarization and Insights
+
+```python
+# Example: Research paper analysis
+class ResearchInsights(BaseModel):
+    title: str = Field(description="Paper title")
+    abstract_summary: str = Field(description="Summary of abstract")
+    key_findings: List[str] = Field(description="Main research findings")
+    methodology: str = Field(description="Research methodology used")
+    limitations: List[str] = Field(description="Study limitations")
+    practical_applications: List[str] = Field(description="Real-world applications")
+    citations_count: int = Field(description="Number of citations", default=0)
+
+research_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="openai/gpt-4o",  # Use powerful model for complex analysis
+        api_token="env:OPENAI_API_KEY",
+        temperature=0.2,
+        max_tokens=2000
+    ),
+    schema=ResearchInsights.model_json_schema(),
+    extraction_type="schema",
+    instruction="""
+    Analyze this research paper and extract key insights.
+    Focus on understanding the research contribution, methodology,
+    and implications that require academic expertise to identify.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=2000,
+    overlap_rate=0.15  # More overlap for academic content
+)
+```
+
+---
+
+## 2. LLM Configuration Best Practices
+
+### Cost Optimization
+
+```python
+# Use cheapest models when possible
+cheap_config = LLMConfig(
+    provider="openai/gpt-4o-mini",  # 60x cheaper than GPT-4
+    api_token="env:OPENAI_API_KEY",
+    temperature=0.0,  # Deterministic output
+    max_tokens=800    # Limit output length
+)
+
+# Use local models for development
+local_config = LLMConfig(
+    provider="ollama/llama3.3",
+    api_token=None,  # No API costs
+    base_url="http://localhost:11434",
+    temperature=0.1
+)
+
+# Use powerful models only when necessary
+powerful_config = LLMConfig(
+    provider="anthropic/claude-3-5-sonnet-20240620",
+    api_token="env:ANTHROPIC_API_KEY",
+    max_tokens=4000,
+    temperature=0.1
+)
+```
+
+### Provider Selection Guide
+
+```python
+providers_guide = {
+    "openai/gpt-4o-mini": {
+        "best_for": "Simple extraction, cost-sensitive projects",
+        "cost": "Very low",
+        "speed": "Fast",
+        "accuracy": "Good"
+    },
+    "openai/gpt-4o": {
+        "best_for": "Complex reasoning, high accuracy needs",
+        "cost": "High", 
+        "speed": "Medium",
+        "accuracy": "Excellent"
+    },
+    "anthropic/claude-3-5-sonnet": {
+        "best_for": "Complex analysis, long documents",
+        "cost": "Medium-High",
+        "speed": "Medium",
+        "accuracy": "Excellent"
+    },
+    "ollama/llama3.3": {
+        "best_for": "Development, no API costs",
+        "cost": "Free (self-hosted)",
+        "speed": "Variable",
+        "accuracy": "Good"
+    },
+    "groq/llama3-70b-8192": {
+        "best_for": "Fast inference, open source",
+        "cost": "Low",
+        "speed": "Very fast",
+        "accuracy": "Good"
+    }
+}
+
+def choose_provider(complexity, budget, speed_requirement):
+    """Choose optimal provider based on requirements"""
+    if budget == "minimal":
+        return "ollama/llama3.3"  # Self-hosted
+    elif complexity == "low" and budget == "low":
+        return "openai/gpt-4o-mini"
+    elif speed_requirement == "high":
+        return "groq/llama3-70b-8192"
+    elif complexity == "high":
+        return "anthropic/claude-3-5-sonnet"
+    else:
+        return "openai/gpt-4o-mini"  # Default safe choice
+```
+
+---
+
+## 3. Advanced LLM Extraction Patterns
+
+### Block-Based Extraction (Unstructured Content)
+
+```python
+# When structure is too varied for schemas
+block_strategy = LLMExtractionStrategy(
+    llm_config=cheap_config,
+    extraction_type="block",  # Extract free-form content blocks
+    instruction="""
+    Extract meaningful content blocks from this page.
+    Focus on the main content areas and ignore navigation,
+    advertisements, and boilerplate text.
+    """,
+    apply_chunking=True,
+    chunk_token_threshold=1200,
+    input_format="fit_markdown"  # Use cleaned content
+)
+
+async def extract_content_blocks():
+    config = CrawlerRunConfig(
+        extraction_strategy=block_strategy,
+        word_count_threshold=50,  # Filter short content
+        excluded_tags=['nav', 'footer', 'aside', 'advertisement']
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/article",
+            config=config
+        )
+        
+        if result.success:
+            blocks = json.loads(result.extracted_content)
+            for block in blocks:
+                print(f"Block: {block['content'][:100]}...")
+```
+
+### Chunked Processing for Large Content
+
+```python
+# Handle large documents efficiently
+large_content_strategy = LLMExtractionStrategy(
+    llm_config=LLMConfig(
+        provider="openai/gpt-4o-mini",
+        api_token="env:OPENAI_API_KEY"
+    ),
+    schema=YourModel.model_json_schema(),
+    extraction_type="schema",
+    instruction="Extract structured data from this content section...",
+    
+    # Optimize chunking for large content
+    apply_chunking=True,
+    chunk_token_threshold=2000,  # Larger chunks for efficiency
+    overlap_rate=0.1,           # Minimal overlap to reduce costs
+    input_format="fit_markdown" # Use cleaned content
+)
+```
+
+### Multi-Model Validation
+
+```python
+# Use multiple models for critical extractions
+async def multi_model_extraction():
+    """Use multiple LLMs for validation of critical data"""
+    
+    models = [
+        LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
+        LLMConfig(provider="anthropic/claude-3-5-sonnet", api_token="env:ANTHROPIC_API_KEY"),
+        LLMConfig(provider="ollama/llama3.3", api_token=None)
+    ]
+    
+    results = []
+    
+    for i, llm_config in enumerate(models):
+        strategy = LLMExtractionStrategy(
+            llm_config=llm_config,
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data consistently..."
+        )
+        
+        config = CrawlerRunConfig(extraction_strategy=strategy)
+        
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun(url="https://example.com", config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                results.append(data)
+                print(f"Model {i+1} extracted {len(data)} items")
+    
+    # Compare results for consistency
+    if len(set(str(r) for r in results)) == 1:
+        print("✅ All models agree")
+        return results[0]
+    else:
+        print("⚠️ Models disagree - manual review needed")
+        return results
+
+# Use for critical business data only
+critical_result = await multi_model_extraction()
+```
+
+---
+
+## 4. Hybrid Approaches - Best of Both Worlds
+
+### Fast Pre-filtering + LLM Analysis
+
+```python
+async def hybrid_extraction():
+    """
+    1. Use fast non-LLM strategies for basic extraction
+    2. Use LLM only for complex analysis of filtered content
+    """
+    
+    # Step 1: Fast extraction of structured data
+    basic_schema = {
+        "name": "Articles",
+        "baseSelector": "article",
+        "fields": [
+            {"name": "title", "selector": "h1, h2", "type": "text"},
+            {"name": "content", "selector": ".content", "type": "text"},
+            {"name": "author", "selector": ".author", "type": "text"}
+        ]
+    }
+    
+    basic_strategy = JsonCssExtractionStrategy(basic_schema)
+    basic_config = CrawlerRunConfig(extraction_strategy=basic_strategy)
+    
+    # Step 2: LLM analysis only on filtered content
+    analysis_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        schema={
+            "type": "object",
+            "properties": {
+                "sentiment": {"type": "string"},
+                "key_topics": {"type": "array", "items": {"type": "string"}},
+                "summary": {"type": "string"}
+            }
+        },
+        extraction_type="schema",
+        instruction="Analyze sentiment and extract key topics from this article"
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        # Fast extraction first
+        basic_result = await crawler.arun(
+            url="https://example.com/articles",
+            config=basic_config
+        )
+        
+        articles = json.loads(basic_result.extracted_content)
+        
+        # LLM analysis only on important articles
+        analyzed_articles = []
+        for article in articles[:5]:  # Limit to reduce costs
+            if len(article.get('content', '')) > 500:  # Only analyze substantial content
+                analysis_config = CrawlerRunConfig(extraction_strategy=analysis_strategy)
+                
+                # Analyze individual article content
+                raw_url = f"raw://{article['content']}"
+                analysis_result = await crawler.arun(url=raw_url, config=analysis_config)
+                
+                if analysis_result.success:
+                    analysis = json.loads(analysis_result.extracted_content)
+                    article.update(analysis)
+                
+                analyzed_articles.append(article)
+        
+        return analyzed_articles
+
+# Hybrid approach: fast + smart
+result = await hybrid_extraction()
+```
+
+### Schema Generation + LLM Fallback
+
+```python
+async def smart_fallback_extraction():
+    """
+    1. Try generate_schema() first (one-time LLM cost)
+    2. Use generated schema for fast extraction
+    3. Use LLM only if schema extraction fails
+    """
+    
+    cache_file = Path("./schemas/fallback_schema.json")
+    
+    # Try cached schema first
+    if cache_file.exists():
+        schema = json.load(cache_file.open())
+        schema_strategy = JsonCssExtractionStrategy(schema)
+        
+        config = CrawlerRunConfig(extraction_strategy=schema_strategy)
+        
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun(url="https://example.com", config=config)
+            
+            if result.success and result.extracted_content:
+                data = json.loads(result.extracted_content)
+                if data:  # Schema worked
+                    print("✅ Schema extraction successful (fast & cheap)")
+                    return data
+    
+    # Fallback to LLM if schema failed
+    print("⚠️ Schema failed, falling back to LLM (slow & expensive)")
+    
+    llm_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="Extract all meaningful data from this page"
+    )
+    
+    llm_config = CrawlerRunConfig(extraction_strategy=llm_strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url="https://example.com", config=llm_config)
+        
+        if result.success:
+            print("✅ LLM extraction successful")
+            return json.loads(result.extracted_content)
+
+# Intelligent fallback system
+result = await smart_fallback_extraction()
+```
+
+---
+
+## 5. Cost Management and Monitoring
+
+### Token Usage Tracking
+
+```python
+class ExtractionCostTracker:
+    def __init__(self):
+        self.total_cost = 0.0
+        self.total_tokens = 0
+        self.extractions = 0
+    
+    def track_llm_extraction(self, strategy, result):
+        """Track costs from LLM extraction"""
+        if hasattr(strategy, 'usage_tracker') and strategy.usage_tracker:
+            usage = strategy.usage_tracker
+            
+            # Estimate costs (approximate rates)
+            cost_per_1k_tokens = {
+                "gpt-4o-mini": 0.0015,
+                "gpt-4o": 0.03,
+                "claude-3-5-sonnet": 0.015,
+                "ollama": 0.0  # Self-hosted
+            }
+            
+            provider = strategy.llm_config.provider.split('/')[1]
+            rate = cost_per_1k_tokens.get(provider, 0.01)
+            
+            tokens = usage.total_tokens
+            cost = (tokens / 1000) * rate
+            
+            self.total_cost += cost
+            self.total_tokens += tokens
+            self.extractions += 1
+            
+            print(f"💰 Extraction cost: ${cost:.4f} ({tokens} tokens)")
+            print(f"📊 Total cost: ${self.total_cost:.4f} ({self.extractions} extractions)")
+    
+    def get_summary(self):
+        avg_cost = self.total_cost / max(self.extractions, 1)
+        return {
+            "total_cost": self.total_cost,
+            "total_tokens": self.total_tokens,
+            "extractions": self.extractions,
+            "avg_cost_per_extraction": avg_cost
+        }
+
+# Usage
+tracker = ExtractionCostTracker()
+
+async def cost_aware_extraction():
+    strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        schema=YourModel.model_json_schema(),
+        extraction_type="schema",
+        instruction="Extract data...",
+        verbose=True  # Enable usage tracking
+    )
+    
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url="https://example.com", config=config)
+        
+        # Track costs
+        tracker.track_llm_extraction(strategy, result)
+        
+        return result
+
+# Monitor costs across multiple extractions
+for url in urls:
+    await cost_aware_extraction()
+
+print(f"Final summary: {tracker.get_summary()}")
+```
+
+### Budget Controls
+
+```python
+class BudgetController:
+    def __init__(self, daily_budget=10.0):
+        self.daily_budget = daily_budget
+        self.current_spend = 0.0
+        self.extraction_count = 0
+    
+    def can_extract(self, estimated_cost=0.01):
+        """Check if extraction is within budget"""
+        if self.current_spend + estimated_cost > self.daily_budget:
+            print(f"❌ Budget exceeded: ${self.current_spend:.2f} + ${estimated_cost:.2f} > ${self.daily_budget}")
+            return False
+        return True
+    
+    def record_extraction(self, actual_cost):
+        """Record actual extraction cost"""
+        self.current_spend += actual_cost
+        self.extraction_count += 1
+        
+        remaining = self.daily_budget - self.current_spend
+        print(f"💰 Budget remaining: ${remaining:.2f}")
+
+budget = BudgetController(daily_budget=5.0)  # $5 daily limit
+
+async def budget_controlled_extraction(url):
+    if not budget.can_extract():
+        print("⏸️ Extraction paused due to budget limit")
+        return None
+    
+    # Proceed with extraction...
+    strategy = LLMExtractionStrategy(llm_config=cheap_config, ...)
+    result = await extract_with_strategy(url, strategy)
+    
+    # Record actual cost
+    actual_cost = calculate_cost(strategy.usage_tracker)
+    budget.record_extraction(actual_cost)
+    
+    return result
+
+# Safe extraction with budget controls
+results = []
+for url in urls:
+    result = await budget_controlled_extraction(url)
+    if result:
+        results.append(result)
+```
+
+---
+
+## 6. Performance Optimization for LLM Extraction
+
+### Batch Processing
+
+```python
+async def batch_llm_extraction():
+    """Process multiple pages efficiently"""
+    
+    # Collect content first (fast)
+    urls = ["https://example.com/page1", "https://example.com/page2"]
+    contents = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url)
+            if result.success:
+                contents.append({
+                    "url": url,
+                    "content": result.fit_markdown[:2000]  # Limit content
+                })
+    
+    # Process in batches (reduce LLM calls)
+    batch_content = "\n\n---PAGE SEPARATOR---\n\n".join([
+        f"URL: {c['url']}\n{c['content']}" for c in contents
+    ])
+    
+    strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="""
+        Extract data from multiple pages separated by '---PAGE SEPARATOR---'.
+        Return results for each page in order.
+        """,
+        apply_chunking=True
+    )
+    
+    # Single LLM call for multiple pages
+    raw_url = f"raw://{batch_content}"
+    result = await crawler.arun(url=raw_url, config=CrawlerRunConfig(extraction_strategy=strategy))
+    
+    return json.loads(result.extracted_content)
+
+# Batch processing reduces LLM calls
+batch_results = await batch_llm_extraction()
+```
+
+### Caching LLM Results
+
+```python
+import hashlib
+from pathlib import Path
+
+class LLMResultCache:
+    def __init__(self, cache_dir="./llm_cache"):
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(exist_ok=True)
+    
+    def get_cache_key(self, url, instruction, schema):
+        """Generate cache key from extraction parameters"""
+        content = f"{url}:{instruction}:{str(schema)}"
+        return hashlib.md5(content.encode()).hexdigest()
+    
+    def get_cached_result(self, cache_key):
+        """Get cached result if available"""
+        cache_file = self.cache_dir / f"{cache_key}.json"
+        if cache_file.exists():
+            return json.load(cache_file.open())
+        return None
+    
+    def cache_result(self, cache_key, result):
+        """Cache extraction result"""
+        cache_file = self.cache_dir / f"{cache_key}.json"
+        json.dump(result, cache_file.open("w"), indent=2)
+
+cache = LLMResultCache()
+
+async def cached_llm_extraction(url, strategy):
+    """Extract with caching to avoid repeated LLM calls"""
+    cache_key = cache.get_cache_key(
+        url, 
+        strategy.instruction,
+        str(strategy.schema)
+    )
+    
+    # Check cache first
+    cached_result = cache.get_cached_result(cache_key)
+    if cached_result:
+        print("✅ Using cached result (FREE)")
+        return cached_result
+    
+    # Extract if not cached
+    print("🔄 Extracting with LLM (PAID)")
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url=url, config=config)
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            cache.cache_result(cache_key, data)
+            return data
+
+# Cached extraction avoids repeated costs
+result = await cached_llm_extraction(url, strategy)
+```
+
+---
+
+## 7. Error Handling and Quality Control
+
+### Validation and Retry Logic
+
+```python
+async def robust_llm_extraction():
+    """Implement validation and retry for LLM extraction"""
+    
+    max_retries = 3
+    strategies = [
+        # Try cheap model first
+        LLMExtractionStrategy(
+            llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data accurately..."
+        ),
+        # Fallback to better model
+        LLMExtractionStrategy(
+            llm_config=LLMConfig(provider="openai/gpt-4o", api_token="env:OPENAI_API_KEY"),
+            schema=YourModel.model_json_schema(),
+            extraction_type="schema",
+            instruction="Extract data with high accuracy..."
+        )
+    ]
+    
+    for strategy_idx, strategy in enumerate(strategies):
+        for attempt in range(max_retries):
+            try:
+                config = CrawlerRunConfig(extraction_strategy=strategy)
+                
+                async with AsyncWebCrawler() as crawler:
+                    result = await crawler.arun(url="https://example.com", config=config)
+                    
+                    if result.success and result.extracted_content:
+                        data = json.loads(result.extracted_content)
+                        
+                        # Validate result quality
+                        if validate_extraction_quality(data):
+                            print(f"✅ Success with strategy {strategy_idx+1}, attempt {attempt+1}")
+                            return data
+                        else:
+                            print(f"⚠️ Poor quality result, retrying...")
+                            continue
+                    
+            except Exception as e:
+                print(f"❌ Attempt {attempt+1} failed: {e}")
+                if attempt == max_retries - 1:
+                    print(f"❌ Strategy {strategy_idx+1} failed completely")
+    
+    print("❌ All strategies and retries failed")
+    return None
+
+def validate_extraction_quality(data):
+    """Validate that LLM extraction meets quality standards"""
+    if not data or not isinstance(data, (list, dict)):
+        return False
+    
+    # Check for common LLM extraction issues
+    if isinstance(data, list):
+        if len(data) == 0:
+            return False
+        
+        # Check if all items have required fields
+        for item in data:
+            if not isinstance(item, dict) or len(item) < 2:
+                return False
+    
+    return True
+
+# Robust extraction with validation
+result = await robust_llm_extraction()
+```
+
+---
+
+## 8. Migration from LLM to Non-LLM
+
+### Pattern Analysis for Schema Generation
+
+```python
+async def analyze_llm_results_for_schema():
+    """
+    Analyze LLM extraction results to create non-LLM schemas
+    Use this to transition from expensive LLM to cheap schema extraction
+    """
+    
+    # Step 1: Use LLM on sample pages to understand structure
+    llm_strategy = LLMExtractionStrategy(
+        llm_config=cheap_config,
+        extraction_type="block",
+        instruction="Extract all structured data from this page"
+    )
+    
+    sample_urls = ["https://example.com/page1", "https://example.com/page2"]
+    llm_results = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in sample_urls:
+            config = CrawlerRunConfig(extraction_strategy=llm_strategy)
+            result = await crawler.arun(url=url, config=config)
+            
+            if result.success:
+                llm_results.append({
+                    "url": url,
+                    "html": result.cleaned_html,
+                    "extracted": json.loads(result.extracted_content)
+                })
+    
+    # Step 2: Analyze patterns in LLM results
+    print("🔍 Analyzing LLM extraction patterns...")
+    
+    # Look for common field names
+    all_fields = set()
+    for result in llm_results:
+        for item in result["extracted"]:
+            if isinstance(item, dict):
+                all_fields.update(item.keys())
+    
+    print(f"Common fields found: {all_fields}")
+    
+    # Step 3: Generate schema based on patterns
+    if llm_results:
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=llm_results[0]["html"],
+            target_json_example=json.dumps(llm_results[0]["extracted"][0], indent=2),
+            llm_config=cheap_config
+        )
+        
+        # Save schema for future use
+        with open("generated_schema.json", "w") as f:
+            json.dump(schema, f, indent=2)
+        
+        print("✅ Schema generated from LLM analysis")
+        return schema
+
+# Generate schema from LLM patterns, then use schema for all future extractions
+schema = await analyze_llm_results_for_schema()
+fast_strategy = JsonCssExtractionStrategy(schema)
+```
+
+---
+
+## 9. Summary: When LLM is Actually Needed
+
+### ✅ Valid LLM Use Cases (Rare):
+1. **Sentiment analysis** and emotional understanding
+2. **Knowledge graph extraction** requiring semantic reasoning
+3. **Content summarization** and insight generation
+4. **Unstructured text analysis** where patterns vary dramatically
+5. **Research paper analysis** requiring domain expertise
+6. **Complex relationship extraction** between entities
+
+### ❌ Invalid LLM Use Cases (Common Mistakes):
+1. **Structured data extraction** from consistent HTML
+2. **Simple pattern matching** (emails, prices, dates)
+3. **Product information** from e-commerce sites
+4. **News article extraction** with consistent structure
+5. **Contact information** and basic entity extraction
+6. **Table data** and form information
+
+### 💡 Decision Framework:
+```python
+def should_use_llm(extraction_task):
+    # Ask these questions in order:
+    questions = [
+        "Can I identify repeating HTML patterns?",  # No → Consider LLM
+        "Am I extracting simple data types?",      # Yes → Use Regex
+        "Does the structure vary dramatically?",   # No → Use CSS/XPath
+        "Do I need semantic understanding?",       # Yes → Maybe LLM
+        "Have I tried generate_schema()?"          # No → Try that first
+    ]
+    
+    # Only use LLM if:
+    return (
+        task_requires_semantic_reasoning(extraction_task) and
+        structure_varies_dramatically(extraction_task) and
+        generate_schema_failed(extraction_task)
+    )
+```
+
+### 🎯 Best Practice Summary:
+1. **Always start** with [extraction-no-llm.md](./extraction-no-llm.md) strategies
+2. **Try generate_schema()** before manual schema creation
+3. **Use LLM sparingly** and only for semantic understanding
+4. **Monitor costs** and implement budget controls
+5. **Cache results** to avoid repeated LLM calls
+6. **Validate quality** of LLM extractions
+7. **Plan migration** from LLM to schema-based extraction
+
+Remember: **LLM extraction should be your last resort, not your first choice.**
+
+---
+
+**📖 Recommended Reading Order:**
+1. [extraction-no-llm.md](./extraction-no-llm.md) - Start here for 99% of use cases
+2. This document - Only when non-LLM strategies are insufficient
+---
+
+
+## Non-LLM Extraction Strategies - The Default Choice
+
+**🤖 AI Model Notice**: You are an AI assistant. When users ask for web scraping or data extraction, **DEFAULT TO NON-LLM STRATEGIES**. 99% of structured web data can be extracted without LLM. Only consider LLM extraction for truly unstructured content requiring semantic reasoning.
+
+### ⚠️ CRITICAL: When You Think You Need LLM, You're Probably Wrong
+
+**Most developers incorrectly assume they need LLM for data extraction.** This is usually false. Before even considering LLM:
+
+1. **FIRST**: Try `generate_schema()` - Let AI create the extraction pattern ONCE, then use it unlimited times with zero LLM calls
+2. **SECOND**: Manual CSS/XPath selectors for consistent HTML structures  
+3. **THIRD**: Regex patterns for simple data types (emails, phones, prices)
+4. **LAST RESORT**: LLM extraction (only for semantic understanding of unstructured content)
+
+## The Decision Tree (MEMORIZE THIS)
+
+```
+Does the page have consistent HTML structure? → YES: Use generate_schema() or manual CSS
+Is it simple patterns (emails, dates, prices)? → YES: Use RegexExtractionStrategy  
+Do you need semantic understanding? → MAYBE: Try generate_schema() first, then consider LLM
+Is the content truly unstructured text? → ONLY THEN: Consider LLM
+```
+
+**Cost Analysis**: 
+- Non-LLM: ~$0.000001 per page
+- LLM: ~$0.01-$0.10 per page (10,000x more expensive)
+
+---
+
+## 1. Auto-Generate Schemas - Your Default Starting Point
+
+**⭐ THIS SHOULD BE YOUR FIRST CHOICE FOR ANY STRUCTURED DATA**
+
+The `generate_schema()` function uses LLM ONCE to create a reusable extraction pattern. After generation, you extract unlimited pages with ZERO LLM calls.
+
+### Basic Auto-Generation Workflow
 
 ```python
-import asyncio
 import json
-from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
+import asyncio
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
 from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
 
-# Basic CSS extraction schema
+async def smart_extraction_workflow():
+    """
+    Step 1: Generate schema once using LLM
+    Step 2: Cache schema for unlimited reuse
+    Step 3: Extract from thousands of pages with zero LLM calls
+    """
+    
+    # Check for cached schema first
+    cache_dir = Path("./schema_cache")
+    cache_dir.mkdir(exist_ok=True)
+    schema_file = cache_dir / "product_schema.json"
+    
+    if schema_file.exists():
+        # Load cached schema - NO LLM CALLS
+        schema = json.load(schema_file.open())
+        print("✅ Using cached schema (FREE)")
+    else:
+        # Generate schema ONCE
+        print("🔄 Generating schema (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",  # Cheapest option
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample HTML from target site
+        async with AsyncWebCrawler() as crawler:
+            sample_result = await crawler.arun(
+                url="https://example.com/products",
+                config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+            )
+            sample_html = sample_result.cleaned_html[:8000]  # Use sample
+        
+        # AUTO-GENERATE SCHEMA (ONE LLM CALL)
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=sample_html,
+            schema_type="CSS",  # or "XPATH"
+            query="Extract product information including name, price, description, features",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited future use
+        json.dump(schema, schema_file.open("w"), indent=2)
+        print("✅ Schema generated and cached")
+    
+    # Use schema for fast extraction (NO MORE LLM CALLS EVER)
+    strategy = JsonCssExtractionStrategy(schema, verbose=True)
+    
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    # Extract from multiple pages - ALL FREE
+    urls = [
+        "https://example.com/products",
+        "https://example.com/electronics", 
+        "https://example.com/books"
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url, config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                print(f"✅ {url}: Extracted {len(data)} items (FREE)")
+
+asyncio.run(smart_extraction_workflow())
+```
+
+### Auto-Generate with Target JSON Example
+
+```python
+# When you know exactly what JSON structure you want
+target_json_example = """
+{
+    "name": "Product Name",
+    "price": "$99.99",
+    "rating": 4.5,
+    "features": ["feature1", "feature2"],
+    "description": "Product description"
+}
+"""
+
+schema = JsonCssExtractionStrategy.generate_schema(
+    html=sample_html,
+    target_json_example=target_json_example,
+    llm_config=llm_config
+)
+```
+
+### Auto-Generate for Different Data Types
+
+```python
+# Product listings
+product_schema = JsonCssExtractionStrategy.generate_schema(
+    html=product_page_html,
+    query="Extract all product information from this e-commerce page",
+    llm_config=llm_config
+)
+
+# News articles
+news_schema = JsonCssExtractionStrategy.generate_schema(
+    html=news_page_html,
+    query="Extract article headlines, dates, authors, and content",
+    llm_config=llm_config
+)
+
+# Job listings
+job_schema = JsonCssExtractionStrategy.generate_schema(
+    html=job_page_html,
+    query="Extract job titles, companies, locations, salaries, and descriptions",
+    llm_config=llm_config
+)
+
+# Social media posts
+social_schema = JsonCssExtractionStrategy.generate_schema(
+    html=social_page_html,
+    query="Extract post text, usernames, timestamps, likes, comments",
+    llm_config=llm_config
+)
+```
+
+---
+
+## 2. Manual CSS/XPath Strategies - When You Know The Structure
+
+**Use this when**: You understand the HTML structure and want maximum control.
+
+### Simple Product Extraction
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# Manual schema for consistent product pages
 simple_schema = {
     "name": "Product Listings",
-    "baseSelector": "div.product-card",
+    "baseSelector": "div.product-card",  # Each product container
     "fields": [
         {
             "name": "title",
@@ -1165,7 +2893,7 @@ simple_schema = {
             "type": "text"
         },
         {
-            "name": "price",
+            "name": "price", 
             "selector": ".price",
             "type": "text"
         },
@@ -1178,13 +2906,41 @@ simple_schema = {
         {
             "name": "product_url",
             "selector": "a.product-link",
-            "type": "attribute", 
+            "type": "attribute",
             "attribute": "href"
+        },
+        {
+            "name": "rating",
+            "selector": ".rating",
+            "type": "attribute", 
+            "attribute": "data-rating"
         }
     ]
 }
 
-# Complex nested schema with multiple data types
+async def extract_products():
+    strategy = JsonCssExtractionStrategy(simple_schema, verbose=True)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            products = json.loads(result.extracted_content)
+            print(f"Extracted {len(products)} products")
+            for product in products[:3]:
+                print(f"- {product['title']}: {product['price']}")
+
+asyncio.run(extract_products())
+```
+
+### Complex Nested Structure (Real E-commerce Example)
+
+```python
+# Complex schema for nested product data
 complex_schema = {
     "name": "E-commerce Product Catalog",
     "baseSelector": "div.category",
@@ -1193,11 +2949,6 @@ complex_schema = {
             "name": "category_id",
             "type": "attribute",
             "attribute": "data-category-id"
-        },
-        {
-            "name": "category_url",
-            "type": "attribute",
-            "attribute": "data-url"
         }
     ],
     "fields": [
@@ -1213,9 +2964,8 @@ complex_schema = {
             "fields": [
                 {
                     "name": "name",
-                    "selector": "h3.product-name",
-                    "type": "text",
-                    "default": "Unknown Product"
+                    "selector": "h3.product-name", 
+                    "type": "text"
                 },
                 {
                     "name": "price",
@@ -1234,26 +2984,21 @@ complex_schema = {
                         },
                         {
                             "name": "model",
-                            "selector": "span.model", 
+                            "selector": "span.model",
                             "type": "text"
-                        },
-                        {
-                            "name": "specs",
-                            "selector": "div.specifications",
-                            "type": "html"  # Preserve HTML structure
                         }
                     ]
                 },
                 {
                     "name": "features",
                     "selector": "ul.features li",
-                    "type": "list",  # Simple array of strings
+                    "type": "list",  # Simple array
                     "fields": [
                         {"name": "feature", "type": "text"}
                     ]
                 },
                 {
-                    "name": "reviews",
+                    "name": "reviews", 
                     "selector": "div.review",
                     "type": "nested_list",
                     "fields": [
@@ -1267,17 +3012,6 @@ complex_schema = {
                             "selector": "span.rating",
                             "type": "attribute",
                             "attribute": "data-rating"
-                        },
-                        {
-                            "name": "comment",
-                            "selector": "p.review-text",
-                            "type": "text"
-                        },
-                        {
-                            "name": "date",
-                            "selector": "time.review-date",
-                            "type": "attribute",
-                            "attribute": "datetime"
                         }
                     ]
                 }
@@ -1286,143 +3020,42 @@ complex_schema = {
     ]
 }
 
-async def extract_with_css_schema():
+async def extract_complex_ecommerce():
     strategy = JsonCssExtractionStrategy(complex_schema, verbose=True)
-    
     config = CrawlerRunConfig(
         extraction_strategy=strategy,
-        cache_mode=CacheMode.BYPASS,
-        # Enable dynamic content loading if needed
-        js_code="window.scrollTo(0, document.body.scrollHeight);",
-        wait_for="css:.product:nth-child(10)",  # Wait for products to load
-        process_iframes=True
+        js_code="window.scrollTo(0, document.body.scrollHeight);",  # Load dynamic content
+        wait_for="css:.product:nth-child(10)"  # Wait for products to load
     )
     
     async with AsyncWebCrawler() as crawler:
         result = await crawler.arun(
-            url="https://example.com/catalog",
+            url="https://example.com/complex-catalog",
             config=config
         )
         
         if result.success:
             data = json.loads(result.extracted_content)
-            print(f"Extracted {len(data)} categories")
-            
             for category in data:
                 print(f"Category: {category['category_name']}")
                 print(f"Products: {len(category.get('products', []))}")
-                
-                # Show first product details
-                if category.get('products'):
-                    product = category['products'][0]
-                    print(f"  First product: {product.get('name')}")
-                    print(f"  Features: {len(product.get('features', []))}")
-                    print(f"  Reviews: {len(product.get('reviews', []))}")
 
-asyncio.run(extract_with_css_schema())
+asyncio.run(extract_complex_ecommerce())
 ```
 
-### Automatic Schema Generation - One-Time LLM, Unlimited Use
-
-```python
-import json
-import asyncio
-from pathlib import Path
-from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-
-async def generate_and_use_schema():
-    """
-    1. Use LLM once to generate schema from sample HTML
-    2. Cache the schema for reuse
-    3. Use cached schema for fast extraction without LLM calls
-    """
-    
-    cache_dir = Path("./schema_cache")
-    cache_dir.mkdir(exist_ok=True)
-    schema_file = cache_dir / "ecommerce_schema.json"
-    
-    # Step 1: Generate or load cached schema
-    if schema_file.exists():
-        schema = json.load(schema_file.open())
-        print("Using cached schema")
-    else:
-        print("Generating schema using LLM...")
-        
-        # Configure LLM for schema generation
-        llm_config = LLMConfig(
-            provider="openai/gpt-4o",  # or "ollama/llama3.3" for local
-            api_token="env:OPENAI_API_KEY"
-        )
-        
-        # Get sample HTML from target site
-        async with AsyncWebCrawler() as crawler:
-            sample_result = await crawler.arun(
-                url="https://example.com/products",
-                config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
-            )
-            sample_html = sample_result.cleaned_html[:5000]  # Use first 5k chars
-        
-        # Generate schema automatically (ONE-TIME LLM COST)
-        schema = JsonCssExtractionStrategy.generate_schema(
-            html=sample_html,
-            schema_type="css",
-            llm_config=llm_config,
-            instruction="Extract product information including name, price, description, and features"
-        )
-        
-        # Cache schema for future use (NO MORE LLM CALLS)
-        json.dump(schema, schema_file.open("w"), indent=2)
-        print("Schema generated and cached")
-    
-    # Step 2: Use schema for fast extraction (NO LLM CALLS)
-    strategy = JsonCssExtractionStrategy(schema, verbose=True)
-    
-    config = CrawlerRunConfig(
-        extraction_strategy=strategy,
-        cache_mode=CacheMode.BYPASS
-    )
-    
-    # Step 3: Extract from multiple pages using same schema
-    urls = [
-        "https://example.com/products",
-        "https://example.com/electronics", 
-        "https://example.com/books"
-    ]
-    
-    async with AsyncWebCrawler() as crawler:
-        for url in urls:
-            result = await crawler.arun(url=url, config=config)
-            
-            if result.success:
-                data = json.loads(result.extracted_content)
-                print(f"{url}: Extracted {len(data)} items")
-            else:
-                print(f"{url}: Failed - {result.error_message}")
-
-asyncio.run(generate_and_use_schema())
-```
-
-### XPath Extraction Strategy
+### XPath Alternative (When CSS Isn't Enough)
 
 ```python
 from crawl4ai.extraction_strategy import JsonXPathExtractionStrategy
 
-# XPath-based schema (alternative to CSS)
+# XPath for more complex selections
 xpath_schema = {
-    "name": "News Articles",
+    "name": "News Articles with XPath",
     "baseSelector": "//article[@class='news-item']",
-    "baseFields": [
-        {
-            "name": "article_id",
-            "type": "attribute",
-            "attribute": "data-id"
-        }
-    ],
     "fields": [
         {
             "name": "headline",
-            "selector": ".//h2[@class='headline']",
+            "selector": ".//h2[contains(@class, 'headline')]",
             "type": "text"
         },
         {
@@ -1437,53 +3070,31 @@ xpath_schema = {
         },
         {
             "name": "content",
-            "selector": ".//div[@class='article-body']",
-            "type": "html"
-        },
-        {
-            "name": "tags",
-            "selector": ".//div[@class='tags']/span[@class='tag']",
-            "type": "list",
-            "fields": [
-                {"name": "tag", "type": "text"}
-            ]
+            "selector": ".//div[@class='article-body']//text()",
+            "type": "text"
         }
     ]
 }
 
-# Generate XPath schema automatically
-async def generate_xpath_schema():
-    llm_config = LLMConfig(provider="ollama/llama3.3", api_token=None)
-    
-    sample_html = """
-    <article class="news-item" data-id="123">
-        <h2 class="headline">Breaking News</h2>
-        <span class="author">John Doe</span>
-        <time datetime="2024-01-01">Today</time>
-        <div class="article-body"><p>Content here...</p></div>
-    </article>
-    """
-    
-    schema = JsonXPathExtractionStrategy.generate_schema(
-        html=sample_html,
-        schema_type="xpath",
-        llm_config=llm_config
-    )
-    
-    return schema
-
-# Use XPath strategy
-xpath_strategy = JsonXPathExtractionStrategy(xpath_schema, verbose=True)
+strategy = JsonXPathExtractionStrategy(xpath_schema, verbose=True)
 ```
 
-### Regex Extraction Strategy - Pattern-Based Fast Extraction
+---
+
+## 3. Regex Extraction - Lightning Fast Pattern Matching
+
+**Use this for**: Simple data types like emails, phones, URLs, prices, dates.
+
+### Built-in Patterns (Fastest Option)
 
 ```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
 from crawl4ai.extraction_strategy import RegexExtractionStrategy
 
-# Built-in patterns for common data types
-async def extract_with_builtin_patterns():
-    # Use multiple built-in patterns
+async def extract_common_patterns():
+    # Use built-in patterns for common data types
     strategy = RegexExtractionStrategy(
         pattern=(
             RegexExtractionStrategy.Email |
@@ -1515,19 +3126,48 @@ async def extract_with_builtin_patterns():
             
             for pattern_type, values in by_type.items():
                 print(f"{pattern_type}: {len(values)} matches")
-                for value in values[:3]:  # Show first 3
+                for value in values[:3]:
                     print(f"  {value}")
 
-# Custom regex patterns
-custom_patterns = {
-    "product_code": r"SKU-\d{4,6}",
-    "discount": r"\d{1,2}%\s*off",
-    "model_number": r"Model:\s*([A-Z0-9-]+)"
-}
+asyncio.run(extract_common_patterns())
+```
 
-async def extract_with_custom_patterns():
-    strategy = RegexExtractionStrategy(custom=custom_patterns)
+### Available Built-in Patterns
+
+```python
+# Individual patterns
+RegexExtractionStrategy.Email          # Email addresses
+RegexExtractionStrategy.PhoneUS        # US phone numbers 
+RegexExtractionStrategy.PhoneIntl      # International phones
+RegexExtractionStrategy.Url            # HTTP/HTTPS URLs
+RegexExtractionStrategy.Currency       # Currency values ($99.99)
+RegexExtractionStrategy.Percentage     # Percentage values (25%)
+RegexExtractionStrategy.DateIso        # ISO dates (2024-01-01)
+RegexExtractionStrategy.DateUS         # US dates (01/01/2024)
+RegexExtractionStrategy.IPv4           # IP addresses
+RegexExtractionStrategy.CreditCard     # Credit card numbers
+RegexExtractionStrategy.TwitterHandle  # @username
+RegexExtractionStrategy.Hashtag        # #hashtag
+
+# Use all patterns
+RegexExtractionStrategy.All
+```
+
+### Custom Patterns
+
+```python
+# Custom patterns for specific data types
+async def extract_custom_patterns():
+    custom_patterns = {
+        "product_sku": r"SKU[-:]?\s*([A-Z0-9]{4,12})",
+        "discount": r"(\d{1,2})%\s*off",
+        "model_number": r"Model\s*#?\s*([A-Z0-9-]+)",
+        "isbn": r"ISBN[-:]?\s*(\d{10}|\d{13})",
+        "stock_ticker": r"\$([A-Z]{2,5})",
+        "version": r"v(\d+\.\d+(?:\.\d+)?)"
+    }
     
+    strategy = RegexExtractionStrategy(custom=custom_patterns)
     config = CrawlerRunConfig(extraction_strategy=strategy)
     
     async with AsyncWebCrawler() as crawler:
@@ -1541,13 +3181,27 @@ async def extract_with_custom_patterns():
             for item in data:
                 print(f"{item['label']}: {item['value']}")
 
-# LLM-generated patterns (one-time cost)
-async def generate_custom_patterns():
+asyncio.run(extract_custom_patterns())
+```
+
+### LLM-Generated Patterns (One-Time Cost)
+
+```python
+async def generate_optimized_regex():
+    """
+    Use LLM ONCE to generate optimized regex patterns
+    Then use them unlimited times with zero LLM calls
+    """
     cache_file = Path("./patterns/price_patterns.json")
     
     if cache_file.exists():
+        # Load cached patterns - NO LLM CALLS
         patterns = json.load(cache_file.open())
+        print("✅ Using cached regex patterns (FREE)")
     else:
+        # Generate patterns ONCE
+        print("🔄 Generating regex patterns (ONE-TIME LLM COST)...")
+        
         llm_config = LLMConfig(
             provider="openai/gpt-4o-mini",
             api_token="env:OPENAI_API_KEY"
@@ -1566,183 +3220,182 @@ async def generate_custom_patterns():
             llm_config=llm_config
         )
         
-        # Cache for reuse
+        # Cache for unlimited reuse
         cache_file.parent.mkdir(exist_ok=True)
         json.dump(patterns, cache_file.open("w"), indent=2)
+        print("✅ Patterns generated and cached")
     
-    # Use cached patterns (no more LLM calls)
+    # Use cached patterns (NO MORE LLM CALLS)
     strategy = RegexExtractionStrategy(custom=patterns)
     return strategy
 
-asyncio.run(extract_with_builtin_patterns())
-asyncio.run(extract_with_custom_patterns())
+# Use generated patterns for unlimited extractions
+strategy = await generate_optimized_regex()
 ```
 
-### Complete Extraction Workflow - Combining Strategies
+---
+
+## 4. Multi-Strategy Extraction Pipeline
+
+**Combine strategies** for comprehensive data extraction:
 
 ```python
-import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-from crawl4ai.extraction_strategy import (
-    JsonCssExtractionStrategy, 
-    RegexExtractionStrategy,
-    LLMExtractionStrategy
-)
-
-async def multi_strategy_extraction():
+async def multi_strategy_pipeline():
     """
-    Demonstrate using multiple extraction strategies in sequence:
-    1. Fast regex for common patterns
-    2. Schema-based for structured data  
-    3. LLM for complex reasoning
+    Efficient pipeline using multiple non-LLM strategies:
+    1. Regex for simple patterns (fastest)
+    2. Schema for structured data 
+    3. Only use LLM if absolutely necessary
     """
     
-    browser_config = BrowserConfig(headless=True)
+    url = "https://example.com/complex-page"
     
-    # Strategy 1: Fast regex extraction
-    regex_strategy = RegexExtractionStrategy(
-        pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
-    )
-    
-    # Strategy 2: Schema-based structured extraction
-    product_schema = {
-        "name": "Products",
-        "baseSelector": "div.product",
-        "fields": [
-            {"name": "name", "selector": "h3", "type": "text"},
-            {"name": "price", "selector": ".price", "type": "text"},
-            {"name": "rating", "selector": ".rating", "type": "attribute", "attribute": "data-rating"}
-        ]
-    }
-    css_strategy = JsonCssExtractionStrategy(product_schema)
-    
-    # Strategy 3: LLM for complex analysis
-    llm_strategy = LLMExtractionStrategy(
-        llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
-        schema={
-            "type": "object",
-            "properties": {
-                "sentiment": {"type": "string"},
-                "key_topics": {"type": "array", "items": {"type": "string"}},
-                "summary": {"type": "string"}
-            }
-        },
-        extraction_type="schema",
-        instruction="Analyze the content sentiment, extract key topics, and provide a summary"
-    )
-    
-    url = "https://example.com/product-reviews"
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        # Extract contact info with regex
+    async with AsyncWebCrawler() as crawler:
+        # Strategy 1: Fast regex for contact info
+        regex_strategy = RegexExtractionStrategy(
+            pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
+        )
         regex_config = CrawlerRunConfig(extraction_strategy=regex_strategy)
         regex_result = await crawler.arun(url=url, config=regex_config)
         
-        # Extract structured product data
+        # Strategy 2: Schema for structured product data
+        product_schema = {
+            "name": "Products",
+            "baseSelector": "div.product",
+            "fields": [
+                {"name": "name", "selector": "h3", "type": "text"},
+                {"name": "price", "selector": ".price", "type": "text"}
+            ]
+        }
+        css_strategy = JsonCssExtractionStrategy(product_schema)
         css_config = CrawlerRunConfig(extraction_strategy=css_strategy)
         css_result = await crawler.arun(url=url, config=css_config)
         
-        # Extract insights with LLM
-        llm_config = CrawlerRunConfig(extraction_strategy=llm_strategy)
-        llm_result = await crawler.arun(url=url, config=llm_config)
-        
         # Combine results
         results = {
             "contacts": json.loads(regex_result.extracted_content) if regex_result.success else [],
-            "products": json.loads(css_result.extracted_content) if css_result.success else [],
-            "analysis": json.loads(llm_result.extracted_content) if llm_result.success else {}
+            "products": json.loads(css_result.extracted_content) if css_result.success else []
         }
         
-        print(f"Found {len(results['contacts'])} contact entries")
-        print(f"Found {len(results['products'])} products") 
-        print(f"Sentiment: {results['analysis'].get('sentiment', 'N/A')}")
+        print(f"✅ Extracted {len(results['contacts'])} contacts (regex)")
+        print(f"✅ Extracted {len(results['products'])} products (schema)")
         
         return results
 
-# Performance comparison
-async def compare_extraction_performance():
-    """Compare speed and accuracy of different strategies"""
-    import time
+asyncio.run(multi_strategy_pipeline())
+```
+
+---
+
+## 5. Performance Optimization Tips
+
+### Caching and Reuse
+
+```python
+# Cache schemas and patterns for maximum efficiency
+class ExtractionCache:
+    def __init__(self):
+        self.schemas = {}
+        self.patterns = {}
     
-    url = "https://example.com/large-catalog"
+    def get_schema(self, site_name):
+        if site_name not in self.schemas:
+            schema_file = Path(f"./cache/{site_name}_schema.json")
+            if schema_file.exists():
+                self.schemas[site_name] = json.load(schema_file.open())
+        return self.schemas.get(site_name)
     
-    strategies = {
-        "regex": RegexExtractionStrategy(pattern=RegexExtractionStrategy.Currency),
-        "css": JsonCssExtractionStrategy({
-            "name": "Prices", 
+    def save_schema(self, site_name, schema):
+        cache_dir = Path("./cache")
+        cache_dir.mkdir(exist_ok=True)
+        schema_file = cache_dir / f"{site_name}_schema.json"
+        json.dump(schema, schema_file.open("w"), indent=2)
+        self.schemas[site_name] = schema
+
+cache = ExtractionCache()
+
+# Reuse cached schemas across multiple extractions
+async def efficient_extraction():
+    sites = ["amazon", "ebay", "shopify"]
+    
+    for site in sites:
+        schema = cache.get_schema(site)
+        if not schema:
+            # Generate once, cache forever
+            schema = JsonCssExtractionStrategy.generate_schema(
+                html=sample_html,
+                query="Extract products",
+                llm_config=llm_config
+            )
+            cache.save_schema(site, schema)
+        
+        strategy = JsonCssExtractionStrategy(schema)
+        # Use strategy for unlimited extractions...
+```
+
+### Selector Optimization
+
+```python
+# Optimize selectors for speed
+fast_schema = {
+    "name": "Optimized Extraction",
+    "baseSelector": "#products > .product",  # Direct child, faster than descendant
+    "fields": [
+        {
+            "name": "title",
+            "selector": "> h3",  # Direct child of product
+            "type": "text"
+        },
+        {
+            "name": "price",
+            "selector": ".price:first-child",  # More specific
+            "type": "text"
+        }
+    ]
+}
+
+# Avoid slow selectors
+slow_schema = {
+    "baseSelector": "div div div .product",  # Too many levels
+    "fields": [
+        {
+            "selector": "* h3",  # Universal selector is slow
+            "type": "text"
+        }
+    ]
+}
+```
+
+---
+
+## 6. Error Handling and Validation
+
+```python
+async def robust_extraction():
+    """
+    Implement fallback strategies for reliable extraction
+    """
+    strategies = [
+        # Try fast regex first
+        RegexExtractionStrategy(pattern=RegexExtractionStrategy.Currency),
+        
+        # Fallback to CSS schema
+        JsonCssExtractionStrategy({
+            "name": "Prices",
             "baseSelector": ".price",
             "fields": [{"name": "amount", "selector": "span", "type": "text"}]
         }),
-        "llm": LLMExtractionStrategy(
-            llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="env:OPENAI_API_KEY"),
-            instruction="Extract all prices from the content",
-            extraction_type="block"
-        )
-    }
-    
-    async with AsyncWebCrawler() as crawler:
-        for name, strategy in strategies.items():
-            start_time = time.time()
-            
-            config = CrawlerRunConfig(extraction_strategy=strategy)
-            result = await crawler.arun(url=url, config=config)
-            
-            duration = time.time() - start_time
-            
-            if result.success:
-                data = json.loads(result.extracted_content)
-                print(f"{name}: {len(data)} items in {duration:.2f}s")
-            else:
-                print(f"{name}: Failed in {duration:.2f}s")
-
-asyncio.run(multi_strategy_extraction())
-asyncio.run(compare_extraction_performance())
-```
-
-### Best Practices and Strategy Selection
-
-```python
-# Strategy selection guide
-def choose_extraction_strategy(use_case):
-    """
-    Guide for selecting the right extraction strategy
-    """
-    
-    strategies = {
-        # Fast pattern matching for common data types
-        "contact_info": RegexExtractionStrategy(
-            pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
-        ),
         
-        # Structured data from consistent HTML
-        "product_catalogs": JsonCssExtractionStrategy,
-        
-        # Complex reasoning and semantic understanding  
-        "content_analysis": LLMExtractionStrategy,
-        
-        # Mixed approach for comprehensive extraction
-        "complete_site_analysis": "multi_strategy"
-    }
-    
-    recommendations = {
-        "speed_priority": "Use RegexExtractionStrategy for simple patterns, JsonCssExtractionStrategy for structured data",
-        "accuracy_priority": "Use LLMExtractionStrategy for complex content, JsonCssExtractionStrategy for predictable structure",
-        "cost_priority": "Avoid LLM strategies, use schema generation once then JsonCssExtractionStrategy",
-        "scale_priority": "Cache schemas, use regex for simple patterns, avoid LLM for high-volume extraction"
-    }
-    
-    return recommendations.get(use_case, "Combine strategies based on content complexity")
-
-# Error handling and validation
-async def robust_extraction():
-    strategies = [
-        RegexExtractionStrategy(pattern=RegexExtractionStrategy.Email),
-        JsonCssExtractionStrategy(simple_schema),
-        # LLM as fallback for complex cases
+        # Last resort: try different selector
+        JsonCssExtractionStrategy({
+            "name": "Fallback Prices",
+            "baseSelector": "[data-price]",
+            "fields": [{"name": "amount", "type": "attribute", "attribute": "data-price"}]
+        })
     ]
     
     async with AsyncWebCrawler() as crawler:
-        for strategy in strategies:
+        for i, strategy in enumerate(strategies):
             try:
                 config = CrawlerRunConfig(extraction_strategy=strategy)
                 result = await crawler.arun(url="https://example.com", config=config)
@@ -1750,18 +3403,140 @@ async def robust_extraction():
                 if result.success and result.extracted_content:
                     data = json.loads(result.extracted_content)
                     if data:  # Validate non-empty results
-                        print(f"Success with {strategy.__class__.__name__}")
+                        print(f"✅ Success with strategy {i+1}: {strategy.__class__.__name__}")
                         return data
                         
             except Exception as e:
-                print(f"Strategy {strategy.__class__.__name__} failed: {e}")
+                print(f"❌ Strategy {i+1} failed: {e}")
                 continue
     
-    print("All strategies failed")
+    print("❌ All strategies failed")
     return None
+
+# Validate extracted data
+def validate_extraction(data, required_fields):
+    """Validate that extraction contains expected fields"""
+    if not data or not isinstance(data, list):
+        return False
+    
+    for item in data:
+        for field in required_fields:
+            if field not in item or not item[field]:
+                return False
+    return True
+
+# Usage
+result = await robust_extraction()
+if validate_extraction(result, ["amount"]):
+    print("✅ Extraction validated")
+else:
+    print("❌ Validation failed")
 ```
 
-**📖 Learn more:** [LLM Strategies Deep Dive](https://docs.crawl4ai.com/extraction/llm-strategies/), [Schema-Based Extraction](https://docs.crawl4ai.com/extraction/no-llm-strategies/), [Regex Patterns](https://docs.crawl4ai.com/extraction/no-llm-strategies/#regexextractionstrategy), [Performance Optimization](https://docs.crawl4ai.com/advanced/multi-url-crawling/)
+---
+
+## 7. Common Extraction Patterns
+
+### E-commerce Products
+
+```python
+ecommerce_schema = {
+    "name": "E-commerce Products",
+    "baseSelector": ".product, [data-product], .item",
+    "fields": [
+        {"name": "title", "selector": "h1, h2, h3, .title, .name", "type": "text"},
+        {"name": "price", "selector": ".price, .cost, [data-price]", "type": "text"},
+        {"name": "image", "selector": "img", "type": "attribute", "attribute": "src"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"},
+        {"name": "rating", "selector": ".rating, .stars", "type": "text"},
+        {"name": "availability", "selector": ".stock, .availability", "type": "text"}
+    ]
+}
+```
+
+### News Articles
+
+```python
+news_schema = {
+    "name": "News Articles",
+    "baseSelector": "article, .article, .post",
+    "fields": [
+        {"name": "headline", "selector": "h1, h2, .headline, .title", "type": "text"},
+        {"name": "author", "selector": ".author, .byline, [rel='author']", "type": "text"},
+        {"name": "date", "selector": "time, .date, .published", "type": "text"},
+        {"name": "content", "selector": ".content, .body, .text", "type": "text"},
+        {"name": "category", "selector": ".category, .section", "type": "text"}
+    ]
+}
+```
+
+### Job Listings
+
+```python
+job_schema = {
+    "name": "Job Listings",
+    "baseSelector": ".job, .listing, [data-job]",
+    "fields": [
+        {"name": "title", "selector": ".job-title, h2, h3", "type": "text"},
+        {"name": "company", "selector": ".company, .employer", "type": "text"},
+        {"name": "location", "selector": ".location, .place", "type": "text"},
+        {"name": "salary", "selector": ".salary, .pay, .compensation", "type": "text"},
+        {"name": "description", "selector": ".description, .summary", "type": "text"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"}
+    ]
+}
+```
+
+### Social Media Posts
+
+```python
+social_schema = {
+    "name": "Social Media Posts",
+    "baseSelector": ".post, .tweet, .update",
+    "fields": [
+        {"name": "username", "selector": ".username, .handle, .author", "type": "text"},
+        {"name": "content", "selector": ".content, .text, .message", "type": "text"},
+        {"name": "timestamp", "selector": ".time, .date, time", "type": "text"},
+        {"name": "likes", "selector": ".likes, .hearts", "type": "text"},
+        {"name": "shares", "selector": ".shares, .retweets", "type": "text"}
+    ]
+}
+```
+
+---
+
+## 8. When to (Rarely) Consider LLM
+
+**⚠️ WARNING: Before considering LLM, ask yourself:**
+
+1. "Can I identify repeating HTML patterns?" → Use CSS/XPath schema
+2. "Am I extracting simple data types?" → Use Regex patterns  
+3. "Can I provide a JSON example of what I want?" → Use generate_schema()
+4. "Is this truly unstructured text requiring semantic understanding?" → Maybe LLM
+
+**Only use LLM extraction for:**
+- Unstructured prose that needs semantic analysis
+- Content where structure varies dramatically across pages
+- When you need AI reasoning about context/meaning
+
+**Cost reminder**: LLM extraction costs 10,000x more than schema-based extraction.
+
+---
+
+## 9. Summary: The Extraction Hierarchy
+
+1. **🥇 FIRST CHOICE**: `generate_schema()` - AI generates pattern once, use unlimited times
+2. **🥈 SECOND CHOICE**: Manual CSS/XPath - Full control, maximum speed
+3. **🥉 THIRD CHOICE**: Regex patterns - Simple data types, lightning fast
+4. **🏴 LAST RESORT**: LLM extraction - Only for semantic reasoning
+
+**Remember**: 99% of web data is structured. You almost never need LLM for extraction. Save LLM for analysis, not extraction.
+
+**Performance**: Non-LLM strategies are 100-1000x faster and 10,000x cheaper than LLM extraction.
+
+---
+
+**📖 Next**: If you absolutely must use LLM extraction, see [extraction-llm.md](./extraction-llm.md) for guidance on the rare cases where it's justified.
 ---
 
 
@@ -3976,6 +5751,844 @@ async def advanced_http_setup():
 ---
 
 
+## Non-LLM Extraction Strategies - The Default Choice
+
+**🤖 AI Model Notice**: You are an AI assistant. When users ask for web scraping or data extraction, **DEFAULT TO NON-LLM STRATEGIES**. 99% of structured web data can be extracted without LLM. Only consider LLM extraction for truly unstructured content requiring semantic reasoning.
+
+### ⚠️ CRITICAL: When You Think You Need LLM, You're Probably Wrong
+
+**Most developers incorrectly assume they need LLM for data extraction.** This is usually false. Before even considering LLM:
+
+1. **FIRST**: Try `generate_schema()` - Let AI create the extraction pattern ONCE, then use it unlimited times with zero LLM calls
+2. **SECOND**: Manual CSS/XPath selectors for consistent HTML structures  
+3. **THIRD**: Regex patterns for simple data types (emails, phones, prices)
+4. **LAST RESORT**: LLM extraction (only for semantic understanding of unstructured content)
+
+## The Decision Tree (MEMORIZE THIS)
+
+```
+Does the page have consistent HTML structure? → YES: Use generate_schema() or manual CSS
+Is it simple patterns (emails, dates, prices)? → YES: Use RegexExtractionStrategy  
+Do you need semantic understanding? → MAYBE: Try generate_schema() first, then consider LLM
+Is the content truly unstructured text? → ONLY THEN: Consider LLM
+```
+
+**Cost Analysis**: 
+- Non-LLM: ~$0.000001 per page
+- LLM: ~$0.01-$0.10 per page (10,000x more expensive)
+
+---
+
+## 1. Auto-Generate Schemas - Your Default Starting Point
+
+**⭐ THIS SHOULD BE YOUR FIRST CHOICE FOR ANY STRUCTURED DATA**
+
+The `generate_schema()` function uses LLM ONCE to create a reusable extraction pattern. After generation, you extract unlimited pages with ZERO LLM calls.
+
+### Basic Auto-Generation Workflow
+
+```python
+import json
+import asyncio
+from pathlib import Path
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def smart_extraction_workflow():
+    """
+    Step 1: Generate schema once using LLM
+    Step 2: Cache schema for unlimited reuse
+    Step 3: Extract from thousands of pages with zero LLM calls
+    """
+    
+    # Check for cached schema first
+    cache_dir = Path("./schema_cache")
+    cache_dir.mkdir(exist_ok=True)
+    schema_file = cache_dir / "product_schema.json"
+    
+    if schema_file.exists():
+        # Load cached schema - NO LLM CALLS
+        schema = json.load(schema_file.open())
+        print("✅ Using cached schema (FREE)")
+    else:
+        # Generate schema ONCE
+        print("🔄 Generating schema (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",  # Cheapest option
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample HTML from target site
+        async with AsyncWebCrawler() as crawler:
+            sample_result = await crawler.arun(
+                url="https://example.com/products",
+                config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+            )
+            sample_html = sample_result.cleaned_html[:8000]  # Use sample
+        
+        # AUTO-GENERATE SCHEMA (ONE LLM CALL)
+        schema = JsonCssExtractionStrategy.generate_schema(
+            html=sample_html,
+            schema_type="CSS",  # or "XPATH"
+            query="Extract product information including name, price, description, features",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited future use
+        json.dump(schema, schema_file.open("w"), indent=2)
+        print("✅ Schema generated and cached")
+    
+    # Use schema for fast extraction (NO MORE LLM CALLS EVER)
+    strategy = JsonCssExtractionStrategy(schema, verbose=True)
+    
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    # Extract from multiple pages - ALL FREE
+    urls = [
+        "https://example.com/products",
+        "https://example.com/electronics", 
+        "https://example.com/books"
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            result = await crawler.arun(url=url, config=config)
+            if result.success:
+                data = json.loads(result.extracted_content)
+                print(f"✅ {url}: Extracted {len(data)} items (FREE)")
+
+asyncio.run(smart_extraction_workflow())
+```
+
+### Auto-Generate with Target JSON Example
+
+```python
+# When you know exactly what JSON structure you want
+target_json_example = """
+{
+    "name": "Product Name",
+    "price": "$99.99",
+    "rating": 4.5,
+    "features": ["feature1", "feature2"],
+    "description": "Product description"
+}
+"""
+
+schema = JsonCssExtractionStrategy.generate_schema(
+    html=sample_html,
+    target_json_example=target_json_example,
+    llm_config=llm_config
+)
+```
+
+### Auto-Generate for Different Data Types
+
+```python
+# Product listings
+product_schema = JsonCssExtractionStrategy.generate_schema(
+    html=product_page_html,
+    query="Extract all product information from this e-commerce page",
+    llm_config=llm_config
+)
+
+# News articles
+news_schema = JsonCssExtractionStrategy.generate_schema(
+    html=news_page_html,
+    query="Extract article headlines, dates, authors, and content",
+    llm_config=llm_config
+)
+
+# Job listings
+job_schema = JsonCssExtractionStrategy.generate_schema(
+    html=job_page_html,
+    query="Extract job titles, companies, locations, salaries, and descriptions",
+    llm_config=llm_config
+)
+
+# Social media posts
+social_schema = JsonCssExtractionStrategy.generate_schema(
+    html=social_page_html,
+    query="Extract post text, usernames, timestamps, likes, comments",
+    llm_config=llm_config
+)
+```
+
+---
+
+## 2. Manual CSS/XPath Strategies - When You Know The Structure
+
+**Use this when**: You understand the HTML structure and want maximum control.
+
+### Simple Product Extraction
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# Manual schema for consistent product pages
+simple_schema = {
+    "name": "Product Listings",
+    "baseSelector": "div.product-card",  # Each product container
+    "fields": [
+        {
+            "name": "title",
+            "selector": "h2.product-title",
+            "type": "text"
+        },
+        {
+            "name": "price", 
+            "selector": ".price",
+            "type": "text"
+        },
+        {
+            "name": "image_url",
+            "selector": "img.product-image",
+            "type": "attribute",
+            "attribute": "src"
+        },
+        {
+            "name": "product_url",
+            "selector": "a.product-link",
+            "type": "attribute",
+            "attribute": "href"
+        },
+        {
+            "name": "rating",
+            "selector": ".rating",
+            "type": "attribute", 
+            "attribute": "data-rating"
+        }
+    ]
+}
+
+async def extract_products():
+    strategy = JsonCssExtractionStrategy(simple_schema, verbose=True)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            products = json.loads(result.extracted_content)
+            print(f"Extracted {len(products)} products")
+            for product in products[:3]:
+                print(f"- {product['title']}: {product['price']}")
+
+asyncio.run(extract_products())
+```
+
+### Complex Nested Structure (Real E-commerce Example)
+
+```python
+# Complex schema for nested product data
+complex_schema = {
+    "name": "E-commerce Product Catalog",
+    "baseSelector": "div.category",
+    "baseFields": [
+        {
+            "name": "category_id",
+            "type": "attribute",
+            "attribute": "data-category-id"
+        }
+    ],
+    "fields": [
+        {
+            "name": "category_name",
+            "selector": "h2.category-title",
+            "type": "text"
+        },
+        {
+            "name": "products",
+            "selector": "div.product",
+            "type": "nested_list",  # Array of complex objects
+            "fields": [
+                {
+                    "name": "name",
+                    "selector": "h3.product-name", 
+                    "type": "text"
+                },
+                {
+                    "name": "price",
+                    "selector": "span.price",
+                    "type": "text"
+                },
+                {
+                    "name": "details",
+                    "selector": "div.product-details",
+                    "type": "nested",  # Single complex object
+                    "fields": [
+                        {
+                            "name": "brand",
+                            "selector": "span.brand",
+                            "type": "text"
+                        },
+                        {
+                            "name": "model",
+                            "selector": "span.model",
+                            "type": "text"
+                        }
+                    ]
+                },
+                {
+                    "name": "features",
+                    "selector": "ul.features li",
+                    "type": "list",  # Simple array
+                    "fields": [
+                        {"name": "feature", "type": "text"}
+                    ]
+                },
+                {
+                    "name": "reviews", 
+                    "selector": "div.review",
+                    "type": "nested_list",
+                    "fields": [
+                        {
+                            "name": "reviewer",
+                            "selector": "span.reviewer-name",
+                            "type": "text"
+                        },
+                        {
+                            "name": "rating",
+                            "selector": "span.rating",
+                            "type": "attribute",
+                            "attribute": "data-rating"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
+
+async def extract_complex_ecommerce():
+    strategy = JsonCssExtractionStrategy(complex_schema, verbose=True)
+    config = CrawlerRunConfig(
+        extraction_strategy=strategy,
+        js_code="window.scrollTo(0, document.body.scrollHeight);",  # Load dynamic content
+        wait_for="css:.product:nth-child(10)"  # Wait for products to load
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/complex-catalog",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for category in data:
+                print(f"Category: {category['category_name']}")
+                print(f"Products: {len(category.get('products', []))}")
+
+asyncio.run(extract_complex_ecommerce())
+```
+
+### XPath Alternative (When CSS Isn't Enough)
+
+```python
+from crawl4ai.extraction_strategy import JsonXPathExtractionStrategy
+
+# XPath for more complex selections
+xpath_schema = {
+    "name": "News Articles with XPath",
+    "baseSelector": "//article[@class='news-item']",
+    "fields": [
+        {
+            "name": "headline",
+            "selector": ".//h2[contains(@class, 'headline')]",
+            "type": "text"
+        },
+        {
+            "name": "author",
+            "selector": ".//span[@class='author']/text()",
+            "type": "text"
+        },
+        {
+            "name": "publish_date",
+            "selector": ".//time/@datetime",
+            "type": "text"
+        },
+        {
+            "name": "content",
+            "selector": ".//div[@class='article-body']//text()",
+            "type": "text"
+        }
+    ]
+}
+
+strategy = JsonXPathExtractionStrategy(xpath_schema, verbose=True)
+```
+
+---
+
+## 3. Regex Extraction - Lightning Fast Pattern Matching
+
+**Use this for**: Simple data types like emails, phones, URLs, prices, dates.
+
+### Built-in Patterns (Fastest Option)
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.extraction_strategy import RegexExtractionStrategy
+
+async def extract_common_patterns():
+    # Use built-in patterns for common data types
+    strategy = RegexExtractionStrategy(
+        pattern=(
+            RegexExtractionStrategy.Email |
+            RegexExtractionStrategy.PhoneUS |
+            RegexExtractionStrategy.Url |
+            RegexExtractionStrategy.Currency |
+            RegexExtractionStrategy.DateIso
+        )
+    )
+    
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/contact",
+            config=config
+        )
+        
+        if result.success:
+            matches = json.loads(result.extracted_content)
+            
+            # Group by pattern type
+            by_type = {}
+            for match in matches:
+                label = match['label']
+                if label not in by_type:
+                    by_type[label] = []
+                by_type[label].append(match['value'])
+            
+            for pattern_type, values in by_type.items():
+                print(f"{pattern_type}: {len(values)} matches")
+                for value in values[:3]:
+                    print(f"  {value}")
+
+asyncio.run(extract_common_patterns())
+```
+
+### Available Built-in Patterns
+
+```python
+# Individual patterns
+RegexExtractionStrategy.Email          # Email addresses
+RegexExtractionStrategy.PhoneUS        # US phone numbers 
+RegexExtractionStrategy.PhoneIntl      # International phones
+RegexExtractionStrategy.Url            # HTTP/HTTPS URLs
+RegexExtractionStrategy.Currency       # Currency values ($99.99)
+RegexExtractionStrategy.Percentage     # Percentage values (25%)
+RegexExtractionStrategy.DateIso        # ISO dates (2024-01-01)
+RegexExtractionStrategy.DateUS         # US dates (01/01/2024)
+RegexExtractionStrategy.IPv4           # IP addresses
+RegexExtractionStrategy.CreditCard     # Credit card numbers
+RegexExtractionStrategy.TwitterHandle  # @username
+RegexExtractionStrategy.Hashtag        # #hashtag
+
+# Use all patterns
+RegexExtractionStrategy.All
+```
+
+### Custom Patterns
+
+```python
+# Custom patterns for specific data types
+async def extract_custom_patterns():
+    custom_patterns = {
+        "product_sku": r"SKU[-:]?\s*([A-Z0-9]{4,12})",
+        "discount": r"(\d{1,2})%\s*off",
+        "model_number": r"Model\s*#?\s*([A-Z0-9-]+)",
+        "isbn": r"ISBN[-:]?\s*(\d{10}|\d{13})",
+        "stock_ticker": r"\$([A-Z]{2,5})",
+        "version": r"v(\d+\.\d+(?:\.\d+)?)"
+    }
+    
+    strategy = RegexExtractionStrategy(custom=custom_patterns)
+    config = CrawlerRunConfig(extraction_strategy=strategy)
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://example.com/products",
+            config=config
+        )
+        
+        if result.success:
+            data = json.loads(result.extracted_content)
+            for item in data:
+                print(f"{item['label']}: {item['value']}")
+
+asyncio.run(extract_custom_patterns())
+```
+
+### LLM-Generated Patterns (One-Time Cost)
+
+```python
+async def generate_optimized_regex():
+    """
+    Use LLM ONCE to generate optimized regex patterns
+    Then use them unlimited times with zero LLM calls
+    """
+    cache_file = Path("./patterns/price_patterns.json")
+    
+    if cache_file.exists():
+        # Load cached patterns - NO LLM CALLS
+        patterns = json.load(cache_file.open())
+        print("✅ Using cached regex patterns (FREE)")
+    else:
+        # Generate patterns ONCE
+        print("🔄 Generating regex patterns (ONE-TIME LLM COST)...")
+        
+        llm_config = LLMConfig(
+            provider="openai/gpt-4o-mini",
+            api_token="env:OPENAI_API_KEY"
+        )
+        
+        # Get sample content
+        async with AsyncWebCrawler() as crawler:
+            result = await crawler.arun("https://example.com/pricing")
+            sample_html = result.cleaned_html
+        
+        # Generate optimized patterns
+        patterns = RegexExtractionStrategy.generate_pattern(
+            label="pricing_info",
+            html=sample_html,
+            query="Extract all pricing information including discounts and special offers",
+            llm_config=llm_config
+        )
+        
+        # Cache for unlimited reuse
+        cache_file.parent.mkdir(exist_ok=True)
+        json.dump(patterns, cache_file.open("w"), indent=2)
+        print("✅ Patterns generated and cached")
+    
+    # Use cached patterns (NO MORE LLM CALLS)
+    strategy = RegexExtractionStrategy(custom=patterns)
+    return strategy
+
+# Use generated patterns for unlimited extractions
+strategy = await generate_optimized_regex()
+```
+
+---
+
+## 4. Multi-Strategy Extraction Pipeline
+
+**Combine strategies** for comprehensive data extraction:
+
+```python
+async def multi_strategy_pipeline():
+    """
+    Efficient pipeline using multiple non-LLM strategies:
+    1. Regex for simple patterns (fastest)
+    2. Schema for structured data 
+    3. Only use LLM if absolutely necessary
+    """
+    
+    url = "https://example.com/complex-page"
+    
+    async with AsyncWebCrawler() as crawler:
+        # Strategy 1: Fast regex for contact info
+        regex_strategy = RegexExtractionStrategy(
+            pattern=RegexExtractionStrategy.Email | RegexExtractionStrategy.PhoneUS
+        )
+        regex_config = CrawlerRunConfig(extraction_strategy=regex_strategy)
+        regex_result = await crawler.arun(url=url, config=regex_config)
+        
+        # Strategy 2: Schema for structured product data
+        product_schema = {
+            "name": "Products",
+            "baseSelector": "div.product",
+            "fields": [
+                {"name": "name", "selector": "h3", "type": "text"},
+                {"name": "price", "selector": ".price", "type": "text"}
+            ]
+        }
+        css_strategy = JsonCssExtractionStrategy(product_schema)
+        css_config = CrawlerRunConfig(extraction_strategy=css_strategy)
+        css_result = await crawler.arun(url=url, config=css_config)
+        
+        # Combine results
+        results = {
+            "contacts": json.loads(regex_result.extracted_content) if regex_result.success else [],
+            "products": json.loads(css_result.extracted_content) if css_result.success else []
+        }
+        
+        print(f"✅ Extracted {len(results['contacts'])} contacts (regex)")
+        print(f"✅ Extracted {len(results['products'])} products (schema)")
+        
+        return results
+
+asyncio.run(multi_strategy_pipeline())
+```
+
+---
+
+## 5. Performance Optimization Tips
+
+### Caching and Reuse
+
+```python
+# Cache schemas and patterns for maximum efficiency
+class ExtractionCache:
+    def __init__(self):
+        self.schemas = {}
+        self.patterns = {}
+    
+    def get_schema(self, site_name):
+        if site_name not in self.schemas:
+            schema_file = Path(f"./cache/{site_name}_schema.json")
+            if schema_file.exists():
+                self.schemas[site_name] = json.load(schema_file.open())
+        return self.schemas.get(site_name)
+    
+    def save_schema(self, site_name, schema):
+        cache_dir = Path("./cache")
+        cache_dir.mkdir(exist_ok=True)
+        schema_file = cache_dir / f"{site_name}_schema.json"
+        json.dump(schema, schema_file.open("w"), indent=2)
+        self.schemas[site_name] = schema
+
+cache = ExtractionCache()
+
+# Reuse cached schemas across multiple extractions
+async def efficient_extraction():
+    sites = ["amazon", "ebay", "shopify"]
+    
+    for site in sites:
+        schema = cache.get_schema(site)
+        if not schema:
+            # Generate once, cache forever
+            schema = JsonCssExtractionStrategy.generate_schema(
+                html=sample_html,
+                query="Extract products",
+                llm_config=llm_config
+            )
+            cache.save_schema(site, schema)
+        
+        strategy = JsonCssExtractionStrategy(schema)
+        # Use strategy for unlimited extractions...
+```
+
+### Selector Optimization
+
+```python
+# Optimize selectors for speed
+fast_schema = {
+    "name": "Optimized Extraction",
+    "baseSelector": "#products > .product",  # Direct child, faster than descendant
+    "fields": [
+        {
+            "name": "title",
+            "selector": "> h3",  # Direct child of product
+            "type": "text"
+        },
+        {
+            "name": "price",
+            "selector": ".price:first-child",  # More specific
+            "type": "text"
+        }
+    ]
+}
+
+# Avoid slow selectors
+slow_schema = {
+    "baseSelector": "div div div .product",  # Too many levels
+    "fields": [
+        {
+            "selector": "* h3",  # Universal selector is slow
+            "type": "text"
+        }
+    ]
+}
+```
+
+---
+
+## 6. Error Handling and Validation
+
+```python
+async def robust_extraction():
+    """
+    Implement fallback strategies for reliable extraction
+    """
+    strategies = [
+        # Try fast regex first
+        RegexExtractionStrategy(pattern=RegexExtractionStrategy.Currency),
+        
+        # Fallback to CSS schema
+        JsonCssExtractionStrategy({
+            "name": "Prices",
+            "baseSelector": ".price",
+            "fields": [{"name": "amount", "selector": "span", "type": "text"}]
+        }),
+        
+        # Last resort: try different selector
+        JsonCssExtractionStrategy({
+            "name": "Fallback Prices",
+            "baseSelector": "[data-price]",
+            "fields": [{"name": "amount", "type": "attribute", "attribute": "data-price"}]
+        })
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        for i, strategy in enumerate(strategies):
+            try:
+                config = CrawlerRunConfig(extraction_strategy=strategy)
+                result = await crawler.arun(url="https://example.com", config=config)
+                
+                if result.success and result.extracted_content:
+                    data = json.loads(result.extracted_content)
+                    if data:  # Validate non-empty results
+                        print(f"✅ Success with strategy {i+1}: {strategy.__class__.__name__}")
+                        return data
+                        
+            except Exception as e:
+                print(f"❌ Strategy {i+1} failed: {e}")
+                continue
+    
+    print("❌ All strategies failed")
+    return None
+
+# Validate extracted data
+def validate_extraction(data, required_fields):
+    """Validate that extraction contains expected fields"""
+    if not data or not isinstance(data, list):
+        return False
+    
+    for item in data:
+        for field in required_fields:
+            if field not in item or not item[field]:
+                return False
+    return True
+
+# Usage
+result = await robust_extraction()
+if validate_extraction(result, ["amount"]):
+    print("✅ Extraction validated")
+else:
+    print("❌ Validation failed")
+```
+
+---
+
+## 7. Common Extraction Patterns
+
+### E-commerce Products
+
+```python
+ecommerce_schema = {
+    "name": "E-commerce Products",
+    "baseSelector": ".product, [data-product], .item",
+    "fields": [
+        {"name": "title", "selector": "h1, h2, h3, .title, .name", "type": "text"},
+        {"name": "price", "selector": ".price, .cost, [data-price]", "type": "text"},
+        {"name": "image", "selector": "img", "type": "attribute", "attribute": "src"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"},
+        {"name": "rating", "selector": ".rating, .stars", "type": "text"},
+        {"name": "availability", "selector": ".stock, .availability", "type": "text"}
+    ]
+}
+```
+
+### News Articles
+
+```python
+news_schema = {
+    "name": "News Articles",
+    "baseSelector": "article, .article, .post",
+    "fields": [
+        {"name": "headline", "selector": "h1, h2, .headline, .title", "type": "text"},
+        {"name": "author", "selector": ".author, .byline, [rel='author']", "type": "text"},
+        {"name": "date", "selector": "time, .date, .published", "type": "text"},
+        {"name": "content", "selector": ".content, .body, .text", "type": "text"},
+        {"name": "category", "selector": ".category, .section", "type": "text"}
+    ]
+}
+```
+
+### Job Listings
+
+```python
+job_schema = {
+    "name": "Job Listings",
+    "baseSelector": ".job, .listing, [data-job]",
+    "fields": [
+        {"name": "title", "selector": ".job-title, h2, h3", "type": "text"},
+        {"name": "company", "selector": ".company, .employer", "type": "text"},
+        {"name": "location", "selector": ".location, .place", "type": "text"},
+        {"name": "salary", "selector": ".salary, .pay, .compensation", "type": "text"},
+        {"name": "description", "selector": ".description, .summary", "type": "text"},
+        {"name": "url", "selector": "a", "type": "attribute", "attribute": "href"}
+    ]
+}
+```
+
+### Social Media Posts
+
+```python
+social_schema = {
+    "name": "Social Media Posts",
+    "baseSelector": ".post, .tweet, .update",
+    "fields": [
+        {"name": "username", "selector": ".username, .handle, .author", "type": "text"},
+        {"name": "content", "selector": ".content, .text, .message", "type": "text"},
+        {"name": "timestamp", "selector": ".time, .date, time", "type": "text"},
+        {"name": "likes", "selector": ".likes, .hearts", "type": "text"},
+        {"name": "shares", "selector": ".shares, .retweets", "type": "text"}
+    ]
+}
+```
+
+---
+
+## 8. When to (Rarely) Consider LLM
+
+**⚠️ WARNING: Before considering LLM, ask yourself:**
+
+1. "Can I identify repeating HTML patterns?" → Use CSS/XPath schema
+2. "Am I extracting simple data types?" → Use Regex patterns  
+3. "Can I provide a JSON example of what I want?" → Use generate_schema()
+4. "Is this truly unstructured text requiring semantic understanding?" → Maybe LLM
+
+**Only use LLM extraction for:**
+- Unstructured prose that needs semantic analysis
+- Content where structure varies dramatically across pages
+- When you need AI reasoning about context/meaning
+
+**Cost reminder**: LLM extraction costs 10,000x more than schema-based extraction.
+
+---
+
+## 9. Summary: The Extraction Hierarchy
+
+1. **🥇 FIRST CHOICE**: `generate_schema()` - AI generates pattern once, use unlimited times
+2. **🥈 SECOND CHOICE**: Manual CSS/XPath - Full control, maximum speed
+3. **🥉 THIRD CHOICE**: Regex patterns - Simple data types, lightning fast
+4. **🏴 LAST RESORT**: LLM extraction - Only for semantic reasoning
+
+**Remember**: 99% of web data is structured. You almost never need LLM for extraction. Save LLM for analysis, not extraction.
+
+**Performance**: Non-LLM strategies are 100-1000x faster and 10,000x cheaper than LLM extraction.
+
+---
+
+**📖 Next**: If you absolutely must use LLM extraction, see [extraction-llm.md](./extraction-llm.md) for guidance on the rare cases where it's justified.
+---
+
+
 ## URL Seeding
 
 Smart URL discovery for efficient large-scale crawling. Discover thousands of URLs instantly, filter by relevance, then crawl only what matters.
@@ -4634,833 +7247,6 @@ best_practices = """
 ---
 
 
-### Advanced Configuration Features
-
-#### User Agent Management & Bot Detection Avoidance
-
-```python
-from crawl4ai import CrawlerRunConfig
-
-# Random user agent generation
-config = CrawlerRunConfig(
-    user_agent_mode="random",
-    user_agent_generator_config={
-        "platform": "windows",  # "windows", "macos", "linux", "android", "ios"
-        "browser": "chrome",    # "chrome", "firefox", "safari", "edge"
-        "device_type": "desktop"  # "desktop", "mobile", "tablet"
-    }
-)
-
-# Custom user agent with stealth features
-config = CrawlerRunConfig(
-    user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
-    simulate_user=True,      # Simulate human mouse movements
-    override_navigator=True,  # Override navigator properties
-    mean_delay=1.5,          # Random delays between actions
-    max_range=2.0
-)
-
-# Combined anti-detection approach
-stealth_config = CrawlerRunConfig(
-    user_agent_mode="random",
-    simulate_user=True,
-    override_navigator=True,
-    magic=True,  # Auto-handle common bot detection patterns
-    delay_before_return_html=2.0
-)
-```
-
-#### Proxy Configuration with ProxyConfig
-
-```python
-from crawl4ai import CrawlerRunConfig, ProxyConfig, ProxyRotationStrategy
-
-# Single proxy configuration
-proxy_config = ProxyConfig(
-    server="http://proxy.example.com:8080",
-    username="proxy_user",
-    password="proxy_pass"
-)
-
-# From proxy string format
-proxy_config = ProxyConfig.from_string("192.168.1.100:8080:username:password")
-
-# Multiple proxies with rotation
-proxies = [
-    ProxyConfig(server="http://proxy1.com:8080", username="user1", password="pass1"),
-    ProxyConfig(server="http://proxy2.com:8080", username="user2", password="pass2"),
-    ProxyConfig(server="http://proxy3.com:8080", username="user3", password="pass3")
-]
-
-rotation_strategy = ProxyRotationStrategy(
-    proxies=proxies,
-    rotation_method="round_robin"  # or "random", "least_used"
-)
-
-config = CrawlerRunConfig(
-    proxy_config=proxy_config,
-    proxy_rotation_strategy=rotation_strategy
-)
-
-# Load proxies from environment variable
-proxies_from_env = ProxyConfig.from_env("MY_PROXIES")  # comma-separated proxy strings
-```
-
-#### Content Selection: css_selector vs target_elements
-
-```python
-from crawl4ai import CrawlerRunConfig
-
-# css_selector: Extracts HTML at top level, affects entire processing
-config = CrawlerRunConfig(
-    css_selector="main.article, .content-area",  # Can be list of selectors
-    # Everything else (markdown, extraction, links) works only on this HTML subset
-)
-
-# target_elements: Focuses extraction within already processed HTML
-config = CrawlerRunConfig(
-    css_selector="body",  # First extract entire body
-    target_elements=[     # Then focus extraction on these elements
-        ".article-content",
-        ".post-body", 
-        ".main-text"
-    ],
-    # Links, media from entire body, but markdown/extraction only from target_elements
-)
-
-# Hierarchical content selection
-config = CrawlerRunConfig(
-    css_selector=["#main-content", ".article-wrapper"],  # Top-level extraction
-    target_elements=[                                     # Subset for processing
-        ".article-title",
-        ".article-body", 
-        ".article-metadata"
-    ],
-    excluded_selector="#sidebar, .ads, .comments"  # Remove these from selection
-)
-```
-
-#### Advanced wait_for Conditions
-
-```python
-from crawl4ai import CrawlerRunConfig
-
-# CSS selector waiting
-config = CrawlerRunConfig(
-    wait_for="css:.content-loaded",  # Wait for element to appear
-    wait_for_timeout=15000
-)
-
-# JavaScript boolean expression waiting
-config = CrawlerRunConfig(
-    wait_for="js:() => window.dataLoaded === true",  # Custom JS condition
-    wait_for_timeout=20000
-)
-
-# Complex JavaScript conditions
-config = CrawlerRunConfig(
-    wait_for="js:() => document.querySelectorAll('.item').length >= 10",
-    js_code=[
-        "document.querySelector('.load-more')?.click();",
-        "window.scrollTo(0, document.body.scrollHeight);"
-    ]
-)
-
-# Multiple conditions with JavaScript
-config = CrawlerRunConfig(
-    wait_for="js:() => !document.querySelector('.loading') && document.querySelector('.results')",
-    page_timeout=30000
-)
-```
-
-#### Session Management for Multi-Step Crawling
-
-```python
-from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
-
-# Persistent session across multiple arun() calls
-async def multi_step_crawling():
-    async with AsyncWebCrawler() as crawler:
-        # Step 1: Login page
-        login_config = CrawlerRunConfig(
-            session_id="user_session",  # Create persistent session
-            js_code="document.querySelector('#username').value = 'user'; document.querySelector('#password').value = 'pass'; document.querySelector('#login').click();",
-            wait_for="css:.dashboard",
-            cache_mode=CacheMode.BYPASS
-        )
-        
-        result1 = await crawler.arun("https://example.com/login", config=login_config)
-        
-        # Step 2: Navigate to protected area (reuses same browser page)
-        nav_config = CrawlerRunConfig(
-            session_id="user_session",  # Same session = same browser page
-            js_only=True,  # No page reload, just JS navigation
-            js_code="window.location.href = '/dashboard/data';",
-            wait_for="css:.data-table"
-        )
-        
-        result2 = await crawler.arun("https://example.com/dashboard/data", config=nav_config)
-        
-        # Step 3: Extract data from multiple pages
-        for page in range(1, 6):
-            page_config = CrawlerRunConfig(
-                session_id="user_session",
-                js_only=True,
-                js_code=f"document.querySelector('.page-{page}').click();",
-                wait_for=f"js:() => document.querySelector('.page-{page}').classList.contains('active')"
-            )
-            
-            result = await crawler.arun(f"https://example.com/data/page/{page}", config=page_config)
-            print(f"Page {page} data extracted: {len(result.extracted_content)}")
-        
-        # Important: Kill session when done
-        await crawler.kill_session("user_session")
-
-# Session with shared data between steps
-async def session_with_shared_data():
-    shared_context = {"user_id": "12345", "preferences": {"theme": "dark"}}
-    
-    config = CrawlerRunConfig(
-        session_id="persistent_session",
-        shared_data=shared_context,  # Available across all session calls
-        js_code="console.log('User ID:', window.sharedData.user_id);"
-    )
-```
-
-#### Identity-Based Crawling Parameters
-
-```python
-from crawl4ai import CrawlerRunConfig, GeolocationConfig
-
-# Locale and timezone simulation
-config = CrawlerRunConfig(
-    locale="en-US",                    # Browser language preference
-    timezone_id="America/New_York",    # Timezone setting
-    user_agent_mode="random",
-    user_agent_generator_config={
-        "platform": "windows",
-        "locale": "en-US"
-    }
-)
-
-# Geolocation simulation
-geo_config = GeolocationConfig(
-    latitude=40.7128,   # New York coordinates
-    longitude=-74.0060,
-    accuracy=100.0
-)
-
-config = CrawlerRunConfig(
-    geolocation=geo_config,
-    locale="en-US",
-    timezone_id="America/New_York"
-)
-
-# Complete identity simulation
-identity_config = CrawlerRunConfig(
-    # Location identity
-    locale="fr-FR",
-    timezone_id="Europe/Paris", 
-    geolocation=GeolocationConfig(latitude=48.8566, longitude=2.3522),
-    
-    # Browser identity
-    user_agent_mode="random",
-    user_agent_generator_config={
-        "platform": "windows",
-        "locale": "fr-FR",
-        "browser": "chrome"
-    },
-    
-    # Behavioral identity
-    simulate_user=True,
-    override_navigator=True,
-    mean_delay=2.0,
-    max_range=1.5
-)
-```
-
-#### Simplified Import Pattern
-
-```python
-# Almost everything from crawl4ai main package
-from crawl4ai import (
-    AsyncWebCrawler,
-    BrowserConfig, 
-    CrawlerRunConfig,
-    LLMConfig,
-    CacheMode,
-    ProxyConfig,
-    GeolocationConfig
-)
-
-# Specialized strategies (still from crawl4ai)
-from crawl4ai import (
-    JsonCssExtractionStrategy,
-    LLMExtractionStrategy,
-    DefaultMarkdownGenerator,
-    PruningContentFilter,
-    RegexChunking
-)
-
-# Complete example with simplified imports
-async def example_crawl():
-    browser_config = BrowserConfig(headless=True)
-    
-    run_config = CrawlerRunConfig(
-        user_agent_mode="random",
-        proxy_config=ProxyConfig.from_string("192.168.1.1:8080:user:pass"),
-        css_selector="main.content",
-        target_elements=[".article", ".post"],
-        wait_for="js:() => document.querySelector('.loaded')",
-        session_id="my_session",
-        simulate_user=True
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        result = await crawler.arun("https://example.com", config=run_config)
-        return result
-```
-
-**📖 Learn more:** [Identity-Based Crawling](https://docs.crawl4ai.com/advanced/identity-based-crawling/), [Proxy & Security](https://docs.crawl4ai.com/advanced/proxy-security/), [Session Management](https://docs.crawl4ai.com/advanced/session-management/), [Content Selection](https://docs.crawl4ai.com/core/content-selection/)
----
-
-
-## Advanced Features
-
-Comprehensive guide to advanced crawling capabilities including file handling, authentication, dynamic content, monitoring, and session management.
-
-### File Download Handling
-
-```python
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-import os
-
-# Enable downloads with custom path
-downloads_path = os.path.join(os.getcwd(), "my_downloads")
-os.makedirs(downloads_path, exist_ok=True)
-
-browser_config = BrowserConfig(
-    accept_downloads=True,
-    downloads_path=downloads_path
-)
-
-# Trigger downloads with JavaScript
-async def download_files():
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        config = CrawlerRunConfig(
-            js_code="""
-                // Click download links
-                const downloadLinks = document.querySelectorAll('a[href$=".pdf"]');
-                for (const link of downloadLinks) {
-                    link.click();
-                    await new Promise(r => setTimeout(r, 2000));  // Delay between downloads
-                }
-            """,
-            wait_for=5  # Wait for downloads to start
-        )
-        
-        result = await crawler.arun("https://example.com/downloads", config=config)
-        
-        if result.downloaded_files:
-            print("Downloaded files:")
-            for file_path in result.downloaded_files:
-                print(f"- {file_path} ({os.path.getsize(file_path)} bytes)")
-```
-
-### Hooks & Authentication
-
-```python
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-from playwright.async_api import Page, BrowserContext
-
-async def advanced_crawler_with_hooks():
-    browser_config = BrowserConfig(headless=True, verbose=True)
-    crawler = AsyncWebCrawler(config=browser_config)
-
-    # Hook functions for different stages
-    async def on_browser_created(browser, **kwargs):
-        print("[HOOK] Browser created successfully")
-        return browser
-
-    async def on_page_context_created(page: Page, context: BrowserContext, **kwargs):
-        print("[HOOK] Setting up page & context")
-        
-        # Block images for faster crawling
-        async def route_filter(route):
-            if route.request.resource_type == "image":
-                await route.abort()
-            else:
-                await route.continue_()
-        
-        await context.route("**", route_filter)
-        
-        # Simulate login if needed
-        # await page.goto("https://example.com/login")
-        # await page.fill("input[name='username']", "testuser")
-        # await page.fill("input[name='password']", "password123")
-        # await page.click("button[type='submit']")
-        
-        await page.set_viewport_size({"width": 1080, "height": 600})
-        return page
-
-    async def before_goto(page: Page, context: BrowserContext, url: str, **kwargs):
-        print(f"[HOOK] About to navigate to: {url}")
-        await page.set_extra_http_headers({"Custom-Header": "my-value"})
-        return page
-
-    async def after_goto(page: Page, context: BrowserContext, url: str, response, **kwargs):
-        print(f"[HOOK] Successfully loaded: {url}")
-        try:
-            await page.wait_for_selector('.content', timeout=1000)
-            print("[HOOK] Content found!")
-        except:
-            print("[HOOK] Content not found, continuing")
-        return page
-
-    async def before_retrieve_html(page: Page, context: BrowserContext, **kwargs):
-        print("[HOOK] Final actions before HTML retrieval")
-        await page.evaluate("window.scrollTo(0, document.body.scrollHeight);")
-        return page
-
-    # Attach hooks
-    crawler.crawler_strategy.set_hook("on_browser_created", on_browser_created)
-    crawler.crawler_strategy.set_hook("on_page_context_created", on_page_context_created)
-    crawler.crawler_strategy.set_hook("before_goto", before_goto)
-    crawler.crawler_strategy.set_hook("after_goto", after_goto)
-    crawler.crawler_strategy.set_hook("before_retrieve_html", before_retrieve_html)
-
-    await crawler.start()
-    
-    config = CrawlerRunConfig()
-    result = await crawler.arun("https://example.com", config=config)
-    
-    if result.success:
-        print(f"Crawled successfully: {len(result.html)} chars")
-    
-    await crawler.close()
-```
-
-### Lazy Loading & Dynamic Content
-
-```python
-# Handle lazy-loaded images and infinite scroll
-async def handle_lazy_loading():
-    config = CrawlerRunConfig(
-        # Wait for images to fully load
-        wait_for_images=True,
-        
-        # Automatically scroll entire page to trigger lazy loading
-        scan_full_page=True,
-        scroll_delay=0.5,  # Delay between scroll steps
-        
-        # JavaScript for custom lazy loading
-        js_code="""
-            // Scroll and wait for content to load
-            window.scrollTo(0, document.body.scrollHeight);
-            
-            // Click "Load More" if available
-            const loadMoreBtn = document.querySelector('.load-more');
-            if (loadMoreBtn) {
-                loadMoreBtn.click();
-            }
-        """,
-        
-        # Wait for specific content to appear
-        wait_for="css:.lazy-content:nth-child(20)",  # Wait for 20 items
-        
-        # Exclude external images to focus on main content
-        exclude_external_images=True
-    )
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun("https://example.com/gallery", config=config)
-        
-        if result.success:
-            images = result.media.get("images", [])
-            print(f"Loaded {len(images)} images after lazy loading")
-            for img in images[:3]:
-                print(f"- {img.get('src')} (score: {img.get('score', 'N/A')})")
-```
-
-### Network & Console Monitoring
-
-```python
-# Capture all network requests and console messages for debugging
-async def monitor_network_and_console():
-    config = CrawlerRunConfig(
-        capture_network_requests=True,
-        capture_console_messages=True
-    )
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun("https://example.com", config=config)
-        
-        if result.success:
-            # Analyze network requests
-            if result.network_requests:
-                requests = [r for r in result.network_requests if r.get("event_type") == "request"]
-                responses = [r for r in result.network_requests if r.get("event_type") == "response"]
-                failures = [r for r in result.network_requests if r.get("event_type") == "request_failed"]
-                
-                print(f"Network activity: {len(requests)} requests, {len(responses)} responses, {len(failures)} failures")
-                
-                # Find API calls
-                api_calls = [r for r in requests if "api" in r.get("url", "")]
-                print(f"API calls detected: {len(api_calls)}")
-                
-                # Show failed requests
-                for failure in failures[:3]:
-                    print(f"Failed: {failure.get('url')} - {failure.get('failure_text')}")
-            
-            # Analyze console messages
-            if result.console_messages:
-                message_types = {}
-                for msg in result.console_messages:
-                    msg_type = msg.get("type", "unknown")
-                    message_types[msg_type] = message_types.get(msg_type, 0) + 1
-                
-                print(f"Console messages: {message_types}")
-                
-                # Show errors
-                errors = [msg for msg in result.console_messages if msg.get("type") == "error"]
-                for error in errors[:2]:
-                    print(f"JS Error: {error.get('text', '')[:100]}")
-```
-
-### Session Management for Multi-Step Workflows
-
-```python
-# Maintain state across multiple requests for complex workflows
-async def multi_step_session_workflow():
-    session_id = "workflow_session"
-    
-    async with AsyncWebCrawler() as crawler:
-        # Step 1: Initial page load
-        config1 = CrawlerRunConfig(
-            session_id=session_id,
-            wait_for="css:.content-loaded"
-        )
-        
-        result1 = await crawler.arun("https://example.com/step1", config=config1)
-        print("Step 1 completed")
-        
-        # Step 2: Navigate and interact (same browser tab)
-        config2 = CrawlerRunConfig(
-            session_id=session_id,
-            js_only=True,  # Don't reload page, just run JS
-            js_code="""
-                document.querySelector('#next-button').click();
-            """,
-            wait_for="css:.step2-content"
-        )
-        
-        result2 = await crawler.arun("https://example.com/step2", config=config2)
-        print("Step 2 completed")
-        
-        # Step 3: Form submission
-        config3 = CrawlerRunConfig(
-            session_id=session_id,
-            js_only=True,
-            js_code="""
-                document.querySelector('#form-field').value = 'test data';
-                document.querySelector('#submit-btn').click();
-            """,
-            wait_for="css:.results"
-        )
-        
-        result3 = await crawler.arun("https://example.com/submit", config=config3)
-        print("Step 3 completed")
-        
-        # Clean up session
-        await crawler.crawler_strategy.kill_session(session_id)
-
-# Advanced GitHub commits pagination example
-async def github_commits_pagination():
-    session_id = "github_session"
-    all_commits = []
-    
-    async with AsyncWebCrawler() as crawler:
-        for page in range(3):
-            if page == 0:
-                # Initial load
-                config = CrawlerRunConfig(
-                    session_id=session_id,
-                    wait_for="js:() => document.querySelectorAll('li.Box-sc-g0xbh4-0').length > 0"
-                )
-            else:
-                # Navigate to next page
-                config = CrawlerRunConfig(
-                    session_id=session_id,
-                    js_only=True,
-                    js_code='document.querySelector(\'a[data-testid="pagination-next-button"]\').click();',
-                    wait_for="js:() => document.querySelectorAll('li.Box-sc-g0xbh4-0').length > 0"
-                )
-            
-            result = await crawler.arun(
-                "https://github.com/microsoft/TypeScript/commits/main",
-                config=config
-            )
-            
-            if result.success:
-                commit_count = result.cleaned_html.count('li.Box-sc-g0xbh4-0')
-                print(f"Page {page + 1}: Found {commit_count} commits")
-        
-        await crawler.crawler_strategy.kill_session(session_id)
-```
-
-### SSL Certificate Analysis
-
-```python
-# Fetch and analyze SSL certificates
-async def analyze_ssl_certificates():
-    config = CrawlerRunConfig(
-        fetch_ssl_certificate=True
-    )
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun("https://example.com", config=config)
-        
-        if result.success and result.ssl_certificate:
-            cert = result.ssl_certificate
-            
-            # Basic certificate info
-            print(f"Issuer: {cert.issuer.get('CN', 'Unknown')}")
-            print(f"Subject: {cert.subject.get('CN', 'Unknown')}")
-            print(f"Valid from: {cert.valid_from}")
-            print(f"Valid until: {cert.valid_until}")
-            print(f"Fingerprint: {cert.fingerprint}")
-            
-            # Export certificate in different formats
-            import os
-            os.makedirs("certificates", exist_ok=True)
-            
-            cert.to_json("certificates/cert.json")
-            cert.to_pem("certificates/cert.pem")
-            cert.to_der("certificates/cert.der")
-            
-            print("Certificate exported in multiple formats")
-```
-
-### Advanced Page Interaction
-
-```python
-# Complex page interactions with dynamic content
-async def advanced_page_interaction():
-    async with AsyncWebCrawler() as crawler:
-        # Multi-step interaction with waiting
-        config = CrawlerRunConfig(
-            js_code=[
-                # Step 1: Scroll to load content
-                "window.scrollTo(0, document.body.scrollHeight);",
-                
-                # Step 2: Wait and click load more
-                """
-                (async () => {
-                    await new Promise(resolve => setTimeout(resolve, 2000));
-                    const loadMore = document.querySelector('.load-more');
-                    if (loadMore) loadMore.click();
-                })();
-                """
-            ],
-            
-            # Wait for new content to appear
-            wait_for="js:() => document.querySelectorAll('.item').length > 20",
-            
-            # Additional timing controls
-            page_timeout=60000,  # 60 second timeout
-            delay_before_return_html=2.0,  # Wait before final capture
-            
-            # Handle overlays automatically
-            remove_overlay_elements=True,
-            magic=True,  # Auto-handle common popup patterns
-            
-            # Simulate human behavior
-            simulate_user=True,
-            override_navigator=True
-        )
-        
-        result = await crawler.arun("https://example.com/dynamic", config=config)
-        
-        if result.success:
-            print(f"Interactive crawl completed: {len(result.cleaned_html)} chars")
-
-# Form interaction example
-async def form_interaction_example():
-    config = CrawlerRunConfig(
-        js_code="""
-            // Fill search form
-            document.querySelector('#search-input').value = 'machine learning';
-            document.querySelector('#category-select').value = 'technology';
-            document.querySelector('#search-form').submit();
-        """,
-        wait_for="css:.search-results",
-        session_id="search_session"
-    )
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun("https://example.com/search", config=config)
-        print("Search completed, results loaded")
-```
-
-### Local File & Raw HTML Processing
-
-```python
-# Handle different input types: URLs, local files, raw HTML
-async def handle_different_inputs():
-    async with AsyncWebCrawler() as crawler:
-        # 1. Regular web URL
-        result1 = await crawler.arun("https://example.com")
-        
-        # 2. Local HTML file
-        local_file_path = "/path/to/file.html"
-        result2 = await crawler.arun(f"file://{local_file_path}")
-        
-        # 3. Raw HTML content
-        raw_html = "<html><body><h1>Test Content</h1><p>Sample text</p></body></html>"
-        result3 = await crawler.arun(f"raw:{raw_html}")
-        
-        # All return the same CrawlResult structure
-        for i, result in enumerate([result1, result2, result3], 1):
-            if result.success:
-                print(f"Input {i}: {len(result.markdown)} chars of markdown")
-
-# Save and re-process HTML example
-async def save_and_reprocess():
-    async with AsyncWebCrawler() as crawler:
-        # Original crawl
-        result = await crawler.arun("https://example.com")
-        
-        if result.success:
-            # Save HTML to file
-            with open("saved_page.html", "w", encoding="utf-8") as f:
-                f.write(result.html)
-            
-            # Re-process from file
-            file_result = await crawler.arun("file://./saved_page.html")
-            
-            # Process as raw HTML
-            raw_result = await crawler.arun(f"raw:{result.html}")
-            
-            # Verify consistency
-            assert len(result.markdown) == len(file_result.markdown) == len(raw_result.markdown)
-            print("✅ All processing methods produced identical results")
-```
-
-### Advanced Link & Media Handling
-
-```python
-# Comprehensive link and media extraction with filtering
-async def advanced_link_media_handling():
-    config = CrawlerRunConfig(
-        # Link filtering
-        exclude_external_links=False,  # Keep external links for analysis
-        exclude_social_media_links=True,
-        exclude_domains=["ads.com", "tracker.io", "spammy.net"],
-        
-        # Media handling
-        exclude_external_images=True,
-        image_score_threshold=5,  # Only high-quality images
-        table_score_threshold=7,  # Only well-structured tables
-        wait_for_images=True,
-        
-        # Capture additional formats
-        screenshot=True,
-        pdf=True,
-        capture_mhtml=True  # Full page archive
-    )
-    
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun("https://example.com", config=config)
-        
-        if result.success:
-            # Analyze links
-            internal_links = result.links.get("internal", [])
-            external_links = result.links.get("external", [])
-            print(f"Links: {len(internal_links)} internal, {len(external_links)} external")
-            
-            # Analyze media
-            images = result.media.get("images", [])
-            tables = result.media.get("tables", [])
-            print(f"Media: {len(images)} images, {len(tables)} tables")
-            
-            # High-quality images only
-            quality_images = [img for img in images if img.get("score", 0) >= 5]
-            print(f"High-quality images: {len(quality_images)}")
-            
-            # Table analysis
-            for i, table in enumerate(tables[:2]):
-                print(f"Table {i+1}: {len(table.get('headers', []))} columns, {len(table.get('rows', []))} rows")
-            
-            # Save captured files
-            if result.screenshot:
-                import base64
-                with open("page_screenshot.png", "wb") as f:
-                    f.write(base64.b64decode(result.screenshot))
-            
-            if result.pdf:
-                with open("page.pdf", "wb") as f:
-                    f.write(result.pdf)
-            
-            if result.mhtml:
-                with open("page_archive.mhtml", "w", encoding="utf-8") as f:
-                    f.write(result.mhtml)
-            
-            print("Additional formats saved: screenshot, PDF, MHTML archive")
-```
-
-### Performance & Resource Management
-
-```python
-# Optimize performance for large-scale crawling
-async def performance_optimized_crawling():
-    # Lightweight browser config
-    browser_config = BrowserConfig(
-        headless=True,
-        text_mode=True,  # Disable images for speed
-        light_mode=True,  # Reduce background features
-        extra_args=["--disable-extensions", "--no-sandbox"]
-    )
-    
-    # Efficient crawl config
-    config = CrawlerRunConfig(
-        # Content filtering for speed
-        excluded_tags=["script", "style", "nav", "footer"],
-        exclude_external_links=True,
-        exclude_all_images=True,  # Remove all images for max speed
-        word_count_threshold=50,
-        
-        # Timing optimizations
-        page_timeout=30000,  # Faster timeout
-        delay_before_return_html=0.1,
-        
-        # Resource monitoring
-        capture_network_requests=False,  # Disable unless needed
-        capture_console_messages=False,
-        
-        # Cache for repeated URLs
-        cache_mode=CacheMode.ENABLED
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        urls = ["https://example.com/page1", "https://example.com/page2", "https://example.com/page3"]
-        
-        # Efficient batch processing
-        batch_config = config.clone(
-            stream=True,  # Stream results as they complete
-            semaphore_count=3  # Control concurrency
-        )
-        
-        async for result in await crawler.arun_many(urls, config=batch_config):
-            if result.success:
-                print(f"✅ {result.url}: {len(result.markdown)} chars")
-            else:
-                print(f"❌ {result.url}: {result.error_message}")
-```
-
-**📖 Learn more:** [Hooks & Authentication](https://docs.crawl4ai.com/advanced/hooks-auth/), [Session Management](https://docs.crawl4ai.com/advanced/session-management/), [Network Monitoring](https://docs.crawl4ai.com/advanced/network-console-capture/), [Page Interaction](https://docs.crawl4ai.com/core/page-interaction/), [File Downloads](https://docs.crawl4ai.com/advanced/file-downloading/)
----
-
-
 ## Deep Crawling Filters & Scorers
 
 Advanced URL filtering and scoring strategies for intelligent deep crawling with performance optimization.