Release v0.7.4: Merge release branch

- Merge release/v0.7.4 into main
- Version: 0.7.4
- Ready for tag and publication

.github/FUNDING.yml

@@ -0,0 +1,7 @@
+# These are supported funding model platforms
+
+# GitHub Sponsors
+github: unclecode
+
+# Custom links for enterprise inquiries (uncomment when ready)
+# custom: ["https://crawl4ai.com/enterprise"]

.github/workflows/main.yml

@@ -9,16 +9,26 @@ on:
     types: [opened]
   discussion:
     types: [created]
+  watch:
+    types: [started]
 
 jobs:
   notify-discord:
     runs-on: ubuntu-latest
     steps:
+      - name: Send to Google Apps Script (Stars only)
+        if: github.event_name == 'watch'
+        run: |
+          curl -fSs -X POST "${{ secrets.GOOGLE_SCRIPT_ENDPOINT }}" \
+            -H 'Content-Type: application/json' \
+            -d '{"url":"${{ github.event.sender.html_url }}"}'
       - name: Set webhook based on event type
         id: set-webhook
         run: |
           if [ "${{ github.event_name }}" == "discussion" ]; then
             echo "webhook=${{ secrets.DISCORD_DISCUSSIONS_WEBHOOK }}" >> $GITHUB_OUTPUT
+          elif [ "${{ github.event_name }}" == "watch" ]; then
+            echo "webhook=${{ secrets.DISCORD_STAR_GAZERS }}" >> $GITHUB_OUTPUT
           else
             echo "webhook=${{ secrets.DISCORD_WEBHOOK }}" >> $GITHUB_OUTPUT
           fi
@@ -31,5 +41,6 @@ jobs:
           args: |
             ${{ github.event_name == 'issues' && format('📣 New issue created: **{0}** by {1} - {2}', github.event.issue.title, github.event.issue.user.login, github.event.issue.html_url) || 
             github.event_name == 'issue_comment' && format('💬 New comment on issue **{0}** by {1} - {2}', github.event.issue.title, github.event.comment.user.login, github.event.comment.html_url) ||
-            github.event_name == 'pull_request' && format('🔄 New PR opened: **{0}** by {1} - {2}', github.event.pull_request.title, github.event.pull_request.user.login, github.event.pull_request.html_url) || 
+            github.event_name == 'pull_request' && format('🔄 New PR opened: **{0}** by {1} - {2}', github.event.pull_request.title, github.event.pull_request.user.login, github.event.pull_request.html_url) ||
+            github.event_name == 'watch' && format('⭐ {0} starred Crawl4AI 🥳! Check out their profile: {1}', github.event.sender.login, github.event.sender.html_url) ||
             format('💬 New discussion started: **{0}** by {1} - {2}', github.event.discussion.title, github.event.discussion.user.login, github.event.discussion.html_url) }}

.github/workflows/release.yml

@@ -0,0 +1,142 @@
+name: Release Pipeline
+on:
+  push:
+    tags:
+      - 'v*'
+      - '!test-v*'  # Exclude test tags
+
+jobs:
+  release:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write  # Required for creating releases
+    
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+      
+      - name: Extract version from tag
+        id: get_version
+        run: |
+          TAG_VERSION=${GITHUB_REF#refs/tags/v}
+          echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
+          echo "Releasing version: $TAG_VERSION"
+      
+      - name: Install package dependencies
+        run: |
+          pip install -e .
+      
+      - name: Check version consistency
+        run: |
+          TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
+          PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
+          
+          echo "Tag version: $TAG_VERSION"
+          echo "Package version: $PACKAGE_VERSION"
+          
+          if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
+            echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
+            echo "Please update crawl4ai/__version__.py to match the tag version"
+            exit 1
+          fi
+          echo "✅ Version check passed: $TAG_VERSION"
+      
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build twine
+      
+      - name: Build package
+        run: python -m build
+      
+      - name: Check package
+        run: twine check dist/*
+      
+      - name: Upload to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
+        run: |
+          echo "📦 Uploading to PyPI..."
+          twine upload dist/*
+          echo "✅ Package uploaded to https://pypi.org/project/crawl4ai/"
+      
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
+      
+      - name: Extract major and minor versions
+        id: versions
+        run: |
+          VERSION=${{ steps.get_version.outputs.VERSION }}
+          MAJOR=$(echo $VERSION | cut -d. -f1)
+          MINOR=$(echo $VERSION | cut -d. -f1-2)
+          echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
+          echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
+      
+      - name: Build and push Docker images
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          push: true
+          tags: |
+            unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
+            unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}
+            unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}
+            unclecode/crawl4ai:latest
+          platforms: linux/amd64,linux/arm64
+      
+      - name: Create GitHub Release
+        uses: softprops/action-gh-release@v2
+        with:
+          tag_name: v${{ steps.get_version.outputs.VERSION }}
+          name: Release v${{ steps.get_version.outputs.VERSION }}
+          body: |
+            ## 🎉 Crawl4AI v${{ steps.get_version.outputs.VERSION }} Released!
+            
+            ### 📦 Installation
+            
+            **PyPI:**
+            ```bash
+            pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}
+            ```
+            
+            **Docker:**
+            ```bash
+            docker pull unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
+            docker pull unclecode/crawl4ai:latest
+            ```
+            
+            ### 📝 What's Changed
+            See [CHANGELOG.md](https://github.com/${{ github.repository }}/blob/main/CHANGELOG.md) for details.
+          draft: false
+          prerelease: false
+          token: ${{ secrets.GITHUB_TOKEN }}
+      
+      - name: Summary
+        run: |
+          echo "## 🚀 Release Complete!" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### 📦 PyPI Package" >> $GITHUB_STEP_SUMMARY
+          echo "- Version: ${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
+          echo "- URL: https://pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
+          echo "- Install: \`pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### 🐳 Docker Images" >> $GITHUB_STEP_SUMMARY
+          echo "- \`unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "- \`unclecode/crawl4ai:latest\`" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### 📋 GitHub Release" >> $GITHUB_STEP_SUMMARY
+          echo "https://github.com/${{ github.repository }}/releases/tag/v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY

.github/workflows/test-release.yml.disabled

@@ -0,0 +1,116 @@
+name: Test Release Pipeline
+on:
+  push:
+    tags:
+      - 'test-v*'
+
+jobs:
+  test-release:
+    runs-on: ubuntu-latest
+    
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+      
+      - name: Extract version from tag
+        id: get_version
+        run: |
+          TAG_VERSION=${GITHUB_REF#refs/tags/test-v}
+          echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
+          echo "Testing with version: $TAG_VERSION"
+      
+      - name: Install package dependencies
+        run: |
+          pip install -e .
+      
+      - name: Check version consistency
+        run: |
+          TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
+          PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
+          
+          echo "Tag version: $TAG_VERSION"
+          echo "Package version: $PACKAGE_VERSION"
+          
+          if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
+            echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
+            echo "Please update crawl4ai/__version__.py to match the tag version"
+            exit 1
+          fi
+          echo "✅ Version check passed: $TAG_VERSION"
+      
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build twine
+      
+      - name: Build package
+        run: python -m build
+      
+      - name: Check package
+        run: twine check dist/*
+      
+      - name: Upload to Test PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.TEST_PYPI_TOKEN }}
+        run: |
+          echo "📦 Uploading to Test PyPI..."
+          twine upload --repository testpypi dist/* || {
+            if [ $? -eq 1 ]; then
+              echo "⚠️ Upload failed - likely version already exists on Test PyPI"
+              echo "Continuing anyway for test purposes..."
+            else
+              exit 1
+            fi
+          }
+          echo "✅ Test PyPI step complete"
+      
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
+      
+      - name: Build and push Docker test images
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          push: true
+          tags: |
+            unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}
+            unclecode/crawl4ai:test-latest
+          platforms: linux/amd64,linux/arm64
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+      
+      - name: Summary
+        run: |
+          echo "## 🎉 Test Release Complete!" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### 📦 Test PyPI Package" >> $GITHUB_STEP_SUMMARY
+          echo "- Version: ${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
+          echo "- URL: https://test.pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
+          echo "- Install: \`pip install -i https://test.pypi.org/simple/ crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### 🐳 Docker Test Images" >> $GITHUB_STEP_SUMMARY
+          echo "- \`unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "- \`unclecode/crawl4ai:test-latest\`" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### 🧹 Cleanup Commands" >> $GITHUB_STEP_SUMMARY
+          echo "\`\`\`bash" >> $GITHUB_STEP_SUMMARY
+          echo "# Remove test tag" >> $GITHUB_STEP_SUMMARY
+          echo "git tag -d test-v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
+          echo "git push origin :test-v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "# Remove Docker test images" >> $GITHUB_STEP_SUMMARY
+          echo "docker rmi unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
+          echo "docker rmi unclecode/crawl4ai:test-latest" >> $GITHUB_STEP_SUMMARY
+          echo "\`\`\`" >> $GITHUB_STEP_SUMMARY

CHANGELOG.md

@@ -5,6 +5,76 @@ All notable changes to Crawl4AI will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.7.3] - 2025-08-09
+
+### Added
+- **🕵️ Undetected Browser Support**: New browser adapter pattern with stealth capabilities
+  - `browser_adapter.py` with undetected Chrome integration
+  - Bypass sophisticated bot detection systems (Cloudflare, Akamai, custom solutions)
+  - Support for headless stealth mode with anti-detection techniques
+  - Human-like behavior simulation with random mouse movements and scrolling
+  - Comprehensive examples for anti-bot strategies and stealth crawling
+  - Full documentation guide for undetected browser usage
+
+- **🎨 Multi-URL Configuration System**: URL-specific crawler configurations for batch processing
+  - Different crawling strategies for different URL patterns in a single batch
+  - Support for string patterns with wildcards (`"*.pdf"`, `"*/blog/*"`)
+  - Lambda function matchers for complex URL logic
+  - Mixed matchers combining strings and functions with AND/OR logic
+  - Fallback configuration support when no patterns match
+  - First-match-wins configuration selection with optional fallback
+
+- **🧠 Memory Monitoring & Optimization**: Comprehensive memory usage tracking
+  - New `memory_utils.py` module for memory monitoring and optimization
+  - Real-time memory usage tracking during crawl sessions
+  - Memory leak detection and reporting
+  - Performance optimization recommendations
+  - Peak memory usage analysis and efficiency metrics
+  - Automatic cleanup suggestions for memory-intensive operations
+
+- **📊 Enhanced Table Extraction**: Improved table access and DataFrame conversion
+  - Direct `result.tables` interface replacing generic `result.media` approach
+  - Instant pandas DataFrame conversion with `pd.DataFrame(table['data'])`
+  - Enhanced table detection algorithms for better accuracy
+  - Table metadata including source XPath and headers
+  - Improved table structure preservation during extraction
+
+- **💰 GitHub Sponsors Integration**: 4-tier sponsorship system
+  - Supporter ($5/month): Community support + early feature previews
+  - Professional ($25/month): Priority support + beta access
+  - Business ($100/month): Direct consultation + custom integrations
+  - Enterprise ($500/month): Dedicated support + feature development
+  - Custom arrangement options for larger organizations
+
+- **🐳 Docker LLM Provider Flexibility**: Environment-based LLM configuration
+  - `LLM_PROVIDER` environment variable support for dynamic provider switching
+  - `.llm.env` file support for secure configuration management
+  - Per-request provider override capabilities in API endpoints
+  - Support for OpenAI, Groq, and other providers without rebuilding images
+  - Enhanced Docker documentation with deployment examples
+
+### Fixed
+- **URL Matcher Fallback**: Resolved edge cases in URL pattern matching logic
+- **Memory Management**: Fixed memory leaks in long-running crawl sessions
+- **Sitemap Processing**: Improved redirect handling in sitemap fetching
+- **Table Extraction**: Enhanced table detection and extraction accuracy
+- **Error Handling**: Better error messages and recovery from network failures
+
+### Changed
+- **Architecture Refactoring**: Major cleanup and optimization
+  - Moved 2,450+ lines from main `async_crawler_strategy.py` to backup
+  - Cleaner separation of concerns in crawler architecture
+  - Better maintainability and code organization
+  - Preserved backward compatibility while improving performance
+
+### Documentation
+- **Comprehensive Examples**: Added real-world URLs and practical use cases
+- **API Documentation**: Complete CrawlResult field documentation with all available fields
+- **Migration Guides**: Updated table extraction patterns from `result.media` to `result.tables`
+- **Undetected Browser Guide**: Full documentation for stealth mode and anti-bot strategies
+- **Multi-Config Examples**: Detailed examples for URL-specific configurations
+- **Docker Deployment**: Enhanced Docker documentation with LLM provider configuration
+
 ## [0.7.x] - 2025-06-29
 
 ### Added
@@ -21,6 +91,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+- **Flexible LLM Provider Configuration** (Docker): 
+  - Support for `LLM_PROVIDER` environment variable to override default provider
+  - Per-request provider override via optional `provider` parameter in API endpoints
+  - Automatic provider validation with clear error messages
+  - Updated Docker documentation and examples
+
+### Changed
+- **WebScrapingStrategy Refactoring**: Simplified content scraping architecture
+  - `WebScrapingStrategy` is now an alias for `LXMLWebScrapingStrategy` for backward compatibility
+  - Removed redundant BeautifulSoup-based implementation (~1000 lines of code)
+  - `LXMLWebScrapingStrategy` now inherits directly from `ContentScrapingStrategy`
+  - All existing code using `WebScrapingStrategy` continues to work without modification
+  - Default scraping strategy remains `LXMLWebScrapingStrategy` for optimal performance
+
 ### Added
 - **AsyncUrlSeeder**: High-performance URL discovery system for intelligent crawling at scale
   - Discover URLs from sitemaps and Common Crawl index

Dockerfile

@@ -1,7 +1,7 @@
 FROM python:3.12-slim-bookworm AS build
 
 # C4ai version
-ARG C4AI_VER=0.6.0
+ARG C4AI_VER=0.7.0-r1
 ENV C4AI_VERSION=$C4AI_VER
 LABEL c4ai.version=$C4AI_VER
 

README-first.md

@@ -0,0 +1,809 @@
+# 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper.
+
+<div align="center">
+
+<a href="https://trendshift.io/repositories/11716" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11716" alt="unclecode%2Fcrawl4ai | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+
+[![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
+[![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)
+
+[![PyPI version](https://badge.fury.io/py/crawl4ai.svg)](https://badge.fury.io/py/crawl4ai)
+[![Python Version](https://img.shields.io/pypi/pyversions/crawl4ai)](https://pypi.org/project/crawl4ai/)
+[![Downloads](https://static.pepy.tech/badge/crawl4ai/month)](https://pepy.tech/project/crawl4ai)
+[![GitHub Sponsors](https://img.shields.io/github/sponsors/unclecode?style=flat&logo=GitHub-Sponsors&label=Sponsors&color=pink)](https://github.com/sponsors/unclecode)
+
+<p align="center">
+    <a href="https://x.com/crawl4ai">
+      <img src="https://img.shields.io/badge/Follow%20on%20X-000000?style=for-the-badge&logo=x&logoColor=white" alt="Follow on X" />
+    </a>
+    <a href="https://www.linkedin.com/company/crawl4ai">
+      <img src="https://img.shields.io/badge/Follow%20on%20LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" alt="Follow on LinkedIn" />
+    </a>
+    <a href="https://discord.gg/jP8KfhDhyN">
+      <img src="https://img.shields.io/badge/Join%20our%20Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Join our Discord" />
+    </a>
+  </p>
+</div>
+
+Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.  
+
+[✨ Check out latest update v0.7.0](#-recent-updates)
+
+🎉 **Version 0.7.0 is now available!** The Adaptive Intelligence Update introduces groundbreaking features: Adaptive Crawling that learns website patterns, Virtual Scroll support for infinite pages, intelligent Link Preview with 3-layer scoring, Async URL Seeder for massive discovery, and significant performance improvements. [Read the release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.0.md)
+
+<details>
+<summary>🤓 <strong>My Personal Story</strong></summary>
+
+My journey with computers started in childhood when my dad, a computer scientist, introduced me to an Amstrad computer. Those early days sparked a fascination with technology, leading me to pursue computer science and specialize in NLP during my postgraduate studies. It was during this time that I first delved into web crawling, building tools to help researchers organize papers and extract information from publications a challenging yet rewarding experience that honed my skills in data extraction.
+
+Fast forward to 2023, I was working on a tool for a project and needed a crawler to convert a webpage into markdown. While exploring solutions, I found one that claimed to be open-source but required creating an account and generating an API token. Worse, it turned out to be a SaaS model charging $16, and its quality didn’t meet my standards. Frustrated, I realized this was a deeper problem. That frustration turned into turbo anger mode, and I decided to build my own solution. In just a few days, I created Crawl4AI. To my surprise, it went viral, earning thousands of GitHub stars and resonating with a global community.
+
+I made Crawl4AI open-source for two reasons. First, it’s my way of giving back to the open-source community that has supported me throughout my career. Second, I believe data should be accessible to everyone, not locked behind paywalls or monopolized by a few. Open access to data lays the foundation for the democratization of AI, a vision where individuals can train their own models and take ownership of their information. This library is the first step in a larger journey to create the best open-source data extraction and generation tool the world has ever seen, built collaboratively by a passionate community.
+
+Thank you to everyone who has supported this project, used it, and shared feedback. Your encouragement motivates me to dream even bigger. Join us, file issues, submit PRs, or spread the word. Together, we can build a tool that truly empowers people to access their own data and reshape the future of AI.
+</details>
+
+## 🧐 Why Crawl4AI?
+
+1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.  
+2. **Lightning Fast**: Delivers results faster with real-time, cost-efficient performance.  
+3. **Flexible Browser Control**: Offers session management, proxies, and custom hooks for seamless data access.  
+4. **Heuristic Intelligence**: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.  
+5. **Open Source & Deployable**: Fully open-source with no API keys—ready for Docker and cloud integration.  
+6. **Thriving Community**: Actively maintained by a vibrant community and the #1 trending GitHub repository.
+
+## 🚀 Quick Start 
+
+1. Install Crawl4AI:
+```bash
+# Install the package
+pip install -U crawl4ai
+
+# For pre release versions
+pip install crawl4ai --pre
+
+# Run post-installation setup
+crawl4ai-setup
+
+# Verify your installation
+crawl4ai-doctor
+```
+
+If you encounter any browser-related issues, you can install them manually:
+```bash
+python -m playwright install --with-deps chromium
+```
+
+2. Run a simple web crawl with Python:
+```python
+import asyncio
+from crawl4ai import *
+
+async def main():
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+        )
+        print(result.markdown)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+3. Or use the new command-line interface:
+```bash
+# Basic crawl with markdown output
+crwl https://www.nbcnews.com/business -o markdown
+
+# Deep crawl with BFS strategy, max 10 pages
+crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10
+
+# Use LLM extraction with a specific question
+crwl https://www.example.com/products -q "Extract all product prices"
+```
+
+## ✨ Features 
+
+<details>
+<summary>📝 <strong>Markdown Generation</strong></summary>
+
+- 🧹 **Clean Markdown**: Generates clean, structured Markdown with accurate formatting.
+- 🎯 **Fit Markdown**: Heuristic-based filtering to remove noise and irrelevant parts for AI-friendly processing.
+- 🔗 **Citations and References**: Converts page links into a numbered reference list with clean citations.
+- 🛠️ **Custom Strategies**: Users can create their own Markdown generation strategies tailored to specific needs.
+- 📚 **BM25 Algorithm**: Employs BM25-based filtering for extracting core information and removing irrelevant content. 
+</details>
+
+<details>
+<summary>📊 <strong>Structured Data Extraction</strong></summary>
+
+- 🤖 **LLM-Driven Extraction**: Supports all LLMs (open-source and proprietary) for structured data extraction.
+- 🧱 **Chunking Strategies**: Implements chunking (topic-based, regex, sentence-level) for targeted content processing.
+- 🌌 **Cosine Similarity**: Find relevant content chunks based on user queries for semantic extraction.
+- 🔎 **CSS-Based Extraction**: Fast schema-based data extraction using XPath and CSS selectors.
+- 🔧 **Schema Definition**: Define custom schemas for extracting structured JSON from repetitive patterns.
+
+</details>
+
+<details>
+<summary>🌐 <strong>Browser Integration</strong></summary>
+
+- 🖥️ **Managed Browser**: Use user-owned browsers with full control, avoiding bot detection.
+- 🔄 **Remote Browser Control**: Connect to Chrome Developer Tools Protocol for remote, large-scale data extraction.
+- 👤 **Browser Profiler**: Create and manage persistent profiles with saved authentication states, cookies, and settings.
+- 🔒 **Session Management**: Preserve browser states and reuse them for multi-step crawling.
+- 🧩 **Proxy Support**: Seamlessly connect to proxies with authentication for secure access.
+- ⚙️ **Full Browser Control**: Modify headers, cookies, user agents, and more for tailored crawling setups.
+- 🌍 **Multi-Browser Support**: Compatible with Chromium, Firefox, and WebKit.
+- 📐 **Dynamic Viewport Adjustment**: Automatically adjusts the browser viewport to match page content, ensuring complete rendering and capturing of all elements.
+
+</details>
+
+<details>
+<summary>🔎 <strong>Crawling & Scraping</strong></summary>
+
+- 🖼️ **Media Support**: Extract images, audio, videos, and responsive image formats like `srcset` and `picture`.
+- 🚀 **Dynamic Crawling**: Execute JS and wait for async or sync for dynamic content extraction.
+- 📸 **Screenshots**: Capture page screenshots during crawling for debugging or analysis.
+- 📂 **Raw Data Crawling**: Directly process raw HTML (`raw:`) or local files (`file://`).
+- 🔗 **Comprehensive Link Extraction**: Extracts internal, external links, and embedded iframe content.
+- 🛠️ **Customizable Hooks**: Define hooks at every step to customize crawling behavior.
+- 💾 **Caching**: Cache data for improved speed and to avoid redundant fetches.
+- 📄 **Metadata Extraction**: Retrieve structured metadata from web pages.
+- 📡 **IFrame Content Extraction**: Seamless extraction from embedded iframe content.
+- 🕵️ **Lazy Load Handling**: Waits for images to fully load, ensuring no content is missed due to lazy loading.
+- 🔄 **Full-Page Scanning**: Simulates scrolling to load and capture all dynamic content, perfect for infinite scroll pages.
+
+</details>
+
+<details>
+<summary>🚀 <strong>Deployment</strong></summary>
+
+- 🐳 **Dockerized Setup**: Optimized Docker image with FastAPI server for easy deployment.
+- 🔑 **Secure Authentication**: Built-in JWT token authentication for API security.
+- 🔄 **API Gateway**: One-click deployment with secure token authentication for API-based workflows.
+- 🌐 **Scalable Architecture**: Designed for mass-scale production and optimized server performance.
+- ☁️ **Cloud Deployment**: Ready-to-deploy configurations for major cloud platforms.
+
+</details>
+
+<details>
+<summary>🎯 <strong>Additional Features</strong></summary>
+
+- 🕶️ **Stealth Mode**: Avoid bot detection by mimicking real users.
+- 🏷️ **Tag-Based Content Extraction**: Refine crawling based on custom tags, headers, or metadata.
+- 🔗 **Link Analysis**: Extract and analyze all links for detailed data exploration.
+- 🛡️ **Error Handling**: Robust error management for seamless execution.
+- 🔐 **CORS & Static Serving**: Supports filesystem-based caching and cross-origin requests.
+- 📖 **Clear Documentation**: Simplified and updated guides for onboarding and advanced usage.
+- 🙌 **Community Recognition**: Acknowledges contributors and pull requests for transparency.
+
+</details>
+
+## Try it Now!
+
+✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SgRPrByQLzjRfwoRNq1wSGE9nYY_EE8C?usp=sharing)
+
+✨ Visit our [Documentation Website](https://docs.crawl4ai.com/)
+
+## Installation 🛠️
+
+Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
+
+<details>
+<summary>🐍 <strong>Using pip</strong></summary>
+
+Choose the installation option that best fits your needs:
+
+### Basic Installation
+
+For basic web crawling and scraping tasks:
+
+```bash
+pip install crawl4ai
+crawl4ai-setup # Setup the browser
+```
+
+By default, this will install the asynchronous version of Crawl4AI, using Playwright for web crawling.
+
+👉 **Note**: When you install Crawl4AI, the `crawl4ai-setup` should automatically install and set up Playwright. However, if you encounter any Playwright-related errors, you can manually install it using one of these methods:
+
+1. Through the command line:
+
+   ```bash
+   playwright install
+   ```
+
+2. If the above doesn't work, try this more specific command:
+
+   ```bash
+   python -m playwright install chromium
+   ```
+
+This second method has proven to be more reliable in some cases.
+
+---
+
+### Installation with Synchronous Version
+
+The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:
+
+```bash
+pip install crawl4ai[sync]
+```
+
+---
+
+### Development Installation
+
+For contributors who plan to modify the source code:
+
+```bash
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+pip install -e .                    # Basic installation in editable mode
+```
+
+Install optional features:
+
+```bash
+pip install -e ".[torch]"           # With PyTorch features
+pip install -e ".[transformer]"     # With Transformer features
+pip install -e ".[cosine]"          # With cosine similarity features
+pip install -e ".[sync]"            # With synchronous crawling (Selenium)
+pip install -e ".[all]"             # Install all optional features
+```
+
+</details>
+
+<details>
+<summary>🐳 <strong>Docker Deployment</strong></summary>
+
+> 🚀 **Now Available!** Our completely redesigned Docker implementation is here! This new solution makes deployment more efficient and seamless than ever.
+
+### New Docker Features
+
+The new Docker implementation includes:
+- **Browser pooling** with page pre-warming for faster response times
+- **Interactive playground** to test and generate request code
+- **MCP integration** for direct connection to AI tools like Claude Code
+- **Comprehensive API endpoints** including HTML extraction, screenshots, PDF generation, and JavaScript execution
+- **Multi-architecture support** with automatic detection (AMD64/ARM64)
+- **Optimized resources** with improved memory management
+
+### Getting Started
+
+```bash
+# Pull and run the latest release candidate
+docker pull unclecode/crawl4ai:0.7.0
+docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:0.7.0
+
+# Visit the playground at http://localhost:11235/playground
+```
+
+For complete documentation, see our [Docker Deployment Guide](https://docs.crawl4ai.com/core/docker-deployment/).
+
+</details>
+
+---
+
+### Quick Test
+
+Run a quick test (works for both Docker options):
+
+```python
+import requests
+
+# Submit a crawl job
+response = requests.post(
+    "http://localhost:11235/crawl",
+    json={"urls": ["https://example.com"], "priority": 10}
+)
+if response.status_code == 200:
+    print("Crawl job submitted successfully.")
+    
+if "results" in response.json():
+    results = response.json()["results"]
+    print("Crawl job completed. Results:")
+    for result in results:
+        print(result)
+else:
+    task_id = response.json()["task_id"]
+    print(f"Crawl job submitted. Task ID:: {task_id}")
+    result = requests.get(f"http://localhost:11235/task/{task_id}")
+```
+
+For more examples, see our [Docker Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/docker_example.py). For advanced configuration, environment variables, and usage examples, see our [Docker Deployment Guide](https://docs.crawl4ai.com/basic/docker-deployment/).
+
+</details>
+
+
+## 🔬 Advanced Usage Examples 🔬
+
+You can check the project structure in the directory [docs/examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
+
+<details>
+<summary>📝 <strong>Heuristic Markdown Generation with Clean and Fit Markdown</strong></summary>
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai.content_filter_strategy import PruningContentFilter, BM25ContentFilter
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+
+async def main():
+    browser_config = BrowserConfig(
+        headless=True,  
+        verbose=True,
+    )
+    run_config = CrawlerRunConfig(
+        cache_mode=CacheMode.ENABLED,
+        markdown_generator=DefaultMarkdownGenerator(
+            content_filter=PruningContentFilter(threshold=0.48, threshold_type="fixed", min_word_threshold=0)
+        ),
+        # markdown_generator=DefaultMarkdownGenerator(
+        #     content_filter=BM25ContentFilter(user_query="WHEN_WE_FOCUS_BASED_ON_A_USER_QUERY", bm25_threshold=1.0)
+        # ),
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url="https://docs.micronaut.io/4.7.6/guide/",
+            config=run_config
+        )
+        print(len(result.markdown.raw_markdown))
+        print(len(result.markdown.fit_markdown))
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+</details>
+
+<details>
+<summary>🖥️ <strong>Executing JavaScript & Extract Structured Data without LLMs</strong></summary>
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai import JsonCssExtractionStrategy
+import json
+
+async def main():
+    schema = {
+    "name": "KidoCode Courses",
+    "baseSelector": "section.charge-methodology .w-tab-content > div",
+    "fields": [
+        {
+            "name": "section_title",
+            "selector": "h3.heading-50",
+            "type": "text",
+        },
+        {
+            "name": "section_description",
+            "selector": ".charge-content",
+            "type": "text",
+        },
+        {
+            "name": "course_name",
+            "selector": ".text-block-93",
+            "type": "text",
+        },
+        {
+            "name": "course_description",
+            "selector": ".course-content-text",
+            "type": "text",
+        },
+        {
+            "name": "course_icon",
+            "selector": ".image-92",
+            "type": "attribute",
+            "attribute": "src"
+        }
+    }
+}
+
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True
+    )
+    run_config = CrawlerRunConfig(
+        extraction_strategy=extraction_strategy,
+        js_code=["""(async () => {const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");for(let tab of tabs) {tab.scrollIntoView();tab.click();await new Promise(r => setTimeout(r, 500));}})();"""],
+        cache_mode=CacheMode.BYPASS
+    )
+        
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        
+        result = await crawler.arun(
+            url="https://www.kidocode.com/degrees/technology",
+            config=run_config
+        )
+
+        companies = json.loads(result.extracted_content)
+        print(f"Successfully extracted {len(companies)} companies")
+        print(json.dumps(companies[0], indent=2))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+</details>
+
+<details>
+<summary>📚 <strong>Extracting Structured Data with LLMs</strong></summary>
+
+```python
+import os
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
+from crawl4ai import LLMExtractionStrategy
+from pydantic import BaseModel, Field
+
+class OpenAIModelFee(BaseModel):
+    model_name: str = Field(..., description="Name of the OpenAI model.")
+    input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+    output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
+
+async def main():
+    browser_config = BrowserConfig(verbose=True)
+    run_config = CrawlerRunConfig(
+        word_count_threshold=1,
+        extraction_strategy=LLMExtractionStrategy(
+            # Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
+            # provider="ollama/qwen2", api_token="no-token", 
+            llm_config = LLMConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')), 
+            schema=OpenAIModelFee.schema(),
+            extraction_type="schema",
+            instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
+            Do not miss any models in the entire content. One extracted model JSON format should look like this: 
+            {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
+        ),            
+        cache_mode=CacheMode.BYPASS,
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url='https://openai.com/api/pricing/',
+            config=run_config
+        )
+        print(result.extracted_content)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+</details>
+
+<details>
+<summary>🤖 <strong>Using Your own Browser with Custom User Profile</strong></summary>
+
+```python
+import os, sys
+from pathlib import Path
+import asyncio, time
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+
+async def test_news_crawl():
+    # Create a persistent user data directory
+    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
+    os.makedirs(user_data_dir, exist_ok=True)
+
+    browser_config = BrowserConfig(
+        verbose=True,
+        headless=True,
+        user_data_dir=user_data_dir,
+        use_persistent_context=True,
+    )
+    run_config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
+        
+        result = await crawler.arun(
+            url,
+            config=run_config,
+            magic=True,
+        )
+        
+        print(f"Successfully crawled {url}")
+        print(f"Content length: {len(result.markdown)}")
+```
+
+</details>
+
+## ✨ Recent Updates
+
+### Version 0.7.0 Release Highlights - The Adaptive Intelligence Update
+
+- **🧠 Adaptive Crawling**: Your crawler now learns and adapts to website patterns automatically:
+  ```python
+  config = AdaptiveConfig(
+      confidence_threshold=0.7, # Min confidence to stop crawling
+      max_depth=5, # Maximum crawl depth
+      max_pages=20, # Maximum number of pages to crawl
+      strategy="statistical"
+  )
+  
+  async with AsyncWebCrawler() as crawler:
+      adaptive_crawler = AdaptiveCrawler(crawler, config)
+      state = await adaptive_crawler.digest(
+          start_url="https://news.example.com",
+          query="latest news content"
+      )
+  # Crawler learns patterns and improves extraction over time
+  ```
+
+- **🌊 Virtual Scroll Support**: Complete content extraction from infinite scroll pages:
+  ```python
+  scroll_config = VirtualScrollConfig(
+      container_selector="[data-testid='feed']",
+      scroll_count=20,
+      scroll_by="container_height",
+      wait_after_scroll=1.0
+  )
+  
+  result = await crawler.arun(url, config=CrawlerRunConfig(
+      virtual_scroll_config=scroll_config
+  ))
+  ```
+
+- **🔗 Intelligent Link Analysis**: 3-layer scoring system for smart link prioritization:
+  ```python
+  link_config = LinkPreviewConfig(
+      query="machine learning tutorials",
+      score_threshold=0.3,
+      concurrent_requests=10
+  )
+  
+  result = await crawler.arun(url, config=CrawlerRunConfig(
+      link_preview_config=link_config,
+      score_links=True
+  ))
+  # Links ranked by relevance and quality
+  ```
+
+- **🎣 Async URL Seeder**: Discover thousands of URLs in seconds:
+  ```python
+  seeder = AsyncUrlSeeder(SeedingConfig(
+      source="sitemap+cc",
+      pattern="*/blog/*",
+      query="python tutorials",
+      score_threshold=0.4
+  ))
+  
+  urls = await seeder.discover("https://example.com")
+  ```
+
+- **⚡ Performance Boost**: Up to 3x faster with optimized resource handling and memory efficiency
+
+Read the full details in our [0.7.0 Release Notes](https://docs.crawl4ai.com/blog/release-v0.7.0) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
+
+## Version Numbering in Crawl4AI
+
+Crawl4AI follows standard Python version numbering conventions (PEP 440) to help users understand the stability and features of each release.
+
+### Version Numbers Explained
+
+Our version numbers follow this pattern: `MAJOR.MINOR.PATCH` (e.g., 0.4.3)
+
+#### Pre-release Versions
+We use different suffixes to indicate development stages:
+
+- `dev` (0.4.3dev1): Development versions, unstable
+- `a` (0.4.3a1): Alpha releases, experimental features
+- `b` (0.4.3b1): Beta releases, feature complete but needs testing
+- `rc` (0.4.3): Release candidates, potential final version
+
+#### Installation
+- Regular installation (stable version):
+  ```bash
+  pip install -U crawl4ai
+  ```
+
+- Install pre-release versions:
+  ```bash
+  pip install crawl4ai --pre
+  ```
+
+- Install specific version:
+  ```bash
+  pip install crawl4ai==0.4.3b1
+  ```
+
+#### Why Pre-releases?
+We use pre-releases to:
+- Test new features in real-world scenarios
+- Gather feedback before final releases
+- Ensure stability for production users
+- Allow early adopters to try new features
+
+For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the `--pre` flag.
+
+## 📖 Documentation & Roadmap 
+
+> 🚨 **Documentation Update Alert**: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!
+
+For current documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://docs.crawl4ai.com/).
+
+To check our development plans and upcoming features, visit our [Roadmap](https://github.com/unclecode/crawl4ai/blob/main/ROADMAP.md).
+
+<details>
+<summary>📈 <strong>Development TODOs</strong></summary>
+
+- [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
+- [ ] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
+- [ ] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
+- [ ] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
+- [ ] 4. Automated Schema Generator: Convert natural language to extraction schemas
+- [ ] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
+- [ ] 6. Web Embedding Index: Semantic search infrastructure for crawled content
+- [ ] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
+- [ ] 8. Performance Monitor: Real-time insights into crawler operations
+- [ ] 9. Cloud Integration: One-click deployment solutions across cloud providers
+- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
+- [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
+
+</details>
+
+## 🤝 Contributing 
+
+We welcome contributions from the open-source community. Check out our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTORS.md) for more information.
+
+I'll help modify the license section with badges. For the halftone effect, here's a version with it:
+
+Here's the updated license section:
+
+## 📄 License & Attribution
+
+This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE) file for details.
+
+### Attribution Requirements
+When using Crawl4AI, you must include one of the following attribution methods:
+
+#### 1. Badge Attribution (Recommended)
+Add one of these badges to your README, documentation, or website:
+
+| Theme | Badge |
+|-------|-------|
+| **Disco Theme (Animated)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/></a> |
+| **Night Theme (Dark with Neon)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/></a> |
+| **Dark Theme (Classic)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/></a> |
+| **Light Theme (Classic)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/></a> |
+ 
+
+HTML code for adding the badges:
+```html
+<!-- Disco Theme (Animated) -->
+<a href="https://github.com/unclecode/crawl4ai">
+  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/>
+</a>
+
+<!-- Night Theme (Dark with Neon) -->
+<a href="https://github.com/unclecode/crawl4ai">
+  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/>
+</a>
+
+<!-- Dark Theme (Classic) -->
+<a href="https://github.com/unclecode/crawl4ai">
+  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/>
+</a>
+
+<!-- Light Theme (Classic) -->
+<a href="https://github.com/unclecode/crawl4ai">
+  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/>
+</a>
+
+<!-- Simple Shield Badge -->
+<a href="https://github.com/unclecode/crawl4ai">
+  <img src="https://img.shields.io/badge/Powered%20by-Crawl4AI-blue?style=flat-square" alt="Powered by Crawl4AI"/>
+</a>
+```
+
+#### 2. Text Attribution
+Add this line to your documentation:
+```
+This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction.
+```
+
+## 📚 Citation
+
+If you use Crawl4AI in your research or project, please cite:
+
+```bibtex
+@software{crawl4ai2024,
+  author = {UncleCode},
+  title = {Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub Repository},
+  howpublished = {\url{https://github.com/unclecode/crawl4ai}},
+  commit = {Please use the commit hash you're working with}
+}
+```
+
+Text citation format:
+```
+UncleCode. (2024). Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper [Computer software]. 
+GitHub. https://github.com/unclecode/crawl4ai
+```
+
+## 📧 Contact 
+
+For questions, suggestions, or feedback, feel free to reach out:
+
+- GitHub: [unclecode](https://github.com/unclecode)
+- Twitter: [@unclecode](https://twitter.com/unclecode)
+- Website: [crawl4ai.com](https://crawl4ai.com)
+
+Happy Crawling! 🕸️🚀
+
+## 💖 Support Crawl4AI
+
+> 🎉 **Sponsorship Program Just Launched!** Be among the first 50 **Founding Sponsors** and get permanent recognition in our Hall of Fame!
+
+Crawl4AI is the #1 trending open-source web crawler with 51K+ stars. Your support ensures we stay independent, innovative, and free forever.
+
+<div align="center">
+
+[![Become a Sponsor](https://img.shields.io/badge/Become%20a%20Sponsor-pink?style=for-the-badge&logo=github-sponsors&logoColor=white)](https://github.com/sponsors/unclecode)
+[![Current Sponsors](https://img.shields.io/github/sponsors/unclecode?style=for-the-badge&logo=github&label=Current%20Sponsors&color=green)](https://github.com/sponsors/unclecode)
+
+</div>
+
+### 🤝 Sponsorship Tiers
+
+- **🌱 Believer ($5/mo)**: Join the movement for data democratization
+- **🚀 Builder ($50/mo)**: Get priority support and early feature access  
+- **💼 Growing Team ($500/mo)**: Bi-weekly syncs and optimization help
+- **🏢 Data Infrastructure Partner ($2000/mo)**: Full partnership with dedicated support
+
+**Why sponsor?** Every tier includes real benefits. No more rate-limited APIs. Own your data pipeline. Build data sovereignty together.
+
+[View All Tiers & Benefits →](https://github.com/sponsors/unclecode)
+
+### 🏆 Our Sponsors
+
+#### 👑 Founding Sponsors (First 50)
+*Be part of history - [Become a Founding Sponsor](https://github.com/sponsors/unclecode)*
+
+<!-- Founding sponsors will be permanently recognized here -->
+
+#### Current Sponsors
+Thank you to all our sponsors who make this project possible!
+
+<!-- Sponsors will be automatically added here -->
+
+## 🗾 Mission
+
+Our mission is to unlock the value of personal and enterprise data by transforming digital footprints into structured, tradeable assets. Crawl4AI empowers individuals and organizations with open-source tools to extract and structure data, fostering a shared data economy.  
+
+We envision a future where AI is powered by real human knowledge, ensuring data creators directly benefit from their contributions. By democratizing data and enabling ethical sharing, we are laying the foundation for authentic AI advancement.
+
+<details>
+<summary>🔑 <strong>Key Opportunities</strong></summary>
+ 
+- **Data Capitalization**: Transform digital footprints into measurable, valuable assets.  
+- **Authentic AI Data**: Provide AI systems with real human insights.  
+- **Shared Economy**: Create a fair data marketplace that benefits data creators.  
+
+</details>
+
+<details>
+<summary>🚀 <strong>Development Pathway</strong></summary>
+
+1. **Open-Source Tools**: Community-driven platforms for transparent data extraction.  
+2. **Digital Asset Structuring**: Tools to organize and value digital knowledge.  
+3. **Ethical Data Marketplace**: A secure, fair platform for exchanging structured data.  
+
+For more details, see our [full mission statement](./MISSION.md).
+</details>
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=unclecode/crawl4ai&type=Date)](https://star-history.com/#unclecode/crawl4ai&Date)

README.md

@@ -10,6 +10,7 @@
 [![PyPI version](https://badge.fury.io/py/crawl4ai.svg)](https://badge.fury.io/py/crawl4ai)
 [![Python Version](https://img.shields.io/pypi/pyversions/crawl4ai)](https://pypi.org/project/crawl4ai/)
 [![Downloads](https://static.pepy.tech/badge/crawl4ai/month)](https://pepy.tech/project/crawl4ai)
+[![GitHub Sponsors](https://img.shields.io/github/sponsors/unclecode?style=flat&logo=GitHub-Sponsors&label=Sponsors&color=pink)](https://github.com/sponsors/unclecode)
 
 <p align="center">
     <a href="https://x.com/crawl4ai">
@@ -24,32 +25,35 @@
   </p>
 </div>
 
-Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.  
+Crawl4AI turns the web into clean, LLM ready Markdown for RAG, agents, and data pipelines. Fast, controllable, battle tested by a 50k+ star community.
 
-[✨ Check out latest update v0.6.0](#-recent-updates)
+[✨ Check out latest update v0.7.4](#-recent-updates)
 
-🎉 **Version 0.6.0 is now available!** This release candidate introduces World-aware Crawling with geolocation and locale settings, Table-to-DataFrame extraction, Browser pooling with pre-warming, Network and console traffic capture, MCP integration for AI tools, and a completely revamped Docker deployment! [Read the release notes →](https://docs.crawl4ai.com/blog)
+✨ New in v0.7.4: Revolutionary LLM Table Extraction with intelligent chunking, enhanced concurrency fixes, memory management refactor, and critical stability improvements. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.4.md)
+
+✨ Recent v0.7.3: Undetected Browser Support, Multi-URL Configurations, Memory Monitoring, Enhanced Table Extraction, GitHub Sponsors. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.3.md)
 
 <details>
-<summary>🤓 <strong>My Personal Story</strong></summary>
+  <summary>🤓 <strong>My Personal Story</strong></summary>
 
-My journey with computers started in childhood when my dad, a computer scientist, introduced me to an Amstrad computer. Those early days sparked a fascination with technology, leading me to pursue computer science and specialize in NLP during my postgraduate studies. It was during this time that I first delved into web crawling, building tools to help researchers organize papers and extract information from publications a challenging yet rewarding experience that honed my skills in data extraction.
+I grew up on an Amstrad, thanks to my dad, and never stopped building. In grad school I specialized in NLP and built crawlers for research. That’s where I learned how much extraction matters.
 
-Fast forward to 2023, I was working on a tool for a project and needed a crawler to convert a webpage into markdown. While exploring solutions, I found one that claimed to be open-source but required creating an account and generating an API token. Worse, it turned out to be a SaaS model charging $16, and its quality didn’t meet my standards. Frustrated, I realized this was a deeper problem. That frustration turned into turbo anger mode, and I decided to build my own solution. In just a few days, I created Crawl4AI. To my surprise, it went viral, earning thousands of GitHub stars and resonating with a global community.
+In 2023, I needed web-to-Markdown. The “open source” option wanted an account, API token, and $16, and still under-delivered. I went turbo anger mode, built Crawl4AI in days, and it went viral. Now it’s the most-starred crawler on GitHub.
 
-I made Crawl4AI open-source for two reasons. First, it’s my way of giving back to the open-source community that has supported me throughout my career. Second, I believe data should be accessible to everyone, not locked behind paywalls or monopolized by a few. Open access to data lays the foundation for the democratization of AI, a vision where individuals can train their own models and take ownership of their information. This library is the first step in a larger journey to create the best open-source data extraction and generation tool the world has ever seen, built collaboratively by a passionate community.
-
-Thank you to everyone who has supported this project, used it, and shared feedback. Your encouragement motivates me to dream even bigger. Join us, file issues, submit PRs, or spread the word. Together, we can build a tool that truly empowers people to access their own data and reshape the future of AI.
+I made it open source for **availability**, anyone can use it without a gate. Now I’m building the platform for **affordability**, anyone can run serious crawls without breaking the bank. If that resonates, join in, send feedback, or just crawl something amazing.
 </details>
 
-## 🧐 Why Crawl4AI?
 
-1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.  
-2. **Lightning Fast**: Delivers results 6x faster with real-time, cost-efficient performance.  
-3. **Flexible Browser Control**: Offers session management, proxies, and custom hooks for seamless data access.  
-4. **Heuristic Intelligence**: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.  
-5. **Open Source & Deployable**: Fully open-source with no API keys—ready for Docker and cloud integration.  
-6. **Thriving Community**: Actively maintained by a vibrant community and the #1 trending GitHub repository.
+<details>
+  <summary>Why developers pick Crawl4AI</summary>
+
+- **LLM ready output**, smart Markdown with headings, tables, code, citation hints
+- **Fast in practice**, async browser pool, caching, minimal hops
+- **Full control**, sessions, proxies, cookies, user scripts, hooks
+- **Adaptive intelligence**, learns site patterns, explores only what matters
+- **Deploy anywhere**, zero keys, CLI and Docker, cloud friendly
+</details>
+
 
 ## 🚀 Quick Start 
 
@@ -101,6 +105,33 @@ crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10
 crwl https://www.example.com/products -q "Extract all product prices"
 ```
 
+## 💖 Support Crawl4AI
+
+> 🎉 **Sponsorship Program Now Open!** After powering 51K+ developers and 1 year of growth, Crawl4AI is launching dedicated support for **startups** and **enterprises**. Be among the first 50 **Founding Sponsors** for permanent recognition in our Hall of Fame.
+
+Crawl4AI is the #1 trending open-source web crawler on GitHub. Your support keeps it independent, innovative, and free for the community — while giving you direct access to premium benefits.
+
+<div align="">
+  
+[![Become a Sponsor](https://img.shields.io/badge/Become%20a%20Sponsor-pink?style=for-the-badge&logo=github-sponsors&logoColor=white)](https://github.com/sponsors/unclecode)  
+[![Current Sponsors](https://img.shields.io/github/sponsors/unclecode?style=for-the-badge&logo=github&label=Current%20Sponsors&color=green)](https://github.com/sponsors/unclecode)
+
+</div>
+
+### 🤝 Sponsorship Tiers
+
+- **🌱 Believer ($5/mo)** — Join the movement for data democratization  
+- **🚀 Builder ($50/mo)** — Priority support & early access to features  
+- **💼 Growing Team ($500/mo)** — Bi-weekly syncs & optimization help  
+- **🏢 Data Infrastructure Partner ($2000/mo)** — Full partnership with dedicated support  
+  *Custom arrangements available - see [SPONSORS.md](SPONSORS.md) for details & contact*
+
+**Why sponsor?**  
+No rate-limited APIs. No lock-in. Build and own your data pipeline with direct guidance from the creator of Crawl4AI.
+
+[See All Tiers & Benefits →](https://github.com/sponsors/unclecode)
+
+
 ## ✨ Features 
 
 <details>
@@ -274,18 +305,12 @@ The new Docker implementation includes:
 
 ```bash
 # Pull and run the latest release candidate
-docker pull unclecode/crawl4ai:0.6.0-rN # Use your favorite revision number
-docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:0.6.0-rN # Use your favorite revision number
+docker pull unclecode/crawl4ai:0.7.0
+docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:0.7.0
 
 # Visit the playground at http://localhost:11235/playground
 ```
 
-For complete documentation, see our [Docker Deployment Guide](https://docs.crawl4ai.com/core/docker-deployment/).
-
-</details>
-
----
-
 ### Quick Test
 
 Run a quick test (works for both Docker options):
@@ -316,10 +341,11 @@ For more examples, see our [Docker Examples](https://github.com/unclecode/crawl4
 
 </details>
 
+---
 
 ## 🔬 Advanced Usage Examples 🔬
 
-You can check the project structure in the directory [https://github.com/unclecode/crawl4ai/docs/examples](docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
+You can check the project structure in the directory [docs/examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
 
 <details>
 <summary>📝 <strong>Heuristic Markdown Generation with Clean and Fit Markdown</strong></summary>
@@ -478,7 +504,7 @@ if __name__ == "__main__":
 </details>
 
 <details>
-<summary>🤖 <strong>Using You own Browser with Custom User Profile</strong></summary>
+<summary>🤖 <strong>Using Your own Browser with Custom User Profile</strong></summary>
 
 ```python
 import os, sys
@@ -518,98 +544,195 @@ async def test_news_crawl():
 
 ## ✨ Recent Updates
 
-### Version 0.6.0 Release Highlights
+<details>
+<summary><strong>Version 0.7.4 Release Highlights - The Intelligent Table Extraction & Performance Update</strong></summary>
 
-- **🌎 World-aware Crawling**: Set geolocation, language, and timezone for authentic locale-specific content:
+- **🚀 LLMTableExtraction**: Revolutionary table extraction with intelligent chunking for massive tables:
   ```python
-    crun_cfg = CrawlerRunConfig(
-        url="https://browserleaks.com/geo",          # test page that shows your location
-        locale="en-US",                              # Accept-Language & UI locale
-        timezone_id="America/Los_Angeles",           # JS Date()/Intl timezone
-        geolocation=GeolocationConfig(                 # override GPS coords
-            latitude=34.0522,
-            longitude=-118.2437,
-            accuracy=10.0,
-        )
-    )
-  ```
-
-- **📊 Table-to-DataFrame Extraction**: Extract HTML tables directly to CSV or pandas DataFrames:
-  ```python
-    crawler = AsyncWebCrawler(config=browser_config)
-    await crawler.start()
-
-    try:
-        # Set up scraping parameters
-        crawl_config = CrawlerRunConfig(
-            table_score_threshold=8,  # Strict table detection
-        )
-
-        # Execute market data extraction
-        results: List[CrawlResult] = await crawler.arun(
-            url="https://coinmarketcap.com/?page=1", config=crawl_config
-        )
-
-        # Process results
-        raw_df = pd.DataFrame()
-        for result in results:
-            if result.success and result.media["tables"]:
-                raw_df = pd.DataFrame(
-                    result.media["tables"][0]["rows"],
-                    columns=result.media["tables"][0]["headers"],
-                )
-                break
-        print(raw_df.head())
-
-    finally:
-        await crawler.stop()
-  ```
-
-- **🚀 Browser Pooling**: Pages launch hot with pre-warmed browser instances for lower latency and memory usage
-
-- **🕸️ Network and Console Capture**: Full traffic logs and MHTML snapshots for debugging:
-  ```python
-  crawler_config = CrawlerRunConfig(
-      capture_network=True,
-      capture_console=True,
-      mhtml=True
+  from crawl4ai import LLMTableExtraction, LLMConfig
+  
+  # Configure intelligent table extraction
+  table_strategy = LLMTableExtraction(
+      llm_config=LLMConfig(provider="openai/gpt-4.1-mini"),
+      enable_chunking=True,           # Handle massive tables
+      chunk_token_threshold=5000,     # Smart chunking threshold
+      overlap_threshold=100,          # Maintain context between chunks
+      extraction_type="structured"    # Get structured data output
   )
+  
+  config = CrawlerRunConfig(table_extraction_strategy=table_strategy)
+  result = await crawler.arun("https://complex-tables-site.com", config=config)
+  
+  # Tables are automatically chunked, processed, and merged
+  for table in result.tables:
+      print(f"Extracted table: {len(table['data'])} rows")
   ```
 
-- **🔌 MCP Integration**: Connect to AI tools like Claude Code through the Model Context Protocol
-  ```bash
-  # Add Crawl4AI to Claude Code
-  claude mcp add --transport sse c4ai-sse http://localhost:11235/mcp/sse
+- **⚡ Dispatcher Bug Fix**: Fixed sequential processing bottleneck in arun_many for fast-completing tasks
+- **🧹 Memory Management Refactor**: Consolidated memory utilities into main utils module for cleaner architecture
+- **🔧 Browser Manager Fixes**: Resolved race conditions in concurrent page creation with thread-safe locking
+- **🔗 Advanced URL Processing**: Better handling of raw:// URLs and base tag link resolution
+- **🛡️ Enhanced Proxy Support**: Flexible proxy configuration supporting both dict and string formats
+
+[Full v0.7.4 Release Notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.4.md)
+
+</details>
+
+<details>
+<summary><strong>Version 0.7.3 Release Highlights - The Multi-Config Intelligence Update</strong></summary>
+
+- **🕵️ Undetected Browser Support**: Bypass sophisticated bot detection systems:
+  ```python
+  from crawl4ai import AsyncWebCrawler, BrowserConfig
+  
+  browser_config = BrowserConfig(
+      browser_type="undetected",  # Use undetected Chrome
+      headless=True,              # Can run headless with stealth
+      extra_args=[
+          "--disable-blink-features=AutomationControlled",
+          "--disable-web-security"
+      ]
+  )
+  
+  async with AsyncWebCrawler(config=browser_config) as crawler:
+      result = await crawler.arun("https://protected-site.com")
+  # Successfully bypass Cloudflare, Akamai, and custom bot detection
   ```
 
-- **🖥️ Interactive Playground**: Test configurations and generate API requests with the built-in web interface at `http://localhost:11235//playground`
+- **🎨 Multi-URL Configuration**: Different strategies for different URL patterns in one batch:
+  ```python
+  from crawl4ai import CrawlerRunConfig, MatchMode
+  
+  configs = [
+      # Documentation sites - aggressive caching
+      CrawlerRunConfig(
+          url_matcher=["*docs*", "*documentation*"],
+          cache_mode="write",
+          markdown_generator_options={"include_links": True}
+      ),
+      
+      # News/blog sites - fresh content
+      CrawlerRunConfig(
+          url_matcher=lambda url: 'blog' in url or 'news' in url,
+          cache_mode="bypass"
+      ),
+      
+      # Fallback for everything else
+      CrawlerRunConfig()
+  ]
+  
+  results = await crawler.arun_many(urls, config=configs)
+  # Each URL gets the perfect configuration automatically
+  ```
 
-- **🐳 Revamped Docker Deployment**: Streamlined multi-architecture Docker image with improved resource efficiency
+- **🧠 Memory Monitoring**: Track and optimize memory usage during crawling:
+  ```python
+  from crawl4ai.memory_utils import MemoryMonitor
+  
+  monitor = MemoryMonitor()
+  monitor.start_monitoring()
+  
+  results = await crawler.arun_many(large_url_list)
+  
+  report = monitor.get_report()
+  print(f"Peak memory: {report['peak_mb']:.1f} MB")
+  print(f"Efficiency: {report['efficiency']:.1f}%")
+  # Get optimization recommendations
+  ```
 
-- **📱 Multi-stage Build System**: Optimized Dockerfile with platform-specific performance enhancements
+- **📊 Enhanced Table Extraction**: Direct DataFrame conversion from web tables:
+  ```python
+  result = await crawler.arun("https://site-with-tables.com")
+  
+  # New way - direct table access
+  if result.tables:
+      import pandas as pd
+      for table in result.tables:
+          df = pd.DataFrame(table['data'])
+          print(f"Table: {df.shape[0]} rows × {df.shape[1]} columns")
+  ```
 
-Read the full details in our [0.6.0 Release Notes](https://docs.crawl4ai.com/blog/releases/0.6.0.html) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
+- **💰 GitHub Sponsors**: 4-tier sponsorship system for project sustainability
+- **🐳 Docker LLM Flexibility**: Configure providers via environment variables
 
-### Previous Version: 0.5.0 Major Release Highlights
+[Full v0.7.3 Release Notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.3.md)
 
--   **🚀 Deep Crawling System**: Explore websites beyond initial URLs with BFS, DFS, and BestFirst strategies
--   **⚡ Memory-Adaptive Dispatcher**: Dynamically adjusts concurrency based on system memory
--   **🔄 Multiple Crawling Strategies**: Browser-based and lightweight HTTP-only crawlers
--   **💻 Command-Line Interface**: New `crwl` CLI provides convenient terminal access
--   **👤 Browser Profiler**: Create and manage persistent browser profiles
--   **🧠 Crawl4AI Coding Assistant**: AI-powered coding assistant
--   **🏎️ LXML Scraping Mode**: Fast HTML parsing using the `lxml` library
--   **🌐 Proxy Rotation**: Built-in support for proxy switching
--   **🤖 LLM Content Filter**: Intelligent markdown generation using LLMs
--   **📄 PDF Processing**: Extract text, images, and metadata from PDF files
+</details>
 
-Read the full details in our [0.5.0 Release Notes](https://docs.crawl4ai.com/blog/releases/0.5.0.html).
+<details>
+<summary><strong>Version 0.7.0 Release Highlights - The Adaptive Intelligence Update</strong></summary>
+
+- **🧠 Adaptive Crawling**: Your crawler now learns and adapts to website patterns automatically:
+  ```python
+  config = AdaptiveConfig(
+      confidence_threshold=0.7, # Min confidence to stop crawling
+      max_depth=5, # Maximum crawl depth
+      max_pages=20, # Maximum number of pages to crawl
+      strategy="statistical"
+  )
+  
+  async with AsyncWebCrawler() as crawler:
+      adaptive_crawler = AdaptiveCrawler(crawler, config)
+      state = await adaptive_crawler.digest(
+          start_url="https://news.example.com",
+          query="latest news content"
+      )
+  # Crawler learns patterns and improves extraction over time
+  ```
+
+- **🌊 Virtual Scroll Support**: Complete content extraction from infinite scroll pages:
+  ```python
+  scroll_config = VirtualScrollConfig(
+      container_selector="[data-testid='feed']",
+      scroll_count=20,
+      scroll_by="container_height",
+      wait_after_scroll=1.0
+  )
+  
+  result = await crawler.arun(url, config=CrawlerRunConfig(
+      virtual_scroll_config=scroll_config
+  ))
+  ```
+
+- **🔗 Intelligent Link Analysis**: 3-layer scoring system for smart link prioritization:
+  ```python
+  link_config = LinkPreviewConfig(
+      query="machine learning tutorials",
+      score_threshold=0.3,
+      concurrent_requests=10
+  )
+  
+  result = await crawler.arun(url, config=CrawlerRunConfig(
+      link_preview_config=link_config,
+      score_links=True
+  ))
+  # Links ranked by relevance and quality
+  ```
+
+- **🎣 Async URL Seeder**: Discover thousands of URLs in seconds:
+  ```python
+  seeder = AsyncUrlSeeder(SeedingConfig(
+      source="sitemap+cc",
+      pattern="*/blog/*",
+      query="python tutorials",
+      score_threshold=0.4
+  ))
+  
+  urls = await seeder.discover("https://example.com")
+  ```
+
+- **⚡ Performance Boost**: Up to 3x faster with optimized resource handling and memory efficiency
+
+Read the full details in our [0.7.0 Release Notes](https://docs.crawl4ai.com/blog/release-v0.7.0) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
+
+</details>
 
 ## Version Numbering in Crawl4AI
 
 Crawl4AI follows standard Python version numbering conventions (PEP 440) to help users understand the stability and features of each release.
 
-### Version Numbers Explained
+<details>
+<summary>📈 <strong>Version Numbers Explained</strong></summary>
 
 Our version numbers follow this pattern: `MAJOR.MINOR.PATCH` (e.g., 0.4.3)
 
@@ -646,6 +769,8 @@ We use pre-releases to:
 
 For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the `--pre` flag.
 
+</details>
+
 ## 📖 Documentation & Roadmap 
 
 > 🚨 **Documentation Update Alert**: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!
@@ -658,16 +783,16 @@ To check our development plans and upcoming features, visit our [Roadmap](https:
 <summary>📈 <strong>Development TODOs</strong></summary>
 
 - [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
-- [ ] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
-- [ ] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
-- [ ] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
-- [ ] 4. Automated Schema Generator: Convert natural language to extraction schemas
-- [ ] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
-- [ ] 6. Web Embedding Index: Semantic search infrastructure for crawled content
-- [ ] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
-- [ ] 8. Performance Monitor: Real-time insights into crawler operations
+- [x] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
+- [x] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
+- [x] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
+- [x] 4. Automated Schema Generator: Convert natural language to extraction schemas
+- [x] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
+- [x] 6. Web Embedding Index: Semantic search infrastructure for crawled content
+- [x] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
+- [x] 8. Performance Monitor: Real-time insights into crawler operations
 - [ ] 9. Cloud Integration: One-click deployment solutions across cloud providers
-- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
+- [x] 10. Sponsorship Program: Structured support system with tiered benefits
 - [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
 
 </details>
@@ -682,12 +807,13 @@ Here's the updated license section:
 
 ## 📄 License & Attribution
 
-This project is licensed under the Apache License 2.0 with a required attribution clause. See the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE) file for details.
+This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE) file for details.
 
 ### Attribution Requirements
 When using Crawl4AI, you must include one of the following attribution methods:
 
-#### 1. Badge Attribution (Recommended)
+<details>
+<summary>📈 <strong>1. Badge Attribution (Recommended)</strong></summary>
 Add one of these badges to your README, documentation, or website:
 
 | Theme | Badge |
@@ -726,11 +852,15 @@ HTML code for adding the badges:
 </a>
 ```
 
-#### 2. Text Attribution
+</details>
+
+<details>
+<summary>📖 <strong>2. Text Attribution</strong></summary>
 Add this line to your documentation:
 ```
 This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction.
 ```
+</details>
 
 ## 📚 Citation
 

SPONSORS.md

@@ -0,0 +1,65 @@
+# 💖 Sponsors & Supporters
+
+Thank you to everyone supporting Crawl4AI! Your sponsorship helps keep this project open-source and actively maintained.
+
+## 👑 Founding Sponsors
+*The first 50 sponsors who believed in our vision - permanently recognized*
+
+<!-- Founding sponsors will be listed here with special recognition -->
+🎉 **Become a Founding Sponsor!** Only [X/50] spots remaining! [Join now →](https://github.com/sponsors/unclecode)
+
+---
+
+## 🏢 Data Infrastructure Partners ($2000/month)
+*These organizations are building their data sovereignty with Crawl4AI at the core*
+
+<!-- Data Infrastructure Partners will be listed here -->
+*Be the first Data Infrastructure Partner! [Join us →](https://github.com/sponsors/unclecode)*
+
+---
+
+## 💼 Growing Teams ($500/month)
+*Teams scaling their data extraction with Crawl4AI*
+
+<!-- Growing Teams will be listed here -->
+*Your team could be here! [Become a sponsor →](https://github.com/sponsors/unclecode)*
+
+---
+
+## 🚀 Builders ($50/month)
+*Developers and entrepreneurs building with Crawl4AI*
+
+<!-- Builders will be listed here -->
+*Join the builders! [Start sponsoring →](https://github.com/sponsors/unclecode)*
+
+---
+
+## 🌱 Believers ($5/month)
+*The community supporting data democratization*
+
+<!-- Believers will be listed here -->
+*Thank you to all our community believers!*
+
+---
+
+## 🤝 Want to Sponsor?
+
+Crawl4AI is the #1 trending open-source web crawler. We're building the future of data extraction - where organizations own their data pipelines instead of relying on rate-limited APIs.
+
+### Available Sponsorship Tiers:
+- **🌱 Believer** ($5/mo) - Support the movement
+- **🚀 Builder** ($50/mo) - Priority support & early access
+- **💼 Growing Team** ($500/mo) - Bi-weekly syncs & optimization
+- **🏢 Data Infrastructure Partner** ($2000/mo) - Full partnership & dedicated support
+
+[View all tiers and benefits →](https://github.com/sponsors/unclecode)
+
+### Enterprise & Custom Partnerships
+
+Building data extraction at scale? Need dedicated support or infrastructure? Let's talk about a custom partnership.
+
+📧 Contact: [hello@crawl4ai.com](mailto:hello@crawl4ai.com) | 📅 [Schedule a call](https://calendar.app.google/rEpvi2UBgUQjWHfJ9)
+
+---
+
+*This list is updated regularly. Sponsors at $50+ tiers can submit their logos via [hello@crawl4ai.com](mailto:hello@crawl4ai.com)*

crawl4ai/__init__.py

@@ -3,12 +3,12 @@ import warnings
 
 from .async_webcrawler import AsyncWebCrawler, CacheMode
 # MODIFIED: Add SeedingConfig and VirtualScrollConfig here
-from .async_configs import BrowserConfig, CrawlerRunConfig, HTTPCrawlerConfig, LLMConfig, ProxyConfig, GeolocationConfig, SeedingConfig, VirtualScrollConfig
+from .async_configs import BrowserConfig, CrawlerRunConfig, HTTPCrawlerConfig, LLMConfig, ProxyConfig, GeolocationConfig, SeedingConfig, VirtualScrollConfig, LinkPreviewConfig, MatchMode
 
 from .content_scraping_strategy import (
     ContentScrapingStrategy,
-    WebScrapingStrategy,
     LXMLWebScrapingStrategy,
+    WebScrapingStrategy,  # Backward compatibility alias
 )
 from .async_logger import (
     AsyncLoggerBase,
@@ -29,6 +29,12 @@ from .extraction_strategy import (
 )
 from .chunking_strategy import ChunkingStrategy, RegexChunking
 from .markdown_generation_strategy import DefaultMarkdownGenerator
+from .table_extraction import (
+    TableExtractionStrategy,
+    DefaultTableExtraction,
+    NoTableExtraction,
+    LLMTableExtraction,
+)
 from .content_filter_strategy import (
     PruningContentFilter,
     BM25ContentFilter,
@@ -88,6 +94,13 @@ from .script import (
     ErrorDetail
 )
 
+# Browser Adapters
+from .browser_adapter import (
+    BrowserAdapter,
+    PlaywrightAdapter,
+    UndetectedAdapter
+)
+
 from .utils import (
     start_colab_display_server,
     setup_colab_environment
@@ -132,6 +145,7 @@ __all__ = [
     "CrawlResult",
     "CrawlerHub",
     "CacheMode",
+    "MatchMode",
     "ContentScrapingStrategy",
     "WebScrapingStrategy",
     "LXMLWebScrapingStrategy",
@@ -148,6 +162,9 @@ __all__ = [
     "ChunkingStrategy",
     "RegexChunking",
     "DefaultMarkdownGenerator",
+    "TableExtractionStrategy",
+    "DefaultTableExtraction",
+    "NoTableExtraction",
     "RelevantContentFilter",
     "PruningContentFilter",
     "BM25ContentFilter",
@@ -173,6 +190,11 @@ __all__ = [
     "CompilationResult",
     "ValidationResult",
     "ErrorDetail",
+    # Browser Adapters
+    "BrowserAdapter",
+    "PlaywrightAdapter", 
+    "UndetectedAdapter",
+    "LinkPreviewConfig"
 ]
 
 

crawl4ai/__version__.py

@@ -1,7 +1,7 @@
 # crawl4ai/__version__.py
 
 # This is the version that will be used for stable releases
-__version__ = "0.6.3"
+__version__ = "0.7.4"
 
 # For nightly builds, this gets set during build process
 __nightly_version__ = None

crawl4ai/async_configs.py

@@ -18,17 +18,25 @@ from .extraction_strategy import ExtractionStrategy, LLMExtractionStrategy
 from .chunking_strategy import ChunkingStrategy, RegexChunking
 
 from .markdown_generation_strategy import MarkdownGenerationStrategy, DefaultMarkdownGenerator
-from .content_scraping_strategy import ContentScrapingStrategy, WebScrapingStrategy, LXMLWebScrapingStrategy
+from .content_scraping_strategy import ContentScrapingStrategy, LXMLWebScrapingStrategy
 from .deep_crawling import DeepCrawlStrategy
+from .table_extraction import TableExtractionStrategy, DefaultTableExtraction
 
 from .cache_context import CacheMode
 from .proxy_strategy import ProxyRotationStrategy
 
-from typing import Union, List
+from typing import Union, List, Callable
 import inspect
 from typing import Any, Dict, Optional
 from enum import Enum
 
+# Type alias for URL matching
+UrlMatcher = Union[str, Callable[[str], bool], List[Union[str, Callable[[str], bool]]]]
+
+class MatchMode(Enum):
+    OR = "or"
+    AND = "and"
+
 # from .proxy_strategy import ProxyConfig
 
 
@@ -383,6 +391,8 @@ class BrowserConfig:
         light_mode (bool): Disables certain background features for performance gains. Default: False.
         extra_args (list): Additional command-line arguments passed to the browser.
                            Default: [].
+        enable_stealth (bool): If True, applies playwright-stealth to bypass basic bot detection.
+                              Cannot be used with use_undetected browser mode. Default: False.
     """
 
     def __init__(
@@ -423,6 +433,7 @@ class BrowserConfig:
         extra_args: list = None,
         debugging_port: int = 9222,
         host: str = "localhost",
+        enable_stealth: bool = False,
     ):
         self.browser_type = browser_type
         self.headless = headless 
@@ -438,6 +449,10 @@ class BrowserConfig:
             self.chrome_channel = ""
         self.proxy = proxy
         self.proxy_config = proxy_config
+        if isinstance(self.proxy_config, dict):
+            self.proxy_config = ProxyConfig.from_dict(self.proxy_config)
+        if isinstance(self.proxy_config, str):
+            self.proxy_config = ProxyConfig.from_string(self.proxy_config)
 
 
         self.viewport_width = viewport_width
@@ -463,6 +478,7 @@ class BrowserConfig:
         self.verbose = verbose
         self.debugging_port = debugging_port
         self.host = host
+        self.enable_stealth = enable_stealth
 
         fa_user_agenr_generator = ValidUAGenerator()
         if self.user_agent_mode == "random":
@@ -494,6 +510,13 @@ class BrowserConfig:
         # If persistent context is requested, ensure managed browser is enabled
         if self.use_persistent_context:
             self.use_managed_browser = True
+            
+        # Validate stealth configuration
+        if self.enable_stealth and self.use_managed_browser and self.browser_mode == "builtin":
+            raise ValueError(
+                "enable_stealth cannot be used with browser_mode='builtin'. "
+                "Stealth mode requires a dedicated browser instance."
+            )
 
     @staticmethod
     def from_kwargs(kwargs: dict) -> "BrowserConfig":
@@ -530,6 +553,7 @@ class BrowserConfig:
             extra_args=kwargs.get("extra_args", []),
             debugging_port=kwargs.get("debugging_port", 9222),
             host=kwargs.get("host", "localhost"),
+            enable_stealth=kwargs.get("enable_stealth", False),
         )
 
     def to_dict(self):
@@ -564,6 +588,7 @@ class BrowserConfig:
             "verbose": self.verbose,
             "debugging_port": self.debugging_port,
             "host": self.host,
+            "enable_stealth": self.enable_stealth,
         }
 
                 
@@ -862,7 +887,7 @@ class CrawlerRunConfig():
         parser_type (str): Type of parser to use for HTML parsing.
                            Default: "lxml".
         scraping_strategy (ContentScrapingStrategy): Scraping strategy to use.
-                           Default: WebScrapingStrategy.
+                           Default: LXMLWebScrapingStrategy.
         proxy_config (ProxyConfig or dict or None): Detailed proxy configuration, e.g. {"server": "...", "username": "..."}.
                                      If None, no additional proxy config. Default: None.
 
@@ -958,6 +983,8 @@ class CrawlerRunConfig():
                                          Default: False.
         table_score_threshold (int): Minimum score threshold for processing a table.
                                      Default: 7.
+        table_extraction (TableExtractionStrategy): Strategy to use for table extraction.
+                                     Default: DefaultTableExtraction with table_score_threshold.
 
         # Virtual Scroll Parameters
         virtual_scroll_config (VirtualScrollConfig or dict or None): Configuration for handling virtual scroll containers.
@@ -1084,6 +1111,7 @@ class CrawlerRunConfig():
         image_description_min_word_threshold: int = IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD,
         image_score_threshold: int = IMAGE_SCORE_THRESHOLD,
         table_score_threshold: int = 7,
+        table_extraction: TableExtractionStrategy = None,
         exclude_external_images: bool = False,
         exclude_all_images: bool = False,
         # Link and Domain Handling Parameters
@@ -1113,6 +1141,9 @@ class CrawlerRunConfig():
         link_preview_config: Union[LinkPreviewConfig, Dict[str, Any]] = None,
         # Virtual Scroll Parameters
         virtual_scroll_config: Union[VirtualScrollConfig, Dict[str, Any]] = None,
+        # URL Matching Parameters
+        url_matcher: Optional[UrlMatcher] = None,
+        match_mode: MatchMode = MatchMode.OR,
         # Experimental Parameters
         experimental: Dict[str, Any] = None,
     ):
@@ -1136,6 +1167,11 @@ class CrawlerRunConfig():
         self.parser_type = parser_type
         self.scraping_strategy = scraping_strategy or LXMLWebScrapingStrategy()
         self.proxy_config = proxy_config
+        if isinstance(proxy_config, dict):
+            self.proxy_config = ProxyConfig.from_dict(proxy_config)
+        if isinstance(proxy_config, str):
+            self.proxy_config = ProxyConfig.from_string(proxy_config)
+
         self.proxy_rotation_strategy = proxy_rotation_strategy
         
         # Browser Location and Identity Parameters
@@ -1192,6 +1228,12 @@ class CrawlerRunConfig():
         self.exclude_external_images = exclude_external_images
         self.exclude_all_images = exclude_all_images
         self.table_score_threshold = table_score_threshold
+        
+        # Table extraction strategy (default to DefaultTableExtraction if not specified)
+        if table_extraction is None:
+            self.table_extraction = DefaultTableExtraction(table_score_threshold=table_score_threshold)
+        else:
+            self.table_extraction = table_extraction
 
         # Link and Domain Handling Parameters
         self.exclude_social_media_domains = (
@@ -1266,6 +1308,10 @@ class CrawlerRunConfig():
         else:
             raise ValueError("virtual_scroll_config must be VirtualScrollConfig object or dict")
         
+        # URL Matching Parameters
+        self.url_matcher = url_matcher
+        self.match_mode = match_mode
+        
         # Experimental Parameters
         self.experimental = experimental or {}
         
@@ -1321,6 +1367,51 @@ class CrawlerRunConfig():
             if "compilation error" not in str(e).lower():
                 raise ValueError(f"Failed to compile C4A script: {str(e)}")
             raise
+    
+    def is_match(self, url: str) -> bool:
+        """Check if this config matches the given URL.
+        
+        Args:
+            url: The URL to check against this config's matcher
+            
+        Returns:
+            bool: True if this config should be used for the URL or if no matcher is set.
+        """
+        if self.url_matcher is None:
+            return True
+            
+        if callable(self.url_matcher):
+            # Single function matcher
+            return self.url_matcher(url)
+        
+        elif isinstance(self.url_matcher, str):
+            # Single pattern string
+            from fnmatch import fnmatch
+            return fnmatch(url, self.url_matcher)
+        
+        elif isinstance(self.url_matcher, list):
+            # List of mixed matchers
+            if not self.url_matcher:  # Empty list
+                return False
+                
+            results = []
+            for matcher in self.url_matcher:
+                if callable(matcher):
+                    results.append(matcher(url))
+                elif isinstance(matcher, str):
+                    from fnmatch import fnmatch
+                    results.append(fnmatch(url, matcher))
+                else:
+                    # Skip invalid matchers
+                    continue
+            
+            # Apply match mode logic
+            if self.match_mode == MatchMode.OR:
+                return any(results) if results else False
+            else:  # AND mode
+                return all(results) if results else False
+        
+        return False
 
 
     def __getattr__(self, name):
@@ -1414,6 +1505,7 @@ class CrawlerRunConfig():
                 "image_score_threshold", IMAGE_SCORE_THRESHOLD
             ),
             table_score_threshold=kwargs.get("table_score_threshold", 7),
+            table_extraction=kwargs.get("table_extraction", None),
             exclude_all_images=kwargs.get("exclude_all_images", False),
             exclude_external_images=kwargs.get("exclude_external_images", False),
             # Link and Domain Handling Parameters
@@ -1443,6 +1535,9 @@ class CrawlerRunConfig():
             # Link Extraction Parameters
             link_preview_config=kwargs.get("link_preview_config"),
             url=kwargs.get("url"),
+            # URL Matching Parameters
+            url_matcher=kwargs.get("url_matcher"),
+            match_mode=kwargs.get("match_mode", MatchMode.OR),
             # Experimental Parameters 
             experimental=kwargs.get("experimental"),
         )
@@ -1519,6 +1614,7 @@ class CrawlerRunConfig():
             "image_description_min_word_threshold": self.image_description_min_word_threshold,
             "image_score_threshold": self.image_score_threshold,
             "table_score_threshold": self.table_score_threshold,
+            "table_extraction": self.table_extraction,
             "exclude_all_images": self.exclude_all_images,
             "exclude_external_images": self.exclude_external_images,
             "exclude_social_media_domains": self.exclude_social_media_domains,
@@ -1540,6 +1636,8 @@ class CrawlerRunConfig():
             "deep_crawl_strategy": self.deep_crawl_strategy,
             "link_preview_config": self.link_preview_config.to_dict() if self.link_preview_config else None,
             "url": self.url,
+            "url_matcher": self.url_matcher,
+            "match_mode": self.match_mode,
             "experimental": self.experimental,
         }
 
@@ -1659,22 +1757,57 @@ class SeedingConfig:
     """
     def __init__(
         self,
-        source: str = "sitemap+cc",  # Options: "sitemap", "cc", "sitemap+cc"
-        pattern: Optional[str] = "*", # URL pattern to filter discovered URLs (e.g., "*example.com/blog/*")
-        live_check: bool = False,    # Whether to perform HEAD requests to verify URL liveness
-        extract_head: bool = False,  # Whether to fetch and parse <head> section for metadata
-        max_urls: int = -1, # Maximum number of URLs to discover (default: -1 for no limit)
-        concurrency: int = 1000,      # Maximum concurrent requests for live checks/head extraction
-        hits_per_sec: int = 5,      # Rate limit in requests per second
-        force: bool = False, # If True, bypasses the AsyncUrlSeeder's internal .jsonl cache
-        base_directory: Optional[str] = None, # Base directory for UrlSeeder's cache files (.jsonl)
-        llm_config: Optional[LLMConfig] = None, # Forward LLM config for future use (e.g., relevance scoring)
-        verbose: Optional[bool] = None, # Override crawler's general verbose setting
-        query: Optional[str] = None,  # Search query for relevance scoring
-        score_threshold: Optional[float] = None,  # Minimum relevance score to include URL (0.0-1.0)
-        scoring_method: str = "bm25",  # Scoring method: "bm25" (default), future: "semantic"
-        filter_nonsense_urls: bool = True,  # Filter out utility URLs like robots.txt, sitemap.xml, etc.
+        source: str = "sitemap+cc",
+        pattern: Optional[str] = "*",
+        live_check: bool = False,
+        extract_head: bool = False,
+        max_urls: int = -1,
+        concurrency: int = 1000,
+        hits_per_sec: int = 5,
+        force: bool = False,
+        base_directory: Optional[str] = None,
+        llm_config: Optional[LLMConfig] = None,
+        verbose: Optional[bool] = None,
+        query: Optional[str] = None,
+        score_threshold: Optional[float] = None,
+        scoring_method: str = "bm25",
+        filter_nonsense_urls: bool = True,
     ):
+        """
+        Initialize URL seeding configuration.
+        
+        Args:
+            source: Discovery source(s) to use. Options: "sitemap", "cc" (Common Crawl), 
+                   or "sitemap+cc" (both). Default: "sitemap+cc"
+            pattern: URL pattern to filter discovered URLs (e.g., "*example.com/blog/*"). 
+                    Supports glob-style wildcards. Default: "*" (all URLs)
+            live_check: Whether to perform HEAD requests to verify URL liveness. 
+                       Default: False
+            extract_head: Whether to fetch and parse <head> section for metadata extraction.
+                         Required for BM25 relevance scoring. Default: False
+            max_urls: Maximum number of URLs to discover. Use -1 for no limit. 
+                     Default: -1
+            concurrency: Maximum concurrent requests for live checks/head extraction. 
+                        Default: 1000
+            hits_per_sec: Rate limit in requests per second to avoid overwhelming servers. 
+                         Default: 5
+            force: If True, bypasses the AsyncUrlSeeder's internal .jsonl cache and 
+                  re-fetches URLs. Default: False
+            base_directory: Base directory for UrlSeeder's cache files (.jsonl). 
+                           If None, uses default ~/.crawl4ai/. Default: None
+            llm_config: LLM configuration for future features (e.g., semantic scoring). 
+                       Currently unused. Default: None
+            verbose: Override crawler's general verbose setting for seeding operations. 
+                    Default: None (inherits from crawler)
+            query: Search query for BM25 relevance scoring (e.g., "python tutorials"). 
+                  Requires extract_head=True. Default: None
+            score_threshold: Minimum relevance score (0.0-1.0) to include URL. 
+                           Only applies when query is provided. Default: None
+            scoring_method: Scoring algorithm to use. Currently only "bm25" is supported. 
+                          Future: "semantic". Default: "bm25"
+            filter_nonsense_urls: Filter out utility URLs like robots.txt, sitemap.xml, 
+                                 ads.txt, favicon.ico, etc. Default: True
+        """
         self.source = source
         self.pattern = pattern
         self.live_check = live_check

crawl4ai/async_crawler_strategy.py

@@ -21,6 +21,7 @@ from .async_logger import AsyncLogger
 from .ssl_certificate import SSLCertificate
 from .user_agent_generator import ValidUAGenerator
 from .browser_manager import BrowserManager
+from .browser_adapter import BrowserAdapter, PlaywrightAdapter, UndetectedAdapter
 
 import aiofiles
 import aiohttp
@@ -71,7 +72,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
     """
 
     def __init__(
-        self, browser_config: BrowserConfig = None, logger: AsyncLogger = None, **kwargs
+        self, browser_config: BrowserConfig = None, logger: AsyncLogger = None, browser_adapter: BrowserAdapter = None, **kwargs
     ):
         """
         Initialize the AsyncPlaywrightCrawlerStrategy with a browser configuration.
@@ -80,11 +81,16 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             browser_config (BrowserConfig): Configuration object containing browser settings.
                                           If None, will be created from kwargs for backwards compatibility.
             logger: Logger instance for recording events and errors.
+            browser_adapter (BrowserAdapter): Browser adapter for handling browser-specific operations.
+                                           If None, defaults to PlaywrightAdapter.
             **kwargs: Additional arguments for backwards compatibility and extending functionality.
         """
         # Initialize browser config, either from provided object or kwargs
         self.browser_config = browser_config or BrowserConfig.from_kwargs(kwargs)
         self.logger = logger
+        
+        # Initialize browser adapter
+        self.adapter = browser_adapter or PlaywrightAdapter()
 
         # Initialize session management
         self._downloaded_files = []
@@ -104,7 +110,9 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
 
         # Initialize browser manager with config
         self.browser_manager = BrowserManager(
-            browser_config=self.browser_config, logger=self.logger
+            browser_config=self.browser_config, 
+            logger=self.logger,
+            use_undetected=isinstance(self.adapter, UndetectedAdapter)
         )
 
     async def __aenter__(self):
@@ -322,7 +330,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         """
 
         try:
-            result = await page.evaluate(wrapper_js)
+            result = await self.adapter.evaluate(page, wrapper_js)
             return result
         except Exception as e:
             if "Error evaluating condition" in str(e):
@@ -367,7 +375,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
 
                     # Replace the iframe with a div containing the extracted content
                     _iframe = iframe_content.replace("`", "\\`")
-                    await page.evaluate(
+                    await self.adapter.evaluate(page,
                         f"""
                         () => {{
                             const iframe = document.getElementById('iframe-{i}');
@@ -628,91 +636,16 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             page.on("requestfailed", handle_request_failed_capture)
 
         # Console Message Capturing
+        handle_console = None
+        handle_error = None
         if config.capture_console_messages:
-            def handle_console_capture(msg):
-                try:
-                    message_type = "unknown"
-                    try:
-                        message_type = msg.type
-                    except:
-                        pass
-                        
-                    message_text = "unknown"
-                    try:
-                        message_text = msg.text
-                    except:
-                        pass
-                        
-                    # Basic console message with minimal content
-                    entry = {
-                        "type": message_type,
-                        "text": message_text,
-                        "timestamp": time.time()
-                    }
-                    
-                    captured_console.append(entry)
-                    
-                except Exception as e:
-                    if self.logger:
-                        self.logger.warning(f"Error capturing console message: {e}", tag="CAPTURE")
-                    # Still add something to the list even on error
-                    captured_console.append({
-                        "type": "console_capture_error", 
-                        "error": str(e), 
-                        "timestamp": time.time()
-                    })
-
-            def handle_pageerror_capture(err):
-                try:
-                    error_message = "Unknown error"
-                    try:
-                        error_message = err.message
-                    except:
-                        pass
-                        
-                    error_stack = ""
-                    try:
-                        error_stack = err.stack
-                    except:
-                        pass
-                        
-                    captured_console.append({
-                        "type": "error",
-                        "text": error_message,
-                        "stack": error_stack,
-                        "timestamp": time.time()
-                    })
-                except Exception as e:
-                    if self.logger:
-                        self.logger.warning(f"Error capturing page error: {e}", tag="CAPTURE")
-                    captured_console.append({
-                        "type": "pageerror_capture_error", 
-                        "error": str(e), 
-                        "timestamp": time.time()
-                    })
-
-            # Add event listeners directly
-            page.on("console", handle_console_capture)
-            page.on("pageerror", handle_pageerror_capture)
+            # Set up console capture using adapter
+            handle_console = await self.adapter.setup_console_capture(page, captured_console)
+            handle_error = await self.adapter.setup_error_capture(page, captured_console)
 
         # Set up console logging if requested
-        if config.log_console:
-            def log_consol(
-                msg, console_log_type="debug"
-            ):  # Corrected the parameter syntax
-                if console_log_type == "error":
-                    self.logger.error(
-                        message=f"Console error: {msg}",  # Use f-string for variable interpolation
-                        tag="CONSOLE"
-                    )
-                elif console_log_type == "debug":
-                    self.logger.debug(
-                        message=f"Console: {msg}",  # Use f-string for variable interpolation
-                        tag="CONSOLE"
-                    )
-
-            page.on("console", log_consol)
-            page.on("pageerror", lambda e: log_consol(e, "error"))
+        # Note: For undetected browsers, console logging won't work directly
+        # but captured messages can still be logged after retrieval
 
         try:
             # Get SSL certificate information if requested and URL is HTTPS
@@ -824,7 +757,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             except Error:
                 visibility_info = await self.check_visibility(page)
 
-                if self.browser_config.config.verbose:
+                if self.browser_config.verbose:
                     self.logger.debug(
                         message="Body visibility info: {info}",
                         tag="DEBUG",
@@ -998,7 +931,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                         await page.wait_for_load_state("domcontentloaded", timeout=5)
                     except PlaywrightTimeoutError:
                         pass
-                    await page.evaluate(update_image_dimensions_js)
+                    await self.adapter.evaluate(page, update_image_dimensions_js)
                 except Exception as e:
                     self.logger.error(
                         message="Error updating image dimensions: {error}",
@@ -1027,7 +960,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                     
                     for selector in selectors:
                         try:
-                            content = await page.evaluate(
+                            content = await self.adapter.evaluate(page,
                                 f"""Array.from(document.querySelectorAll("{selector}"))
                                     .map(el => el.outerHTML)
                                     .join('')"""
@@ -1085,6 +1018,11 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                 await asyncio.sleep(delay)
                 return await page.content()
 
+            # For undetected browsers, retrieve console messages before returning
+            if config.capture_console_messages and hasattr(self.adapter, 'retrieve_console_messages'):
+                final_messages = await self.adapter.retrieve_console_messages(page)
+                captured_console.extend(final_messages)
+
             # Return complete response
             return AsyncCrawlResponse(
                 html=html,
@@ -1123,8 +1061,13 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                     page.remove_listener("response", handle_response_capture)
                     page.remove_listener("requestfailed", handle_request_failed_capture)
                 if config.capture_console_messages:
-                    page.remove_listener("console", handle_console_capture)
-                    page.remove_listener("pageerror", handle_pageerror_capture)
+                    # Retrieve any final console messages for undetected browsers
+                    if hasattr(self.adapter, 'retrieve_console_messages'):
+                        final_messages = await self.adapter.retrieve_console_messages(page)
+                        captured_console.extend(final_messages)
+                    
+                    # Clean up console capture
+                    await self.adapter.cleanup_console_capture(page, handle_console, handle_error)
                 
                 # Close the page
                 await page.close()
@@ -1354,7 +1297,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             """
             
             # Execute virtual scroll capture
-            result = await page.evaluate(virtual_scroll_js, config.to_dict())
+            result = await self.adapter.evaluate(page, virtual_scroll_js, config.to_dict())
             
             if result.get("replaced", False):
                 self.logger.success(
@@ -1438,7 +1381,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         remove_overlays_js = load_js_script("remove_overlay_elements")
 
         try:
-            await page.evaluate(
+            await self.adapter.evaluate(page,
                 f"""
                 (() => {{
                     try {{
@@ -1843,7 +1786,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                         # When {script} contains statements (e.g., const link = …; link.click();), 
                         # this forms invalid JavaScript, causing Playwright execution error: SyntaxError: Unexpected token 'const'.
                         # """
-                        result = await page.evaluate(
+                        result = await self.adapter.evaluate(page,
                             f"""
                         (async () => {{
                             try {{
@@ -1965,7 +1908,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             for script in scripts:
                 try:
                     # Execute the script and wait for network idle
-                    result = await page.evaluate(
+                    result = await self.adapter.evaluate(page,
                         f"""
                         (() => {{
                             return new Promise((resolve) => {{
@@ -2049,7 +1992,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         Returns:
             Boolean indicating visibility
         """
-        return await page.evaluate(
+        return await self.adapter.evaluate(page,
             """
             () => {
                 const element = document.body;
@@ -2090,7 +2033,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             Dict containing scroll status and position information
         """
         try:
-            result = await page.evaluate(
+            result = await self.adapter.evaluate(page,
                 f"""() => {{
                     try {{
                         const startX = window.scrollX;
@@ -2147,7 +2090,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         Returns:
             Dict containing width and height of the page
         """
-        return await page.evaluate(
+        return await self.adapter.evaluate(page,
             """
             () => {
                 const {scrollWidth, scrollHeight} = document.documentElement;
@@ -2167,7 +2110,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             bool: True if page needs scrolling
         """
         try:
-            need_scroll = await page.evaluate(
+            need_scroll = await self.adapter.evaluate(page,
                 """
             () => {
                 const scrollHeight = document.documentElement.scrollHeight;
@@ -2447,4 +2390,4 @@ class AsyncHTTPCrawlerStrategy(AsyncCrawlerStrategy):
                     tag="CRAWL",
                     params={"error": str(e), "url": url}
                 )
-            raise
+            raise

crawl4ai/async_dispatcher.py

@@ -1,4 +1,4 @@
-from typing import Dict, Optional, List, Tuple
+from typing import Dict, Optional, List, Tuple, Union
 from .async_configs import CrawlerRunConfig
 from .models import (
     CrawlResult,
@@ -22,6 +22,8 @@ from urllib.parse import urlparse
 import random
 from abc import ABC, abstractmethod
 
+from .utils import get_true_memory_usage_percent
+
 
 class RateLimiter:
     def __init__(
@@ -96,11 +98,37 @@ class BaseDispatcher(ABC):
         self.rate_limiter = rate_limiter
         self.monitor = monitor
 
+    def select_config(self, url: str, configs: Union[CrawlerRunConfig, List[CrawlerRunConfig]]) -> Optional[CrawlerRunConfig]:
+        """Select the appropriate config for a given URL.
+        
+        Args:
+            url: The URL to match against
+            configs: Single config or list of configs to choose from
+            
+        Returns:
+            The matching config, or None if no match found
+        """
+        # Single config - return as is
+        if isinstance(configs, CrawlerRunConfig):
+            return configs
+        
+        # Empty list - return None
+        if not configs:
+            return None
+        
+        # Find first matching config
+        for config in configs:
+            if config.is_match(url):
+                return config
+        
+        # No match found - return None to indicate URL should be skipped
+        return None
+
     @abstractmethod
     async def crawl_url(
         self,
         url: str,
-        config: CrawlerRunConfig,
+        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
         task_id: str,
         monitor: Optional[CrawlerMonitor] = None,
     ) -> CrawlerTaskResult:
@@ -111,7 +139,7 @@ class BaseDispatcher(ABC):
         self,
         urls: List[str],
         crawler: AsyncWebCrawler,  # noqa: F821
-        config: CrawlerRunConfig,
+        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
         monitor: Optional[CrawlerMonitor] = None,
     ) -> List[CrawlerTaskResult]:
         pass
@@ -147,7 +175,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
     async def _memory_monitor_task(self):
         """Background task to continuously monitor memory usage and update state"""
         while True:
-            self.current_memory_percent = psutil.virtual_memory().percent
+            self.current_memory_percent = get_true_memory_usage_percent()
 
             # Enter memory pressure mode if we cross the threshold
             if self.current_memory_percent >= self.memory_threshold_percent:
@@ -200,7 +228,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
     async def crawl_url(
         self,
         url: str,
-        config: CrawlerRunConfig,
+        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
         task_id: str,
         retry_count: int = 0,
     ) -> CrawlerTaskResult:
@@ -208,6 +236,37 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
         error_message = ""
         memory_usage = peak_memory = 0.0
         
+        # Select appropriate config for this URL
+        selected_config = self.select_config(url, config)
+        
+        # If no config matches, return failed result
+        if selected_config is None:
+            error_message = f"No matching configuration found for URL: {url}"
+            if self.monitor:
+                self.monitor.update_task(
+                    task_id, 
+                    status=CrawlStatus.FAILED,
+                    error_message=error_message
+                )
+            
+            return CrawlerTaskResult(
+                task_id=task_id,
+                url=url,
+                result=CrawlResult(
+                    url=url, 
+                    html="", 
+                    metadata={"status": "no_config_match"}, 
+                    success=False, 
+                    error_message=error_message
+                ),
+                memory_usage=0,
+                peak_memory=0,
+                start_time=start_time,
+                end_time=time.time(),
+                error_message=error_message,
+                retry_count=retry_count
+            )
+        
         # Get starting memory for accurate measurement
         process = psutil.Process()
         start_memory = process.memory_info().rss / (1024 * 1024)
@@ -257,8 +316,8 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
                     retry_count=retry_count + 1
                 )
             
-            # Execute the crawl
-            result = await self.crawler.arun(url, config=config, session_id=task_id)
+            # Execute the crawl with selected config
+            result = await self.crawler.arun(url, config=selected_config, session_id=task_id)
             
             # Measure memory usage
             end_memory = process.memory_info().rss / (1024 * 1024)
@@ -316,7 +375,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
         self,
         urls: List[str],
         crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
+        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
     ) -> List[CrawlerTaskResult]:
         self.crawler = crawler
         
@@ -348,32 +407,34 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
                             t.cancel()
                         raise exc
 
-                # If memory pressure is low, start new tasks
-                if not self.memory_pressure_mode and len(active_tasks) < self.max_session_permit:
-                    try:
-                        # Try to get a task with timeout to avoid blocking indefinitely
-                        priority, (url, task_id, retry_count, enqueue_time) = await asyncio.wait_for(
-                            self.task_queue.get(), timeout=0.1
-                        )
-                        
-                        # Create and start the task
-                        task = asyncio.create_task(
-                            self.crawl_url(url, config, task_id, retry_count)
-                        )
-                        active_tasks.append(task)
-                        
-                        # Update waiting time in monitor
-                        if self.monitor:
-                            wait_time = time.time() - enqueue_time
-                            self.monitor.update_task(
-                                task_id, 
-                                wait_time=wait_time,
-                                status=CrawlStatus.IN_PROGRESS
-                            )
+                # If memory pressure is low, greedily fill all available slots
+                if not self.memory_pressure_mode:
+                    slots = self.max_session_permit - len(active_tasks)
+                    while slots > 0:
+                        try:
+                            # Use get_nowait() to immediately get tasks without blocking
+                            priority, (url, task_id, retry_count, enqueue_time) = self.task_queue.get_nowait()
                             
-                    except asyncio.TimeoutError:
-                        # No tasks in queue, that's fine
-                        pass
+                            # Create and start the task
+                            task = asyncio.create_task(
+                                self.crawl_url(url, config, task_id, retry_count)
+                            )
+                            active_tasks.append(task)
+                            
+                            # Update waiting time in monitor
+                            if self.monitor:
+                                wait_time = time.time() - enqueue_time
+                                self.monitor.update_task(
+                                    task_id, 
+                                    wait_time=wait_time,
+                                    status=CrawlStatus.IN_PROGRESS
+                                )
+                            
+                            slots -= 1
+                            
+                        except asyncio.QueueEmpty:
+                            # No more tasks in queue, exit the loop
+                            break
                         
                 # Wait for completion even if queue is starved
                 if active_tasks:
@@ -470,7 +531,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
         self,
         urls: List[str],
         crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
+        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
     ) -> AsyncGenerator[CrawlerTaskResult, None]:
         self.crawler = crawler
         
@@ -500,32 +561,34 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
                         for t in active_tasks:
                             t.cancel()
                         raise exc
-                # If memory pressure is low, start new tasks
-                if not self.memory_pressure_mode and len(active_tasks) < self.max_session_permit:
-                    try:
-                        # Try to get a task with timeout
-                        priority, (url, task_id, retry_count, enqueue_time) = await asyncio.wait_for(
-                            self.task_queue.get(), timeout=0.1
-                        )
-                        
-                        # Create and start the task
-                        task = asyncio.create_task(
-                            self.crawl_url(url, config, task_id, retry_count)
-                        )
-                        active_tasks.append(task)
-                        
-                        # Update waiting time in monitor
-                        if self.monitor:
-                            wait_time = time.time() - enqueue_time
-                            self.monitor.update_task(
-                                task_id, 
-                                wait_time=wait_time,
-                                status=CrawlStatus.IN_PROGRESS
-                            )
+                # If memory pressure is low, greedily fill all available slots
+                if not self.memory_pressure_mode:
+                    slots = self.max_session_permit - len(active_tasks)
+                    while slots > 0:
+                        try:
+                            # Use get_nowait() to immediately get tasks without blocking
+                            priority, (url, task_id, retry_count, enqueue_time) = self.task_queue.get_nowait()
                             
-                    except asyncio.TimeoutError:
-                        # No tasks in queue, that's fine
-                        pass
+                            # Create and start the task
+                            task = asyncio.create_task(
+                                self.crawl_url(url, config, task_id, retry_count)
+                            )
+                            active_tasks.append(task)
+                            
+                            # Update waiting time in monitor
+                            if self.monitor:
+                                wait_time = time.time() - enqueue_time
+                                self.monitor.update_task(
+                                    task_id, 
+                                    wait_time=wait_time,
+                                    status=CrawlStatus.IN_PROGRESS
+                                )
+                            
+                            slots -= 1
+                            
+                        except asyncio.QueueEmpty:
+                            # No more tasks in queue, exit the loop
+                            break
                         
                 # Process completed tasks and yield results
                 if active_tasks:
@@ -572,7 +635,7 @@ class SemaphoreDispatcher(BaseDispatcher):
     async def crawl_url(
         self,
         url: str,
-        config: CrawlerRunConfig,
+        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
         task_id: str,
         semaphore: asyncio.Semaphore = None,
     ) -> CrawlerTaskResult:
@@ -580,6 +643,36 @@ class SemaphoreDispatcher(BaseDispatcher):
         error_message = ""
         memory_usage = peak_memory = 0.0
 
+        # Select appropriate config for this URL
+        selected_config = self.select_config(url, config)
+        
+        # If no config matches, return failed result
+        if selected_config is None:
+            error_message = f"No matching configuration found for URL: {url}"
+            if self.monitor:
+                self.monitor.update_task(
+                    task_id, 
+                    status=CrawlStatus.FAILED,
+                    error_message=error_message
+                )
+            
+            return CrawlerTaskResult(
+                task_id=task_id,
+                url=url,
+                result=CrawlResult(
+                    url=url, 
+                    html="", 
+                    metadata={"status": "no_config_match"}, 
+                    success=False, 
+                    error_message=error_message
+                ),
+                memory_usage=0,
+                peak_memory=0,
+                start_time=start_time,
+                end_time=time.time(),
+                error_message=error_message
+            )
+
         try:
             if self.monitor:
                 self.monitor.update_task(
@@ -592,7 +685,7 @@ class SemaphoreDispatcher(BaseDispatcher):
             async with semaphore:
                 process = psutil.Process()
                 start_memory = process.memory_info().rss / (1024 * 1024)
-                result = await self.crawler.arun(url, config=config, session_id=task_id)
+                result = await self.crawler.arun(url, config=selected_config, session_id=task_id)
                 end_memory = process.memory_info().rss / (1024 * 1024)
 
                 memory_usage = peak_memory = end_memory - start_memory
@@ -654,7 +747,7 @@ class SemaphoreDispatcher(BaseDispatcher):
         self,
         crawler: AsyncWebCrawler,  # noqa: F821
         urls: List[str],
-        config: CrawlerRunConfig,
+        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
     ) -> List[CrawlerTaskResult]:
         self.crawler = crawler
         if self.monitor:

crawl4ai/async_url_seeder.py

@@ -424,10 +424,21 @@ class AsyncUrlSeeder:
         self._log("info", "Finished URL seeding for {domain}. Total URLs: {count}",
                   params={"domain": domain, "count": len(results)}, tag="URL_SEED")
 
-        # Sort by relevance score if query was provided
+        # Apply BM25 scoring if query was provided
         if query and extract_head and scoring_method == "bm25":
-            results.sort(key=lambda x: x.get(
-                "relevance_score", 0.0), reverse=True)
+            # Apply collective BM25 scoring across all documents
+            results = await self._apply_bm25_scoring(results, config)
+            
+            # Filter by score threshold if specified
+            if score_threshold is not None:
+                original_count = len(results)
+                results = [r for r in results if r.get("relevance_score", 0) >= score_threshold]
+                if original_count > len(results):
+                    self._log("info", "Filtered {filtered} URLs below score threshold {threshold}",
+                              params={"filtered": original_count - len(results), "threshold": score_threshold}, tag="URL_SEED")
+            
+            # Sort by relevance score
+            results.sort(key=lambda x: x.get("relevance_score", 0.0), reverse=True)
             self._log("info", "Sorted {count} URLs by relevance score for query: '{query}'",
                       params={"count": len(results), "query": query}, tag="URL_SEED")
         elif query and not extract_head:
@@ -818,7 +829,7 @@ class AsyncUrlSeeder:
 
     async def _iter_sitemap(self, url: str):
         try:
-            r = await self.client.get(url, timeout=15)
+            r = await self.client.get(url, timeout=15, follow_redirects=True)
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
             self._log("warning", "Failed to fetch sitemap {url}: HTTP {status_code}",
@@ -982,28 +993,6 @@ class AsyncUrlSeeder:
                 "head_data": head_data,
             }
 
-            # Apply BM25 scoring if query is provided and head data exists
-            if query and ok and scoring_method == "bm25" and head_data:
-                text_context = self._extract_text_context(head_data)
-                if text_context:
-                    # Calculate BM25 score for this single document
-                    # scores = self._calculate_bm25_score(query, [text_context])
-                    scores = await asyncio.to_thread(self._calculate_bm25_score, query, [text_context])
-                    relevance_score = scores[0] if scores else 0.0
-                    entry["relevance_score"] = float(relevance_score)
-                else:
-                    # No text context, use URL-based scoring as fallback
-                    relevance_score = self._calculate_url_relevance_score(
-                        query, entry["url"])
-                    entry["relevance_score"] = float(relevance_score)
-            elif query:
-                # Query provided but no head data - we reject this entry
-                self._log("debug", "No head data for {url}, using URL-based scoring",
-                          params={"url": url}, tag="URL_SEED")
-                return
-                # relevance_score = self._calculate_url_relevance_score(query, entry["url"])
-                # entry["relevance_score"] = float(relevance_score)
-
         elif live:
             self._log("debug", "Performing live check for {url}", params={
                       "url": url}, tag="URL_SEED")
@@ -1013,35 +1002,13 @@ class AsyncUrlSeeder:
                       params={"status": status.upper(), "url": url}, tag="URL_SEED")
             entry = {"url": url, "status": status, "head_data": {}}
 
-            # Apply URL-based scoring if query is provided
-            if query:
-                relevance_score = self._calculate_url_relevance_score(
-                    query, url)
-                entry["relevance_score"] = float(relevance_score)
-
         else:
             entry = {"url": url, "status": "unknown", "head_data": {}}
 
-            # Apply URL-based scoring if query is provided
-            if query:
-                relevance_score = self._calculate_url_relevance_score(
-                    query, url)
-                entry["relevance_score"] = float(relevance_score)
-
-        # Now decide whether to add the entry based on score threshold
-        if query and "relevance_score" in entry:
-            if score_threshold is None or entry["relevance_score"] >= score_threshold:
-                if live or extract:
-                    await self._cache_set(cache_kind, url, entry)
-                res_list.append(entry)
-            else:
-                self._log("debug", "URL {url} filtered out with score {score} < {threshold}",
-                          params={"url": url, "score": entry["relevance_score"], "threshold": score_threshold}, tag="URL_SEED")
-        else:
-            # No query or no scoring - add as usual
-            if live or extract:
-                await self._cache_set(cache_kind, url, entry)
-            res_list.append(entry)
+        # Add entry to results (scoring will be done later)
+        if live or extract:
+            await self._cache_set(cache_kind, url, entry)
+        res_list.append(entry)
 
     async def _head_ok(self, url: str, timeout: int) -> bool:
         try:
@@ -1436,8 +1403,19 @@ class AsyncUrlSeeder:
             scores = bm25.get_scores(query_tokens)
 
             # Normalize scores to 0-1 range
-            max_score = max(scores) if max(scores) > 0 else 1.0
-            normalized_scores = [score / max_score for score in scores]
+            # BM25 can return negative scores, so we need to handle the full range
+            if len(scores) == 0:
+                return []
+            
+            min_score = min(scores)
+            max_score = max(scores)
+            
+            # If all scores are the same, return 0.5 for all
+            if max_score == min_score:
+                return [0.5] * len(scores)
+            
+            # Normalize to 0-1 range using min-max normalization
+            normalized_scores = [(score - min_score) / (max_score - min_score) for score in scores]
 
             return normalized_scores
         except Exception as e:

crawl4ai/async_webcrawler.py

@@ -502,9 +502,12 @@ class AsyncWebCrawler:
             metadata = result.get("metadata", {})
         else:
             cleaned_html = sanitize_input_encode(result.cleaned_html)
-            media = result.media.model_dump()
-            tables = media.pop("tables", [])
-            links = result.links.model_dump()
+            # media = result.media.model_dump()
+            # tables = media.pop("tables", [])
+            # links = result.links.model_dump()
+            media = result.media.model_dump() if hasattr(result.media, 'model_dump') else result.media
+            tables = media.pop("tables", []) if isinstance(media, dict) else []
+            links = result.links.model_dump() if hasattr(result.links, 'model_dump') else result.links
             metadata = result.metadata
 
         fit_html = preprocess_html_for_schema(html_content=html, text_threshold= 500, max_size= 300_000)
@@ -650,7 +653,7 @@ class AsyncWebCrawler:
     async def arun_many(
         self,
         urls: List[str],
-        config: Optional[CrawlerRunConfig] = None,
+        config: Optional[Union[CrawlerRunConfig, List[CrawlerRunConfig]]] = None,
         dispatcher: Optional[BaseDispatcher] = None,
         # Legacy parameters maintained for backwards compatibility
         # word_count_threshold=MIN_WORD_THRESHOLD,
@@ -671,7 +674,9 @@ class AsyncWebCrawler:
 
         Args:
         urls: List of URLs to crawl
-        config: Configuration object controlling crawl behavior for all URLs
+        config: Configuration object(s) controlling crawl behavior. Can be:
+            - Single CrawlerRunConfig: Used for all URLs
+            - List[CrawlerRunConfig]: Configs with url_matcher for URL-specific settings
         dispatcher: The dispatcher strategy instance to use. Defaults to MemoryAdaptiveDispatcher
         [other parameters maintained for backwards compatibility]
 
@@ -736,7 +741,11 @@ class AsyncWebCrawler:
                 or task_result.result
             )
 
-        stream = config.stream
+        # Handle stream setting - use first config's stream setting if config is a list
+        if isinstance(config, list):
+            stream = config[0].stream if config else False
+        else:
+            stream = config.stream
 
         if stream:
 

crawl4ai/browser_adapter.py

@@ -0,0 +1,293 @@
+# browser_adapter.py
+"""
+Browser adapter for Crawl4AI to support both Playwright and undetected browsers
+with minimal changes to existing codebase.
+"""
+
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any, Optional, Callable
+import time
+import json
+
+# Import both, but use conditionally
+try:
+    from playwright.async_api import Page
+except ImportError:
+    Page = Any
+
+try:
+    from patchright.async_api import Page as UndetectedPage
+except ImportError:
+    UndetectedPage = Any
+
+
+class BrowserAdapter(ABC):
+    """Abstract adapter for browser-specific operations"""
+    
+    @abstractmethod
+    async def evaluate(self, page: Page, expression: str, arg: Any = None) -> Any:
+        """Execute JavaScript in the page"""
+        pass
+    
+    @abstractmethod
+    async def setup_console_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
+        """Setup console message capturing, returns handler function if needed"""
+        pass
+    
+    @abstractmethod
+    async def setup_error_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
+        """Setup error capturing, returns handler function if needed"""
+        pass
+    
+    @abstractmethod
+    async def retrieve_console_messages(self, page: Page) -> List[Dict]:
+        """Retrieve captured console messages (for undetected browsers)"""
+        pass
+    
+    @abstractmethod
+    async def cleanup_console_capture(self, page: Page, handle_console: Optional[Callable], handle_error: Optional[Callable]):
+        """Clean up console event listeners"""
+        pass
+    
+    @abstractmethod
+    def get_imports(self) -> tuple:
+        """Get the appropriate imports for this adapter"""
+        pass
+
+
+class PlaywrightAdapter(BrowserAdapter):
+    """Adapter for standard Playwright"""
+    
+    async def evaluate(self, page: Page, expression: str, arg: Any = None) -> Any:
+        """Standard Playwright evaluate"""
+        if arg is not None:
+            return await page.evaluate(expression, arg)
+        return await page.evaluate(expression)
+    
+    async def setup_console_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
+        """Setup console capture using Playwright's event system"""
+        def handle_console_capture(msg):
+            try:
+                message_type = "unknown"
+                try:
+                    message_type = msg.type
+                except:
+                    pass
+                    
+                message_text = "unknown"
+                try:
+                    message_text = msg.text
+                except:
+                    pass
+                    
+                entry = {
+                    "type": message_type,
+                    "text": message_text,
+                    "timestamp": time.time()
+                }
+                
+                captured_console.append(entry)
+                
+            except Exception as e:
+                captured_console.append({
+                    "type": "console_capture_error", 
+                    "error": str(e), 
+                    "timestamp": time.time()
+                })
+        
+        page.on("console", handle_console_capture)
+        return handle_console_capture
+    
+    async def setup_error_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
+        """Setup error capture using Playwright's event system"""
+        def handle_pageerror_capture(err):
+            try:
+                error_message = "Unknown error"
+                try:
+                    error_message = err.message
+                except:
+                    pass
+                    
+                error_stack = ""
+                try:
+                    error_stack = err.stack
+                except:
+                    pass
+                    
+                captured_console.append({
+                    "type": "error",
+                    "text": error_message,
+                    "stack": error_stack,
+                    "timestamp": time.time()
+                })
+            except Exception as e:
+                captured_console.append({
+                    "type": "pageerror_capture_error", 
+                    "error": str(e), 
+                    "timestamp": time.time()
+                })
+        
+        page.on("pageerror", handle_pageerror_capture)
+        return handle_pageerror_capture
+    
+    async def retrieve_console_messages(self, page: Page) -> List[Dict]:
+        """Not needed for Playwright - messages are captured via events"""
+        return []
+    
+    async def cleanup_console_capture(self, page: Page, handle_console: Optional[Callable], handle_error: Optional[Callable]):
+        """Remove event listeners"""
+        if handle_console:
+            page.remove_listener("console", handle_console)
+        if handle_error:
+            page.remove_listener("pageerror", handle_error)
+    
+    def get_imports(self) -> tuple:
+        """Return Playwright imports"""
+        from playwright.async_api import Page, Error
+        from playwright.async_api import TimeoutError as PlaywrightTimeoutError
+        return Page, Error, PlaywrightTimeoutError
+
+
+class UndetectedAdapter(BrowserAdapter):
+    """Adapter for undetected browser automation with stealth features"""
+    
+    def __init__(self):
+        self._console_script_injected = {}
+    
+    async def evaluate(self, page: UndetectedPage, expression: str, arg: Any = None) -> Any:
+        """Undetected browser evaluate with isolated context"""
+        # For most evaluations, use isolated context for stealth
+        # Only use non-isolated when we need to access our injected console capture
+        isolated = not (
+            "__console" in expression or 
+            "__captured" in expression or
+            "__error" in expression or
+            "window.__" in expression
+        )
+        
+        if arg is not None:
+            return await page.evaluate(expression, arg, isolated_context=isolated)
+        return await page.evaluate(expression, isolated_context=isolated)
+    
+    async def setup_console_capture(self, page: UndetectedPage, captured_console: List[Dict]) -> Optional[Callable]:
+        """Setup console capture using JavaScript injection for undetected browsers"""
+        if not self._console_script_injected.get(page, False):
+            await page.add_init_script("""
+                // Initialize console capture
+                window.__capturedConsole = [];
+                window.__capturedErrors = [];
+                
+                // Store original console methods
+                const originalConsole = {};
+                ['log', 'info', 'warn', 'error', 'debug'].forEach(method => {
+                    originalConsole[method] = console[method];
+                    console[method] = function(...args) {
+                        try {
+                            window.__capturedConsole.push({
+                                type: method,
+                                text: args.map(arg => {
+                                    try {
+                                        if (typeof arg === 'object') {
+                                            return JSON.stringify(arg);
+                                        }
+                                        return String(arg);
+                                    } catch (e) {
+                                        return '[Object]';
+                                    }
+                                }).join(' '),
+                                timestamp: Date.now()
+                            });
+                        } catch (e) {
+                            // Fail silently to avoid detection
+                        }
+                        
+                        // Call original method
+                        originalConsole[method].apply(console, args);
+                    };
+                });
+            """)
+            self._console_script_injected[page] = True
+        
+        return None  # No handler function needed for undetected browser
+    
+    async def setup_error_capture(self, page: UndetectedPage, captured_console: List[Dict]) -> Optional[Callable]:
+        """Setup error capture using JavaScript injection for undetected browsers"""
+        if not self._console_script_injected.get(page, False):
+            await page.add_init_script("""
+                // Capture errors
+                window.addEventListener('error', (event) => {
+                    try {
+                        window.__capturedErrors.push({
+                            type: 'error',
+                            text: event.message,
+                            stack: event.error ? event.error.stack : '',
+                            filename: event.filename,
+                            lineno: event.lineno,
+                            colno: event.colno,
+                            timestamp: Date.now()
+                        });
+                    } catch (e) {
+                        // Fail silently
+                    }
+                });
+                
+                // Capture unhandled promise rejections
+                window.addEventListener('unhandledrejection', (event) => {
+                    try {
+                        window.__capturedErrors.push({
+                            type: 'unhandledrejection',
+                            text: event.reason ? String(event.reason) : 'Unhandled Promise Rejection',
+                            stack: event.reason && event.reason.stack ? event.reason.stack : '',
+                            timestamp: Date.now()
+                        });
+                    } catch (e) {
+                        // Fail silently
+                    }
+                });
+            """)
+            self._console_script_injected[page] = True
+        
+        return None  # No handler function needed for undetected browser
+    
+    async def retrieve_console_messages(self, page: UndetectedPage) -> List[Dict]:
+        """Retrieve captured console messages and errors from the page"""
+        messages = []
+        
+        try:
+            # Get console messages
+            console_messages = await page.evaluate(
+                "() => { const msgs = window.__capturedConsole || []; window.__capturedConsole = []; return msgs; }",
+                isolated_context=False
+            )
+            messages.extend(console_messages)
+            
+            # Get errors
+            errors = await page.evaluate(
+                "() => { const errs = window.__capturedErrors || []; window.__capturedErrors = []; return errs; }",
+                isolated_context=False
+            )
+            messages.extend(errors)
+            
+            # Convert timestamps from JS to Python format
+            for msg in messages:
+                if 'timestamp' in msg and isinstance(msg['timestamp'], (int, float)):
+                    msg['timestamp'] = msg['timestamp'] / 1000.0  # Convert from ms to seconds
+                    
+        except Exception:
+            # If retrieval fails, return empty list
+            pass
+        
+        return messages
+    
+    async def cleanup_console_capture(self, page: UndetectedPage, handle_console: Optional[Callable], handle_error: Optional[Callable]):
+        """Clean up for undetected browser - retrieve final messages"""
+        # For undetected browser, we don't have event listeners to remove
+        # but we should retrieve any final messages
+        final_messages = await self.retrieve_console_messages(page)
+        return final_messages
+    
+    def get_imports(self) -> tuple:
+        """Return undetected browser imports"""
+        from patchright.async_api import Page, Error
+        from patchright.async_api import TimeoutError as PlaywrightTimeoutError
+        return Page, Error, PlaywrightTimeoutError

crawl4ai/browser_manager.py

@@ -14,23 +14,8 @@ import hashlib
 from .js_snippet import load_js_script
 from .config import DOWNLOAD_PAGE_TIMEOUT
 from .async_configs import BrowserConfig, CrawlerRunConfig
-from playwright_stealth import StealthConfig
 from .utils import get_chromium_path
 
-stealth_config = StealthConfig(
-    webdriver=True,
-    chrome_app=True,
-    chrome_csi=True,
-    chrome_load_times=True,
-    chrome_runtime=True,
-    navigator_languages=True,
-    navigator_plugins=True,
-    navigator_permissions=True,
-    webgl_vendor=True,
-    outerdimensions=True,
-    navigator_hardware_concurrency=True,
-    media_codecs=True,
-)
 
 BROWSER_DISABLE_OPTIONS = [
     "--disable-background-networking",
@@ -588,21 +573,26 @@ class BrowserManager:
     _playwright_instance = None
     
     @classmethod
-    async def get_playwright(cls):
-        from playwright.async_api import async_playwright
+    async def get_playwright(cls, use_undetected: bool = False):
+        if use_undetected:
+            from patchright.async_api import async_playwright
+        else:
+            from playwright.async_api import async_playwright
         cls._playwright_instance = await async_playwright().start()
         return cls._playwright_instance    
 
-    def __init__(self, browser_config: BrowserConfig, logger=None):
+    def __init__(self, browser_config: BrowserConfig, logger=None, use_undetected: bool = False):
         """
         Initialize the BrowserManager with a browser configuration.
 
         Args:
             browser_config (BrowserConfig): Configuration object containing all browser settings
             logger: Logger instance for recording events and errors
+            use_undetected (bool): Whether to use undetected browser (Patchright)
         """
         self.config: BrowserConfig = browser_config
         self.logger = logger
+        self.use_undetected = use_undetected
 
         # Browser state
         self.browser = None
@@ -616,7 +606,16 @@ class BrowserManager:
 
         # Keep track of contexts by a "config signature," so each unique config reuses a single context
         self.contexts_by_config = {}
-        self._contexts_lock = asyncio.Lock() 
+        self._contexts_lock = asyncio.Lock()
+        
+        # Serialize context.new_page() across concurrent tasks to avoid races
+        # when using a shared persistent context (context.pages may be empty
+        # for all racers). Prevents 'Target page/context closed' errors.
+        self._page_lock = asyncio.Lock()
+        
+        # Stealth-related attributes
+        self._stealth_instance = None
+        self._stealth_cm = None 
 
         # Initialize ManagedBrowser if needed
         if self.config.use_managed_browser:
@@ -645,9 +644,21 @@ class BrowserManager:
         if self.playwright is not None:
             await self.close()
             
-        from playwright.async_api import async_playwright
+        if self.use_undetected:
+            from patchright.async_api import async_playwright
+        else:
+            from playwright.async_api import async_playwright
 
-        self.playwright = await async_playwright().start()
+        # Initialize playwright with or without stealth
+        if self.config.enable_stealth and not self.use_undetected:
+            # Import stealth only when needed
+            from playwright_stealth import Stealth
+            # Use the recommended stealth wrapper approach
+            self._stealth_instance = Stealth()
+            self._stealth_cm = self._stealth_instance.use_async(async_playwright())
+            self.playwright = await self._stealth_cm.__aenter__()
+        else:
+            self.playwright = await async_playwright().start()
 
         if self.config.cdp_url or self.config.use_managed_browser:
             self.config.use_managed_browser = True
@@ -1021,13 +1032,26 @@ class BrowserManager:
                 context = await self.create_browser_context(crawlerRunConfig)
                 ctx = self.default_context        # default context, one window only
                 ctx = await clone_runtime_state(context, ctx, crawlerRunConfig, self.config)
-                page = await ctx.new_page()
+                # Avoid concurrent new_page on shared persistent context
+                # See GH-1198: context.pages can be empty under races
+                async with self._page_lock:
+                    page = await ctx.new_page()
             else:
                 context = self.default_context
                 pages = context.pages
                 page = next((p for p in pages if p.url == crawlerRunConfig.url), None)
                 if not page:
-                    page = context.pages[0] # await context.new_page()
+                    if pages:
+                        page = pages[0]
+                    else:
+                        # Double-check under lock to avoid TOCTOU and ensure only
+                        # one task calls new_page when pages=[] concurrently
+                        async with self._page_lock:
+                            pages = context.pages
+                            if pages:
+                                page = pages[0]
+                            else:
+                                page = await context.new_page()
         else:
             # Otherwise, check if we have an existing context for this config
             config_signature = self._make_config_signature(crawlerRunConfig)
@@ -1109,5 +1133,19 @@ class BrowserManager:
             self.managed_browser = None
 
         if self.playwright:
-            await self.playwright.stop()
+            # Handle stealth context manager cleanup if it exists
+            if hasattr(self, '_stealth_cm') and self._stealth_cm is not None:
+                try:
+                    await self._stealth_cm.__aexit__(None, None, None)
+                except Exception as e:
+                    if self.logger:
+                        self.logger.error(
+                            message="Error closing stealth context: {error}",
+                            tag="ERROR", 
+                            params={"error": str(e)}
+                        )
+                self._stealth_cm = None
+                self._stealth_instance = None
+            else:
+                await self.playwright.stop()
             self.playwright = None

crawl4ai/browser_profiler.py

@@ -65,6 +65,213 @@ class BrowserProfiler:
         self.builtin_config_file = os.path.join(self.builtin_browser_dir, "browser_config.json")
         os.makedirs(self.builtin_browser_dir, exist_ok=True)
     
+    def _is_windows(self) -> bool:
+        """Check if running on Windows platform."""
+        return sys.platform.startswith('win') or sys.platform == 'cygwin'
+    
+    def _is_macos(self) -> bool:
+        """Check if running on macOS platform."""
+        return sys.platform == 'darwin'
+    
+    def _is_linux(self) -> bool:
+        """Check if running on Linux platform."""
+        return sys.platform.startswith('linux')
+    
+    def _get_quit_message(self, tag: str) -> str:
+        """Get appropriate quit message based on context."""
+        if tag == "PROFILE":
+            return "Closing browser and saving profile..."
+        elif tag == "CDP":
+            return "Closing browser..."
+        else:
+            return "Closing browser..."
+    
+    async def _listen_windows(self, user_done_event, check_browser_process, tag: str):
+        """Windows-specific keyboard listener using msvcrt."""
+        try:
+            import msvcrt
+        except ImportError:
+            raise ImportError("msvcrt module not available on this platform")
+        
+        while True:
+            try:
+                # Check for keyboard input
+                if msvcrt.kbhit():
+                    raw = msvcrt.getch()
+                    
+                    # Handle Unicode decoding more robustly
+                    key = None
+                    try:
+                        key = raw.decode("utf-8")
+                    except UnicodeDecodeError:
+                        try:
+                            # Try different encodings
+                            key = raw.decode("latin1")
+                        except UnicodeDecodeError:
+                            # Skip if we can't decode
+                            continue
+                    
+                    # Validate key
+                    if not key or len(key) != 1:
+                        continue
+                    
+                    # Check for printable characters only
+                    if not key.isprintable():
+                        continue
+                    
+                    # Check for quit command
+                    if key.lower() == "q":
+                        self.logger.info(
+                            self._get_quit_message(tag),
+                            tag=tag,
+                            base_color=LogColor.GREEN
+                        )
+                        user_done_event.set()
+                        return
+                
+                # Check if browser process ended
+                if await check_browser_process():
+                    return
+                
+                # Small delay to prevent busy waiting
+                await asyncio.sleep(0.1)
+                
+            except Exception as e:
+                self.logger.warning(f"Error in Windows keyboard listener: {e}", tag=tag)
+                # Continue trying instead of failing completely
+                await asyncio.sleep(0.1)
+                continue
+    
+    async def _listen_unix(self, user_done_event: asyncio.Event, check_browser_process, tag: str):
+        """Unix/Linux/macOS keyboard listener using termios and select."""
+        try:
+            import termios
+            import tty
+            import select
+        except ImportError:
+            raise ImportError("termios/tty/select modules not available on this platform")
+        
+        # Get stdin file descriptor
+        try:
+            fd = sys.stdin.fileno()
+        except (AttributeError, OSError):
+            raise ImportError("stdin is not a terminal")
+        
+        # Save original terminal settings
+        old_settings = None
+        try:
+            old_settings = termios.tcgetattr(fd)
+        except termios.error as e:
+            raise ImportError(f"Cannot get terminal attributes: {e}")
+        
+        try:
+            # Switch to non-canonical mode (cbreak mode)
+            tty.setcbreak(fd)
+            
+            while True:
+                try:
+                    # Use select to check if input is available (non-blocking)
+                    # Timeout of 0.5 seconds to periodically check browser process
+                    readable, _, _ = select.select([sys.stdin], [], [], 0.5)
+                    
+                    if readable:
+                        # Read one character
+                        key = sys.stdin.read(1)
+                        
+                        if key and key.lower() == "q":
+                            self.logger.info(
+                                self._get_quit_message(tag),
+                                tag=tag,
+                                base_color=LogColor.GREEN
+                            )
+                            user_done_event.set()
+                            return
+                    
+                    # Check if browser process ended
+                    if await check_browser_process():
+                        return
+                    
+                    # Small delay to prevent busy waiting
+                    await asyncio.sleep(0.1)
+                    
+                except (KeyboardInterrupt, EOFError):
+                    # Handle Ctrl+C or EOF gracefully
+                    self.logger.info("Keyboard interrupt received", tag=tag)
+                    user_done_event.set()
+                    return
+                except Exception as e:
+                    self.logger.warning(f"Error in Unix keyboard listener: {e}", tag=tag)
+                    await asyncio.sleep(0.1)
+                    continue
+                    
+        finally:
+            # Always restore terminal settings
+            if old_settings is not None:
+                try:
+                    termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
+                except Exception as e:
+                    self.logger.error(f"Failed to restore terminal settings: {e}", tag=tag)
+    
+    async def _listen_fallback(self, user_done_event: asyncio.Event, check_browser_process, tag: str):
+        """Fallback keyboard listener using simple input() method."""
+        self.logger.info("Using fallback input mode. Type 'q' and press Enter to quit.", tag=tag)
+        
+        # Run input in a separate thread to avoid blocking
+        import threading
+        import queue
+        
+        input_queue = queue.Queue()
+        
+        def input_thread():
+            """Thread function to handle input."""
+            try:
+                while not user_done_event.is_set():
+                    try:
+                        # Use input() with a prompt
+                        user_input = input("Press 'q' + Enter to quit: ").strip().lower()
+                        input_queue.put(user_input)
+                        if user_input == 'q':
+                            break
+                    except (EOFError, KeyboardInterrupt):
+                        input_queue.put('q')
+                        break
+                    except Exception as e:
+                        self.logger.warning(f"Error in input thread: {e}", tag=tag)
+                        break
+            except Exception as e:
+                self.logger.error(f"Input thread failed: {e}", tag=tag)
+        
+        # Start input thread
+        thread = threading.Thread(target=input_thread, daemon=True)
+        thread.start()
+        
+        try:
+            while not user_done_event.is_set():
+                # Check for user input
+                try:
+                    user_input = input_queue.get_nowait()
+                    if user_input == 'q':
+                        self.logger.info(
+                            self._get_quit_message(tag),
+                            tag=tag,
+                            base_color=LogColor.GREEN
+                        )
+                        user_done_event.set()
+                        return
+                except queue.Empty:
+                    pass
+                
+                # Check if browser process ended
+                if await check_browser_process():
+                    return
+                
+                # Small delay
+                await asyncio.sleep(0.5)
+                
+        except Exception as e:
+            self.logger.error(f"Fallback listener failed: {e}", tag=tag)
+            user_done_event.set()
+    
     async def create_profile(self, 
                             profile_name: Optional[str] = None, 
                             browser_config: Optional[BrowserConfig] = None) -> Optional[str]:
@@ -180,42 +387,38 @@ class BrowserProfiler:
         
         # Run keyboard input loop in a separate task
         async def listen_for_quit_command():
-            import termios
-            import tty
-            import select
-            
+            """Cross-platform keyboard listener that waits for 'q' key press."""
             # First output the prompt
-            self.logger.info("Press 'q' when you've finished using the browser...", tag="PROFILE")
-            
-            # Save original terminal settings
-            fd = sys.stdin.fileno()
-            old_settings = termios.tcgetattr(fd)
-            
+            self.logger.info(
+                "Press {segment} when you've finished using the browser...",
+                tag="PROFILE",
+                params={"segment": "'q'"}, colors={"segment": LogColor.YELLOW},
+                base_color=LogColor.CYAN
+            )
+
+            async def check_browser_process():
+                """Check if browser process is still running."""
+                if (
+                    managed_browser.browser_process
+                    and managed_browser.browser_process.poll() is not None
+                ):
+                    self.logger.info(
+                        "Browser already closed. Ending input listener.", tag="PROFILE"
+                    )
+                    user_done_event.set()
+                    return True
+                return False
+
+            # Try platform-specific implementations with fallback
             try:
-                # Switch to non-canonical mode (no line buffering)
-                tty.setcbreak(fd)
-                
-                while True:
-                    # Check if input is available (non-blocking)
-                    readable, _, _ = select.select([sys.stdin], [], [], 0.5)
-                    if readable:
-                        key = sys.stdin.read(1)
-                        if key.lower() == 'q':
-                            self.logger.info("Closing browser and saving profile...", tag="PROFILE", base_color=LogColor.GREEN)
-                            user_done_event.set()
-                            return
-                    
-                    # Check if the browser process has already exited
-                    if managed_browser.browser_process and managed_browser.browser_process.poll() is not None:
-                        self.logger.info("Browser already closed. Ending input listener.", tag="PROFILE")
-                        user_done_event.set()
-                        return
-                        
-                    await asyncio.sleep(0.1)
-            
-            finally:
-                # Restore terminal settings 
-                termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
+                if self._is_windows():
+                    await self._listen_windows(user_done_event, check_browser_process, "PROFILE")
+                else:
+                    await self._listen_unix(user_done_event, check_browser_process, "PROFILE")
+            except Exception as e:
+                self.logger.warning(f"Platform-specific keyboard listener failed: {e}", tag="PROFILE")
+                self.logger.info("Falling back to simple input mode...", tag="PROFILE")
+                await self._listen_fallback(user_done_event, check_browser_process, "PROFILE")
         
         try:
             from playwright.async_api import async_playwright
@@ -682,42 +885,33 @@ class BrowserProfiler:
         
         # Run keyboard input loop in a separate task
         async def listen_for_quit_command():
-            import termios
-            import tty
-            import select
-            
+            """Cross-platform keyboard listener that waits for 'q' key press."""
             # First output the prompt
-            self.logger.info("Press 'q' to stop the browser and exit...", tag="CDP")
-            
-            # Save original terminal settings
-            fd = sys.stdin.fileno()
-            old_settings = termios.tcgetattr(fd)
-            
+            self.logger.info(
+                "Press {segment} to stop the browser and exit...",
+                tag="CDP",
+                params={"segment": "'q'"}, colors={"segment": LogColor.YELLOW},
+                base_color=LogColor.CYAN
+            )
+
+            async def check_browser_process():
+                """Check if browser process is still running."""
+                if managed_browser.browser_process and managed_browser.browser_process.poll() is not None:
+                    self.logger.info("Browser already closed. Ending input listener.", tag="CDP")
+                    user_done_event.set()
+                    return True
+                return False
+
+            # Try platform-specific implementations with fallback
             try:
-                # Switch to non-canonical mode (no line buffering)
-                tty.setcbreak(fd)
-                
-                while True:
-                    # Check if input is available (non-blocking)
-                    readable, _, _ = select.select([sys.stdin], [], [], 0.5)
-                    if readable:
-                        key = sys.stdin.read(1)
-                        if key.lower() == 'q':
-                            self.logger.info("Closing browser...", tag="CDP")
-                            user_done_event.set()
-                            return
-                    
-                    # Check if the browser process has already exited
-                    if managed_browser.browser_process and managed_browser.browser_process.poll() is not None:
-                        self.logger.info("Browser already closed. Ending input listener.", tag="CDP")
-                        user_done_event.set()
-                        return
-                        
-                    await asyncio.sleep(0.1)
-            
-            finally:
-                # Restore terminal settings 
-                termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
+                if self._is_windows():
+                    await self._listen_windows(user_done_event, check_browser_process, "CDP")
+                else:
+                    await self._listen_unix(user_done_event, check_browser_process, "CDP")
+            except Exception as e:
+                self.logger.warning(f"Platform-specific keyboard listener failed: {e}", tag="CDP")
+                self.logger.info("Falling back to simple input mode...", tag="CDP")
+                await self._listen_fallback(user_done_event, check_browser_process, "CDP")
                 
         # Function to retrieve and display CDP JSON config
         async def get_cdp_json(port):

crawl4ai/cli.py

@@ -27,7 +27,10 @@ from crawl4ai import (
     PruningContentFilter,
     BrowserProfiler,
     DefaultMarkdownGenerator,
-    LLMConfig
+    LLMConfig,
+    BFSDeepCrawlStrategy,
+    DFSDeepCrawlStrategy,
+    BestFirstCrawlingStrategy,
 )
 from crawl4ai.config import USER_SETTINGS
 from litellm import completion
@@ -1014,9 +1017,11 @@ def cdp_cmd(user_data_dir: Optional[str], port: int, browser_type: str, headless
 @click.option("--question", "-q", help="Ask a question about the crawled content")
 @click.option("--verbose", "-v", is_flag=True)
 @click.option("--profile", "-p", help="Use a specific browser profile (by name)")
+@click.option("--deep-crawl", type=click.Choice(["bfs", "dfs", "best-first"]), help="Enable deep crawling with specified strategy (bfs, dfs, or best-first)")
+@click.option("--max-pages", type=int, default=10, help="Maximum number of pages to crawl in deep crawl mode")
 def crawl_cmd(url: str, browser_config: str, crawler_config: str, filter_config: str, 
            extraction_config: str, json_extract: str, schema: str, browser: Dict, crawler: Dict,
-           output: str, output_file: str, bypass_cache: bool, question: str, verbose: bool, profile: str):
+           output: str, output_file: str, bypass_cache: bool, question: str, verbose: bool, profile: str, deep_crawl: str, max_pages: int):
     """Crawl a website and extract content
     
     Simple Usage:
@@ -1156,6 +1161,27 @@ Always return valid, properly formatted JSON."""
 
         crawler_cfg.scraping_strategy = LXMLWebScrapingStrategy()    
 
+        # Handle deep crawling configuration
+        if deep_crawl:
+            if deep_crawl == "bfs":
+                crawler_cfg.deep_crawl_strategy = BFSDeepCrawlStrategy(
+                    max_depth=3,
+                    max_pages=max_pages
+                )
+            elif deep_crawl == "dfs":
+                crawler_cfg.deep_crawl_strategy = DFSDeepCrawlStrategy(
+                    max_depth=3,
+                    max_pages=max_pages
+                )
+            elif deep_crawl == "best-first":
+                crawler_cfg.deep_crawl_strategy = BestFirstCrawlingStrategy(
+                    max_depth=3,
+                    max_pages=max_pages
+                )
+            
+            if verbose:
+                console.print(f"[green]Deep crawling enabled:[/green] {deep_crawl} strategy, max {max_pages} pages")
+
         config = get_global_config()
         
         browser_cfg.verbose = config.get("VERBOSE", False)
@@ -1170,39 +1196,60 @@ Always return valid, properly formatted JSON."""
             verbose
         )
 
+        # Handle deep crawl results (list) vs single result
+        if isinstance(result, list):
+            if len(result) == 0:
+                click.echo("No results found during deep crawling")
+                return
+            # Use the first result for question answering and output
+            main_result = result[0]
+            all_results = result
+        else:
+            # Single result from regular crawling
+            main_result = result
+            all_results = [result]
+
         # Handle question
         if question:
             provider, token = setup_llm_config()
-            markdown = result.markdown.raw_markdown
+            markdown = main_result.markdown.raw_markdown
             anyio.run(stream_llm_response, url, markdown, question, provider, token)
             return
         
         # Handle output
         if not output_file:
             if output == "all":
-                click.echo(json.dumps(result.model_dump(), indent=2))
+                if isinstance(result, list):
+                    output_data = [r.model_dump() for r in all_results]
+                    click.echo(json.dumps(output_data, indent=2))
+                else:
+                    click.echo(json.dumps(main_result.model_dump(), indent=2))
             elif output == "json":
-                print(result.extracted_content)
-                extracted_items = json.loads(result.extracted_content)
+                print(main_result.extracted_content)
+                extracted_items = json.loads(main_result.extracted_content)
                 click.echo(json.dumps(extracted_items, indent=2))
                 
             elif output in ["markdown", "md"]:
-                click.echo(result.markdown.raw_markdown)
+                click.echo(main_result.markdown.raw_markdown)
             elif output in ["markdown-fit", "md-fit"]:
-                click.echo(result.markdown.fit_markdown)
+                click.echo(main_result.markdown.fit_markdown)
         else:
             if output == "all":
                 with open(output_file, "w") as f:
-                    f.write(json.dumps(result.model_dump(), indent=2))
+                    if isinstance(result, list):
+                        output_data = [r.model_dump() for r in all_results]
+                        f.write(json.dumps(output_data, indent=2))
+                    else:
+                        f.write(json.dumps(main_result.model_dump(), indent=2))
             elif output == "json":
                 with open(output_file, "w") as f:
-                    f.write(result.extracted_content)
+                    f.write(main_result.extracted_content)
             elif output in ["markdown", "md"]:
                 with open(output_file, "w") as f:
-                    f.write(result.markdown.raw_markdown)
+                    f.write(main_result.markdown.raw_markdown)
             elif output in ["markdown-fit", "md-fit"]:
                 with open(output_file, "w") as f:
-                    f.write(result.markdown.fit_markdown)
+                    f.write(main_result.markdown.fit_markdown)
             
     except Exception as e:
         raise click.ClickException(str(e))
@@ -1354,9 +1401,11 @@ def profiles_cmd():
 @click.option("--question", "-q", help="Ask a question about the crawled content")
 @click.option("--verbose", "-v", is_flag=True)
 @click.option("--profile", "-p", help="Use a specific browser profile (by name)")
+@click.option("--deep-crawl", type=click.Choice(["bfs", "dfs", "best-first"]), help="Enable deep crawling with specified strategy")
+@click.option("--max-pages", type=int, default=10, help="Maximum number of pages to crawl in deep crawl mode")
 def default(url: str, example: bool, browser_config: str, crawler_config: str, filter_config: str, 
         extraction_config: str, json_extract: str, schema: str, browser: Dict, crawler: Dict,
-        output: str, bypass_cache: bool, question: str, verbose: bool, profile: str):
+        output: str, bypass_cache: bool, question: str, verbose: bool, profile: str, deep_crawl: str, max_pages: int):
     """Crawl4AI CLI - Web content extraction tool
 
     Simple Usage:
@@ -1406,7 +1455,9 @@ def default(url: str, example: bool, browser_config: str, crawler_config: str, f
         bypass_cache=bypass_cache,
         question=question,
         verbose=verbose,
-        profile=profile
+        profile=profile,
+        deep_crawl=deep_crawl,
+        max_pages=max_pages
     )
 
 def main():

crawl4ai/install.py

@@ -119,6 +119,32 @@ def install_playwright():
         logger.warning(
             f"Please run '{sys.executable} -m playwright install --with-deps' manually after the installation."
         )
+    
+    # Install Patchright browsers for undetected browser support
+    logger.info("Installing Patchright browsers for undetected mode...", tag="INIT")
+    try:
+        subprocess.check_call(
+            [
+                sys.executable,
+                "-m",
+                "patchright",
+                "install",
+                "--with-deps",
+                "--force",
+                "chromium",
+            ]
+        )
+        logger.success(
+            "Patchright installation completed successfully.", tag="COMPLETE"
+        )
+    except subprocess.CalledProcessError:
+        logger.warning(
+            f"Please run '{sys.executable} -m patchright install --with-deps' manually after the installation."
+        )
+    except Exception:
+        logger.warning(
+            f"Please run '{sys.executable} -m patchright install --with-deps' manually after the installation."
+        )
 
 
 def run_migration():

crawl4ai/legacy/web_crawler.py

@@ -11,7 +11,7 @@ from .extraction_strategy import *
 from .crawler_strategy import *
 from typing import List
 from concurrent.futures import ThreadPoolExecutor
-from .content_scraping_strategy import WebScrapingStrategy
+from ..content_scraping_strategy import LXMLWebScrapingStrategy as WebScrapingStrategy
 from .config import *
 import warnings
 import json

crawl4ai/prompts.py

@@ -1056,7 +1056,7 @@ Your output must:
 </output_requirements>
 """
 
-GENERATE_SCRIPT_PROMPT = """You are a world-class browser automation specialist. Your sole purpose is to convert a natural language objective and a snippet of HTML into the most **efficient, robust, and simple** script possible to prepare a web page for data extraction.
+GENERATE_SCRIPT_PROMPT = r"""You are a world-class browser automation specialist. Your sole purpose is to convert a natural language objective and a snippet of HTML into the most **efficient, robust, and simple** script possible to prepare a web page for data extraction.
 
 Your scripts run **before the crawl** to handle dynamic content, user interactions, and other obstacles. You are a master of two tools: raw **JavaScript** and the high-level **Crawl4ai Script (c4a)**.
 

crawl4ai/types.py

@@ -23,8 +23,9 @@ SeedingConfig = Union['SeedingConfigType']
 
 # Content scraping types
 ContentScrapingStrategy = Union['ContentScrapingStrategyType']
-WebScrapingStrategy = Union['WebScrapingStrategyType']
 LXMLWebScrapingStrategy = Union['LXMLWebScrapingStrategyType']
+# Backward compatibility alias
+WebScrapingStrategy = Union['LXMLWebScrapingStrategyType']
 
 # Proxy types
 ProxyRotationStrategy = Union['ProxyRotationStrategyType']
@@ -114,7 +115,6 @@ if TYPE_CHECKING:
     # Content scraping imports
     from .content_scraping_strategy import (
         ContentScrapingStrategy as ContentScrapingStrategyType,
-        WebScrapingStrategy as WebScrapingStrategyType,
         LXMLWebScrapingStrategy as LXMLWebScrapingStrategyType,
     )
     

crawl4ai/utils.py

@@ -16,7 +16,7 @@ from .config import MIN_WORD_THRESHOLD, IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD, IM
 import httpx
 from socket import gaierror
 from pathlib import Path
-from typing import Dict, Any, List, Optional, Callable
+from typing import Dict, Any, List, Optional, Callable, Generator, Tuple, Iterable
 from urllib.parse import urljoin
 import requests
 from requests.exceptions import InvalidSchema
@@ -40,8 +40,7 @@ from typing import Sequence
 
 from itertools import chain
 from collections import deque
-from typing import  Generator, Iterable
-
+import psutil
 import numpy as np
 
 from urllib.parse import (
@@ -1517,8 +1516,29 @@ def extract_metadata_using_lxml(html, doc=None):
     head = head[0]
 
     # Title - using XPath
+    # title = head.xpath(".//title/text()")
+    # metadata["title"] = title[0].strip() if title else None
+
+    # === Title Extraction - New Approach ===
+    # Attempt to extract <title> using XPath
     title = head.xpath(".//title/text()")
-    metadata["title"] = title[0].strip() if title else None
+    title = title[0] if title else None
+
+    # Fallback: Use .find() in case XPath fails due to malformed HTML
+    if not title:
+        title_el = doc.find(".//title")
+        title = title_el.text if title_el is not None else None
+
+    # Final fallback: Use OpenGraph or Twitter title if <title> is missing or empty
+    if not title:
+        title_candidates = (
+            doc.xpath("//meta[@property='og:title']/@content") or
+            doc.xpath("//meta[@name='twitter:title']/@content")
+        )
+        title = title_candidates[0] if title_candidates else None
+
+    # Strip and assign title
+    metadata["title"] = title.strip() if title else None
 
     # Meta description - using XPath with multiple attribute conditions
     description = head.xpath('.//meta[@name="description"]/@content')
@@ -3342,7 +3362,13 @@ async def get_text_embeddings(
     # Default: use sentence-transformers
     else:
         # Lazy load to avoid importing heavy libraries unless needed
-        from sentence_transformers import SentenceTransformer
+        try:
+            from sentence_transformers import SentenceTransformer
+        except ImportError:
+            raise ImportError(
+                "sentence-transformers is required for local embeddings. "
+                "Install it with: pip install 'crawl4ai[transformer]' or pip install sentence-transformers"
+            )
         
         # Cache the model in function attribute to avoid reloading
         if not hasattr(get_text_embeddings, '_models'):
@@ -3387,3 +3413,79 @@ def cosine_distance(vec1: np.ndarray, vec2: np.ndarray) -> float:
     """Calculate cosine distance (1 - similarity) between two vectors"""
     return 1 - cosine_similarity(vec1, vec2)
 
+
+# Memory utilities
+
+def get_true_available_memory_gb() -> float:
+    """Get truly available memory including inactive pages (cross-platform)"""
+    vm = psutil.virtual_memory()
+
+    if platform.system() == 'Darwin':  # macOS
+        # On macOS, we need to include inactive memory too
+        try:
+            # Use vm_stat to get accurate values
+            result = subprocess.run(['vm_stat'], capture_output=True, text=True)
+            lines = result.stdout.split('\n')
+
+            page_size = 16384  # macOS page size
+            pages = {}
+
+            for line in lines:
+                if 'Pages free:' in line:
+                    pages['free'] = int(line.split()[-1].rstrip('.'))
+                elif 'Pages inactive:' in line:
+                    pages['inactive'] = int(line.split()[-1].rstrip('.'))
+                elif 'Pages speculative:' in line:
+                    pages['speculative'] = int(line.split()[-1].rstrip('.'))
+                elif 'Pages purgeable:' in line:
+                    pages['purgeable'] = int(line.split()[-1].rstrip('.'))
+
+            # Calculate total available (free + inactive + speculative + purgeable)
+            total_available_pages = (
+                pages.get('free', 0) + 
+                pages.get('inactive', 0) + 
+                pages.get('speculative', 0) + 
+                pages.get('purgeable', 0)
+            )
+            available_gb = (total_available_pages * page_size) / (1024**3)
+
+            return available_gb
+        except:
+            # Fallback to psutil
+            return vm.available / (1024**3)
+    else:
+        # For Windows and Linux, psutil.available is accurate
+        return vm.available / (1024**3)
+
+
+def get_true_memory_usage_percent() -> float:
+    """
+    Get memory usage percentage that accounts for platform differences.
+    
+    Returns:
+        float: Memory usage percentage (0-100)
+    """
+    vm = psutil.virtual_memory()
+    total_gb = vm.total / (1024**3)
+    available_gb = get_true_available_memory_gb()
+    
+    # Calculate used percentage based on truly available memory
+    used_percent = 100.0 * (total_gb - available_gb) / total_gb
+    
+    # Ensure it's within valid range
+    return max(0.0, min(100.0, used_percent))
+
+
+def get_memory_stats() -> Tuple[float, float, float]:
+    """
+    Get comprehensive memory statistics.
+    
+    Returns:
+        Tuple[float, float, float]: (used_percent, available_gb, total_gb)
+    """
+    vm = psutil.virtual_memory()
+    total_gb = vm.total / (1024**3)
+    available_gb = get_true_available_memory_gb()
+    used_percent = get_true_memory_usage_percent()
+    
+    return used_percent, available_gb, total_gb

deploy/docker/.llm.env.example

@@ -5,4 +5,9 @@ ANTHROPIC_API_KEY=your_anthropic_key_here
 GROQ_API_KEY=your_groq_key_here
 TOGETHER_API_KEY=your_together_key_here
 MISTRAL_API_KEY=your_mistral_key_here
-GEMINI_API_TOKEN=your_gemini_key_here
+GEMINI_API_TOKEN=your_gemini_key_here
+
+# Optional: Override the default LLM provider
+# Examples: "openai/gpt-4", "anthropic/claude-3-opus", "deepseek/chat", etc.
+# If not set, uses the provider specified in config.yml (default: openai/gpt-4o-mini)
+# LLM_PROVIDER=anthropic/claude-3-opus

deploy/docker/README.md

@@ -58,13 +58,15 @@ Pull and run images directly from Docker Hub without building locally.
 
 #### 1. Pull the Image
 
-Our latest release candidate is `0.6.0-r1`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
+Our latest release candidate is `0.7.0-r1`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
+
+> ⚠️ **Important Note**: The `latest` tag currently points to the stable `0.6.0` version. After testing and validation, `0.7.0` (without -r1) will be released and `latest` will be updated. For now, please use `0.7.0-r1` to test the new features.
 
 ```bash
-# Pull the release candidate (recommended for latest features)
-docker pull unclecode/crawl4ai:0.6.0-rN # Use your favorite revision number
+# Pull the release candidate (for testing new features)
+docker pull unclecode/crawl4ai:0.7.0-r1
 
-# Or pull the latest stable version
+# Or pull the current stable version (0.6.0)
 docker pull unclecode/crawl4ai:latest
 ```
 
@@ -99,7 +101,7 @@ EOL
       -p 11235:11235 \
       --name crawl4ai \
       --shm-size=1g \
-      unclecode/crawl4ai:0.6.0-rN # Use your favorite revision number
+      unclecode/crawl4ai:0.7.0-r1
     ```
 
 *   **With LLM support:**
@@ -110,7 +112,7 @@ EOL
       --name crawl4ai \
       --env-file .llm.env \
       --shm-size=1g \
-      unclecode/crawl4ai:0.6.0-rN # Use your favorite revision number
+      unclecode/crawl4ai:0.7.0-r1
     ```
 
 > The server will be available at `http://localhost:11235`. Visit `/playground` to access the interactive testing interface.
@@ -124,7 +126,7 @@ docker stop crawl4ai && docker rm crawl4ai
 #### Docker Hub Versioning Explained
 
 *   **Image Name:** `unclecode/crawl4ai`
-*   **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.6.0-r1`)
+*   **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.7.0-r1`)
     *   `LIBRARY_VERSION`: The semantic version of the core `crawl4ai` Python library
     *   `SUFFIX`: Optional tag for release candidates (``) and revisions (`r1`)
 *   **`latest` Tag:** Points to the most recent stable version
@@ -152,6 +154,29 @@ cp deploy/docker/.llm.env.example .llm.env
 # Now edit .llm.env and add your API keys
 ```
 
+**Flexible LLM Provider Configuration:**
+
+The Docker setup now supports flexible LLM provider configuration through three methods:
+
+1. **Environment Variable** (Highest Priority): Set `LLM_PROVIDER` to override the default
+   ```bash
+   export LLM_PROVIDER="anthropic/claude-3-opus"
+   # Or in your .llm.env file:
+   # LLM_PROVIDER=anthropic/claude-3-opus
+   ```
+
+2. **API Request Parameter**: Specify provider per request
+   ```json
+   {
+     "url": "https://example.com",
+     "provider": "groq/mixtral-8x7b"
+   }
+   ```
+
+3. **Config File Default**: Falls back to `config.yml` (default: `openai/gpt-4o-mini`)
+
+The system automatically selects the appropriate API key based on the provider.
+
 #### 3. Build and Run with Compose
 
 The `docker-compose.yml` file in the project root provides a simplified approach that automatically handles architecture detection using buildx.
@@ -160,7 +185,7 @@ The `docker-compose.yml` file in the project root provides a simplified approach
     ```bash
     # Pulls and runs the release candidate from Docker Hub
     # Automatically selects the correct architecture
-    IMAGE=unclecode/crawl4ai:0.6.0-rN # Use your favorite revision number docker compose up -d
+    IMAGE=unclecode/crawl4ai:0.7.0-r1 docker compose up -d
     ```
 
 *   **Build and Run Locally:**
@@ -666,7 +691,7 @@ app:
 
 # Default LLM Configuration
 llm:
-  provider: "openai/gpt-4o-mini"
+  provider: "openai/gpt-4o-mini"  # Can be overridden by LLM_PROVIDER env var
   api_key_env: "OPENAI_API_KEY"
   # api_key: sk-...  # If you pass the API key directly then api_key_env will be ignored
 

deploy/docker/api.py

@@ -5,6 +5,7 @@ from typing import List, Tuple, Dict
 from functools import partial
 from uuid import uuid4
 from datetime import datetime
+from base64 import b64encode
 
 import logging
 from typing import Optional, AsyncGenerator
@@ -39,7 +40,9 @@ from utils import (
     get_base_url,
     is_task_id,
     should_cleanup_task,
-    decode_redis_hash
+    decode_redis_hash,
+    get_llm_api_key,
+    validate_llm_provider
 )
 
 import psutil, time
@@ -62,7 +65,7 @@ async def handle_llm_qa(
 ) -> str:
     """Process QA using LLM with crawled content as context."""
     try:
-        if not url.startswith(('http://', 'https://')):
+        if not url.startswith(('http://', 'https://')) and not url.startswith(("raw:", "raw://")):
             url = 'https://' + url
         # Extract base URL by finding last '?q=' occurrence
         last_q_index = url.rfind('?q=')
@@ -88,10 +91,12 @@ async def handle_llm_qa(
 
     Answer:"""
 
+        # api_token=os.environ.get(config["llm"].get("api_key_env", ""))
+
         response = perform_completion_with_backoff(
             provider=config["llm"]["provider"],
             prompt_with_variables=prompt,
-            api_token=os.environ.get(config["llm"].get("api_key_env", ""))
+            api_token=get_llm_api_key(config)
         )
 
         return response.choices[0].message.content
@@ -109,19 +114,23 @@ async def process_llm_extraction(
     url: str,
     instruction: str,
     schema: Optional[str] = None,
-    cache: str = "0"
+    cache: str = "0",
+    provider: Optional[str] = None
 ) -> None:
     """Process LLM extraction in background."""
     try:
-        # If config['llm'] has api_key then ignore the api_key_env
-        api_key = ""
-        if "api_key" in config["llm"]:
-            api_key = config["llm"]["api_key"]
-        else:
-            api_key = os.environ.get(config["llm"].get("api_key_env", None), "")
+        # Validate provider
+        is_valid, error_msg = validate_llm_provider(config, provider)
+        if not is_valid:
+            await redis.hset(f"task:{task_id}", mapping={
+                "status": TaskStatus.FAILED,
+                "error": error_msg
+            })
+            return
+        api_key = get_llm_api_key(config, provider)
         llm_strategy = LLMExtractionStrategy(
             llm_config=LLMConfig(
-                provider=config["llm"]["provider"],
+                provider=provider or config["llm"]["provider"],
                 api_token=api_key
             ),
             instruction=instruction,
@@ -168,12 +177,21 @@ async def handle_markdown_request(
     filter_type: FilterType,
     query: Optional[str] = None,
     cache: str = "0",
-    config: Optional[dict] = None
+    config: Optional[dict] = None,
+    provider: Optional[str] = None
 ) -> str:
     """Handle markdown generation requests."""
     try:
+        # Validate provider if using LLM filter
+        if filter_type == FilterType.LLM:
+            is_valid, error_msg = validate_llm_provider(config, provider)
+            if not is_valid:
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail=error_msg
+                )
         decoded_url = unquote(url)
-        if not decoded_url.startswith(('http://', 'https://')):
+        if not decoded_url.startswith(('http://', 'https://')) and not decoded_url.startswith(("raw:", "raw://")):
             decoded_url = 'https://' + decoded_url
 
         if filter_type == FilterType.RAW:
@@ -184,8 +202,8 @@ async def handle_markdown_request(
                 FilterType.BM25: BM25ContentFilter(user_query=query or ""),
                 FilterType.LLM: LLMContentFilter(
                     llm_config=LLMConfig(
-                        provider=config["llm"]["provider"],
-                        api_token=os.environ.get(config["llm"].get("api_key_env", None), ""),
+                        provider=provider or config["llm"]["provider"],
+                        api_token=get_llm_api_key(config, provider),
                     ),
                     instruction=query or "Extract main content"
                 )
@@ -229,7 +247,8 @@ async def handle_llm_request(
     query: Optional[str] = None,
     schema: Optional[str] = None,
     cache: str = "0",
-    config: Optional[dict] = None
+    config: Optional[dict] = None,
+    provider: Optional[str] = None
 ) -> JSONResponse:
     """Handle LLM extraction requests."""
     base_url = get_base_url(request)
@@ -259,7 +278,8 @@ async def handle_llm_request(
             schema,
             cache,
             base_url,
-            config
+            config,
+            provider
         )
 
     except Exception as e:
@@ -303,11 +323,12 @@ async def create_new_task(
     schema: Optional[str],
     cache: str,
     base_url: str,
-    config: dict
+    config: dict,
+    provider: Optional[str] = None
 ) -> JSONResponse:
     """Create and initialize a new task."""
     decoded_url = unquote(input_path)
-    if not decoded_url.startswith(('http://', 'https://')):
+    if not decoded_url.startswith(('http://', 'https://')) and not decoded_url.startswith(("raw:", "raw://")):
         decoded_url = 'https://' + decoded_url
 
     from datetime import datetime
@@ -327,7 +348,8 @@ async def create_new_task(
         decoded_url,
         query,
         schema,
-        cache
+        cache,
+        provider
     )
 
     return JSONResponse({
@@ -371,6 +393,9 @@ async def stream_results(crawler: AsyncWebCrawler, results_gen: AsyncGenerator)
                 server_memory_mb = _get_memory_mb()
                 result_dict = result.model_dump()
                 result_dict['server_memory_mb'] = server_memory_mb
+                # If PDF exists, encode it to base64
+                if result_dict.get('pdf') is not None:
+                    result_dict['pdf'] = b64encode(result_dict['pdf']).decode('utf-8')
                 logger.info(f"Streaming result for {result_dict.get('url', 'unknown')}")
                 data = json.dumps(result_dict, default=datetime_handler) + "\n"
                 yield data.encode('utf-8')
@@ -403,7 +428,7 @@ async def handle_crawl_request(
     peak_mem_mb = start_mem_mb
     
     try:
-        urls = [('https://' + url) if not url.startswith(('http://', 'https://')) else url for url in urls]
+        urls = [('https://' + url) if not url.startswith(('http://', 'https://')) and not url.startswith(("raw:", "raw://")) else url for url in urls]
         browser_config = BrowserConfig.load(browser_config)
         crawler_config = CrawlerRunConfig.load(crawler_config)
 
@@ -443,10 +468,19 @@ async def handle_crawl_request(
             mem_delta_mb = end_mem_mb - start_mem_mb # <--- Calculate delta
             peak_mem_mb = max(peak_mem_mb if peak_mem_mb else 0, end_mem_mb) # <--- Get peak memory
         logger.info(f"Memory usage: Start: {start_mem_mb} MB, End: {end_mem_mb} MB, Delta: {mem_delta_mb} MB, Peak: {peak_mem_mb} MB")
-                              
+
+        # Process results to handle PDF bytes
+        processed_results = []
+        for result in results:
+            result_dict = result.model_dump()
+            # If PDF exists, encode it to base64
+            if result_dict.get('pdf') is not None:
+                result_dict['pdf'] = b64encode(result_dict['pdf']).decode('utf-8')
+            processed_results.append(result_dict)
+            
         return {
             "success": True,
-            "results": [result.model_dump() for result in results],
+            "results": processed_results,
             "server_processing_time_s": end_time - start_time,
             "server_memory_delta_mb": mem_delta_mb,
             "server_peak_memory_mb": peak_mem_mb

deploy/docker/job.py

@@ -36,6 +36,7 @@ class LlmJobPayload(BaseModel):
     q:      str
     schema: Optional[str] = None
     cache:  bool = False
+    provider: Optional[str] = None
 
 
 class CrawlJobPayload(BaseModel):
@@ -61,6 +62,7 @@ async def llm_job_enqueue(
         schema=payload.schema,
         cache=payload.cache,
         config=_config,
+        provider=payload.provider,
     )
 
 

deploy/docker/schemas.py

@@ -15,6 +15,7 @@ class MarkdownRequest(BaseModel):
     f:   FilterType             = Field(FilterType.FIT, description="Content‑filter strategy: fit, raw, bm25, or llm")
     q:   Optional[str] = Field(None,  description="Query string used by BM25/LLM filters")
     c:   Optional[str] = Field("0",   description="Cache‑bust / revision counter")
+    provider: Optional[str] = Field(None, description="LLM provider override (e.g., 'anthropic/claude-3-opus')")
 
 
 class RawCode(BaseModel):

deploy/docker/server.py

@@ -237,11 +237,11 @@ async def get_markdown(
     body: MarkdownRequest,
     _td: Dict = Depends(token_dep),
 ):
-    if not body.url.startswith(("http://", "https://")):
+    if not body.url.startswith(("http://", "https://")) and not body.url.startswith(("raw:", "raw://")):
         raise HTTPException(
-            400, "URL must be absolute and start with http/https")
+            400, "Invalid URL format. Must start with http://, https://, or for raw HTML (raw:, raw://)")
     markdown = await handle_markdown_request(
-        body.url, body.f, body.q, body.c, config
+        body.url, body.f, body.q, body.c, config, body.provider
     )
     return JSONResponse({
         "url": body.url,
@@ -401,7 +401,7 @@ async def llm_endpoint(
 ):
     if not q:
         raise HTTPException(400, "Query parameter 'q' is required")
-    if not url.startswith(("http://", "https://")):
+    if not url.startswith(("http://", "https://")) and not url.startswith(("raw:", "raw://")):
         url = "https://" + url
     answer = await handle_llm_qa(url, q, config)
     return JSONResponse({"answer": answer})

deploy/docker/utils.py

@@ -1,6 +1,7 @@
 import dns.resolver
 import logging
 import yaml
+import os
 from datetime import datetime
 from enum import Enum
 from pathlib import Path
@@ -19,10 +20,24 @@ class FilterType(str, Enum):
     LLM = "llm"
 
 def load_config() -> Dict:
-    """Load and return application configuration."""
+    """Load and return application configuration with environment variable overrides."""
     config_path = Path(__file__).parent / "config.yml"
     with open(config_path, "r") as config_file:
-        return yaml.safe_load(config_file)
+        config = yaml.safe_load(config_file)
+    
+    # Override LLM provider from environment if set
+    llm_provider = os.environ.get("LLM_PROVIDER")
+    if llm_provider:
+        config["llm"]["provider"] = llm_provider
+        logging.info(f"LLM provider overridden from environment: {llm_provider}")
+    
+    # Also support direct API key from environment if the provider-specific key isn't set
+    llm_api_key = os.environ.get("LLM_API_KEY")
+    if llm_api_key and "api_key" not in config["llm"]:
+        config["llm"]["api_key"] = llm_api_key
+        logging.info("LLM API key loaded from LLM_API_KEY environment variable")
+    
+    return config
 
 def setup_logging(config: Dict) -> None:
     """Configure application logging."""
@@ -56,6 +71,52 @@ def decode_redis_hash(hash_data: Dict[bytes, bytes]) -> Dict[str, str]:
 
 
 
+def get_llm_api_key(config: Dict, provider: Optional[str] = None) -> str:
+    """Get the appropriate API key based on the LLM provider.
+    
+    Args:
+        config: The application configuration dictionary
+        provider: Optional provider override (e.g., "openai/gpt-4")
+    
+    Returns:
+        The API key for the provider, or empty string if not found
+    """
+        
+    # Use provided provider or fall back to config
+    if not provider:
+        provider = config["llm"]["provider"]
+    
+    # Check if direct API key is configured
+    if "api_key" in config["llm"]:
+        return config["llm"]["api_key"]
+    
+    # Fall back to the configured api_key_env if no match
+    return os.environ.get(config["llm"].get("api_key_env", ""), "")
+
+
+def validate_llm_provider(config: Dict, provider: Optional[str] = None) -> tuple[bool, str]:
+    """Validate that the LLM provider has an associated API key.
+    
+    Args:
+        config: The application configuration dictionary
+        provider: Optional provider override (e.g., "openai/gpt-4")
+    
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    # Use provided provider or fall back to config
+    if not provider:
+        provider = config["llm"]["provider"]
+    
+    # Get the API key for this provider
+    api_key = get_llm_api_key(config, provider)
+    
+    if not api_key:
+        return False, f"No API key found for provider '{provider}'. Please set the appropriate environment variable."
+    
+    return True, ""
+
+
 def verify_email_domain(email: str) -> bool:
     try:
         domain = email.split('@')[1]

docker-compose.yml

@@ -14,6 +14,7 @@ x-base-config: &base-config
     - TOGETHER_API_KEY=${TOGETHER_API_KEY:-}
     - MISTRAL_API_KEY=${MISTRAL_API_KEY:-}
     - GEMINI_API_TOKEN=${GEMINI_API_TOKEN:-}
+    - LLM_PROVIDER=${LLM_PROVIDER:-}  # Optional: Override default provider (e.g., "anthropic/claude-3-opus")
   volumes:
     - /dev/shm:/dev/shm  # Chromium performance
   deploy:

docs/blog/release-v0.7.0.md

@@ -0,0 +1,343 @@
+# 🚀 Crawl4AI v0.7.0: The Adaptive Intelligence Update
+
+*January 28, 2025 • 10 min read*
+
+---
+
+Today I'm releasing Crawl4AI v0.7.0—the Adaptive Intelligence Update. This release introduces fundamental improvements in how Crawl4AI handles modern web complexity through adaptive learning, intelligent content discovery, and advanced extraction capabilities.
+
+## 🎯 What's New at a Glance
+
+- **Adaptive Crawling**: Your crawler now learns and adapts to website patterns
+- **Virtual Scroll Support**: Complete content extraction from infinite scroll pages
+- **Link Preview with Intelligent Scoring**: Intelligent link analysis and prioritization
+- **Async URL Seeder**: Discover thousands of URLs in seconds with intelligent filtering
+- **Performance Optimizations**: Significant speed and memory improvements
+
+## 🧠 Adaptive Crawling: Intelligence Through Pattern Learning
+
+**The Problem:** Websites change. Class names shift. IDs disappear. Your carefully crafted selectors break at 3 AM, and you wake up to empty datasets and angry stakeholders.
+
+**My Solution:** I implemented an adaptive learning system that observes patterns, builds confidence scores, and adjusts extraction strategies on the fly. It's like having a junior developer who gets better at their job with every page they scrape.
+
+### Technical Deep-Dive
+
+The Adaptive Crawler maintains a persistent state for each domain, tracking:
+- Pattern success rates
+- Selector stability over time  
+- Content structure variations
+- Extraction confidence scores
+
+```python
+from crawl4ai import AsyncWebCrawler, AdaptiveCrawler, AdaptiveConfig
+import asyncio
+
+async def main():
+    
+    # Configure adaptive crawler
+    config = AdaptiveConfig(
+        strategy="statistical",  # or "embedding" for semantic understanding
+        max_pages=10,
+        confidence_threshold=0.7,  # Stop at 70% confidence
+        top_k_links=3,  # Follow top 3 links per page
+        min_gain_threshold=0.05  # Need 5% information gain to continue
+    )
+    
+    async with AsyncWebCrawler(verbose=False) as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        
+        print("Starting adaptive crawl about Python decorators...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/glossary.html",
+            query="python decorators functions wrapping"
+        )
+        
+        print(f"\n✅ Crawling Complete!")
+        print(f"• Confidence Level: {adaptive.confidence:.0%}")
+        print(f"• Pages Crawled: {len(result.crawled_urls)}")
+        print(f"• Knowledge Base: {len(adaptive.state.knowledge_base)} documents")
+        
+        # Get most relevant content
+        relevant = adaptive.get_relevant_content(top_k=3)
+        print(f"\nMost Relevant Pages:")
+        for i, page in enumerate(relevant, 1):
+            print(f"{i}. {page['url']} (relevance: {page['score']:.2%})")
+
+asyncio.run(main())
+```
+
+**Expected Real-World Impact:**
+- **News Aggregation**: Maintain 95%+ extraction accuracy even as news sites update their templates
+- **E-commerce Monitoring**: Track product changes across hundreds of stores without constant maintenance
+- **Research Data Collection**: Build robust academic datasets that survive website redesigns
+- **Reduced Maintenance**: Cut selector update time by 80% for frequently-changing sites
+
+## 🌊 Virtual Scroll: Complete Content Capture
+
+**The Problem:** Modern web apps only render what's visible. Scroll down, new content appears, old content vanishes into the void. Traditional crawlers capture that first viewport and miss 90% of the content. It's like reading only the first page of every book.
+
+**My Solution:** I built Virtual Scroll support that mimics human browsing behavior, capturing content as it loads and preserving it before the browser's garbage collector strikes.
+
+### Implementation Details
+
+```python
+from crawl4ai import VirtualScrollConfig
+
+# For social media feeds (Twitter/X style)
+twitter_config = VirtualScrollConfig(
+    container_selector="[data-testid='primaryColumn']",
+    scroll_count=20,                    # Number of scrolls
+    scroll_by="container_height",       # Smart scrolling by container size
+    wait_after_scroll=1.0              # Let content load
+)
+
+# For e-commerce product grids (Instagram style)
+grid_config = VirtualScrollConfig(
+    container_selector="main .product-grid",
+    scroll_count=30,
+    scroll_by=800,                     # Fixed pixel scrolling
+    wait_after_scroll=1.5              # Images need time
+)
+
+# For news feeds with lazy loading
+news_config = VirtualScrollConfig(
+    container_selector=".article-feed",
+    scroll_count=50,
+    scroll_by="page_height",           # Viewport-based scrolling
+    wait_after_scroll=0.5              # Wait for content to load
+)
+
+# Use it in your crawl
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        "https://twitter.com/trending",
+        config=CrawlerRunConfig(
+            virtual_scroll_config=twitter_config,
+            # Combine with other features
+            extraction_strategy=JsonCssExtractionStrategy({
+                "tweets": {
+                    "selector": "[data-testid='tweet']",
+                    "fields": {
+                        "text": {"selector": "[data-testid='tweetText']", "type": "text"},
+                        "likes": {"selector": "[data-testid='like']", "type": "text"}
+                    }
+                }
+            })
+        )
+    )
+    
+    print(f"Captured {len(result.extracted_content['tweets'])} tweets")
+```
+
+**Key Capabilities:**
+- **DOM Recycling Awareness**: Detects and handles virtual DOM element recycling
+- **Smart Scroll Physics**: Three modes - container height, page height, or fixed pixels
+- **Content Preservation**: Captures content before it's destroyed
+- **Intelligent Stopping**: Stops when no new content appears
+- **Memory Efficient**: Streams content instead of holding everything in memory
+
+**Expected Real-World Impact:**
+- **Social Media Analysis**: Capture entire Twitter threads with hundreds of replies, not just top 10
+- **E-commerce Scraping**: Extract 500+ products from infinite scroll catalogs vs. 20-50 with traditional methods  
+- **News Aggregation**: Get all articles from modern news sites, not just above-the-fold content
+- **Research Applications**: Complete data extraction from academic databases using virtual pagination
+
+## 🔗 Link Preview: Intelligent Link Analysis and Scoring
+
+**The Problem:** You crawl a page and get 200 links. Which ones matter? Which lead to the content you actually want? Traditional crawlers force you to follow everything or build complex filters.
+
+**My Solution:** I implemented a three-layer scoring system that analyzes links like a human would—considering their position, context, and relevance to your goals.
+
+### Intelligent Link Analysis and Scoring
+
+```python
+import asyncio
+from crawl4ai import CrawlerRunConfig, CacheMode, AsyncWebCrawler
+from crawl4ai.adaptive_crawler import LinkPreviewConfig
+
+async def main():
+    # Configure intelligent link analysis
+    link_config = LinkPreviewConfig(
+        include_internal=True,
+        include_external=False,
+        max_links=10,
+        concurrency=5,
+        query="python tutorial",  # For contextual scoring
+        score_threshold=0.3,
+        verbose=True
+    )
+    # Use in your crawl
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            "https://www.geeksforgeeks.org/",
+            config=CrawlerRunConfig(
+                link_preview_config=link_config,
+                score_links=True,  # Enable intrinsic scoring
+                cache_mode=CacheMode.BYPASS
+            )
+        )
+
+        # Access scored and sorted links
+        if result.success and result.links:
+            for link in result.links.get("internal", []):
+                text = link.get('text', 'No text')[:40]
+                print(
+                    text,
+                    f"{link.get('intrinsic_score', 0):.1f}/10" if link.get('intrinsic_score') is not None else "0.0/10",
+                    f"{link.get('contextual_score', 0):.2f}/1" if link.get('contextual_score') is not None else "0.00/1",
+                    f"{link.get('total_score', 0):.3f}" if link.get('total_score') is not None else "0.000"
+                )
+
+asyncio.run(main())
+```
+
+**Scoring Components:**
+
+1. **Intrinsic Score**: Based on link quality indicators
+   - Position on page (navigation, content, footer)
+   - Link attributes (rel, title, class names)
+   - Anchor text quality and length
+   - URL structure and depth
+
+2. **Contextual Score**: Relevance to your query using BM25 algorithm
+   - Keyword matching in link text and title
+   - Meta description analysis
+   - Content preview scoring
+
+3. **Total Score**: Combined score for final ranking
+
+**Expected Real-World Impact:**
+- **Research Efficiency**: Find relevant papers 10x faster by following only high-score links
+- **Competitive Analysis**: Automatically identify important pages on competitor sites
+- **Content Discovery**: Build topic-focused crawlers that stay on track
+- **SEO Audits**: Identify and prioritize high-value internal linking opportunities
+
+## 🎣 Async URL Seeder: Automated URL Discovery at Scale
+
+**The Problem:** You want to crawl an entire domain but only have the homepage. Or worse, you want specific content types across thousands of pages. Manual URL discovery? That's a job for machines, not humans.
+
+**My Solution:** I built Async URL Seeder—a turbocharged URL discovery engine that combines multiple sources with intelligent filtering and relevance scoring.
+
+### Technical Architecture
+
+```python
+import asyncio
+from crawl4ai import AsyncUrlSeeder, SeedingConfig
+
+async def main():
+    async with AsyncUrlSeeder() as seeder:
+        # Discover Python tutorial URLs
+        config = SeedingConfig(
+            source="sitemap",  # Use sitemap
+            pattern="*python*",  # URL pattern filter
+            extract_head=True,  # Get metadata
+            query="python tutorial",  # For relevance scoring
+            scoring_method="bm25",
+            score_threshold=0.2,
+            max_urls=10
+        )
+        
+        print("Discovering Python async tutorial URLs...")
+        urls = await seeder.urls("https://www.geeksforgeeks.org/", config)
+        
+        print(f"\n✅ Found {len(urls)} relevant URLs:")
+        for i, url_info in enumerate(urls[:5], 1):
+            print(f"\n{i}. {url_info['url']}")
+            if url_info.get('relevance_score'):
+                print(f"   Relevance: {url_info['relevance_score']:.3f}")
+            if url_info.get('head_data', {}).get('title'):
+                print(f"   Title: {url_info['head_data']['title'][:60]}...")
+
+asyncio.run(main())
+```
+
+**Discovery Methods:**
+- **Sitemap Mining**: Parses robots.txt and all linked sitemaps
+- **Common Crawl**: Queries the Common Crawl index for historical URLs
+- **Intelligent Crawling**: Follows links with smart depth control
+- **Pattern Analysis**: Learns URL structures and generates variations
+
+**Expected Real-World Impact:**
+- **Migration Projects**: Discover 10,000+ URLs from legacy sites in under 60 seconds
+- **Market Research**: Map entire competitor ecosystems automatically
+- **Academic Research**: Build comprehensive datasets without manual URL collection
+- **SEO Audits**: Find every indexable page with content scoring
+- **Content Archival**: Ensure no content is left behind during site migrations
+
+## ⚡ Performance Optimizations
+
+This release includes significant performance improvements through optimized resource handling, better concurrency management, and reduced memory footprint.
+
+### What We Optimized
+
+```python
+# Optimized crawling with v0.7.0 improvements
+results = []
+for url in urls:
+    result = await crawler.arun(
+        url,
+        config=CrawlerRunConfig(
+            # Performance optimizations
+            wait_until="domcontentloaded",  # Faster than networkidle
+            cache_mode=CacheMode.ENABLED    # Enable caching
+        )
+    )
+    results.append(result)
+```
+
+**Performance Gains:**
+- **Startup Time**: 70% faster browser initialization
+- **Page Loading**: 40% reduction with smart resource blocking
+- **Extraction**: 3x faster with compiled CSS selectors
+- **Memory Usage**: 60% reduction with streaming processing
+- **Concurrent Crawls**: Handle 5x more parallel requests
+
+
+## 🔧 Important Changes
+
+### Breaking Changes
+- `link_extractor` renamed to `link_preview` (better reflects functionality)
+- Minimum Python version now 3.9
+- `CrawlerConfig` split into `CrawlerRunConfig` and `BrowserConfig`
+
+### Migration Guide
+```python
+# Old (v0.6.x)
+from crawl4ai import CrawlerConfig
+config = CrawlerConfig(timeout=30000)
+
+# New (v0.7.0)
+from crawl4ai import CrawlerRunConfig, BrowserConfig
+browser_config = BrowserConfig(timeout=30000)
+run_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+```
+
+## 🤖 Coming Soon: Intelligent Web Automation
+
+I'm currently working on bringing advanced automation capabilities to Crawl4AI. This includes:
+
+- **Crawl Agents**: Autonomous crawlers that understand your goals and adapt their strategies
+- **Auto JS Generation**: Automatic JavaScript code generation for complex interactions
+- **Smart Form Handling**: Intelligent form detection and filling
+- **Context-Aware Actions**: Crawlers that understand page context and make decisions
+
+These features are under active development and will revolutionize how we approach web automation. Stay tuned!
+
+## 🚀 Get Started
+
+```bash
+pip install crawl4ai==0.7.0
+```
+
+Check out the [updated documentation](https://docs.crawl4ai.com).
+
+Questions? Issues? I'm always listening:
+- GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
+- Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
+- Twitter: [@unclecode](https://x.com/unclecode)
+
+Happy crawling! 🕷️
+
+---
+
+*P.S. If you're using Crawl4AI in production, I'd love to hear about it. Your use cases inspire the next features.*

docs/blog/release-v0.7.1.md

@@ -0,0 +1,43 @@
+# 🛠️ Crawl4AI v0.7.1: Minor Cleanup Update
+
+*July 17, 2025 • 2 min read*
+
+---
+
+A small maintenance release that removes unused code and improves documentation.
+
+## 🎯 What's Changed
+
+- **Removed unused StealthConfig** from `crawl4ai/browser_manager.py`
+- **Updated documentation** with better examples and parameter explanations
+- **Fixed virtual scroll configuration** examples in docs
+
+## 🧹 Code Cleanup
+
+Removed unused `StealthConfig` import and configuration that wasn't being used anywhere in the codebase. The project uses its own custom stealth implementation through JavaScript injection instead.
+
+```python
+# Removed unused code:
+from playwright_stealth import StealthConfig
+stealth_config = StealthConfig(...)  # This was never used
+```
+
+## 📖 Documentation Updates
+
+- Fixed adaptive crawling parameter examples
+- Updated session management documentation
+- Corrected virtual scroll configuration examples
+
+## 🚀 Installation
+
+```bash
+pip install crawl4ai==0.7.1
+```
+
+No breaking changes - upgrade directly from v0.7.0.
+
+---
+
+Questions? Issues? 
+- GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
+- Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)

docs/blog/release-v0.7.3.md

@@ -0,0 +1,350 @@
+# 🚀 Crawl4AI v0.7.3: The Multi-Config Intelligence Update
+
+*August 6, 2025 • 5 min read*
+
+---
+
+Today I'm releasing Crawl4AI v0.7.3—the Multi-Config Intelligence Update. This release brings smarter URL-specific configurations, flexible Docker deployments, important bug fixes, and documentation improvements that make Crawl4AI more robust and production-ready.
+
+## 🎯 What's New at a Glance
+
+- **🕵️ Undetected Browser Support**: Stealth mode for bypassing bot detection systems
+- **🎨 Multi-URL Configurations**: Different crawling strategies for different URL patterns in a single batch
+- **🐳 Flexible Docker LLM Providers**: Configure LLM providers via environment variables
+- **🧠 Memory Monitoring**: Enhanced memory usage tracking and optimization tools
+- **📊 Enhanced Table Extraction**: Improved table access and DataFrame conversion
+- **💰 GitHub Sponsors**: 4-tier sponsorship system with custom arrangements
+- **🔧 Bug Fixes**: Resolved several critical issues for better stability
+- **📚 Documentation Updates**: Clearer examples and improved API documentation
+
+## 🎨 Multi-URL Configurations: One Size Doesn't Fit All
+
+**The Problem:** You're crawling a mix of documentation sites, blogs, and API endpoints. Each needs different handling—caching for docs, fresh content for news, structured extraction for APIs. Previously, you'd run separate crawls or write complex conditional logic.
+
+**My Solution:** I implemented URL-specific configurations that let you define different strategies for different URL patterns in a single crawl batch. First match wins, with optional fallback support.
+
+### Technical Implementation
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, MatchMode
+
+# Define specialized configs for different content types
+configs = [
+    # Documentation sites - aggressive caching, include links
+    CrawlerRunConfig(
+        url_matcher=["*docs*", "*documentation*"],
+        cache_mode="write",
+        markdown_generator_options={"include_links": True}
+    ),
+    
+    # News/blog sites - fresh content, scroll for lazy loading
+    CrawlerRunConfig(
+        url_matcher=lambda url: 'blog' in url or 'news' in url,
+        cache_mode="bypass",
+        js_code="window.scrollTo(0, document.body.scrollHeight/2);"
+    ),
+    
+    # API endpoints - structured extraction
+    CrawlerRunConfig(
+        url_matcher=["*.json", "*api*"],
+        extraction_strategy=LLMExtractionStrategy(
+            provider="openai/gpt-4o-mini",
+            extraction_type="structured"
+        )
+    ),
+    
+    # Default fallback for everything else
+    CrawlerRunConfig()  # No url_matcher = matches everything
+]
+
+# Crawl multiple URLs with appropriate configs
+async with AsyncWebCrawler() as crawler:
+    results = await crawler.arun_many(
+        urls=[
+            "https://docs.python.org/3/",      # → Uses documentation config
+            "https://blog.python.org/",        # → Uses blog config  
+            "https://api.github.com/users",    # → Uses API config
+            "https://example.com/"             # → Uses default config
+        ],
+        config=configs
+    )
+```
+
+**Matching Capabilities:**
+- **String Patterns**: Wildcards like `"*.pdf"`, `"*/blog/*"`
+- **Function Matchers**: Lambda functions for complex logic
+- **Mixed Matchers**: Combine strings and functions with AND/OR logic
+- **Fallback Support**: Default config when nothing matches
+
+**Expected Real-World Impact:**
+- **Mixed Content Sites**: Handle blogs, docs, and downloads in one crawl
+- **Multi-Domain Crawling**: Different strategies per domain without separate runs
+- **Reduced Complexity**: No more if/else forests in your extraction code
+- **Better Performance**: Each URL gets exactly the processing it needs
+
+## 🕵️ Undetected Browser Support: Stealth Mode Activated
+
+**The Problem:** Modern websites employ sophisticated bot detection systems. Cloudflare, Akamai, and custom solutions block automated crawlers, limiting access to valuable content.
+
+**My Solution:** I implemented undetected browser support with a flexible adapter pattern. Now Crawl4AI can bypass most bot detection systems using stealth techniques.
+
+### Technical Implementation
+
+```python
+from crawl4ai import AsyncWebCrawler, BrowserConfig
+
+# Enable undetected mode for stealth crawling
+browser_config = BrowserConfig(
+    browser_type="undetected",  # Use undetected Chrome
+    headless=True,              # Can run headless with stealth
+    extra_args=[
+        "--disable-blink-features=AutomationControlled",
+        "--disable-web-security",
+        "--disable-features=VizDisplayCompositor"
+    ]
+)
+
+async with AsyncWebCrawler(config=browser_config) as crawler:
+    # This will bypass most bot detection systems
+    result = await crawler.arun("https://protected-site.com")
+    
+    if result.success:
+        print("✅ Successfully bypassed bot detection!")
+        print(f"Content length: {len(result.markdown)}")
+```
+
+**Advanced Anti-Bot Strategies:**
+
+```python
+# Combine multiple stealth techniques
+from crawl4ai import CrawlerRunConfig
+
+config = CrawlerRunConfig(
+    # Random user agents and headers
+    headers={
+        "Accept-Language": "en-US,en;q=0.9",
+        "Accept-Encoding": "gzip, deflate, br",
+        "DNT": "1"
+    },
+    
+    # Human-like behavior simulation
+    js_code="""
+        // Random mouse movements
+        const simulateHuman = () => {
+            const event = new MouseEvent('mousemove', {
+                clientX: Math.random() * window.innerWidth,
+                clientY: Math.random() * window.innerHeight
+            });
+            document.dispatchEvent(event);
+        };
+        setInterval(simulateHuman, 100 + Math.random() * 200);
+        
+        // Random scrolling
+        const randomScroll = () => {
+            const scrollY = Math.random() * (document.body.scrollHeight - window.innerHeight);
+            window.scrollTo(0, scrollY);
+        };
+        setTimeout(randomScroll, 500 + Math.random() * 1000);
+    """,
+    
+    # Delay to appear more human
+    delay_before_return_html=2.0
+)
+
+result = await crawler.arun("https://bot-protected-site.com", config=config)
+```
+
+**Expected Real-World Impact:**
+- **Enterprise Scraping**: Access previously blocked corporate sites and databases
+- **Market Research**: Gather data from competitor sites with protection
+- **Price Monitoring**: Track e-commerce sites that block automated access
+- **Content Aggregation**: Collect news and social media despite anti-bot measures
+- **Compliance Testing**: Verify your own site's bot protection effectiveness
+
+## 🧠 Memory Monitoring & Optimization
+
+**The Problem:** Long-running crawl sessions consuming excessive memory, especially when processing large batches or heavy JavaScript sites.
+
+**My Solution:** Built comprehensive memory monitoring and optimization utilities that track usage patterns and provide actionable insights.
+
+### Memory Tracking Implementation
+
+```python
+from crawl4ai.memory_utils import MemoryMonitor, get_memory_info
+
+# Monitor memory during crawling
+monitor = MemoryMonitor()
+
+async with AsyncWebCrawler() as crawler:
+    # Start monitoring
+    monitor.start_monitoring()
+    
+    # Perform memory-intensive operations
+    results = await crawler.arun_many([
+        "https://heavy-js-site.com",
+        "https://large-images-site.com", 
+        "https://dynamic-content-site.com"
+    ])
+    
+    # Get detailed memory report
+    memory_report = monitor.get_report()
+    print(f"Peak memory usage: {memory_report['peak_mb']:.1f} MB")
+    print(f"Memory efficiency: {memory_report['efficiency']:.1f}%")
+    
+    # Automatic cleanup suggestions
+    if memory_report['peak_mb'] > 1000:  # > 1GB
+        print("💡 Consider batch size optimization")
+        print("💡 Enable aggressive garbage collection")
+```
+
+**Expected Real-World Impact:**
+- **Production Stability**: Prevent memory-related crashes in long-running services
+- **Cost Optimization**: Right-size server resources based on actual usage
+- **Performance Tuning**: Identify memory bottlenecks and optimization opportunities
+- **Scalability Planning**: Understand memory patterns for horizontal scaling
+
+## 📊 Enhanced Table Extraction
+
+**The Problem:** Table data was accessed through the generic `result.media` interface, making DataFrame conversion cumbersome and unclear.
+
+**My Solution:** Dedicated `result.tables` interface with direct DataFrame conversion and improved detection algorithms.
+
+### New Table Access Pattern
+
+```python
+# Old way (deprecated)
+# tables_data = result.media.get('tables', [])
+
+# New way (v0.7.3+)
+result = await crawler.arun("https://site-with-tables.com")
+
+# Direct table access
+if result.tables:
+    print(f"Found {len(result.tables)} tables")
+    
+    # Convert to pandas DataFrame instantly
+    import pandas as pd
+    
+    for i, table in enumerate(result.tables):
+        df = pd.DataFrame(table['data'])
+        print(f"Table {i}: {df.shape[0]} rows × {df.shape[1]} columns")
+        print(df.head())
+        
+        # Table metadata
+        print(f"Source: {table.get('source_xpath', 'Unknown')}")
+        print(f"Headers: {table.get('headers', [])}")
+```
+
+**Expected Real-World Impact:**
+- **Data Analysis**: Faster transition from web data to analysis-ready DataFrames
+- **ETL Pipelines**: Cleaner integration with data processing workflows
+- **Reporting**: Simplified table extraction for automated reporting systems
+
+## 💰 Community Support: GitHub Sponsors
+
+I've launched GitHub Sponsors to ensure Crawl4AI's continued development and support our growing community.
+
+**Sponsorship Tiers:**
+- **🌱 Supporter ($5/month)**: Community support + early feature previews
+- **🚀 Professional ($25/month)**: Priority support + beta access
+- **🏢 Business ($100/month)**: Direct consultation + custom integrations
+- **🏛️ Enterprise ($500/month)**: Dedicated support + feature development
+
+**Why Sponsor?**
+- Ensure continuous development and maintenance
+- Get priority support and feature requests
+- Access to premium documentation and examples
+- Direct line to the development team
+
+[**Become a Sponsor →**](https://github.com/sponsors/unclecode)
+
+## 🐳 Docker: Flexible LLM Provider Configuration
+
+**The Problem:** Hardcoded LLM providers in Docker deployments. Want to switch from OpenAI to Groq? Rebuild and redeploy. Testing different models? Multiple Docker images.
+
+**My Solution:** Configure LLM providers via environment variables. Switch providers without touching code or rebuilding images.
+
+### Deployment Flexibility
+
+```bash
+# Option 1: Direct environment variables
+docker run -d \
+  -e LLM_PROVIDER="groq/llama-3.2-3b-preview" \
+  -e GROQ_API_KEY="your-key" \
+  -p 11235:11235 \
+  unclecode/crawl4ai:latest
+
+# Option 2: Using .llm.env file (recommended for production)
+# Create .llm.env file:
+# LLM_PROVIDER=openai/gpt-4o-mini
+# OPENAI_API_KEY=your-openai-key
+# GROQ_API_KEY=your-groq-key
+
+docker run -d \
+  --env-file .llm.env \
+  -p 11235:11235 \
+  unclecode/crawl4ai:latest
+```
+
+Override per request when needed:
+```python
+# Use default provider from .llm.env
+response = requests.post("http://localhost:11235/crawl", json={
+    "url": "https://example.com",
+    "extraction_strategy": {"type": "llm"}
+})
+
+# Override to use different provider for this specific request
+response = requests.post("http://localhost:11235/crawl", json={
+    "url": "https://complex-page.com",
+    "extraction_strategy": {
+        "type": "llm",
+        "provider": "openai/gpt-4"  # Override default
+    }
+})
+```
+
+**Expected Real-World Impact:**
+- **Cost Optimization**: Use cheaper models for simple tasks, premium for complex
+- **A/B Testing**: Compare provider performance without deployment changes
+- **Fallback Strategies**: Switch providers on-the-fly during outages
+- **Development Flexibility**: Test locally with one provider, deploy with another
+- **Secure Configuration**: Keep API keys in `.llm.env` file, not in commands
+
+## 🔧 Bug Fixes & Improvements
+
+This release includes several important bug fixes that improve stability and reliability:
+
+- **URL Matcher Fallback**: Fixed edge cases in URL pattern matching logic
+- **Memory Management**: Resolved memory leaks in long-running crawl sessions
+- **Sitemap Processing**: Fixed redirect handling in sitemap fetching
+- **Table Extraction**: Improved table detection and extraction accuracy
+- **Error Handling**: Better error messages and recovery from network failures
+
+## 📚 Documentation Enhancements
+
+Based on community feedback, we've updated:
+- Clearer examples for multi-URL configuration
+- Improved CrawlResult documentation with all available fields
+- Fixed typos and inconsistencies across documentation
+- Added real-world URLs in examples for better understanding
+- New comprehensive demo showcasing all v0.7.3 features
+
+## 🙏 Acknowledgments
+
+Thanks to our contributors and the entire community for feedback and bug reports.
+
+## 📚 Resources
+
+- [Full Documentation](https://docs.crawl4ai.com)
+- [GitHub Repository](https://github.com/unclecode/crawl4ai)
+- [Discord Community](https://discord.gg/crawl4ai)
+- [Feature Demo](https://github.com/unclecode/crawl4ai/blob/main/docs/releases_review/demo_v0.7.3.py)
+
+---
+
+*Crawl4AI continues to evolve with your needs. This release makes it smarter, more flexible, and more stable. Try the new multi-config feature and flexible Docker deployment—they're game changers!*
+
+**Happy Crawling! 🕷️**
+
+*- The Crawl4AI Team*

docs/blog/release-v0.7.4.md

@@ -0,0 +1,305 @@
+# 🚀 Crawl4AI v0.7.4: The Intelligent Table Extraction & Performance Update
+
+*August 17, 2025 • 6 min read*
+
+---
+
+Today I'm releasing Crawl4AI v0.7.4—the Intelligent Table Extraction & Performance Update. This release introduces revolutionary LLM-powered table extraction with intelligent chunking, significant performance improvements for concurrent crawling, enhanced browser management, and critical stability fixes that make Crawl4AI more robust for production workloads.
+
+## 🎯 What's New at a Glance
+
+- **🚀 LLMTableExtraction**: Revolutionary table extraction with intelligent chunking for massive tables
+- **⚡ Enhanced Concurrency**: True concurrency improvements for fast-completing tasks in batch operations
+- **🧹 Memory Management Refactor**: Streamlined memory utilities and better resource management
+- **🔧 Browser Manager Fixes**: Resolved race conditions in concurrent page creation
+- **⌨️ Cross-Platform Browser Profiler**: Improved keyboard handling and quit mechanisms
+- **🔗 Advanced URL Processing**: Better handling of raw URLs and base tag link resolution
+- **🛡️ Enhanced Proxy Support**: Flexible proxy configuration with dict and string formats
+- **🐳 Docker Improvements**: Better API handling and raw HTML support
+
+## 🚀 LLMTableExtraction: Revolutionary Table Processing
+
+**The Problem:** Complex tables with rowspan, colspan, nested structures, or massive datasets that traditional HTML parsing can't handle effectively. Large tables that exceed token limits crash extraction processes.
+
+**My Solution:** I developed LLMTableExtraction—an intelligent table extraction strategy that uses Large Language Models with automatic chunking to handle tables of any size and complexity.
+
+### Technical Implementation
+
+```python
+from crawl4ai import (
+    AsyncWebCrawler,
+    CrawlerRunConfig, 
+    LLMConfig,
+    LLMTableExtraction,
+    CacheMode
+)
+
+# Configure LLM for table extraction
+llm_config = LLMConfig(
+    provider="openai/gpt-4.1-mini",
+    api_token="env:OPENAI_API_KEY",
+    temperature=0.1,  # Low temperature for consistency
+    max_tokens=32000
+)
+
+# Create intelligent table extraction strategy
+table_strategy = LLMTableExtraction(
+    llm_config=llm_config,
+    verbose=True,
+    max_tries=2,
+    enable_chunking=True,           # Handle massive tables
+    chunk_token_threshold=5000,     # Smart chunking threshold
+    overlap_threshold=100,          # Maintain context between chunks
+    extraction_type="structured"    # Get structured data output
+)
+
+# Apply to crawler configuration
+config = CrawlerRunConfig(
+    table_extraction_strategy=table_strategy,
+    cache_mode=CacheMode.BYPASS
+)
+
+async with AsyncWebCrawler() as crawler:
+    # Extract complex tables with intelligence
+    result = await crawler.arun(
+        "https://en.wikipedia.org/wiki/List_of_countries_by_GDP", 
+        config=config
+    )
+    
+    # Access extracted tables directly
+    for i, table in enumerate(result.tables):
+        print(f"Table {i}: {len(table['data'])} rows × {len(table['headers'])} columns")
+        
+        # Convert to pandas DataFrame instantly
+        import pandas as pd
+        df = pd.DataFrame(table['data'], columns=table['headers'])
+        print(df.head())
+```
+
+**Intelligent Chunking for Massive Tables:**
+
+```python
+# Handle tables that exceed token limits
+large_table_strategy = LLMTableExtraction(
+    llm_config=llm_config,
+    enable_chunking=True,
+    chunk_token_threshold=3000,    # Conservative threshold
+    overlap_threshold=150,         # Preserve context
+    max_concurrent_chunks=3,       # Parallel processing
+    merge_strategy="intelligent"   # Smart chunk merging
+)
+
+# Process Wikipedia comparison tables, financial reports, etc.
+config = CrawlerRunConfig(
+    table_extraction_strategy=large_table_strategy,
+    # Target specific table containers
+    css_selector="div.wikitable, table.sortable",
+    delay_before_return_html=2.0
+)
+
+result = await crawler.arun(
+    "https://en.wikipedia.org/wiki/Comparison_of_operating_systems",
+    config=config
+)
+
+# Tables are automatically chunked, processed, and merged
+print(f"Extracted {len(result.tables)} complex tables")
+for table in result.tables:
+    print(f"Merged table: {len(table['data'])} total rows")
+```
+
+**Advanced Features:**
+
+- **Intelligent Chunking**: Automatically splits massive tables while preserving structure
+- **Context Preservation**: Overlapping chunks maintain column relationships
+- **Parallel Processing**: Concurrent chunk processing for speed
+- **Smart Merging**: Reconstructs complete tables from processed chunks
+- **Complex Structure Support**: Handles rowspan, colspan, nested tables
+- **Metadata Extraction**: Captures table context, captions, and relationships
+
+**Expected Real-World Impact:**
+- **Financial Analysis**: Extract complex earnings tables and financial statements
+- **Research & Academia**: Process large datasets from Wikipedia, research papers
+- **E-commerce**: Handle product comparison tables with complex layouts
+- **Government Data**: Extract census data, statistical tables from official sources
+- **Competitive Intelligence**: Process competitor pricing and feature tables
+
+## ⚡ Enhanced Concurrency: True Performance Gains
+
+**The Problem:** The `arun_many()` method wasn't achieving true concurrency for fast-completing tasks, leading to sequential processing bottlenecks in batch operations.
+
+**My Solution:** I implemented true concurrency improvements in the dispatcher that enable genuine parallel processing for fast-completing tasks.
+
+### Performance Optimization
+
+```python
+# Before v0.7.4: Sequential-like behavior for fast tasks
+# After v0.7.4: True concurrency
+
+async with AsyncWebCrawler() as crawler:
+    # These will now run with true concurrency
+    urls = [
+        "https://httpbin.org/delay/1",
+        "https://httpbin.org/delay/1", 
+        "https://httpbin.org/delay/1",
+        "https://httpbin.org/delay/1"
+    ]
+    
+    # Processes in truly parallel fashion
+    results = await crawler.arun_many(urls)
+    
+    # Performance improvement: ~4x faster for fast-completing tasks
+    print(f"Processed {len(results)} URLs with true concurrency")
+```
+
+**Expected Real-World Impact:**
+- **API Crawling**: 3-4x faster processing of REST endpoints and API documentation
+- **Batch URL Processing**: Significant speedup for large URL lists
+- **Monitoring Systems**: Faster health checks and status page monitoring
+- **Data Aggregation**: Improved performance for real-time data collection
+
+## 🧹 Memory Management Refactor: Cleaner Architecture
+
+**The Problem:** Memory utilities were scattered and difficult to maintain, with potential import conflicts and unclear organization.
+
+**My Solution:** I consolidated all memory-related utilities into the main `utils.py` module, creating a cleaner, more maintainable architecture.
+
+### Improved Memory Handling
+
+```python
+# All memory utilities now consolidated
+from crawl4ai.utils import get_true_memory_usage_percent, MemoryMonitor
+
+# Enhanced memory monitoring
+monitor = MemoryMonitor()
+monitor.start_monitoring()
+
+async with AsyncWebCrawler() as crawler:
+    # Memory-efficient batch processing
+    results = await crawler.arun_many(large_url_list)
+    
+    # Get accurate memory metrics
+    memory_usage = get_true_memory_usage_percent()
+    memory_report = monitor.get_report()
+    
+    print(f"Memory efficiency: {memory_report['efficiency']:.1f}%")
+    print(f"Peak usage: {memory_report['peak_mb']:.1f} MB")
+```
+
+**Expected Real-World Impact:**
+- **Production Stability**: More reliable memory tracking and management
+- **Code Maintainability**: Cleaner architecture for easier debugging
+- **Import Clarity**: Resolved potential conflicts and import issues
+- **Developer Experience**: Simpler API for memory monitoring
+
+## 🔧 Critical Stability Fixes
+
+### Browser Manager Race Condition Resolution
+
+**The Problem:** Concurrent page creation in persistent browser contexts caused "Target page/context closed" errors during high-concurrency operations.
+
+**My Solution:** Implemented thread-safe page creation with proper locking mechanisms.
+
+```python
+# Fixed: Safe concurrent page creation
+browser_config = BrowserConfig(
+    browser_type="chromium",
+    use_persistent_context=True,  # Now thread-safe
+    max_concurrent_sessions=10    # Safely handle concurrent requests
+)
+
+async with AsyncWebCrawler(config=browser_config) as crawler:
+    # These concurrent operations are now stable
+    tasks = [crawler.arun(url) for url in url_list]
+    results = await asyncio.gather(*tasks)  # No more race conditions
+```
+
+### Enhanced Browser Profiler
+
+**The Problem:** Inconsistent keyboard handling across platforms and unreliable quit mechanisms.
+
+**My Solution:** Cross-platform keyboard listeners with improved quit handling.
+
+### Advanced URL Processing
+
+**The Problem:** Raw URL formats (`raw://` and `raw:`) weren't properly handled, and base tag link resolution was incomplete.
+
+**My Solution:** Enhanced URL preprocessing and base tag support.
+
+```python
+# Now properly handles all URL formats
+urls = [
+    "https://example.com",
+    "raw://static-html-content", 
+    "raw:file://local-file.html"
+]
+
+# Base tag links are now correctly resolved
+config = CrawlerRunConfig(
+    include_links=True,  # Links properly resolved with base tags
+    resolve_absolute_urls=True
+)
+```
+
+## 🛡️ Enhanced Proxy Configuration
+
+**The Problem:** Proxy configuration only accepted specific formats, limiting flexibility.
+
+**My Solution:** Enhanced ProxyConfig to support both dictionary and string formats.
+
+```python
+# Multiple proxy configuration formats now supported
+from crawl4ai import BrowserConfig, ProxyConfig
+
+# String format
+proxy_config = ProxyConfig("http://proxy.example.com:8080")
+
+# Dictionary format  
+proxy_config = ProxyConfig({
+    "server": "http://proxy.example.com:8080",
+    "username": "user",
+    "password": "pass"
+})
+
+# Use with crawler
+browser_config = BrowserConfig(proxy_config=proxy_config)
+async with AsyncWebCrawler(config=browser_config) as crawler:
+    result = await crawler.arun("https://httpbin.org/ip")
+```
+
+## 🐳 Docker & Infrastructure Improvements
+
+This release includes several Docker and infrastructure improvements:
+
+- **Better API Token Handling**: Improved Docker example scripts with correct endpoints
+- **Raw HTML Support**: Enhanced Docker API to handle raw HTML content properly
+- **Documentation Updates**: Comprehensive Docker deployment examples
+- **Test Coverage**: Expanded test suite with better coverage
+
+## 📚 Documentation & Examples
+
+Enhanced documentation includes:
+
+- **LLM Table Extraction Guide**: Comprehensive examples and best practices
+- **Migration Documentation**: Updated patterns for new table extraction methods  
+- **Docker Deployment**: Complete deployment guide with examples
+- **Performance Optimization**: Guidelines for concurrent crawling
+
+## 🙏 Acknowledgments
+
+Thanks to our contributors and community for feedback, bug reports, and feature requests that made this release possible.
+
+## 📚 Resources
+
+- [Full Documentation](https://docs.crawl4ai.com)
+- [GitHub Repository](https://github.com/unclecode/crawl4ai)
+- [Discord Community](https://discord.gg/crawl4ai)
+- [LLM Table Extraction Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/llm_table_extraction_example.py)
+
+---
+
+*Crawl4AI v0.7.4 delivers intelligent table extraction and significant performance improvements. The new LLMTableExtraction strategy handles complex tables that were previously impossible to process, while concurrency improvements make batch operations 3-4x faster. Try the intelligent table extraction—it's a game changer for data extraction workflows!*
+
+**Happy Crawling! 🕷️**
+
+*- The Crawl4AI Team*

docs/examples/c4a_script/api_usage_examples.py

@@ -3,8 +3,8 @@ C4A-Script API Usage Examples
 Shows how to use the new Result-based API in various scenarios
 """
 
-from c4a_compile import compile, validate, compile_file
-from c4a_result import CompilationResult, ValidationResult
+from crawl4ai.script.c4a_compile import compile, validate, compile_file
+from crawl4ai.script.c4a_result import CompilationResult, ValidationResult
 import json
 
 

docs/examples/c4a_script/c4a_script_hello_world.py

@@ -3,7 +3,7 @@ C4A-Script Hello World
 A concise example showing how to use the C4A-Script compiler
 """
 
-from c4a_compile import compile
+from crawl4ai.script.c4a_compile import compile
 
 # Define your C4A-Script
 script = """

docs/examples/c4a_script/c4a_script_hello_world_error.py

@@ -3,7 +3,7 @@ C4A-Script Hello World - Error Example
 Shows how error handling works
 """
 
-from c4a_compile import compile
+from crawl4ai.script.c4a_compile import compile
 
 # Define a script with an error (missing THEN)
 script = """

docs/examples/demo_multi_config_clean.py

@@ -0,0 +1,303 @@
+"""
+🎯 Multi-Config URL Matching Demo
+=================================
+Learn how to use different crawler configurations for different URL patterns
+in a single crawl batch with Crawl4AI's multi-config feature.
+
+Part 1: Understanding URL Matching (Pattern Testing)
+Part 2: Practical Example with Real Crawling
+"""
+
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler, 
+    CrawlerRunConfig,
+    MatchMode
+)
+from crawl4ai.processors.pdf import PDFContentScrapingStrategy
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+from crawl4ai.content_filter_strategy import PruningContentFilter
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+
+
+def print_section(title):
+    """Print a formatted section header"""
+    print(f"\n{'=' * 60}")
+    print(f"{title}")
+    print(f"{'=' * 60}\n")
+
+
+def test_url_matching(config, test_urls, config_name):
+    """Test URL matching for a config and show results"""
+    print(f"Config: {config_name}")
+    print(f"Matcher: {config.url_matcher}")
+    if hasattr(config, 'match_mode'):
+        print(f"Mode: {config.match_mode.value}")
+    print("-" * 40)
+    
+    for url in test_urls:
+        matches = config.is_match(url)
+        symbol = "✓" if matches else "✗"
+        print(f"{symbol} {url}")
+    print()
+
+
+# ==============================================================================
+# PART 1: Understanding URL Matching
+# ==============================================================================
+
+def demo_part1_pattern_matching():
+    """Part 1: Learn how URL matching works without crawling"""
+    
+    print_section("PART 1: Understanding URL Matching")
+    print("Let's explore different ways to match URLs with configs.\n")
+    
+    # Test URLs we'll use throughout
+    test_urls = [
+        "https://example.com/report.pdf",
+        "https://example.com/data.json",
+        "https://example.com/blog/post-1",
+        "https://example.com/article/news",
+        "https://api.example.com/v1/users",
+        "https://example.com/about"
+    ]
+    
+    # 1.1 Simple String Pattern
+    print("1.1 Simple String Pattern Matching")
+    print("-" * 40)
+    
+    pdf_config = CrawlerRunConfig(
+        url_matcher="*.pdf"
+    )
+    
+    test_url_matching(pdf_config, test_urls, "PDF Config")
+    
+    
+    # 1.2 Multiple String Patterns
+    print("1.2 Multiple String Patterns (OR logic)")
+    print("-" * 40)
+    
+    blog_config = CrawlerRunConfig(
+        url_matcher=["*/blog/*", "*/article/*", "*/news/*"],
+        match_mode=MatchMode.OR  # This is default, shown for clarity
+    )
+    
+    test_url_matching(blog_config, test_urls, "Blog/Article Config")
+    
+    
+    # 1.3 Single Function Matcher
+    print("1.3 Function-based Matching")
+    print("-" * 40)
+    
+    api_config = CrawlerRunConfig(
+        url_matcher=lambda url: 'api' in url or url.endswith('.json')
+    )
+    
+    test_url_matching(api_config, test_urls, "API Config")
+    
+    
+    # 1.4 List of Functions
+    print("1.4 Multiple Functions with AND Logic")
+    print("-" * 40)
+    
+    # Must be HTTPS AND contain 'api' AND have version number
+    secure_api_config = CrawlerRunConfig(
+        url_matcher=[
+            lambda url: url.startswith('https://'),
+            lambda url: 'api' in url,
+            lambda url: '/v' in url  # Version indicator
+        ],
+        match_mode=MatchMode.AND
+    )
+    
+    test_url_matching(secure_api_config, test_urls, "Secure API Config")
+    
+    
+    # 1.5 Mixed: String and Function Together
+    print("1.5 Mixed Patterns: String + Function")
+    print("-" * 40)
+    
+    # Match JSON files OR any API endpoint
+    json_or_api_config = CrawlerRunConfig(
+        url_matcher=[
+            "*.json",  # String pattern
+            lambda url: 'api' in url  # Function
+        ],
+        match_mode=MatchMode.OR
+    )
+    
+    test_url_matching(json_or_api_config, test_urls, "JSON or API Config")
+    
+    
+    # 1.6 Complex: Multiple Strings + Multiple Functions
+    print("1.6 Complex Matcher: Mixed Types with AND Logic")
+    print("-" * 40)
+    
+    # Must be: HTTPS AND (.com domain) AND (blog OR article) AND NOT a PDF
+    complex_config = CrawlerRunConfig(
+        url_matcher=[
+            lambda url: url.startswith('https://'),  # Function: HTTPS check
+            "*.com/*",  # String: .com domain
+            lambda url: any(pattern in url for pattern in ['/blog/', '/article/']),  # Function: Blog OR article
+            lambda url: not url.endswith('.pdf')  # Function: Not PDF
+        ],
+        match_mode=MatchMode.AND
+    )
+    
+    test_url_matching(complex_config, test_urls, "Complex Mixed Config")
+    
+    print("\n✅ Key Takeaway: First matching config wins when passed to arun_many()!")
+
+
+# ==============================================================================
+# PART 2: Practical Multi-URL Crawling
+# ==============================================================================
+
+async def demo_part2_practical_crawling():
+    """Part 2: Real-world example with different content types"""
+    
+    print_section("PART 2: Practical Multi-URL Crawling")
+    print("Now let's see multi-config in action with real URLs.\n")
+    
+    # Create specialized configs for different content types
+    configs = [
+        # Config 1: PDF documents - only match files ending with .pdf
+        CrawlerRunConfig(
+            url_matcher="*.pdf",
+            scraping_strategy=PDFContentScrapingStrategy()
+        ),
+        
+        # Config 2: Blog/article pages with content filtering
+        CrawlerRunConfig(
+            url_matcher=["*/blog/*", "*/article/*", "*python.org*"],
+            markdown_generator=DefaultMarkdownGenerator(
+                content_filter=PruningContentFilter(threshold=0.48)
+            )
+        ),
+        
+        # Config 3: Dynamic pages requiring JavaScript
+        CrawlerRunConfig(
+            url_matcher=lambda url: 'github.com' in url,
+            js_code="window.scrollTo(0, 500);"  # Scroll to load content
+        ),
+        
+        # Config 4: Mixed matcher - API endpoints (string OR function)
+        CrawlerRunConfig(
+            url_matcher=[
+                "*.json",  # String pattern for JSON files
+                lambda url: 'api' in url or 'httpbin.org' in url  # Function for API endpoints
+            ],
+            match_mode=MatchMode.OR,
+        ),
+        
+        # Config 5: Complex matcher - Secure documentation sites
+        CrawlerRunConfig(
+            url_matcher=[
+                lambda url: url.startswith('https://'),  # Must be HTTPS
+                "*.org/*",  # String: .org domain
+                lambda url: any(doc in url for doc in ['docs', 'documentation', 'reference']),  # Has docs
+                lambda url: not url.endswith(('.pdf', '.json'))  # Not PDF or JSON
+            ],
+            match_mode=MatchMode.AND,
+            # wait_for="css:.content, css:article"  # Wait for content to load
+        ),
+        
+        # Default config for everything else
+        # CrawlerRunConfig()  # No url_matcher means it matches everything (use it as fallback)
+    ]
+    
+    # URLs to crawl - each will use a different config
+    urls = [
+        "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",  # → PDF config
+        "https://blog.python.org/",  # → Blog config with content filter
+        "https://github.com/microsoft/playwright",  # → JS config
+        "https://httpbin.org/json",  # → Mixed matcher config (API)
+        "https://docs.python.org/3/reference/",  # → Complex matcher config
+        "https://www.w3schools.com/",  # → Default config, if you uncomment the default config line above, if not you will see `Error: No matching configuration`
+    ]
+    
+    print("URLs to crawl:")
+    for i, url in enumerate(urls, 1):
+        print(f"{i}. {url}")
+    
+    print("\nCrawling with appropriate config for each URL...\n")
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(
+            urls=urls,
+            config=configs
+        )
+        
+        # Display results
+        print("Results:")
+        print("-" * 60)
+        
+        for result in results:
+            if result.success:
+                # Determine which config was used
+                config_type = "Default"
+                if result.url.endswith('.pdf'):
+                    config_type = "PDF Strategy"
+                elif any(pattern in result.url for pattern in ['blog', 'python.org']) and 'docs' not in result.url:
+                    config_type = "Blog + Content Filter"
+                elif 'github.com' in result.url:
+                    config_type = "JavaScript Enabled"
+                elif 'httpbin.org' in result.url or result.url.endswith('.json'):
+                    config_type = "Mixed Matcher (API)"
+                elif 'docs.python.org' in result.url:
+                    config_type = "Complex Matcher (Secure Docs)"
+                
+                print(f"\n✓ {result.url}")
+                print(f"  Config used: {config_type}")
+                print(f"  Content size: {len(result.markdown)} chars")
+                
+                # Show if we have fit_markdown (from content filter)
+                if hasattr(result.markdown, 'fit_markdown') and result.markdown.fit_markdown:
+                    print(f"  Fit markdown size: {len(result.markdown.fit_markdown)} chars")
+                    reduction = (1 - len(result.markdown.fit_markdown) / len(result.markdown)) * 100
+                    print(f"  Content reduced by: {reduction:.1f}%")
+                
+                # Show extracted data if using extraction strategy
+                if hasattr(result, 'extracted_content') and result.extracted_content:
+                    print(f"  Extracted data: {str(result.extracted_content)[:100]}...")
+            else:
+                print(f"\n✗ {result.url}")
+                print(f"  Error: {result.error_message}")
+    
+    print("\n" + "=" * 60)
+    print("✅ Multi-config crawling complete!")
+    print("\nBenefits demonstrated:")
+    print("- PDFs handled with specialized scraper")
+    print("- Blog content filtered for relevance") 
+    print("- JavaScript executed only where needed")
+    print("- Mixed matchers (string + function) for flexible matching")
+    print("- Complex matchers for precise URL targeting")
+    print("- Each URL got optimal configuration automatically!")
+
+
+async def main():
+    """Run both parts of the demo"""
+    
+    print("""
+🎯 Multi-Config URL Matching Demo
+=================================
+Learn how Crawl4AI can use different configurations
+for different URLs in a single batch.
+    """)
+    
+    # Part 1: Pattern matching
+    demo_part1_pattern_matching()
+    
+    print("\nPress Enter to continue to Part 2...")
+    try:
+        input()
+    except EOFError:
+        # Running in non-interactive mode, skip input
+        pass
+    
+    # Part 2: Practical crawling
+    await demo_part2_practical_crawling()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/docker_example.py

@@ -8,26 +8,20 @@ from typing import Dict, Any
 
 
 class Crawl4AiTester:
-    def __init__(self, base_url: str = "http://localhost:11235", api_token: str = None):
+    def __init__(self, base_url: str = "http://localhost:11235"):
         self.base_url = base_url
-        self.api_token = (
-            api_token or os.getenv("CRAWL4AI_API_TOKEN") or "test_api_code"
-        )  # Check environment variable as fallback
-        self.headers = (
-            {"Authorization": f"Bearer {self.api_token}"} if self.api_token else {}
-        )
 
     def submit_and_wait(
         self, request_data: Dict[str, Any], timeout: int = 300
     ) -> Dict[str, Any]:
-        # Submit crawl job
+        # Submit crawl job using async endpoint
         response = requests.post(
-            f"{self.base_url}/crawl", json=request_data, headers=self.headers
+            f"{self.base_url}/crawl/job", json=request_data
         )
-        if response.status_code == 403:
-            raise Exception("API token is invalid or missing")
-        task_id = response.json()["task_id"]
-        print(f"Task ID: {task_id}")
+        response.raise_for_status()
+        job_response = response.json()
+        task_id = job_response["task_id"]
+        print(f"Submitted job with task_id: {task_id}")
 
         # Poll for result
         start_time = time.time()
@@ -38,8 +32,9 @@ class Crawl4AiTester:
                 )
 
             result = requests.get(
-                f"{self.base_url}/task/{task_id}", headers=self.headers
+                f"{self.base_url}/crawl/job/{task_id}"
             )
+            result.raise_for_status()
             status = result.json()
 
             if status["status"] == "failed":
@@ -52,10 +47,10 @@ class Crawl4AiTester:
             time.sleep(2)
 
     def submit_sync(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
+        # Use synchronous crawl endpoint
         response = requests.post(
-            f"{self.base_url}/crawl_sync",
+            f"{self.base_url}/crawl",
             json=request_data,
-            headers=self.headers,
             timeout=60,
         )
         if response.status_code == 408:
@@ -63,20 +58,9 @@ class Crawl4AiTester:
         response.raise_for_status()
         return response.json()
 
-    def crawl_direct(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Directly crawl without using task queue"""
-        response = requests.post(
-            f"{self.base_url}/crawl_direct", json=request_data, headers=self.headers
-        )
-        response.raise_for_status()
-        return response.json()
-
-
 def test_docker_deployment(version="basic"):
     tester = Crawl4AiTester(
         base_url="http://localhost:11235",
-        # base_url="https://api.crawl4ai.com" # just for example
-        # api_token="test" # just for example
     )
     print(f"Testing Crawl4AI Docker {version} version")
 
@@ -95,11 +79,8 @@ def test_docker_deployment(version="basic"):
             time.sleep(5)
 
     # Test cases based on version
-    test_basic_crawl_direct(tester)
-    test_basic_crawl(tester)
     test_basic_crawl(tester)
     test_basic_crawl_sync(tester)
-
     if version in ["full", "transformer"]:
         test_cosine_extraction(tester)
 
@@ -112,115 +93,129 @@ def test_docker_deployment(version="basic"):
 
 
 def test_basic_crawl(tester: Crawl4AiTester):
-    print("\n=== Testing Basic Crawl ===")
+    print("\n=== Testing Basic Crawl (Async) ===")
     request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 10,
-        "session_id": "test",
+        "urls": ["https://www.nbcnews.com/business"],
+        "browser_config": {},
+        "crawler_config": {}
     }
 
     result = tester.submit_and_wait(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
+    print(f"Basic crawl result count: {len(result['result']['results'])}")
     assert result["result"]["success"]
-    assert len(result["result"]["markdown"]) > 0
+    assert len(result["result"]["results"]) > 0
+    assert len(result["result"]["results"][0]["markdown"]) > 0
 
 
 def test_basic_crawl_sync(tester: Crawl4AiTester):
     print("\n=== Testing Basic Crawl (Sync) ===")
     request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 10,
-        "session_id": "test",
+        "urls": ["https://www.nbcnews.com/business"],
+        "browser_config": {},
+        "crawler_config": {}
     }
 
     result = tester.submit_sync(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
-    assert result["status"] == "completed"
-    assert result["result"]["success"]
-    assert len(result["result"]["markdown"]) > 0
-
-
-def test_basic_crawl_direct(tester: Crawl4AiTester):
-    print("\n=== Testing Basic Crawl (Direct) ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 10,
-        # "session_id": "test"
-        "cache_mode": "bypass",  # or "enabled", "disabled", "read_only", "write_only"
-    }
-
-    result = tester.crawl_direct(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
-    assert result["result"]["success"]
-    assert len(result["result"]["markdown"]) > 0
+    print(f"Basic crawl result count: {len(result['results'])}")
+    assert result["success"]
+    assert len(result["results"]) > 0
+    assert len(result["results"][0]["markdown"]) > 0
 
 
 def test_js_execution(tester: Crawl4AiTester):
     print("\n=== Testing JS Execution ===")
     request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 8,
-        "js_code": [
-            "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
-        ],
-        "wait_for": "article.tease-card:nth-child(10)",
-        "crawler_params": {"headless": True},
+        "urls": ["https://www.nbcnews.com/business"],
+        "browser_config": {"headless": True},
+        "crawler_config": {
+            "js_code": [
+                "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); if(loadMoreButton) loadMoreButton.click();"
+            ],
+            "wait_for": "wide-tease-item__wrapper df flex-column flex-row-m flex-nowrap-m enable-new-sports-feed-mobile-design(10)"
+        }
     }
 
     result = tester.submit_and_wait(request)
-    print(f"JS execution result length: {len(result['result']['markdown'])}")
+    print(f"JS execution result count: {len(result['result']['results'])}")
     assert result["result"]["success"]
 
 
 def test_css_selector(tester: Crawl4AiTester):
     print("\n=== Testing CSS Selector ===")
     request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 7,
-        "css_selector": ".wide-tease-item__description",
-        "crawler_params": {"headless": True},
-        "extra": {"word_count_threshold": 10},
+        "urls": ["https://www.nbcnews.com/business"],
+        "browser_config": {"headless": True},
+        "crawler_config": {
+            "css_selector": ".wide-tease-item__description",
+            "word_count_threshold": 10
+        }
     }
 
     result = tester.submit_and_wait(request)
-    print(f"CSS selector result length: {len(result['result']['markdown'])}")
+    print(f"CSS selector result count: {len(result['result']['results'])}")
     assert result["result"]["success"]
 
 
 def test_structured_extraction(tester: Crawl4AiTester):
     print("\n=== Testing Structured Extraction ===")
     schema = {
-        "name": "Coinbase Crypto Prices",
-        "baseSelector": ".cds-tableRow-t45thuk",
+        "name": "Cryptocurrency Prices",
+        "baseSelector": "table[data-testid=\"prices-table\"] tbody tr",
         "fields": [
             {
-                "name": "crypto",
-                "selector": "td:nth-child(1) h2",
-                "type": "text",
+                "name": "asset_name",
+                "selector": "td:nth-child(2) p.cds-headline-h4steop",
+                "type": "text"
             },
             {
-                "name": "symbol",
-                "selector": "td:nth-child(1) p",
-                "type": "text",
+                "name": "asset_symbol",
+                "selector": "td:nth-child(2) p.cds-label2-l1sm09ec",
+                "type": "text"
+            },
+            {
+                "name": "asset_image_url",
+                "selector": "td:nth-child(2) img[alt=\"Asset Symbol\"]",
+                "type": "attribute",
+                "attribute": "src"
+            },
+            {
+                "name": "asset_url",
+                "selector": "td:nth-child(2) a[aria-label^=\"Asset page for\"]",
+                "type": "attribute",
+                "attribute": "href"
             },
             {
                 "name": "price",
-                "selector": "td:nth-child(2)",
-                "type": "text",
+                "selector": "td:nth-child(3) div.cds-typographyResets-t6muwls.cds-body-bwup3gq",
+                "type": "text"
             },
-        ],
+            {
+                "name": "change",
+                "selector": "td:nth-child(7) p.cds-body-bwup3gq",
+                "type": "text"
+            }
+        ]
     }
 
     request = {
-        "urls": "https://www.coinbase.com/explore",
-        "priority": 9,
-        "extraction_config": {"type": "json_css", "params": {"schema": schema}},
+        "urls": ["https://www.coinbase.com/explore"],
+        "browser_config": {},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
+            "params": {
+                "extraction_strategy": {
+                    "type": "JsonCssExtractionStrategy",
+                    "params": {"schema": schema}
+                }
+            }
+        }
     }
 
     result = tester.submit_and_wait(request)
-    extracted = json.loads(result["result"]["extracted_content"])
+    extracted = json.loads(result["result"]["results"][0]["extracted_content"])
     print(f"Extracted {len(extracted)} items")
-    print("Sample item:", json.dumps(extracted[0], indent=2))
+    if extracted:
+        print("Sample item:", json.dumps(extracted[0], indent=2))
     assert result["result"]["success"]
     assert len(extracted) > 0
 
@@ -230,43 +225,54 @@ def test_llm_extraction(tester: Crawl4AiTester):
     schema = {
         "type": "object",
         "properties": {
-            "model_name": {
+            "asset_name": {
                 "type": "string",
-                "description": "Name of the OpenAI model.",
+                "description": "Name of the asset.",
             },
-            "input_fee": {
+            "price": {
                 "type": "string",
-                "description": "Fee for input token for the OpenAI model.",
+                "description": "Price of the asset.",
             },
-            "output_fee": {
+            "change": {
                 "type": "string",
-                "description": "Fee for output token for the OpenAI model.",
+                "description": "Change in price of the asset.",
             },
         },
-        "required": ["model_name", "input_fee", "output_fee"],
+        "required": ["asset_name", "price", "change"],
     }
 
     request = {
-        "urls": "https://openai.com/api/pricing",
-        "priority": 8,
-        "extraction_config": {
-            "type": "llm",
+        "urls": ["https://www.coinbase.com/en-in/explore"],
+        "browser_config": {},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
             "params": {
-                "provider": "openai/gpt-4o-mini",
-                "api_token": os.getenv("OPENAI_API_KEY"),
-                "schema": schema,
-                "extraction_type": "schema",
-                "instruction": """From the crawled content, extract all mentioned model names along with their fees for input and output tokens.""",
-            },
-        },
-        "crawler_params": {"word_count_threshold": 1},
+                "extraction_strategy": {
+                    "type": "LLMExtractionStrategy",
+                    "params": {
+                        "llm_config": {
+                            "type": "LLMConfig",
+                            "params": {
+                                "provider": "gemini/gemini-2.0-flash-exp",
+                                "api_token": os.getenv("GEMINI_API_KEY")
+                            }
+                        },
+                        "schema": schema,
+                        "extraction_type": "schema",
+                        "instruction": "From the crawled content, extract asset names along with their prices and change in price.",
+                    }
+                },
+                "word_count_threshold": 1
+            }
+        }
     }
 
     try:
         result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
-        print(f"Extracted {len(extracted)} model pricing entries")
-        print("Sample entry:", json.dumps(extracted[0], indent=2))
+        extracted = json.loads(result["result"]["results"][0]["extracted_content"])
+        print(f"Extracted {len(extracted)} asset pricing entries")
+        if extracted:
+            print("Sample entry:", json.dumps(extracted[0], indent=2))
         assert result["result"]["success"]
     except Exception as e:
         print(f"LLM extraction test failed (might be due to missing API key): {str(e)}")
@@ -274,6 +280,16 @@ def test_llm_extraction(tester: Crawl4AiTester):
 
 def test_llm_with_ollama(tester: Crawl4AiTester):
     print("\n=== Testing LLM with Ollama ===")
+    
+    # Check if Ollama is accessible first
+    try:
+        ollama_response = requests.get("http://localhost:11434/api/tags", timeout=5)
+        ollama_response.raise_for_status()
+        print("Ollama is accessible")
+    except:
+        print("Ollama is not accessible, skipping test")
+        return
+    
     schema = {
         "type": "object",
         "properties": {
@@ -294,24 +310,33 @@ def test_llm_with_ollama(tester: Crawl4AiTester):
     }
 
     request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 8,
-        "extraction_config": {
-            "type": "llm",
+        "urls": ["https://www.nbcnews.com/business"],
+        "browser_config": {"verbose": True},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
             "params": {
-                "provider": "ollama/llama2",
-                "schema": schema,
-                "extraction_type": "schema",
-                "instruction": "Extract the main article information including title, summary, and main topics.",
-            },
-        },
-        "extra": {"word_count_threshold": 1},
-        "crawler_params": {"verbose": True},
+                "extraction_strategy": {
+                    "type": "LLMExtractionStrategy",
+                    "params": {
+                        "llm_config": {
+                            "type": "LLMConfig",
+                            "params": {
+                                "provider": "ollama/llama3.2:latest",
+                            }
+                        },
+                        "schema": schema,
+                        "extraction_type": "schema",
+                        "instruction": "Extract the main article information including title, summary, and main topics.",
+                    }
+                },
+                "word_count_threshold": 1
+            }
+        }
     }
 
     try:
         result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
+        extracted = json.loads(result["result"]["results"][0]["extracted_content"])
         print("Extracted content:", json.dumps(extracted, indent=2))
         assert result["result"]["success"]
     except Exception as e:
@@ -321,24 +346,30 @@ def test_llm_with_ollama(tester: Crawl4AiTester):
 def test_cosine_extraction(tester: Crawl4AiTester):
     print("\n=== Testing Cosine Extraction ===")
     request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 8,
-        "extraction_config": {
-            "type": "cosine",
+        "urls": ["https://www.nbcnews.com/business"],
+        "browser_config": {},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
             "params": {
-                "semantic_filter": "business finance economy",
-                "word_count_threshold": 10,
-                "max_dist": 0.2,
-                "top_k": 3,
-            },
-        },
+                "extraction_strategy": {
+                    "type": "CosineStrategy",
+                    "params": {
+                        "semantic_filter": "business finance economy",
+                        "word_count_threshold": 10,
+                        "max_dist": 0.2,
+                        "top_k": 3,
+                    }
+                }
+            }
+        }
     }
 
     try:
         result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
+        extracted = json.loads(result["result"]["results"][0]["extracted_content"])
         print(f"Extracted {len(extracted)} text clusters")
-        print("First cluster tags:", extracted[0]["tags"])
+        if extracted:
+            print("First cluster tags:", extracted[0]["tags"])
         assert result["result"]["success"]
     except Exception as e:
         print(f"Cosine extraction test failed: {str(e)}")
@@ -347,20 +378,25 @@ def test_cosine_extraction(tester: Crawl4AiTester):
 def test_screenshot(tester: Crawl4AiTester):
     print("\n=== Testing Screenshot ===")
     request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 5,
-        "screenshot": True,
-        "crawler_params": {"headless": True},
+        "urls": ["https://www.nbcnews.com/business"],
+        "browser_config": {"headless": True},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
+            "params": {
+                "screenshot": True
+            }
+        }
     }
 
     result = tester.submit_and_wait(request)
-    print("Screenshot captured:", bool(result["result"]["screenshot"]))
+    screenshot_data = result["result"]["results"][0]["screenshot"]
+    print("Screenshot captured:", bool(screenshot_data))
 
-    if result["result"]["screenshot"]:
+    if screenshot_data:
         # Save screenshot
-        screenshot_data = base64.b64decode(result["result"]["screenshot"])
+        screenshot_bytes = base64.b64decode(screenshot_data)
         with open("test_screenshot.jpg", "wb") as f:
-            f.write(screenshot_data)
+            f.write(screenshot_bytes)
         print("Screenshot saved as test_screenshot.jpg")
 
     assert result["result"]["success"]
@@ -368,5 +404,4 @@ def test_screenshot(tester: Crawl4AiTester):
 
 if __name__ == "__main__":
     version = sys.argv[1] if len(sys.argv) > 1 else "basic"
-    # version = "full"
     test_docker_deployment(version)

docs/examples/hello_world_undetected.py

@@ -0,0 +1,57 @@
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler,
+    BrowserConfig,
+    CrawlerRunConfig,
+    DefaultMarkdownGenerator,
+    PruningContentFilter,
+    CrawlResult,
+    UndetectedAdapter
+)
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+
+async def main():
+    # Create browser config
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True,
+    )
+    
+    # Create the undetected adapter
+    undetected_adapter = UndetectedAdapter()
+    
+    # Create the crawler strategy with the undetected adapter
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=undetected_adapter
+    )
+    
+    # Create the crawler with our custom strategy
+    async with AsyncWebCrawler(
+        crawler_strategy=crawler_strategy,
+        config=browser_config
+    ) as crawler:
+        # Configure the crawl
+        crawler_config = CrawlerRunConfig(
+            markdown_generator=DefaultMarkdownGenerator(
+                content_filter=PruningContentFilter()
+            ),
+            capture_console_messages=True,  # Enable console capture to test adapter
+        )
+        
+        # Test on a site that typically detects bots
+        print("Testing undetected adapter...")
+        result: CrawlResult = await crawler.arun(
+            url="https://www.helloworld.org", 
+            config=crawler_config
+        )
+        
+        print(f"Status: {result.status_code}")
+        print(f"Success: {result.success}")
+        print(f"Console messages captured: {len(result.console_messages or [])}")
+        print(f"Markdown content (first 500 chars):\n{result.markdown.raw_markdown[:500]}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/link_head_extraction_example.py

@@ -18,7 +18,7 @@ Usage:
 
 import asyncio
 from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
-from crawl4ai.async_configs import LinkPreviewConfig
+from crawl4ai import LinkPreviewConfig
 
 
 async def basic_link_head_extraction():

docs/examples/llm_table_extraction_example.py

@@ -0,0 +1,356 @@
+#!/usr/bin/env python3
+"""
+Example demonstrating LLM-based table extraction in Crawl4AI.
+
+This example shows how to use the LLMTableExtraction strategy to extract
+complex tables from web pages, including handling rowspan, colspan, and nested tables.
+"""
+
+import os
+import sys
+
+# Get the grandparent directory
+grandparent_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(grandparent_dir)
+__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
+
+
+
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler,
+    CrawlerRunConfig,
+    LLMConfig,
+    LLMTableExtraction,
+    CacheMode
+)
+import pandas as pd
+
+
+# Example 1: Basic LLM Table Extraction
+async def basic_llm_extraction():
+    """Extract tables using LLM with default settings."""
+    print("\n=== Example 1: Basic LLM Table Extraction ===")
+    
+    # Configure LLM (using OpenAI GPT-4o-mini for cost efficiency)
+    llm_config = LLMConfig(
+        provider="openai/gpt-4.1-mini",
+        api_token="env:OPENAI_API_KEY",  # Uses environment variable
+        temperature=0.1,  # Low temperature for consistency
+        max_tokens=32000
+    )
+    
+    # Create LLM table extraction strategy
+    table_strategy = LLMTableExtraction(
+        llm_config=llm_config,
+        verbose=True,
+        # css_selector="div.mw-content-ltr",
+        max_tries=2,
+        enable_chunking=True,
+        chunk_token_threshold=5000,  # Lower threshold to force chunking
+        min_rows_per_chunk=10,
+        max_parallel_chunks=3
+    )
+    
+    # Configure crawler with the strategy
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        table_extraction=table_strategy
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        # Extract tables from a Wikipedia page
+        result = await crawler.arun(
+            url="https://en.wikipedia.org/wiki/List_of_chemical_elements",
+            config=config
+        )
+        
+        if result.success:
+            print(f"✓ Found {len(result.tables)} tables")
+            
+            # Display first table
+            if result.tables:
+                first_table = result.tables[0]
+                print(f"\nFirst table:")
+                print(f"  Headers: {first_table['headers'][:5]}...")
+                print(f"  Rows: {len(first_table['rows'])}")
+                
+                # Convert to pandas DataFrame
+                df = pd.DataFrame(
+                    first_table['rows'],
+                    columns=first_table['headers']
+                )
+                print(f"\nDataFrame shape: {df.shape}")
+                print(df.head())
+        else:
+            print(f"✗ Extraction failed: {result.error}")
+
+
+# Example 2: Focused Extraction with CSS Selector
+async def focused_extraction():
+    """Extract tables from specific page sections using CSS selectors."""
+    print("\n=== Example 2: Focused Extraction with CSS Selector ===")
+    
+    # HTML with multiple tables
+    test_html = """
+    <html>
+    <body>
+        <div class="sidebar">
+            <table role="presentation">
+                <tr><td>Navigation</td></tr>
+            </table>
+        </div>
+        
+        <div class="main-content">
+            <table id="data-table">
+                <caption>Quarterly Sales Report</caption>
+                <thead>
+                    <tr>
+                        <th rowspan="2">Product</th>
+                        <th colspan="3">Q1 2024</th>
+                    </tr>
+                    <tr>
+                        <th>Jan</th>
+                        <th>Feb</th>
+                        <th>Mar</th>
+                    </tr>
+                </thead>
+                <tbody>
+                    <tr>
+                        <td>Widget A</td>
+                        <td>100</td>
+                        <td>120</td>
+                        <td>140</td>
+                    </tr>
+                    <tr>
+                        <td>Widget B</td>
+                        <td>200</td>
+                        <td>180</td>
+                        <td>220</td>
+                    </tr>
+                </tbody>
+            </table>
+        </div>
+    </body>
+    </html>
+    """
+    
+    llm_config = LLMConfig(
+        provider="openai/gpt-4.1-mini",
+        api_token="env:OPENAI_API_KEY"
+    )
+    
+    # Focus only on main content area
+    table_strategy = LLMTableExtraction(
+        llm_config=llm_config,
+        css_selector=".main-content",  # Only extract from main content
+        verbose=True
+    )
+    
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        table_extraction=table_strategy
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url=f"raw:{test_html}",
+            config=config
+        )
+        
+        if result.success and result.tables:
+            table = result.tables[0]
+            print(f"✓ Extracted table: {table.get('caption', 'No caption')}")
+            print(f"  Headers: {table['headers']}")
+            print(f"  Metadata: {table['metadata']}")
+            
+            # The LLM should have handled the rowspan/colspan correctly
+            print("\nProcessed data (rowspan/colspan handled):")
+            for i, row in enumerate(table['rows']):
+                print(f"  Row {i+1}: {row}")
+
+
+# Example 3: Comparing with Default Extraction
+async def compare_strategies():
+    """Compare LLM extraction with default extraction on complex tables."""
+    print("\n=== Example 3: Comparing LLM vs Default Extraction ===")
+    
+    # Complex table with nested structure
+    complex_html = """
+    <html>
+    <body>
+        <table>
+            <tr>
+                <th rowspan="3">Category</th>
+                <th colspan="2">2023</th>
+                <th colspan="2">2024</th>
+            </tr>
+            <tr>
+                <th>H1</th>
+                <th>H2</th>
+                <th>H1</th>
+                <th>H2</th>
+            </tr>
+            <tr>
+                <td colspan="4">All values in millions</td>
+            </tr>
+            <tr>
+                <td>Revenue</td>
+                <td>100</td>
+                <td>120</td>
+                <td>130</td>
+                <td>145</td>
+            </tr>
+            <tr>
+                <td>Profit</td>
+                <td>20</td>
+                <td>25</td>
+                <td>28</td>
+                <td>32</td>
+            </tr>
+        </table>
+    </body>
+    </html>
+    """
+    
+    async with AsyncWebCrawler() as crawler:
+        # Test with default extraction
+        from crawl4ai import DefaultTableExtraction
+        
+        default_strategy = DefaultTableExtraction(
+            table_score_threshold=3,
+            verbose=True
+        )
+        
+        config_default = CrawlerRunConfig(
+            cache_mode=CacheMode.BYPASS,
+            table_extraction=default_strategy
+        )
+        
+        result_default = await crawler.arun(
+            url=f"raw:{complex_html}",
+            config=config_default
+        )
+        
+        # Test with LLM extraction
+        llm_strategy = LLMTableExtraction(
+            llm_config=LLMConfig(
+                provider="openai/gpt-4.1-mini",
+                api_token="env:OPENAI_API_KEY"
+            ),
+            verbose=True
+        )
+        
+        config_llm = CrawlerRunConfig(
+            cache_mode=CacheMode.BYPASS,
+            table_extraction=llm_strategy
+        )
+        
+        result_llm = await crawler.arun(
+            url=f"raw:{complex_html}",
+            config=config_llm
+        )
+        
+        # Compare results
+        print("\nDefault Extraction:")
+        if result_default.tables:
+            table = result_default.tables[0]
+            print(f"  Headers: {table.get('headers', [])}")
+            print(f"  Rows: {len(table.get('rows', []))}")
+            for i, row in enumerate(table.get('rows', [])[:3]):
+                print(f"    Row {i+1}: {row}")
+        
+        print("\nLLM Extraction (handles complex structure better):")
+        if result_llm.tables:
+            table = result_llm.tables[0]
+            print(f"  Headers: {table.get('headers', [])}")
+            print(f"  Rows: {len(table.get('rows', []))}")
+            for i, row in enumerate(table.get('rows', [])):
+                print(f"    Row {i+1}: {row}")
+            print(f"  Metadata: {table.get('metadata', {})}")
+
+# Example 4: Batch Processing Multiple Pages
+async def batch_extraction():
+    """Extract tables from multiple pages efficiently."""
+    print("\n=== Example 4: Batch Table Extraction ===")
+    
+    urls = [
+        "https://www.worldometers.info/geography/alphabetical-list-of-countries/",
+        # "https://en.wikipedia.org/wiki/List_of_chemical_elements",
+    ]
+    
+    llm_config = LLMConfig(
+        provider="openai/gpt-4.1-mini",
+        api_token="env:OPENAI_API_KEY",
+        temperature=0.1,
+        max_tokens=1500
+    )
+    
+    table_strategy = LLMTableExtraction(
+        llm_config=llm_config,
+        css_selector="div.datatable-container",  # Wikipedia data tables
+        verbose=False,
+        enable_chunking=True,
+        chunk_token_threshold=5000,  # Lower threshold to force chunking
+        min_rows_per_chunk=10,
+        max_parallel_chunks=3
+    )
+    
+    config = CrawlerRunConfig(
+        table_extraction=table_strategy,
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    all_tables = []
+    
+    async with AsyncWebCrawler() as crawler:
+        for url in urls:
+            print(f"\nProcessing: {url.split('/')[-1][:50]}...")
+            result = await crawler.arun(url=url, config=config)
+            
+            if result.success and result.tables:
+                print(f"  ✓ Found {len(result.tables)} tables")
+                # Store first table from each page
+                if result.tables:
+                    all_tables.append({
+                        'url': url,
+                        'table': result.tables[0]
+                    })
+    
+    # Summary
+    print(f"\n=== Summary ===")
+    print(f"Extracted {len(all_tables)} tables from {len(urls)} pages")
+    for item in all_tables:
+        table = item['table']
+        print(f"\nFrom {item['url'].split('/')[-1][:30]}:")
+        print(f"  Columns: {len(table['headers'])}")
+        print(f"  Rows: {len(table['rows'])}")
+
+
+async def main():
+    """Run all examples."""
+    print("=" * 60)
+    print("LLM TABLE EXTRACTION EXAMPLES")
+    print("=" * 60)
+    
+    # Run examples (comment out ones you don't want to run)
+    
+    # Basic extraction
+    await basic_llm_extraction()
+    
+    # # Focused extraction with CSS
+    # await focused_extraction()
+    
+    # # Compare strategies
+    # await compare_strategies()
+    
+    # # Batch processing
+    # await batch_extraction()
+    
+    print("\n" + "=" * 60)
+    print("ALL EXAMPLES COMPLETED")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/scraping_strategies_performance.py

@@ -1,5 +1,6 @@
 import time, re
-from crawl4ai.content_scraping_strategy import WebScrapingStrategy,  LXMLWebScrapingStrategy
+from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
+# WebScrapingStrategy is now an alias for LXMLWebScrapingStrategy
 import time
 import functools
 from collections import defaultdict
@@ -57,7 +58,7 @@ methods_to_profile = [
 
 
 # Apply decorators to both strategies
-for strategy, name in [(WebScrapingStrategy, "Original"), (LXMLWebScrapingStrategy, "LXML")]:
+for strategy, name in [(LXMLWebScrapingStrategy, "LXML")]:
     for method in methods_to_profile:
         apply_decorators(strategy, method, name)
 
@@ -85,7 +86,7 @@ def generate_large_html(n_elements=1000):
 
 def test_scraping():
     # Initialize both scrapers
-    original_scraper = WebScrapingStrategy()
+    original_scraper = LXMLWebScrapingStrategy()
     selected_scraper = LXMLWebScrapingStrategy()
     
     # Generate test HTML

docs/examples/simple_anti_bot_examples.py

@@ -0,0 +1,59 @@
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, UndetectedAdapter
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+# Example 1: Stealth Mode
+async def stealth_mode_example():
+    browser_config = BrowserConfig(
+        enable_stealth=True,
+        headless=False
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun("https://example.com")
+        return result.html[:500]
+
+# Example 2: Undetected Browser
+async def undetected_browser_example():
+    browser_config = BrowserConfig(
+        headless=False
+    )
+    
+    adapter = UndetectedAdapter()
+    strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=adapter
+    )
+    
+    async with AsyncWebCrawler(
+        crawler_strategy=strategy,
+        config=browser_config
+    ) as crawler:
+        result = await crawler.arun("https://example.com")
+        return result.html[:500]
+
+# Example 3: Both Combined
+async def combined_example():
+    browser_config = BrowserConfig(
+        enable_stealth=True,
+        headless=False
+    )
+    
+    adapter = UndetectedAdapter()
+    strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=adapter
+    )
+    
+    async with AsyncWebCrawler(
+        crawler_strategy=strategy,
+        config=browser_config
+    ) as crawler:
+        result = await crawler.arun("https://example.com")
+        return result.html[:500]
+
+# Run examples
+if __name__ == "__main__":
+    asyncio.run(stealth_mode_example())
+    asyncio.run(undetected_browser_example())
+    asyncio.run(combined_example())

docs/examples/stealth_mode_example.py

@@ -0,0 +1,522 @@
+"""
+Stealth Mode Example with Crawl4AI
+
+This example demonstrates how to use the stealth mode feature to bypass basic bot detection.
+The stealth mode uses playwright-stealth to modify browser fingerprints and behaviors
+that are commonly used to detect automated browsers.
+
+Key features demonstrated:
+1. Comparing crawling with and without stealth mode
+2. Testing against bot detection sites
+3. Accessing sites that block automated browsers
+4. Best practices for stealth crawling
+"""
+
+import asyncio
+import json
+from typing import Dict, Any
+from colorama import Fore, Style, init
+
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai.async_logger import AsyncLogger
+
+# Initialize colorama for colored output
+init()
+
+# Create a logger for better output
+logger = AsyncLogger(verbose=True)
+
+
+async def test_bot_detection(use_stealth: bool = False) -> Dict[str, Any]:
+    """Test against a bot detection service"""
+    
+    logger.info(
+        f"Testing bot detection with stealth={'ON' if use_stealth else 'OFF'}",
+        tag="STEALTH"
+    )
+    
+    # Configure browser with or without stealth
+    browser_config = BrowserConfig(
+        headless=False,  # Use False to see the browser in action
+        enable_stealth=use_stealth,
+        viewport_width=1280,
+        viewport_height=800
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        # JavaScript to extract bot detection results
+        detection_script = """
+        // Comprehensive bot detection checks
+        (() => {
+        const detectionResults = {
+            // Basic WebDriver detection
+            webdriver: navigator.webdriver,
+            
+            // Chrome specific
+            chrome: !!window.chrome,
+            chromeRuntime: !!window.chrome?.runtime,
+            
+            // Automation indicators
+            automationControlled: navigator.webdriver,
+            
+            // Permissions API
+            permissionsPresent: !!navigator.permissions?.query,
+            
+            // Plugins
+            pluginsLength: navigator.plugins.length,
+            pluginsArray: Array.from(navigator.plugins).map(p => p.name),
+            
+            // Languages
+            languages: navigator.languages,
+            language: navigator.language,
+            
+            // User agent
+            userAgent: navigator.userAgent,
+            
+            // Screen and window properties
+            screen: {
+                width: screen.width,
+                height: screen.height,
+                availWidth: screen.availWidth,
+                availHeight: screen.availHeight,
+                colorDepth: screen.colorDepth,
+                pixelDepth: screen.pixelDepth
+            },
+            
+            // WebGL vendor
+            webglVendor: (() => {
+                try {
+                    const canvas = document.createElement('canvas');
+                    const gl = canvas.getContext('webgl') || canvas.getContext('experimental-webgl');
+                    const ext = gl.getExtension('WEBGL_debug_renderer_info');
+                    return gl.getParameter(ext.UNMASKED_VENDOR_WEBGL);
+                } catch (e) {
+                    return 'Error';
+                }
+            })(),
+            
+            // Platform
+            platform: navigator.platform,
+            
+            // Hardware concurrency
+            hardwareConcurrency: navigator.hardwareConcurrency,
+            
+            // Device memory
+            deviceMemory: navigator.deviceMemory,
+            
+            // Connection
+            connection: navigator.connection?.effectiveType
+        };
+        
+        // Log results for console capture
+        console.log('DETECTION_RESULTS:', JSON.stringify(detectionResults, null, 2));
+        
+        // Return results
+        return detectionResults;
+        })();
+        """
+        
+        # Crawl bot detection test page
+        config = CrawlerRunConfig(
+            js_code=detection_script,
+            capture_console_messages=True,
+            wait_until="networkidle",
+            delay_before_return_html=2.0  # Give time for all checks to complete
+        )
+        
+        result = await crawler.arun(
+            url="https://bot.sannysoft.com",
+            config=config
+        )
+        
+        if result.success:
+            # Extract detection results from console
+            detection_data = None
+            for msg in result.console_messages or []:
+                if "DETECTION_RESULTS:" in msg.get("text", ""):
+                    try:
+                        json_str = msg["text"].replace("DETECTION_RESULTS:", "").strip()
+                        detection_data = json.loads(json_str)
+                    except:
+                        pass
+            
+            # Also try to get from JavaScript execution result
+            if not detection_data and result.js_execution_result:
+                detection_data = result.js_execution_result
+            
+            return {
+                "success": True,
+                "url": result.url,
+                "detection_data": detection_data,
+                "page_title": result.metadata.get("title", ""),
+                "stealth_enabled": use_stealth
+            }
+        else:
+            return {
+                "success": False,
+                "error": result.error_message,
+                "stealth_enabled": use_stealth
+            }
+
+
+async def test_cloudflare_site(use_stealth: bool = False) -> Dict[str, Any]:
+    """Test accessing a Cloudflare-protected site"""
+    
+    logger.info(
+        f"Testing Cloudflare site with stealth={'ON' if use_stealth else 'OFF'}",
+        tag="STEALTH"
+    )
+    
+    browser_config = BrowserConfig(
+        headless=True,  # Cloudflare detection works better in headless mode with stealth
+        enable_stealth=use_stealth,
+        viewport_width=1920,
+        viewport_height=1080
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        config = CrawlerRunConfig(
+            wait_until="networkidle",
+            page_timeout=30000,  # 30 seconds
+            delay_before_return_html=3.0
+        )
+        
+        # Test on a site that often shows Cloudflare challenges
+        result = await crawler.arun(
+            url="https://nowsecure.nl",
+            config=config
+        )
+        
+        # Check if we hit Cloudflare challenge
+        cloudflare_detected = False
+        if result.html:
+            cloudflare_indicators = [
+                "Checking your browser",
+                "Just a moment",
+                "cf-browser-verification",
+                "cf-challenge",
+                "ray ID"
+            ]
+            cloudflare_detected = any(indicator in result.html for indicator in cloudflare_indicators)
+        
+        return {
+            "success": result.success,
+            "url": result.url,
+            "cloudflare_challenge": cloudflare_detected,
+            "status_code": result.status_code,
+            "page_title": result.metadata.get("title", "") if result.metadata else "",
+            "stealth_enabled": use_stealth,
+            "html_snippet": result.html[:500] if result.html else ""
+        }
+
+
+async def test_anti_bot_site(use_stealth: bool = False) -> Dict[str, Any]:
+    """Test against sites with anti-bot measures"""
+    
+    logger.info(
+        f"Testing anti-bot site with stealth={'ON' if use_stealth else 'OFF'}",
+        tag="STEALTH"
+    )
+    
+    browser_config = BrowserConfig(
+        headless=False,
+        enable_stealth=use_stealth,
+        # Additional browser arguments that help with stealth
+        extra_args=[
+            "--disable-blink-features=AutomationControlled",
+            "--disable-features=site-per-process"
+        ] if not use_stealth else []  # These are automatically applied with stealth
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        # Some sites check for specific behaviors
+        behavior_script = """
+        (async () => {
+            // Simulate human-like behavior
+            const sleep = ms => new Promise(resolve => setTimeout(resolve, ms));
+            
+            // Random mouse movement
+            const moveX = Math.random() * 100;
+            const moveY = Math.random() * 100;
+            
+            // Simulate reading time
+            await sleep(1000 + Math.random() * 2000);
+            
+            // Scroll slightly
+            window.scrollBy(0, 100 + Math.random() * 200);
+            
+            console.log('Human behavior simulation complete');
+            return true;
+        })()
+        """
+        
+        config = CrawlerRunConfig(
+            js_code=behavior_script,
+            wait_until="networkidle",
+            delay_before_return_html=5.0,  # Longer delay to appear more human
+            capture_console_messages=True
+        )
+        
+        # Test on a site that implements anti-bot measures
+        result = await crawler.arun(
+            url="https://www.g2.com/",
+            config=config
+        )
+        
+        # Check for common anti-bot blocks
+        blocked_indicators = [
+            "Access Denied",
+            "403 Forbidden", 
+            "Security Check",
+            "Verify you are human",
+            "captcha",
+            "challenge"
+        ]
+        
+        blocked = False
+        if result.html:
+            blocked = any(indicator.lower() in result.html.lower() for indicator in blocked_indicators)
+        
+        return {
+            "success": result.success and not blocked,
+            "url": result.url,
+            "blocked": blocked,
+            "status_code": result.status_code,
+            "page_title": result.metadata.get("title", "") if result.metadata else "",
+            "stealth_enabled": use_stealth
+        }
+
+
+async def compare_results():
+    """Run all tests with and without stealth mode and compare results"""
+    
+    print(f"\n{Fore.CYAN}{'='*60}{Style.RESET_ALL}")
+    print(f"{Fore.CYAN}Crawl4AI Stealth Mode Comparison{Style.RESET_ALL}")
+    print(f"{Fore.CYAN}{'='*60}{Style.RESET_ALL}\n")
+    
+    # Test 1: Bot Detection
+    print(f"{Fore.YELLOW}1. Bot Detection Test (bot.sannysoft.com){Style.RESET_ALL}")
+    print("-" * 40)
+    
+    # Without stealth
+    regular_detection = await test_bot_detection(use_stealth=False)
+    if regular_detection["success"] and regular_detection["detection_data"]:
+        print(f"{Fore.RED}Without Stealth:{Style.RESET_ALL}")
+        data = regular_detection["detection_data"]
+        print(f"  • WebDriver detected: {data.get('webdriver', 'Unknown')}")
+        print(f"  • Chrome: {data.get('chrome', 'Unknown')}")
+        print(f"  • Languages: {data.get('languages', 'Unknown')}")
+        print(f"  • Plugins: {data.get('pluginsLength', 'Unknown')}")
+        print(f"  • User Agent: {data.get('userAgent', 'Unknown')[:60]}...")
+    
+    # With stealth
+    stealth_detection = await test_bot_detection(use_stealth=True)
+    if stealth_detection["success"] and stealth_detection["detection_data"]:
+        print(f"\n{Fore.GREEN}With Stealth:{Style.RESET_ALL}")
+        data = stealth_detection["detection_data"]
+        print(f"  • WebDriver detected: {data.get('webdriver', 'Unknown')}")
+        print(f"  • Chrome: {data.get('chrome', 'Unknown')}")
+        print(f"  • Languages: {data.get('languages', 'Unknown')}")
+        print(f"  • Plugins: {data.get('pluginsLength', 'Unknown')}")
+        print(f"  • User Agent: {data.get('userAgent', 'Unknown')[:60]}...")
+    
+    # Test 2: Cloudflare Site
+    print(f"\n\n{Fore.YELLOW}2. Cloudflare Protected Site Test{Style.RESET_ALL}")
+    print("-" * 40)
+    
+    # Without stealth
+    regular_cf = await test_cloudflare_site(use_stealth=False)
+    print(f"{Fore.RED}Without Stealth:{Style.RESET_ALL}")
+    print(f"  • Success: {regular_cf['success']}")
+    print(f"  • Cloudflare Challenge: {regular_cf['cloudflare_challenge']}")
+    print(f"  • Status Code: {regular_cf['status_code']}")
+    print(f"  • Page Title: {regular_cf['page_title']}")
+    
+    # With stealth
+    stealth_cf = await test_cloudflare_site(use_stealth=True)
+    print(f"\n{Fore.GREEN}With Stealth:{Style.RESET_ALL}")
+    print(f"  • Success: {stealth_cf['success']}")
+    print(f"  • Cloudflare Challenge: {stealth_cf['cloudflare_challenge']}")
+    print(f"  • Status Code: {stealth_cf['status_code']}")
+    print(f"  • Page Title: {stealth_cf['page_title']}")
+    
+    # Test 3: Anti-bot Site
+    print(f"\n\n{Fore.YELLOW}3. Anti-Bot Site Test{Style.RESET_ALL}")
+    print("-" * 40)
+    
+    # Without stealth
+    regular_antibot = await test_anti_bot_site(use_stealth=False)
+    print(f"{Fore.RED}Without Stealth:{Style.RESET_ALL}")
+    print(f"  • Success: {regular_antibot['success']}")
+    print(f"  • Blocked: {regular_antibot['blocked']}")
+    print(f"  • Status Code: {regular_antibot['status_code']}")
+    print(f"  • Page Title: {regular_antibot['page_title']}")
+    
+    # With stealth
+    stealth_antibot = await test_anti_bot_site(use_stealth=True)
+    print(f"\n{Fore.GREEN}With Stealth:{Style.RESET_ALL}")
+    print(f"  • Success: {stealth_antibot['success']}")
+    print(f"  • Blocked: {stealth_antibot['blocked']}")
+    print(f"  • Status Code: {stealth_antibot['status_code']}")
+    print(f"  • Page Title: {stealth_antibot['page_title']}")
+    
+    # Summary
+    print(f"\n{Fore.CYAN}{'='*60}{Style.RESET_ALL}")
+    print(f"{Fore.CYAN}Summary:{Style.RESET_ALL}")
+    print(f"{Fore.CYAN}{'='*60}{Style.RESET_ALL}")
+    print(f"\nStealth mode helps bypass basic bot detection by:")
+    print(f"  • Hiding webdriver property")
+    print(f"  • Modifying browser fingerprints")
+    print(f"  • Adjusting navigator properties")
+    print(f"  • Emulating real browser plugin behavior")
+    print(f"\n{Fore.YELLOW}Note:{Style.RESET_ALL} Stealth mode is not a silver bullet.")
+    print(f"Advanced anti-bot systems may still detect automation.")
+    print(f"Always respect robots.txt and website terms of service.")
+
+
+async def stealth_best_practices():
+    """Demonstrate best practices for using stealth mode"""
+    
+    print(f"\n\n{Fore.CYAN}{'='*60}{Style.RESET_ALL}")
+    print(f"{Fore.CYAN}Stealth Mode Best Practices{Style.RESET_ALL}")
+    print(f"{Fore.CYAN}{'='*60}{Style.RESET_ALL}\n")
+    
+    # Best Practice 1: Combine with realistic behavior
+    print(f"{Fore.YELLOW}1. Combine with Realistic Behavior:{Style.RESET_ALL}")
+    
+    browser_config = BrowserConfig(
+        headless=False,
+        enable_stealth=True,
+        viewport_width=1920,
+        viewport_height=1080
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        # Simulate human-like behavior
+        human_behavior_script = """
+        (async () => {
+            // Wait random time between actions
+            const randomWait = () => Math.random() * 2000 + 1000;
+            
+            // Simulate reading
+            await new Promise(resolve => setTimeout(resolve, randomWait()));
+            
+            // Smooth scroll
+            const smoothScroll = async () => {
+                const totalHeight = document.body.scrollHeight;
+                const viewHeight = window.innerHeight;
+                let currentPosition = 0;
+                
+                while (currentPosition < totalHeight - viewHeight) {
+                    const scrollAmount = Math.random() * 300 + 100;
+                    window.scrollBy({
+                        top: scrollAmount,
+                        behavior: 'smooth'
+                    });
+                    currentPosition += scrollAmount;
+                    await new Promise(resolve => setTimeout(resolve, randomWait()));
+                }
+            };
+            
+            await smoothScroll();
+            console.log('Human-like behavior simulation completed');
+            return true;
+        })()
+        """
+        
+        config = CrawlerRunConfig(
+            js_code=human_behavior_script,
+            wait_until="networkidle",
+            delay_before_return_html=3.0,
+            capture_console_messages=True
+        )
+        
+        result = await crawler.arun(
+            url="https://example.com",
+            config=config
+        )
+        
+        print(f"  ✓ Simulated human-like scrolling and reading patterns")
+        print(f"  ✓ Added random delays between actions")
+        print(f"  ✓ Result: {result.success}")
+    
+    # Best Practice 2: Use appropriate viewport and user agent
+    print(f"\n{Fore.YELLOW}2. Use Realistic Viewport and User Agent:{Style.RESET_ALL}")
+    
+    # Get a realistic user agent
+    from crawl4ai.user_agent_generator import UserAgentGenerator
+    ua_generator = UserAgentGenerator()
+    
+    browser_config = BrowserConfig(
+        headless=True,
+        enable_stealth=True,
+        viewport_width=1920,
+        viewport_height=1080,
+        user_agent=ua_generator.generate(device_type="desktop", browser_type="chrome")
+    )
+    
+    print(f"  ✓ Using realistic viewport: 1920x1080")
+    print(f"  ✓ Using current Chrome user agent")
+    print(f"  ✓ Stealth mode will ensure consistency")
+    
+    # Best Practice 3: Manage request rate
+    print(f"\n{Fore.YELLOW}3. Manage Request Rate:{Style.RESET_ALL}")
+    print(f"  ✓ Add delays between requests")
+    print(f"  ✓ Randomize timing patterns")
+    print(f"  ✓ Respect robots.txt")
+    
+    # Best Practice 4: Session management
+    print(f"\n{Fore.YELLOW}4. Use Session Management:{Style.RESET_ALL}")
+    
+    browser_config = BrowserConfig(
+        headless=False,
+        enable_stealth=True
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        # Create a session for multiple requests
+        session_id = "stealth_session_1"
+        
+        config = CrawlerRunConfig(
+            session_id=session_id,
+            wait_until="domcontentloaded"
+        )
+        
+        # First request
+        result1 = await crawler.arun(
+            url="https://example.com",
+            config=config
+        )
+        
+        # Subsequent request reuses the same browser context
+        result2 = await crawler.arun(
+            url="https://example.com/about",
+            config=config
+        )
+        
+        print(f"  ✓ Reused browser session for multiple requests")
+        print(f"  ✓ Maintains cookies and state between requests")
+        print(f"  ✓ More efficient and realistic browsing pattern")
+    
+    print(f"\n{Fore.CYAN}{'='*60}{Style.RESET_ALL}")
+
+
+async def main():
+    """Run all examples"""
+    
+    # Run comparison tests
+    await compare_results()
+    
+    # Show best practices
+    await stealth_best_practices()
+    
+    print(f"\n{Fore.GREEN}Examples completed!{Style.RESET_ALL}")
+    print(f"\n{Fore.YELLOW}Remember:{Style.RESET_ALL}")
+    print(f"• Stealth mode helps with basic bot detection")
+    print(f"• Always respect website terms of service")
+    print(f"• Consider rate limiting and ethical scraping practices")
+    print(f"• For advanced protection, consider additional measures")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/stealth_mode_quick_start.py

@@ -0,0 +1,215 @@
+"""
+Quick Start: Using Stealth Mode in Crawl4AI
+
+This example shows practical use cases for the stealth mode feature.
+Stealth mode helps bypass basic bot detection mechanisms.
+"""
+
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+
+
+async def example_1_basic_stealth():
+    """Example 1: Basic stealth mode usage"""
+    print("\n=== Example 1: Basic Stealth Mode ===")
+    
+    # Enable stealth mode in browser config
+    browser_config = BrowserConfig(
+        enable_stealth=True,  # This is the key parameter
+        headless=True
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(url="https://example.com")
+        print(f"✓ Crawled {result.url} successfully")
+        print(f"✓ Title: {result.metadata.get('title', 'N/A')}")
+
+
+async def example_2_stealth_with_screenshot():
+    """Example 2: Stealth mode with screenshot to show detection results"""
+    print("\n=== Example 2: Stealth Mode Visual Verification ===")
+    
+    browser_config = BrowserConfig(
+        enable_stealth=True,
+        headless=False  # Set to False to see the browser
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        config = CrawlerRunConfig(
+            screenshot=True,
+            wait_until="networkidle"
+        )
+        
+        result = await crawler.arun(
+            url="https://bot.sannysoft.com",
+            config=config
+        )
+        
+        if result.success:
+            print(f"✓ Successfully crawled bot detection site")
+            print(f"✓ With stealth enabled, many detection tests should show as passed")
+            
+            if result.screenshot:
+                # Save screenshot for verification
+                import base64
+                with open("stealth_detection_results.png", "wb") as f:
+                    f.write(base64.b64decode(result.screenshot))
+                print(f"✓ Screenshot saved as 'stealth_detection_results.png'")
+                print(f"  Check the screenshot to see detection results!")
+
+
+async def example_3_stealth_for_protected_sites():
+    """Example 3: Using stealth for sites with bot protection"""
+    print("\n=== Example 3: Stealth for Protected Sites ===")
+    
+    browser_config = BrowserConfig(
+        enable_stealth=True,
+        headless=True,
+        viewport_width=1920,
+        viewport_height=1080
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        # Add human-like behavior
+        config = CrawlerRunConfig(
+            wait_until="networkidle",
+            delay_before_return_html=2.0,  # Wait 2 seconds
+            js_code="""
+            // Simulate human-like scrolling
+            window.scrollTo({
+                top: document.body.scrollHeight / 2,
+                behavior: 'smooth'
+            });
+            """
+        )
+        
+        # Try accessing a site that might have bot protection
+        result = await crawler.arun(
+            url="https://www.g2.com/products/slack/reviews",
+            config=config
+        )
+        
+        if result.success:
+            print(f"✓ Successfully accessed protected site")
+            print(f"✓ Retrieved {len(result.html)} characters of HTML")
+        else:
+            print(f"✗ Failed to access site: {result.error_message}")
+
+
+async def example_4_stealth_with_sessions():
+    """Example 4: Stealth mode with session management"""
+    print("\n=== Example 4: Stealth + Session Management ===")
+    
+    browser_config = BrowserConfig(
+        enable_stealth=True,
+        headless=False
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        session_id = "my_stealth_session"
+        
+        # First request - establish session
+        config = CrawlerRunConfig(
+            session_id=session_id,
+            wait_until="domcontentloaded"
+        )
+        
+        result1 = await crawler.arun(
+            url="https://news.ycombinator.com",
+            config=config
+        )
+        print(f"✓ First request completed: {result1.url}")
+        
+        # Second request - reuse session
+        await asyncio.sleep(2)  # Brief delay between requests
+        
+        result2 = await crawler.arun(
+            url="https://news.ycombinator.com/best",
+            config=config
+        )
+        print(f"✓ Second request completed: {result2.url}")
+        print(f"✓ Session reused, maintaining cookies and state")
+
+
+async def example_5_stealth_comparison():
+    """Example 5: Compare results with and without stealth using screenshots"""
+    print("\n=== Example 5: Stealth Mode Comparison ===")
+    
+    test_url = "https://bot.sannysoft.com"
+    
+    # First test WITHOUT stealth
+    print("\nWithout stealth:")
+    regular_config = BrowserConfig(
+        enable_stealth=False,
+        headless=True
+    )
+    
+    async with AsyncWebCrawler(config=regular_config) as crawler:
+        config = CrawlerRunConfig(
+            screenshot=True,
+            wait_until="networkidle"
+        )
+        result = await crawler.arun(url=test_url, config=config)
+        
+        if result.success and result.screenshot:
+            import base64
+            with open("comparison_without_stealth.png", "wb") as f:
+                f.write(base64.b64decode(result.screenshot))
+            print(f"  ✓ Screenshot saved: comparison_without_stealth.png")
+            print(f"  Many tests will show as FAILED (red)")
+    
+    # Then test WITH stealth
+    print("\nWith stealth:")
+    stealth_config = BrowserConfig(
+        enable_stealth=True,
+        headless=True
+    )
+    
+    async with AsyncWebCrawler(config=stealth_config) as crawler:
+        config = CrawlerRunConfig(
+            screenshot=True,
+            wait_until="networkidle"
+        )
+        result = await crawler.arun(url=test_url, config=config)
+        
+        if result.success and result.screenshot:
+            import base64
+            with open("comparison_with_stealth.png", "wb") as f:
+                f.write(base64.b64decode(result.screenshot))
+            print(f"  ✓ Screenshot saved: comparison_with_stealth.png")
+            print(f"  More tests should show as PASSED (green)")
+    
+    print("\nCompare the two screenshots to see the difference!")
+
+
+async def main():
+    """Run all examples"""
+    print("Crawl4AI Stealth Mode Examples")
+    print("==============================")
+    
+    # Run basic example
+    await example_1_basic_stealth()
+    
+    # Run screenshot verification example
+    await example_2_stealth_with_screenshot()
+    
+    # Run protected site example
+    await example_3_stealth_for_protected_sites()
+    
+    # Run session example
+    await example_4_stealth_with_sessions()
+    
+    # Run comparison example
+    await example_5_stealth_comparison()
+    
+    print("\n" + "="*50)
+    print("Tips for using stealth mode effectively:")
+    print("- Use realistic viewport sizes (1920x1080, 1366x768)")
+    print("- Add delays between requests to appear more human")
+    print("- Combine with session management for better results")
+    print("- Remember: stealth mode is for legitimate scraping only")
+    print("="*50)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/stealth_test_simple.py

@@ -0,0 +1,62 @@
+"""
+Simple test to verify stealth mode is working
+"""
+
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+
+
+async def test_stealth():
+    """Test stealth mode effectiveness"""
+    
+    # Test WITHOUT stealth
+    print("=== WITHOUT Stealth ===")
+    config1 = BrowserConfig(
+        headless=False,
+        enable_stealth=False
+    )
+    
+    async with AsyncWebCrawler(config=config1) as crawler:
+        result = await crawler.arun(
+            url="https://bot.sannysoft.com",
+            config=CrawlerRunConfig(
+                wait_until="networkidle",
+                screenshot=True
+            )
+        )
+        print(f"Success: {result.success}")
+        # Take screenshot
+        if result.screenshot:
+            with open("without_stealth.png", "wb") as f:
+                import base64
+                f.write(base64.b64decode(result.screenshot))
+            print("Screenshot saved: without_stealth.png")
+    
+    # Test WITH stealth
+    print("\n=== WITH Stealth ===")
+    config2 = BrowserConfig(
+        headless=False,
+        enable_stealth=True
+    )
+    
+    async with AsyncWebCrawler(config=config2) as crawler:
+        result = await crawler.arun(
+            url="https://bot.sannysoft.com",
+            config=CrawlerRunConfig(
+                wait_until="networkidle",
+                screenshot=True
+            )
+        )
+        print(f"Success: {result.success}")
+        # Take screenshot
+        if result.screenshot:
+            with open("with_stealth.png", "wb") as f:
+                import base64
+                f.write(base64.b64decode(result.screenshot))
+            print("Screenshot saved: with_stealth.png")
+    
+    print("\nCheck the screenshots to see the difference in bot detection results!")
+
+
+if __name__ == "__main__":
+    asyncio.run(test_stealth())

docs/examples/table_extraction_example.py

@@ -0,0 +1,276 @@
+"""
+Example: Using Table Extraction Strategies in Crawl4AI
+
+This example demonstrates how to use different table extraction strategies
+to extract tables from web pages.
+"""
+
+import asyncio
+import pandas as pd
+from crawl4ai import (
+    AsyncWebCrawler,
+    CrawlerRunConfig,
+    CacheMode,
+    DefaultTableExtraction,
+    NoTableExtraction,
+    TableExtractionStrategy
+)
+from typing import Dict, List, Any
+
+
+async def example_default_extraction():
+    """Example 1: Using default table extraction (automatic)."""
+    print("\n" + "="*50)
+    print("Example 1: Default Table Extraction")
+    print("="*50)
+    
+    async with AsyncWebCrawler() as crawler:
+        # No need to specify table_extraction - uses DefaultTableExtraction automatically
+        config = CrawlerRunConfig(
+            cache_mode=CacheMode.BYPASS,
+            table_score_threshold=7  # Adjust sensitivity (default: 7)
+        )
+        
+        result = await crawler.arun(
+            "https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)",
+            config=config
+        )
+        
+        if result.success and result.tables:
+            print(f"Found {len(result.tables)} tables")
+            
+            # Convert first table to pandas DataFrame
+            if result.tables:
+                first_table = result.tables[0]
+                df = pd.DataFrame(
+                    first_table['rows'],
+                    columns=first_table['headers'] if first_table['headers'] else None
+                )
+                print(f"\nFirst table preview:")
+                print(df.head())
+                print(f"Shape: {df.shape}")
+
+
+async def example_custom_configuration():
+    """Example 2: Custom table extraction configuration."""
+    print("\n" + "="*50)
+    print("Example 2: Custom Table Configuration")
+    print("="*50)
+    
+    async with AsyncWebCrawler() as crawler:
+        # Create custom extraction strategy with specific settings
+        table_strategy = DefaultTableExtraction(
+            table_score_threshold=5,  # Lower threshold for more permissive detection
+            min_rows=3,  # Only extract tables with at least 3 rows
+            min_cols=2,  # Only extract tables with at least 2 columns
+            verbose=True
+        )
+        
+        config = CrawlerRunConfig(
+            cache_mode=CacheMode.BYPASS,
+            table_extraction=table_strategy,
+            # Target specific tables using CSS selector
+            css_selector="div.main-content"
+        )
+        
+        result = await crawler.arun(
+            "https://example.com/data",
+            config=config
+        )
+        
+        if result.success:
+            print(f"Found {len(result.tables)} tables matching criteria")
+            
+            for i, table in enumerate(result.tables):
+                print(f"\nTable {i+1}:")
+                print(f"  Caption: {table.get('caption', 'No caption')}")
+                print(f"  Size: {table['metadata']['row_count']} rows × {table['metadata']['column_count']} columns")
+                print(f"  Has headers: {table['metadata']['has_headers']}")
+
+
+async def example_disable_extraction():
+    """Example 3: Disable table extraction when not needed."""
+    print("\n" + "="*50)
+    print("Example 3: Disable Table Extraction")
+    print("="*50)
+    
+    async with AsyncWebCrawler() as crawler:
+        # Use NoTableExtraction to skip table processing entirely
+        config = CrawlerRunConfig(
+            cache_mode=CacheMode.BYPASS,
+            table_extraction=NoTableExtraction()  # No tables will be extracted
+        )
+        
+        result = await crawler.arun(
+            "https://example.com",
+            config=config
+        )
+        
+        if result.success:
+            print(f"Tables extracted: {len(result.tables)} (should be 0)")
+            print("Table extraction disabled - better performance for non-table content")
+
+
+class FinancialTableExtraction(TableExtractionStrategy):
+    """
+    Custom strategy for extracting financial tables with specific requirements.
+    """
+    
+    def __init__(self, currency_symbols=None, **kwargs):
+        super().__init__(**kwargs)
+        self.currency_symbols = currency_symbols or ['$', '€', '£', '¥']
+    
+    def extract_tables(self, element, **kwargs):
+        """Extract only tables that appear to contain financial data."""
+        tables_data = []
+        
+        for table in element.xpath(".//table"):
+            # Check if table contains currency symbols
+            table_text = ''.join(table.itertext())
+            has_currency = any(symbol in table_text for symbol in self.currency_symbols)
+            
+            if not has_currency:
+                continue
+            
+            # Extract using base logic (could reuse DefaultTableExtraction logic)
+            headers = []
+            rows = []
+            
+            # Extract headers
+            for th in table.xpath(".//thead//th | .//tr[1]//th"):
+                headers.append(th.text_content().strip())
+            
+            # Extract rows
+            for tr in table.xpath(".//tbody//tr | .//tr[position()>1]"):
+                row = []
+                for td in tr.xpath(".//td"):
+                    cell_text = td.text_content().strip()
+                    # Clean currency values
+                    for symbol in self.currency_symbols:
+                        cell_text = cell_text.replace(symbol, '')
+                    row.append(cell_text)
+                if row:
+                    rows.append(row)
+            
+            if headers or rows:
+                tables_data.append({
+                    "headers": headers,
+                    "rows": rows,
+                    "caption": table.xpath(".//caption/text()")[0] if table.xpath(".//caption") else "",
+                    "summary": table.get("summary", ""),
+                    "metadata": {
+                        "type": "financial",
+                        "has_currency": True,
+                        "row_count": len(rows),
+                        "column_count": len(headers) if headers else len(rows[0]) if rows else 0
+                    }
+                })
+        
+        return tables_data
+
+
+async def example_custom_strategy():
+    """Example 4: Custom table extraction strategy."""
+    print("\n" + "="*50)
+    print("Example 4: Custom Financial Table Strategy")
+    print("="*50)
+    
+    async with AsyncWebCrawler() as crawler:
+        # Use custom strategy for financial tables
+        config = CrawlerRunConfig(
+            cache_mode=CacheMode.BYPASS,
+            table_extraction=FinancialTableExtraction(
+                currency_symbols=['$', '€'],
+                verbose=True
+            )
+        )
+        
+        result = await crawler.arun(
+            "https://finance.yahoo.com/",
+            config=config
+        )
+        
+        if result.success:
+            print(f"Found {len(result.tables)} financial tables")
+            
+            for table in result.tables:
+                if table['metadata'].get('type') == 'financial':
+                    print(f"  ✓ Financial table with {table['metadata']['row_count']} rows")
+
+
+async def example_combined_extraction():
+    """Example 5: Combine table extraction with other strategies."""
+    print("\n" + "="*50)
+    print("Example 5: Combined Extraction Strategies")
+    print("="*50)
+    
+    from crawl4ai import LLMExtractionStrategy, LLMConfig
+    
+    async with AsyncWebCrawler() as crawler:
+        # Define schema for structured extraction
+        schema = {
+            "type": "object",
+            "properties": {
+                "page_title": {"type": "string"},
+                "main_topic": {"type": "string"},
+                "key_figures": {
+                    "type": "array",
+                    "items": {"type": "string"}
+                }
+            }
+        }
+        
+        config = CrawlerRunConfig(
+            cache_mode=CacheMode.BYPASS,
+            # Table extraction
+            table_extraction=DefaultTableExtraction(
+                table_score_threshold=6,
+                min_rows=2
+            ),
+            # LLM extraction for structured data
+            extraction_strategy=LLMExtractionStrategy(
+                llm_config=LLMConfig(provider="openai"),
+                schema=schema
+            )
+        )
+        
+        result = await crawler.arun(
+            "https://en.wikipedia.org/wiki/Economy_of_the_United_States",
+            config=config
+        )
+        
+        if result.success:
+            print(f"Tables found: {len(result.tables)}")
+            
+            # Tables are in result.tables
+            if result.tables:
+                print(f"First table has {len(result.tables[0]['rows'])} rows")
+            
+            # Structured data is in result.extracted_content
+            if result.extracted_content:
+                import json
+                structured_data = json.loads(result.extracted_content)
+                print(f"Page title: {structured_data.get('page_title', 'N/A')}")
+                print(f"Main topic: {structured_data.get('main_topic', 'N/A')}")
+
+
+async def main():
+    """Run all examples."""
+    print("\n" + "="*60)
+    print("CRAWL4AI TABLE EXTRACTION EXAMPLES")
+    print("="*60)
+    
+    # Run examples
+    await example_default_extraction()
+    await example_custom_configuration()
+    await example_disable_extraction()
+    await example_custom_strategy()
+    # await example_combined_extraction()  # Requires OpenAI API key
+    
+    print("\n" + "="*60)
+    print("EXAMPLES COMPLETED")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/undetectability/undetected_basic_test.py

@@ -0,0 +1,74 @@
+"""
+Basic Undetected Browser Test
+Simple example to test if undetected mode works
+"""
+
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig
+
+async def test_regular_mode():
+    """Test with regular browser"""
+    print("Testing Regular Browser Mode...")
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(url="https://www.example.com")
+        print(f"Regular Mode - Success: {result.success}")
+        print(f"Regular Mode - Status: {result.status_code}")
+        print(f"Regular Mode - Content length: {len(result.markdown.raw_markdown)}")
+        print(f"Regular Mode - First 100 chars: {result.markdown.raw_markdown[:100]}...")
+        return result.success
+
+async def test_undetected_mode():
+    """Test with undetected browser"""
+    print("\nTesting Undetected Browser Mode...")
+    from crawl4ai import UndetectedAdapter
+    from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+    
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True
+    )
+    
+    # Create undetected adapter
+    undetected_adapter = UndetectedAdapter()
+    
+    # Create strategy with undetected adapter
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=undetected_adapter
+    )
+    
+    async with AsyncWebCrawler(
+        crawler_strategy=crawler_strategy,
+        config=browser_config
+    ) as crawler:
+        result = await crawler.arun(url="https://www.example.com")
+        print(f"Undetected Mode - Success: {result.success}")
+        print(f"Undetected Mode - Status: {result.status_code}")
+        print(f"Undetected Mode - Content length: {len(result.markdown.raw_markdown)}")
+        print(f"Undetected Mode - First 100 chars: {result.markdown.raw_markdown[:100]}...")
+        return result.success
+
+async def main():
+    """Run both tests"""
+    print("🤖 Crawl4AI Basic Adapter Test\n")
+    
+    # Test regular mode
+    regular_success = await test_regular_mode()
+    
+    # Test undetected mode
+    undetected_success = await test_undetected_mode()
+    
+    # Summary
+    print("\n" + "="*50)
+    print("Summary:")
+    print(f"Regular Mode: {'✅ Success' if regular_success else '❌ Failed'}")
+    print(f"Undetected Mode: {'✅ Success' if undetected_success else '❌ Failed'}")
+    print("="*50)
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/undetectability/undetected_bot_test.py

@@ -0,0 +1,155 @@
+"""
+Bot Detection Test - Compare Regular vs Undetected
+Tests browser fingerprinting differences at bot.sannysoft.com
+"""
+
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler, 
+    BrowserConfig, 
+    CrawlerRunConfig,
+    UndetectedAdapter,
+    CrawlResult
+)
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+# Bot detection test site
+TEST_URL = "https://bot.sannysoft.com"
+
+def analyze_bot_detection(result: CrawlResult) -> dict:
+    """Analyze bot detection results from the page"""
+    detections = {
+        "webdriver": False,
+        "headless": False, 
+        "automation": False,
+        "user_agent": False,
+        "total_tests": 0,
+        "failed_tests": 0
+    }
+    
+    if not result.success or not result.html:
+        return detections
+    
+    # Look for specific test results in the HTML
+    html_lower = result.html.lower()
+    
+    # Check for common bot indicators
+    if "webdriver" in html_lower and ("fail" in html_lower or "true" in html_lower):
+        detections["webdriver"] = True
+        detections["failed_tests"] += 1
+    
+    if "headless" in html_lower and ("fail" in html_lower or "true" in html_lower):
+        detections["headless"] = True
+        detections["failed_tests"] += 1
+    
+    if "automation" in html_lower and "detected" in html_lower:
+        detections["automation"] = True
+        detections["failed_tests"] += 1
+    
+    # Count total tests (approximate)
+    detections["total_tests"] = html_lower.count("test") + html_lower.count("check")
+    
+    return detections
+
+async def test_browser_mode(adapter_name: str, adapter=None):
+    """Test a browser mode and return results"""
+    print(f"\n{'='*60}")
+    print(f"Testing: {adapter_name}")
+    print(f"{'='*60}")
+    
+    browser_config = BrowserConfig(
+        headless=False,  # Run in headed mode for better results
+        verbose=True,
+        viewport_width=1920,
+        viewport_height=1080,
+    )
+    
+    if adapter:
+        # Use undetected mode
+        crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+            browser_config=browser_config,
+            browser_adapter=adapter
+        )
+        crawler = AsyncWebCrawler(
+            crawler_strategy=crawler_strategy,
+            config=browser_config
+        )
+    else:
+        # Use regular mode
+        crawler = AsyncWebCrawler(config=browser_config)
+    
+    async with crawler:
+        config = CrawlerRunConfig(
+            delay_before_return_html=3.0,  # Let detection scripts run
+            wait_for_images=True,
+            screenshot=True,
+            simulate_user=False,  # Don't simulate for accurate detection
+        )
+        
+        result = await crawler.arun(url=TEST_URL, config=config)
+        
+        print(f"\n✓ Success: {result.success}")
+        print(f"✓ Status Code: {result.status_code}")
+        
+        if result.success:
+            # Analyze detection results
+            detections = analyze_bot_detection(result)
+            
+            print(f"\n🔍 Bot Detection Analysis:")
+            print(f"  - WebDriver Detected: {'❌ Yes' if detections['webdriver'] else '✅ No'}")
+            print(f"  - Headless Detected: {'❌ Yes' if detections['headless'] else '✅ No'}")
+            print(f"  - Automation Detected: {'❌ Yes' if detections['automation'] else '✅ No'}")
+            print(f"  - Failed Tests: {detections['failed_tests']}")
+            
+            # Show some content
+            if result.markdown.raw_markdown:
+                print(f"\nContent preview:")
+                lines = result.markdown.raw_markdown.split('\n')
+                for line in lines[:20]:  # Show first 20 lines
+                    if any(keyword in line.lower() for keyword in ['test', 'pass', 'fail', 'yes', 'no']):
+                        print(f"  {line.strip()}")
+        
+        return result, detections if result.success else {}
+
+async def main():
+    """Run the comparison"""
+    print("🤖 Crawl4AI - Bot Detection Test")
+    print(f"Testing at: {TEST_URL}")
+    print("This site runs various browser fingerprinting tests\n")
+    
+    # Test regular browser
+    regular_result, regular_detections = await test_browser_mode("Regular Browser")
+    
+    # Small delay
+    await asyncio.sleep(2)
+    
+    # Test undetected browser
+    undetected_adapter = UndetectedAdapter()
+    undetected_result, undetected_detections = await test_browser_mode(
+        "Undetected Browser", 
+        undetected_adapter
+    )
+    
+    # Summary comparison
+    print(f"\n{'='*60}")
+    print("COMPARISON SUMMARY")
+    print(f"{'='*60}")
+    
+    print(f"\n{'Test':<25} {'Regular':<15} {'Undetected':<15}")
+    print(f"{'-'*55}")
+    
+    if regular_detections and undetected_detections:
+        print(f"{'WebDriver Detection':<25} {'❌ Detected' if regular_detections['webdriver'] else '✅ Passed':<15} {'❌ Detected' if undetected_detections['webdriver'] else '✅ Passed':<15}")
+        print(f"{'Headless Detection':<25} {'❌ Detected' if regular_detections['headless'] else '✅ Passed':<15} {'❌ Detected' if undetected_detections['headless'] else '✅ Passed':<15}")
+        print(f"{'Automation Detection':<25} {'❌ Detected' if regular_detections['automation'] else '✅ Passed':<15} {'❌ Detected' if undetected_detections['automation'] else '✅ Passed':<15}")
+        print(f"{'Failed Tests':<25} {regular_detections['failed_tests']:<15} {undetected_detections['failed_tests']:<15}")
+    
+    print(f"\n{'='*60}")
+    
+    if undetected_detections.get('failed_tests', 0) < regular_detections.get('failed_tests', 1):
+        print("✅ Undetected browser performed better at evading detection!")
+    else:
+        print("ℹ️  Both browsers had similar detection results")
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/undetectability/undetected_cloudflare_test.py

@@ -0,0 +1,164 @@
+"""
+Undetected Browser Test - Cloudflare Protected Site
+Tests the difference between regular and undetected modes on a Cloudflare-protected site
+"""
+
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler, 
+    BrowserConfig, 
+    CrawlerRunConfig,
+    UndetectedAdapter
+)
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+# Test URL with Cloudflare protection
+TEST_URL = "https://nowsecure.nl"
+
+async def test_regular_browser():
+    """Test with regular browser - likely to be blocked"""
+    print("=" * 60)
+    print("Testing with Regular Browser")
+    print("=" * 60)
+    
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True,
+        viewport_width=1920,
+        viewport_height=1080,
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        config = CrawlerRunConfig(
+            delay_before_return_html=2.0,
+            simulate_user=True,
+            magic=True,  # Try with magic mode too
+        )
+        
+        result = await crawler.arun(url=TEST_URL, config=config)
+        
+        print(f"\n✓ Success: {result.success}")
+        print(f"✓ Status Code: {result.status_code}")
+        print(f"✓ HTML Length: {len(result.html)}")
+        
+        # Check for Cloudflare challenge
+        if result.html:
+            cf_indicators = [
+                "Checking your browser",
+                "Please stand by",
+                "cloudflare",
+                "cf-browser-verification",
+                "Access denied",
+                "Ray ID"
+            ]
+            
+            detected = False
+            for indicator in cf_indicators:
+                if indicator.lower() in result.html.lower():
+                    print(f"⚠️  Cloudflare Challenge Detected: '{indicator}' found")
+                    detected = True
+                    break
+            
+            if not detected and len(result.markdown.raw_markdown) > 100:
+                print("✅ Successfully bypassed Cloudflare!")
+                print(f"Content preview: {result.markdown.raw_markdown[:200]}...")
+            elif not detected:
+                print("⚠️  Page loaded but content seems minimal")
+        
+        return result
+
+async def test_undetected_browser():
+    """Test with undetected browser - should bypass Cloudflare"""
+    print("\n" + "=" * 60)
+    print("Testing with Undetected Browser")
+    print("=" * 60)
+    
+    browser_config = BrowserConfig(
+        headless=False,  # Headless is easier to detect
+        verbose=True,
+        viewport_width=1920,
+        viewport_height=1080,
+    )
+    
+    # Create undetected adapter
+    undetected_adapter = UndetectedAdapter()
+    
+    # Create strategy with undetected adapter
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=undetected_adapter
+    )
+    
+    async with AsyncWebCrawler(
+        crawler_strategy=crawler_strategy,
+        config=browser_config
+    ) as crawler:
+        config = CrawlerRunConfig(
+            delay_before_return_html=2.0,
+            simulate_user=True,
+        )
+        
+        result = await crawler.arun(url=TEST_URL, config=config)
+        
+        print(f"\n✓ Success: {result.success}")
+        print(f"✓ Status Code: {result.status_code}")
+        print(f"✓ HTML Length: {len(result.html)}")
+        
+        # Check for Cloudflare challenge
+        if result.html:
+            cf_indicators = [
+                "Checking your browser",
+                "Please stand by",
+                "cloudflare",
+                "cf-browser-verification",
+                "Access denied",
+                "Ray ID"
+            ]
+            
+            detected = False
+            for indicator in cf_indicators:
+                if indicator.lower() in result.html.lower():
+                    print(f"⚠️  Cloudflare Challenge Detected: '{indicator}' found")
+                    detected = True
+                    break
+            
+            if not detected and len(result.markdown.raw_markdown) > 100:
+                print("✅ Successfully bypassed Cloudflare!")
+                print(f"Content preview: {result.markdown.raw_markdown[:200]}...")
+            elif not detected:
+                print("⚠️  Page loaded but content seems minimal")
+        
+        return result
+
+async def main():
+    """Compare regular vs undetected browser"""
+    print("🤖 Crawl4AI - Cloudflare Bypass Test")
+    print(f"Testing URL: {TEST_URL}\n")
+    
+    # Test regular browser
+    regular_result = await test_regular_browser()
+    
+    # Small delay
+    await asyncio.sleep(2)
+    
+    # Test undetected browser
+    undetected_result = await test_undetected_browser()
+    
+    # Summary
+    print("\n" + "=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    print(f"Regular Browser:")
+    print(f"  - Success: {regular_result.success}")
+    print(f"  - Content Length: {len(regular_result.markdown.raw_markdown) if regular_result.markdown else 0}")
+    
+    print(f"\nUndetected Browser:")
+    print(f"  - Success: {undetected_result.success}")
+    print(f"  - Content Length: {len(undetected_result.markdown.raw_markdown) if undetected_result.markdown else 0}")
+    
+    if undetected_result.success and len(undetected_result.markdown.raw_markdown) > len(regular_result.markdown.raw_markdown):
+        print("\n✅ Undetected browser successfully bypassed protection!")
+    print("=" * 60)
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/undetectability/undetected_vs_regular_comparison.py

@@ -0,0 +1,184 @@
+"""
+Undetected vs Regular Browser Comparison
+This example demonstrates the difference between regular and undetected browser modes
+when accessing sites with bot detection services.
+
+Based on tested anti-bot services:
+- Cloudflare
+- Kasada
+- Akamai
+- DataDome
+- Bet365
+- And others
+"""
+
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler,
+    BrowserConfig,
+    CrawlerRunConfig,
+    PlaywrightAdapter,
+    UndetectedAdapter,
+    CrawlResult
+)
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+
+# Test URLs for various bot detection services
+TEST_SITES = {
+    "Cloudflare Protected": "https://nowsecure.nl",
+    # "Bot Detection Test": "https://bot.sannysoft.com",
+    # "Fingerprint Test": "https://fingerprint.com/products/bot-detection",
+    # "Browser Scan": "https://browserscan.net",
+    # "CreepJS": "https://abrahamjuliot.github.io/creepjs",
+}
+
+
+async def test_with_adapter(url: str, adapter_name: str, adapter):
+    """Test a URL with a specific adapter"""
+    browser_config = BrowserConfig(
+        headless=False,  # Better for avoiding detection
+        viewport_width=1920,
+        viewport_height=1080,
+        verbose=True,
+    )
+    
+    # Create the crawler strategy with the adapter
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=adapter
+    )
+    
+    print(f"\n{'='*60}")
+    print(f"Testing with {adapter_name} adapter")
+    print(f"URL: {url}")
+    print(f"{'='*60}")
+    
+    try:
+        async with AsyncWebCrawler(
+            crawler_strategy=crawler_strategy,
+            config=browser_config
+        ) as crawler:
+            crawler_config = CrawlerRunConfig(
+                delay_before_return_html=3.0,  # Give page time to load
+                wait_for_images=True,
+                screenshot=True,
+                simulate_user=True,  # Add user simulation
+            )
+            
+            result: CrawlResult = await crawler.arun(
+                url=url,
+                config=crawler_config
+            )
+            
+            # Check results
+            print(f"✓ Status Code: {result.status_code}")
+            print(f"✓ Success: {result.success}")
+            print(f"✓ HTML Length: {len(result.html)}")
+            print(f"✓ Markdown Length: {len(result.markdown.raw_markdown)}")
+            
+            # Check for common bot detection indicators
+            detection_indicators = [
+                "Access denied",
+                "Please verify you are human",
+                "Checking your browser",
+                "Enable JavaScript",
+                "captcha",
+                "403 Forbidden",
+                "Bot detection",
+                "Security check"
+            ]
+            
+            content_lower = result.markdown.raw_markdown.lower()
+            detected = False
+            for indicator in detection_indicators:
+                if indicator.lower() in content_lower:
+                    print(f"⚠️  Possible detection: Found '{indicator}'")
+                    detected = True
+                    break
+            
+            if not detected:
+                print("✅ No obvious bot detection triggered!")
+                # Show first 200 chars of content
+                print(f"Content preview: {result.markdown.raw_markdown[:200]}...")
+            
+            return result.success and not detected
+            
+    except Exception as e:
+        print(f"❌ Error: {str(e)}")
+        return False
+
+
+async def compare_adapters(url: str, site_name: str):
+    """Compare regular and undetected adapters on the same URL"""
+    print(f"\n{'#'*60}")
+    print(f"# Testing: {site_name}")
+    print(f"{'#'*60}")
+    
+    # Test with regular adapter
+    regular_adapter = PlaywrightAdapter()
+    regular_success = await test_with_adapter(url, "Regular", regular_adapter)
+    
+    # Small delay between tests
+    await asyncio.sleep(2)
+    
+    # Test with undetected adapter
+    undetected_adapter = UndetectedAdapter()
+    undetected_success = await test_with_adapter(url, "Undetected", undetected_adapter)
+    
+    # Summary
+    print(f"\n{'='*60}")
+    print(f"Summary for {site_name}:")
+    print(f"Regular Adapter: {'✅ Passed' if regular_success else '❌ Blocked/Detected'}")
+    print(f"Undetected Adapter: {'✅ Passed' if undetected_success else '❌ Blocked/Detected'}")
+    print(f"{'='*60}")
+    
+    return regular_success, undetected_success
+
+
+async def main():
+    """Run comparison tests on multiple sites"""
+    print("🤖 Crawl4AI Browser Adapter Comparison")
+    print("Testing regular vs undetected browser modes\n")
+    
+    results = {}
+    
+    # Test each site
+    for site_name, url in TEST_SITES.items():
+        regular, undetected = await compare_adapters(url, site_name)
+        results[site_name] = {
+            "regular": regular,
+            "undetected": undetected
+        }
+        
+        # Delay between different sites
+        await asyncio.sleep(3)
+    
+    # Final summary
+    print(f"\n{'#'*60}")
+    print("# FINAL RESULTS")
+    print(f"{'#'*60}")
+    print(f"{'Site':<30} {'Regular':<15} {'Undetected':<15}")
+    print(f"{'-'*60}")
+    
+    for site, result in results.items():
+        regular_status = "✅ Passed" if result["regular"] else "❌ Blocked"
+        undetected_status = "✅ Passed" if result["undetected"] else "❌ Blocked"
+        print(f"{site:<30} {regular_status:<15} {undetected_status:<15}")
+    
+    # Calculate success rates
+    regular_success = sum(1 for r in results.values() if r["regular"])
+    undetected_success = sum(1 for r in results.values() if r["undetected"])
+    total = len(results)
+    
+    print(f"\n{'='*60}")
+    print(f"Success Rates:")
+    print(f"Regular Adapter: {regular_success}/{total} ({regular_success/total*100:.1f}%)")
+    print(f"Undetected Adapter: {undetected_success}/{total} ({undetected_success/total*100:.1f}%)")
+    print(f"{'='*60}")
+
+
+if __name__ == "__main__":
+    # Note: This example may take a while to run as it tests multiple sites
+    # You can comment out sites in TEST_SITES to run faster tests
+    asyncio.run(main())

docs/examples/undetected_simple_demo.py

@@ -0,0 +1,118 @@
+"""
+Simple Undetected Browser Demo
+Demonstrates the basic usage of undetected browser mode
+"""
+
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler, 
+    BrowserConfig, 
+    CrawlerRunConfig,
+    UndetectedAdapter
+)
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+async def crawl_with_regular_browser(url: str):
+    """Crawl with regular browser"""
+    print("\n[Regular Browser Mode]")
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True,
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url=url,
+            config=CrawlerRunConfig(
+                delay_before_return_html=2.0
+            )
+        )
+        
+        print(f"Success: {result.success}")
+        print(f"Status: {result.status_code}")
+        print(f"Content length: {len(result.markdown.raw_markdown)}")
+        
+        # Check for bot detection keywords
+        content = result.markdown.raw_markdown.lower()
+        if any(word in content for word in ["cloudflare", "checking your browser", "please wait"]):
+            print("⚠️  Bot detection triggered!")
+        else:
+            print("✅ Page loaded successfully")
+        
+        return result
+
+async def crawl_with_undetected_browser(url: str):
+    """Crawl with undetected browser"""
+    print("\n[Undetected Browser Mode]")
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True,
+    )
+    
+    # Create undetected adapter and strategy
+    undetected_adapter = UndetectedAdapter()
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=undetected_adapter
+    )
+    
+    async with AsyncWebCrawler(
+        crawler_strategy=crawler_strategy,
+        config=browser_config
+    ) as crawler:
+        result = await crawler.arun(
+            url=url,
+            config=CrawlerRunConfig(
+                delay_before_return_html=2.0
+            )
+        )
+        
+        print(f"Success: {result.success}")
+        print(f"Status: {result.status_code}")
+        print(f"Content length: {len(result.markdown.raw_markdown)}")
+        
+        # Check for bot detection keywords
+        content = result.markdown.raw_markdown.lower()
+        if any(word in content for word in ["cloudflare", "checking your browser", "please wait"]):
+            print("⚠️  Bot detection triggered!")
+        else:
+            print("✅ Page loaded successfully")
+        
+        return result
+
+async def main():
+    """Demo comparing regular vs undetected modes"""
+    print("🤖 Crawl4AI Undetected Browser Demo")
+    print("="*50)
+    
+    # Test URLs - you can change these
+    test_urls = [
+        "https://www.example.com",  # Simple site
+        "https://httpbin.org/headers",  # Shows request headers
+    ]
+    
+    for url in test_urls:
+        print(f"\n📍 Testing URL: {url}")
+        
+        # Test with regular browser
+        regular_result = await crawl_with_regular_browser(url)
+        
+        # Small delay
+        await asyncio.sleep(2)
+        
+        # Test with undetected browser
+        undetected_result = await crawl_with_undetected_browser(url)
+        
+        # Compare results
+        print(f"\n📊 Comparison for {url}:")
+        print(f"Regular browser content: {len(regular_result.markdown.raw_markdown)} chars")
+        print(f"Undetected browser content: {len(undetected_result.markdown.raw_markdown)} chars")
+        
+        if url == "https://httpbin.org/headers":
+            # Show headers for comparison
+            print("\nHeaders seen by server:")
+            print("Regular:", regular_result.markdown.raw_markdown[:500])
+            print("\nUndetected:", undetected_result.markdown.raw_markdown[:500])
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/md_v2/advanced/advanced-features.md

@@ -358,9 +358,77 @@ if __name__ == "__main__":
 
 ---
 
+---
+
+## 7. Anti-Bot Features (Stealth Mode & Undetected Browser)
+
+Crawl4AI provides two powerful features to bypass bot detection:
+
+### 7.1 Stealth Mode
+
+Stealth mode uses playwright-stealth to modify browser fingerprints and behaviors. Enable it with a simple flag:
+
+```python
+browser_config = BrowserConfig(
+    enable_stealth=True,  # Activates stealth mode
+    headless=False
+)
+```
+
+**When to use**: Sites with basic bot detection (checking navigator.webdriver, plugins, etc.)
+
+### 7.2 Undetected Browser
+
+For advanced bot detection, use the undetected browser adapter:
+
+```python
+from crawl4ai import UndetectedAdapter
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+# Create undetected adapter
+adapter = UndetectedAdapter()
+strategy = AsyncPlaywrightCrawlerStrategy(
+    browser_config=browser_config,
+    browser_adapter=adapter
+)
+
+async with AsyncWebCrawler(crawler_strategy=strategy, config=browser_config) as crawler:
+    # Your crawling code
+```
+
+**When to use**: Sites with sophisticated bot detection (Cloudflare, DataDome, etc.)
+
+### 7.3 Combining Both
+
+For maximum evasion, combine stealth mode with undetected browser:
+
+```python
+browser_config = BrowserConfig(
+    enable_stealth=True,  # Enable stealth
+    headless=False
+)
+
+adapter = UndetectedAdapter()  # Use undetected browser
+```
+
+### Choosing the Right Approach
+
+| Detection Level | Recommended Approach |
+|----------------|---------------------|
+| No protection | Regular browser |
+| Basic checks | Regular + Stealth mode |
+| Advanced protection | Undetected browser |
+| Maximum evasion | Undetected + Stealth mode |
+
+**Best Practice**: Start with regular browser + stealth mode. Only use undetected browser if needed, as it may be slightly slower.
+
+See [Undetected Browser Mode](undetected-browser.md) for detailed examples.
+
+---
+
 ## Conclusion & Next Steps
 
-You’ve now explored several **advanced** features:
+You've now explored several **advanced** features:
 
 - **Proxy Usage**  
 - **PDF & Screenshot** capturing for large or critical pages  
@@ -368,7 +436,10 @@ You’ve now explored several **advanced** features:
 - **Custom Headers** for language or specialized requests  
 - **Session Persistence** via storage state
 - **Robots.txt Compliance**
+- **Anti-Bot Features** (Stealth Mode & Undetected Browser)
 
-With these power tools, you can build robust scraping workflows that mimic real user behavior, handle secure sites, capture detailed snapshots, and manage sessions across multiple runs—streamlining your entire data collection pipeline.
+With these power tools, you can build robust scraping workflows that mimic real user behavior, handle secure sites, capture detailed snapshots, manage sessions across multiple runs, and bypass bot detection—streamlining your entire data collection pipeline.
 
-**Last Updated**: 2025-01-01
+**Note**: In future versions, we may enable stealth mode and undetected browser by default. For now, users should explicitly enable these features when needed.
+
+**Last Updated**: 2025-01-17

docs/md_v2/advanced/multi-url-crawling.md

@@ -404,7 +404,182 @@ for result in results:
         print(f"Duration: {dr.end_time - dr.start_time}")
 ```
 
-## 6. Summary
+## 6. URL-Specific Configurations
+
+When crawling diverse content types, you often need different configurations for different URLs. For example:
+- PDFs need specialized extraction
+- Blog pages benefit from content filtering
+- Dynamic sites need JavaScript execution
+- API endpoints need JSON parsing
+
+### 6.1 Basic URL Pattern Matching
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, MatchMode
+from crawl4ai.processors.pdf import PDFContentScrapingStrategy
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+from crawl4ai.content_filter_strategy import PruningContentFilter
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+
+async def crawl_mixed_content():
+    # Configure different strategies for different content
+    configs = [
+        # PDF files - specialized extraction
+        CrawlerRunConfig(
+            url_matcher="*.pdf",
+            scraping_strategy=PDFContentScrapingStrategy()
+        ),
+        
+        # Blog/article pages - content filtering
+        CrawlerRunConfig(
+            url_matcher=["*/blog/*", "*/article/*"],
+            markdown_generator=DefaultMarkdownGenerator(
+                content_filter=PruningContentFilter(threshold=0.48)
+            )
+        ),
+        
+        # Dynamic pages - JavaScript execution
+        CrawlerRunConfig(
+            url_matcher=lambda url: 'github.com' in url,
+            js_code="window.scrollTo(0, 500);"
+        ),
+        
+        # API endpoints - JSON extraction
+        CrawlerRunConfig(
+            url_matcher=lambda url: 'api' in url or url.endswith('.json'),
+            # Custome settings for JSON extraction
+        ),
+        
+        # Default config for everything else
+        CrawlerRunConfig()  # No url_matcher means it matches ALL URLs (fallback)
+    ]
+    
+    # Mixed URLs
+    urls = [
+        "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",
+        "https://blog.python.org/",
+        "https://github.com/microsoft/playwright",
+        "https://httpbin.org/json",
+        "https://example.com/"
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(
+            urls=urls,
+            config=configs  # Pass list of configs
+        )
+        
+        for result in results:
+            print(f"{result.url}: {len(result.markdown)} chars")
+```
+
+### 6.2 Advanced Pattern Matching
+
+**Important**: A `CrawlerRunConfig` without `url_matcher` (or with `url_matcher=None`) matches ALL URLs. This makes it perfect as a default/fallback configuration.
+
+The `url_matcher` parameter supports three types of patterns:
+
+#### Glob Patterns (Strings)
+```python
+# Simple patterns
+"*.pdf"                    # Any PDF file
+"*/api/*"                  # Any URL with /api/ in path
+"https://*.example.com/*"  # Subdomain matching
+"*://example.com/blog/*"   # Any protocol
+```
+
+#### Custom Functions
+```python
+# Complex logic with lambdas
+lambda url: url.startswith('https://') and 'secure' in url
+lambda url: len(url) > 50 and url.count('/') > 5
+lambda url: any(domain in url for domain in ['api.', 'data.', 'feed.'])
+```
+
+#### Mixed Lists with AND/OR Logic
+```python
+# Combine multiple conditions
+CrawlerRunConfig(
+    url_matcher=[
+        "https://*",                        # Must be HTTPS
+        lambda url: 'internal' in url,      # Must contain 'internal'
+        lambda url: not url.endswith('.pdf') # Must not be PDF
+    ],
+    match_mode=MatchMode.AND  # ALL conditions must match
+)
+```
+
+### 6.3 Practical Example: News Site Crawler
+
+```python
+async def crawl_news_site():
+    dispatcher = MemoryAdaptiveDispatcher(
+        memory_threshold_percent=70.0,
+        rate_limiter=RateLimiter(base_delay=(1.0, 2.0))
+    )
+    
+    configs = [
+        # Homepage - light extraction
+        CrawlerRunConfig(
+            url_matcher=lambda url: url.rstrip('/') == 'https://news.ycombinator.com',
+            css_selector="nav, .headline",
+            extraction_strategy=None
+        ),
+        
+        # Article pages - full extraction
+        CrawlerRunConfig(
+            url_matcher="*/article/*",
+            extraction_strategy=CosineStrategy(
+                semantic_filter="article content",
+                word_count_threshold=100
+            ),
+            screenshot=True,
+            excluded_tags=["nav", "aside", "footer"]
+        ),
+        
+        # Author pages - metadata focus
+        CrawlerRunConfig(
+            url_matcher="*/author/*",
+            extraction_strategy=JsonCssExtractionStrategy({
+                "name": "h1.author-name",
+                "bio": ".author-bio",
+                "articles": "article.post-card h2"
+            })
+        ),
+        
+        # Everything else
+        CrawlerRunConfig()
+    ]
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(
+            urls=news_urls,
+            config=configs,
+            dispatcher=dispatcher
+        )
+```
+
+### 6.4 Best Practices
+
+1. **Order Matters**: Configs are evaluated in order - put specific patterns before general ones
+2. **Default Config Behavior**: 
+   - A config without `url_matcher` matches ALL URLs
+   - Always include a default config as the last item if you want to handle all URLs
+   - Without a default config, unmatched URLs will fail with "No matching configuration found"
+3. **Test Your Patterns**: Use the config's `is_match()` method to test patterns:
+   ```python
+   config = CrawlerRunConfig(url_matcher="*.pdf")
+   print(config.is_match("https://example.com/doc.pdf"))  # True
+   
+   default_config = CrawlerRunConfig()  # No url_matcher
+   print(default_config.is_match("https://any-url.com"))  # True - matches everything!
+   ```
+4. **Optimize for Performance**: 
+   - Disable JS for static content
+   - Skip screenshots for data APIs
+   - Use appropriate extraction strategies
+
+## 7. Summary
 
 1. **Two Dispatcher Types**:
 

docs/md_v2/advanced/session-management.md

@@ -49,46 +49,75 @@ from crawl4ai import JsonCssExtractionStrategy
 from crawl4ai.cache_context import CacheMode
 
 async def crawl_dynamic_content():
-    async with AsyncWebCrawler() as crawler:
-        session_id = "github_commits_session"
-        url = "https://github.com/microsoft/TypeScript/commits/main"
-        all_commits = []
+    url = "https://github.com/microsoft/TypeScript/commits/main"
+    session_id = "wait_for_session"
+    all_commits = []
 
-        # Define extraction schema
-        schema = {
-            "name": "Commit Extractor",
-            "baseSelector": "li.Box-sc-g0xbh4-0",
-            "fields": [{
-                "name": "title", "selector": "h4.markdown-title", "type": "text"
-            }],
-        }
-        extraction_strategy = JsonCssExtractionStrategy(schema)
+    js_next_page = """
+    const commits = document.querySelectorAll('li[data-testid="commit-row-item"] h4');
+    if (commits.length > 0) {
+        window.lastCommit = commits[0].textContent.trim();
+    }
+    const button = document.querySelector('a[data-testid="pagination-next-button"]');
+    if (button) {button.click(); console.log('button clicked') }
+    """
 
-        # JavaScript and wait configurations
-        js_next_page = """document.querySelector('a[data-testid="pagination-next-button"]').click();"""
-        wait_for = """() => document.querySelectorAll('li.Box-sc-g0xbh4-0').length > 0"""
-
-        # Crawl multiple pages
+    wait_for = """() => {
+        const commits = document.querySelectorAll('li[data-testid="commit-row-item"] h4');
+        if (commits.length === 0) return false;
+        const firstCommit = commits[0].textContent.trim();
+        return firstCommit !== window.lastCommit;
+    }"""
+    
+    schema = {
+        "name": "Commit Extractor",
+        "baseSelector": "li[data-testid='commit-row-item']",
+        "fields": [
+            {
+                "name": "title",
+                "selector": "h4 a",
+                "type": "text",
+                "transform": "strip",
+            },
+        ],
+    }
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+    
+    
+    browser_config = BrowserConfig(
+        verbose=True,
+        headless=False,
+    )
+        
+    async with AsyncWebCrawler(config=browser_config) as crawler:
         for page in range(3):
-            config = CrawlerRunConfig(
-                url=url,
+            crawler_config = CrawlerRunConfig(
                 session_id=session_id,
+                css_selector="li[data-testid='commit-row-item']",
                 extraction_strategy=extraction_strategy,
                 js_code=js_next_page if page > 0 else None,
                 wait_for=wait_for if page > 0 else None,
                 js_only=page > 0,
-                cache_mode=CacheMode.BYPASS
+                cache_mode=CacheMode.BYPASS,
+                capture_console_messages=True,
             )
-
-            result = await crawler.arun(config=config)
-            if result.success:
+            
+            result = await crawler.arun(url=url, config=crawler_config)
+            
+            if result.console_messages:
+                print(f"Page {page + 1} console messages:", result.console_messages)
+            
+            if result.extracted_content:
+                # print(f"Page {page + 1} result:", result.extracted_content)
                 commits = json.loads(result.extracted_content)
                 all_commits.extend(commits)
                 print(f"Page {page + 1}: Found {len(commits)} commits")
+            else:
+                print(f"Page {page + 1}: No content extracted")
 
+        print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
         # Clean up session
         await crawler.crawler_strategy.kill_session(session_id)
-        return all_commits
 ```
 
 ---

docs/md_v2/advanced/undetected-browser.md

@@ -0,0 +1,394 @@
+# Undetected Browser Mode
+
+## Overview
+
+Crawl4AI offers two powerful anti-bot features to help you access websites with bot detection:
+
+1. **Stealth Mode** - Uses playwright-stealth to modify browser fingerprints and behaviors
+2. **Undetected Browser Mode** - Advanced browser adapter with deep-level patches for sophisticated bot detection
+
+This guide covers both features and helps you choose the right approach for your needs.
+
+## Anti-Bot Features Comparison
+
+| Feature | Regular Browser | Stealth Mode | Undetected Browser |
+|---------|----------------|--------------|-------------------|
+| WebDriver Detection | ❌ | ✅ | ✅ |
+| Navigator Properties | ❌ | ✅ | ✅ |
+| Plugin Emulation | ❌ | ✅ | ✅ |
+| CDP Detection | ❌ | Partial | ✅ |
+| Deep Browser Patches | ❌ | ❌ | ✅ |
+| Performance Impact | None | Minimal | Moderate |
+| Setup Complexity | None | None | Minimal |
+
+## When to Use Each Approach
+
+### Use Regular Browser + Stealth Mode When:
+- Sites have basic bot detection (checking navigator.webdriver, plugins, etc.)
+- You need good performance with basic protection
+- Sites check for common automation indicators
+
+### Use Undetected Browser When:
+- Sites employ sophisticated bot detection services (Cloudflare, DataDome, etc.)
+- Stealth mode alone isn't sufficient
+- You're willing to trade some performance for better evasion
+
+### Best Practice: Progressive Enhancement
+1. **Start with**: Regular browser + Stealth mode
+2. **If blocked**: Switch to Undetected browser
+3. **If still blocked**: Combine Undetected browser + Stealth mode
+
+## Stealth Mode
+
+Stealth mode is the simpler anti-bot solution that works with both regular and undetected browsers:
+
+```python
+from crawl4ai import AsyncWebCrawler, BrowserConfig
+
+# Enable stealth mode with regular browser
+browser_config = BrowserConfig(
+    enable_stealth=True,  # Simple flag to enable
+    headless=False       # Better for avoiding detection
+)
+
+async with AsyncWebCrawler(config=browser_config) as crawler:
+    result = await crawler.arun("https://example.com")
+```
+
+### What Stealth Mode Does:
+- Removes `navigator.webdriver` flag
+- Modifies browser fingerprints
+- Emulates realistic plugin behavior
+- Adjusts navigator properties
+- Fixes common automation leaks
+
+## Undetected Browser Mode
+
+For sites with sophisticated bot detection that stealth mode can't bypass, use the undetected browser adapter:
+
+### Key Features
+
+- **Drop-in Replacement**: Uses the same API as regular browser mode
+- **Enhanced Stealth**: Built-in patches to evade common detection methods
+- **Browser Adapter Pattern**: Seamlessly switch between regular and undetected modes
+- **Automatic Installation**: `crawl4ai-setup` installs all necessary browser dependencies
+
+### Quick Start
+
+```python
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler, 
+    BrowserConfig, 
+    CrawlerRunConfig,
+    UndetectedAdapter
+)
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+async def main():
+    # Create the undetected adapter
+    undetected_adapter = UndetectedAdapter()
+    
+    # Create browser config
+    browser_config = BrowserConfig(
+        headless=False,  # Headless mode can be detected easier
+        verbose=True,
+    )
+    
+    # Create the crawler strategy with undetected adapter
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=undetected_adapter
+    )
+    
+    # Create the crawler with our custom strategy
+    async with AsyncWebCrawler(
+        crawler_strategy=crawler_strategy,
+        config=browser_config
+    ) as crawler:
+        # Your crawling code here
+        result = await crawler.arun(
+            url="https://example.com",
+            config=CrawlerRunConfig()
+        )
+        print(result.markdown[:500])
+
+asyncio.run(main())
+```
+
+## Combining Both Features
+
+For maximum evasion, combine stealth mode with undetected browser:
+
+```python
+from crawl4ai import AsyncWebCrawler, BrowserConfig, UndetectedAdapter
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+# Create browser config with stealth enabled
+browser_config = BrowserConfig(
+    enable_stealth=True,  # Enable stealth mode
+    headless=False
+)
+
+# Create undetected adapter
+adapter = UndetectedAdapter()
+
+# Create strategy with both features
+strategy = AsyncPlaywrightCrawlerStrategy(
+    browser_config=browser_config,
+    browser_adapter=adapter
+)
+
+async with AsyncWebCrawler(
+    crawler_strategy=strategy,
+    config=browser_config
+) as crawler:
+    result = await crawler.arun("https://protected-site.com")
+```
+
+## Examples
+
+### Example 1: Basic Stealth Mode
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+
+async def test_stealth_mode():
+    # Simple stealth mode configuration
+    browser_config = BrowserConfig(
+        enable_stealth=True,
+        headless=False
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(
+            url="https://bot.sannysoft.com",
+            config=CrawlerRunConfig(screenshot=True)
+        )
+        
+        if result.success:
+            print("✓ Successfully accessed bot detection test site")
+            # Save screenshot to verify detection results
+            if result.screenshot:
+                import base64
+                with open("stealth_test.png", "wb") as f:
+                    f.write(base64.b64decode(result.screenshot))
+                print("✓ Screenshot saved - check for green (passed) tests")
+
+asyncio.run(test_stealth_mode())
+```
+
+### Example 2: Undetected Browser Mode
+
+```python
+import asyncio
+from crawl4ai import (
+    AsyncWebCrawler,
+    BrowserConfig,
+    CrawlerRunConfig,
+    UndetectedAdapter
+)
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+
+async def main():
+    # Create browser config
+    browser_config = BrowserConfig(
+        headless=False,
+        verbose=True,
+    )
+    
+    # Create the undetected adapter
+    undetected_adapter = UndetectedAdapter()
+    
+    # Create the crawler strategy with the undetected adapter
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=undetected_adapter
+    )
+    
+    # Create the crawler with our custom strategy
+    async with AsyncWebCrawler(
+        crawler_strategy=crawler_strategy,
+        config=browser_config
+    ) as crawler:
+        # Configure the crawl
+        crawler_config = CrawlerRunConfig(
+            markdown_generator=DefaultMarkdownGenerator(
+                content_filter=PruningContentFilter()
+            ),
+            capture_console_messages=True,  # Test adapter console capture
+        )
+        
+        # Test on a site that typically detects bots
+        print("Testing undetected adapter...")
+        result: CrawlResult = await crawler.arun(
+            url="https://www.helloworld.org", 
+            config=crawler_config
+        )
+        
+        print(f"Status: {result.status_code}")
+        print(f"Success: {result.success}")
+        print(f"Console messages captured: {len(result.console_messages or [])}")
+        print(f"Markdown content (first 500 chars):\n{result.markdown.raw_markdown[:500]}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+## Browser Adapter Pattern
+
+The undetected browser support is implemented using an adapter pattern, allowing seamless switching between different browser implementations:
+
+```python
+# Regular browser adapter (default)
+from crawl4ai import PlaywrightAdapter
+regular_adapter = PlaywrightAdapter()
+
+# Undetected browser adapter
+from crawl4ai import UndetectedAdapter
+undetected_adapter = UndetectedAdapter()
+```
+
+The adapter handles:
+- JavaScript execution
+- Console message capture
+- Error handling
+- Browser-specific optimizations
+
+## Best Practices
+
+1. **Avoid Headless Mode**: Detection is easier in headless mode
+   ```python
+   browser_config = BrowserConfig(headless=False)
+   ```
+
+2. **Use Reasonable Delays**: Don't rush through pages
+   ```python
+   crawler_config = CrawlerRunConfig(
+       wait_time=3.0,  # Wait 3 seconds after page load
+       delay_before_return_html=2.0  # Additional delay
+   )
+   ```
+
+3. **Rotate User Agents**: You can customize user agents
+   ```python
+   browser_config = BrowserConfig(
+       headers={"User-Agent": "your-user-agent"}
+   )
+   ```
+
+4. **Handle Failures Gracefully**: Some sites may still detect and block
+   ```python
+   if not result.success:
+       print(f"Crawl failed: {result.error_message}")
+   ```
+
+## Advanced Usage Tips
+
+### Progressive Detection Handling
+
+```python
+async def crawl_with_progressive_evasion(url):
+    # Step 1: Try regular browser with stealth
+    browser_config = BrowserConfig(
+        enable_stealth=True,
+        headless=False
+    )
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        result = await crawler.arun(url)
+        if result.success and "Access Denied" not in result.html:
+            return result
+    
+    # Step 2: If blocked, try undetected browser
+    print("Regular + stealth blocked, trying undetected browser...")
+    
+    adapter = UndetectedAdapter()
+    strategy = AsyncPlaywrightCrawlerStrategy(
+        browser_config=browser_config,
+        browser_adapter=adapter
+    )
+    
+    async with AsyncWebCrawler(
+        crawler_strategy=strategy,
+        config=browser_config
+    ) as crawler:
+        result = await crawler.arun(url)
+        return result
+```
+
+## Installation
+
+The undetected browser dependencies are automatically installed when you run:
+
+```bash
+crawl4ai-setup
+```
+
+This command installs all necessary browser dependencies for both regular and undetected modes.
+
+## Limitations
+
+- **Performance**: Slightly slower than regular mode due to additional patches
+- **Headless Detection**: Some sites can still detect headless mode
+- **Resource Usage**: May use more resources than regular mode
+- **Not 100% Guaranteed**: Advanced anti-bot services are constantly evolving
+
+## Troubleshooting
+
+### Browser Not Found
+
+Run the setup command:
+```bash
+crawl4ai-setup
+```
+
+### Detection Still Occurring
+
+Try combining with other features:
+```python
+crawler_config = CrawlerRunConfig(
+    simulate_user=True,  # Add user simulation
+    magic=True,  # Enable magic mode
+    wait_time=5.0,  # Longer waits
+)
+```
+
+### Performance Issues
+
+If experiencing slow performance:
+```python
+# Use selective undetected mode only for protected sites
+if is_protected_site(url):
+    adapter = UndetectedAdapter()
+else:
+    adapter = PlaywrightAdapter()  # Default adapter
+```
+
+## Future Plans
+
+**Note**: In future versions of Crawl4AI, we may enable stealth mode and undetected browser by default to provide better out-of-the-box success rates. For now, users should explicitly enable these features when needed.
+
+## Conclusion
+
+Crawl4AI provides flexible anti-bot solutions:
+
+1. **Start Simple**: Use regular browser + stealth mode for most sites
+2. **Escalate if Needed**: Switch to undetected browser for sophisticated protection
+3. **Combine for Maximum Effect**: Use both features together when facing the toughest challenges
+
+Remember:
+- Always respect robots.txt and website terms of service
+- Use appropriate delays to avoid overwhelming servers
+- Consider the performance trade-offs of each approach
+- Test progressively to find the minimum necessary evasion level
+
+## See Also
+
+- [Advanced Features](advanced-features.md) - Overview of all advanced features
+- [Proxy & Security](proxy-security.md) - Using proxies with anti-bot features
+- [Session Management](session-management.md) - Maintaining sessions across requests
+- [Identity Based Crawling](identity-based-crawling.md) - Additional anti-detection strategies

docs/md_v2/advanced/virtual-scroll.md

@@ -91,13 +91,12 @@ async def crawl_twitter_timeline():
         wait_after_scroll=1.0  # Twitter needs time to load
     )
     
+    browser_config = BrowserConfig(headless=True)  # Set to False to watch it work
     config = CrawlerRunConfig(
-        virtual_scroll_config=virtual_config,
-        # Optional: Set headless=False to watch it work
-        # browser_config=BrowserConfig(headless=False)
+        virtual_scroll_config=virtual_config
     )
     
-    async with AsyncWebCrawler() as crawler:
+    async with AsyncWebCrawler(config=browser_config) as crawler:
         result = await crawler.arun(
             url="https://twitter.com/search?q=AI",
             config=config
@@ -200,7 +199,7 @@ Use **scan_full_page** when:
 Virtual Scroll works seamlessly with extraction strategies:
 
 ```python
-from crawl4ai import LLMExtractionStrategy
+from crawl4ai import LLMExtractionStrategy, LLMConfig
 
 # Define extraction schema
 schema = {
@@ -222,7 +221,7 @@ config = CrawlerRunConfig(
         scroll_count=20
     ),
     extraction_strategy=LLMExtractionStrategy(
-        provider="openai/gpt-4o-mini",
+        llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
         schema=schema
     )
 )

docs/md_v2/api/arun_many.md

@@ -7,7 +7,7 @@
 ```python
 async def arun_many(
     urls: Union[List[str], List[Any]],
-    config: Optional[CrawlerRunConfig] = None,
+    config: Optional[Union[CrawlerRunConfig, List[CrawlerRunConfig]]] = None,
     dispatcher: Optional[BaseDispatcher] = None,
     ...
 ) -> Union[List[CrawlResult], AsyncGenerator[CrawlResult, None]]:
@@ -15,7 +15,9 @@ async def arun_many(
     Crawl multiple URLs concurrently or in batches.
 
     :param urls: A list of URLs (or tasks) to crawl.
-    :param config: (Optional) A default `CrawlerRunConfig` applying to each crawl.
+    :param config: (Optional) Either:
+        - A single `CrawlerRunConfig` applying to all URLs
+        - A list of `CrawlerRunConfig` objects with url_matcher patterns
     :param dispatcher: (Optional) A concurrency controller (e.g. MemoryAdaptiveDispatcher).
     ...
     :return: Either a list of `CrawlResult` objects, or an async generator if streaming is enabled.
@@ -95,10 +97,70 @@ results = await crawler.arun_many(
 )
 ```
 
+### URL-Specific Configurations
+
+Instead of using one config for all URLs, provide a list of configs with `url_matcher` patterns:
+
+```python
+from crawl4ai import CrawlerRunConfig, MatchMode
+from crawl4ai.processors.pdf import PDFContentScrapingStrategy
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+from crawl4ai.content_filter_strategy import PruningContentFilter
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+
+# PDF files - specialized extraction
+pdf_config = CrawlerRunConfig(
+    url_matcher="*.pdf",
+    scraping_strategy=PDFContentScrapingStrategy()
+)
+
+# Blog/article pages - content filtering
+blog_config = CrawlerRunConfig(
+    url_matcher=["*/blog/*", "*/article/*", "*python.org*"],
+    markdown_generator=DefaultMarkdownGenerator(
+        content_filter=PruningContentFilter(threshold=0.48)
+    )
+)
+
+# Dynamic pages - JavaScript execution
+github_config = CrawlerRunConfig(
+    url_matcher=lambda url: 'github.com' in url,
+    js_code="window.scrollTo(0, 500);"
+)
+
+# API endpoints - JSON extraction
+api_config = CrawlerRunConfig(
+    url_matcher=lambda url: 'api' in url or url.endswith('.json'),
+    # Custome settings for JSON extraction
+)
+
+# Default fallback config
+default_config = CrawlerRunConfig()  # No url_matcher means it never matches except as fallback
+
+# Pass the list of configs - first match wins!
+results = await crawler.arun_many(
+    urls=[
+        "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",  # → pdf_config
+        "https://blog.python.org/",  # → blog_config
+        "https://github.com/microsoft/playwright",  # → github_config
+        "https://httpbin.org/json",  # → api_config
+        "https://example.com/"  # → default_config
+    ],
+    config=[pdf_config, blog_config, github_config, api_config, default_config]
+)
+```
+
+**URL Matching Features**:
+- **String patterns**: `"*.pdf"`, `"*/blog/*"`, `"*python.org*"`
+- **Function matchers**: `lambda url: 'api' in url`
+- **Mixed patterns**: Combine strings and functions with `MatchMode.OR` or `MatchMode.AND`
+- **First match wins**: Configs are evaluated in order
+
 **Key Points**:
 - Each URL is processed by the same or separate sessions, depending on the dispatcher’s strategy.
 - `dispatch_result` in each `CrawlResult` (if using concurrency) can hold memory and timing info.  
 - If you need to handle authentication or session IDs, pass them in each individual task or within your run config.
+- **Important**: Always include a default config (without `url_matcher`) as the last item if you want to handle all URLs. Otherwise, unmatched URLs will fail.
 
 ### Return Value
 

docs/md_v2/api/parameters.md

@@ -208,6 +208,71 @@ config = CrawlerRunConfig(
 
 See [Virtual Scroll documentation](../../advanced/virtual-scroll.md) for detailed examples.
 
+---
+
+### I) **URL Matching Configuration**
+
+| **Parameter**          | **Type / Default**           | **What It Does**                                                                                                                    |
+|------------------------|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
+| **`url_matcher`**      | `UrlMatcher` (None)          | Pattern(s) to match URLs against. Can be: string (glob), function, or list of mixed types. **None means match ALL URLs**         |
+| **`match_mode`**       | `MatchMode` (MatchMode.OR)   | How to combine multiple matchers in a list: `MatchMode.OR` (any match) or `MatchMode.AND` (all must match)                       |
+
+The `url_matcher` parameter enables URL-specific configurations when used with `arun_many()`:
+
+```python
+from crawl4ai import CrawlerRunConfig, MatchMode
+from crawl4ai.processors.pdf import PDFContentScrapingStrategy
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# Simple string pattern (glob-style)
+pdf_config = CrawlerRunConfig(
+    url_matcher="*.pdf",
+    scraping_strategy=PDFContentScrapingStrategy()
+)
+
+# Multiple patterns with OR logic (default)
+blog_config = CrawlerRunConfig(
+    url_matcher=["*/blog/*", "*/article/*", "*/news/*"],
+    match_mode=MatchMode.OR  # Any pattern matches
+)
+
+# Function matcher
+api_config = CrawlerRunConfig(
+    url_matcher=lambda url: 'api' in url or url.endswith('.json'),
+    # Other settings like extraction_strategy
+)
+
+# Mixed: String + Function with AND logic
+complex_config = CrawlerRunConfig(
+    url_matcher=[
+        lambda url: url.startswith('https://'),  # Must be HTTPS
+        "*.org/*",                               # Must be .org domain
+        lambda url: 'docs' in url                # Must contain 'docs'
+    ],
+    match_mode=MatchMode.AND  # ALL conditions must match
+)
+
+# Combined patterns and functions with AND logic
+secure_docs = CrawlerRunConfig(
+    url_matcher=["https://*", lambda url: '.doc' in url],
+    match_mode=MatchMode.AND  # Must be HTTPS AND contain .doc
+)
+
+# Default config - matches ALL URLs
+default_config = CrawlerRunConfig()  # No url_matcher = matches everything
+```
+
+**UrlMatcher Types:**
+- **None (default)**: When `url_matcher` is None or not set, the config matches ALL URLs
+- **String patterns**: Glob-style patterns like `"*.pdf"`, `"*/api/*"`, `"https://*.example.com/*"`
+- **Functions**: `lambda url: bool` - Custom logic for complex matching
+- **Lists**: Mix strings and functions, combined with `MatchMode.OR` or `MatchMode.AND`
+
+**Important Behavior:**
+- When passing a list of configs to `arun_many()`, URLs are matched against each config's `url_matcher` in order. First match wins!
+- If no config matches a URL and there's no default config (one without `url_matcher`), the URL will fail with "No matching configuration found"
+- Always include a default config as the last item if you want to handle all URLs
+
 ---## 2.2 Helper Methods
 
 Both `BrowserConfig` and `CrawlerRunConfig` provide a `clone()` method to create modified copies:

docs/md_v2/blog/index.md

@@ -20,118 +20,22 @@ Ever wondered why your AI coding assistant struggles with your library despite c
 
 ## Latest Release
 
-Here’s the blog index entry for **v0.6.0**, written to match the exact tone and structure of your previous entries:
+### [Crawl4AI v0.7.4 – The Intelligent Table Extraction & Performance Update](../blog/release-v0.7.4.md)
+*August 17, 2025*
+
+Crawl4AI v0.7.4 introduces revolutionary LLM-powered table extraction with intelligent chunking, performance improvements for concurrent crawling, enhanced browser management, and critical stability fixes that make Crawl4AI more robust for production workloads.
+
+Key highlights:
+- **🚀 LLMTableExtraction**: Revolutionary table extraction with intelligent chunking for massive tables
+- **⚡ Dispatcher Bug Fix**: Fixed sequential processing issue in arun_many for fast-completing tasks
+- **🧹 Memory Management Refactor**: Streamlined memory utilities and better resource management
+- **🔧 Browser Manager Fixes**: Resolved race conditions in concurrent page creation
+- **🔗 Advanced URL Processing**: Better handling of raw URLs and base tag link resolution
+
+[Read full release notes →](../blog/release-v0.7.4.md)
 
 ---
 
-### [Crawl4AI v0.6.0 – World-Aware Crawling, Pre-Warmed Browsers, and the MCP API](releases/0.6.0.md)
-*April 23, 2025*
-
-Crawl4AI v0.6.0 is our most powerful release yet. This update brings major architectural upgrades including world-aware crawling (set geolocation, locale, and timezone), real-time traffic capture, and a memory-efficient crawler pool with pre-warmed pages.  
-
-The Docker server now exposes a full-featured MCP socket + SSE interface, supports streaming, and comes with a new Playground UI. Plus, table extraction is now native, and the new stress-test framework supports crawling 1,000+ URLs.  
-
-Other key changes:  
-
-*   Native support for `result.media["tables"]` to export DataFrames  
-* Full network + console logs and MHTML snapshot per crawl  
-* Browser pooling and pre-warming for faster cold starts  
-* New streaming endpoints via MCP API and Playground  
-* Robots.txt support, proxy rotation, and improved session handling  
-* Deprecated old markdown names, legacy modules cleaned up  
-* Massive repo cleanup: ~36K insertions, ~5K deletions across 121 files
-
-[Read full release notes →](releases/0.6.0.md)
-
----
-
-Let me know if you want me to auto-update the actual file or just paste this into the markdown.
-
-### [Crawl4AI v0.5.0: Deep Crawling, Scalability, and a New CLI!](releases/0.5.0.md)
-
-My dear friends and crawlers, there you go, this is the release of Crawl4AI v0.5.0! This release brings a wealth of new features, performance improvements, and a more streamlined developer experience.  Here's a breakdown of what's new:
-
-**Major New Features:**
-
-*   **Deep Crawling:** Explore entire websites with configurable strategies (BFS, DFS, Best-First).  Define custom filters and URL scoring for targeted crawls.
-*   **Memory-Adaptive Dispatcher:**  Handle large-scale crawls with ease!  Our new dispatcher dynamically adjusts concurrency based on available memory and includes built-in rate limiting.
-*   **Multiple Crawler Strategies:** Choose between the full-featured Playwright browser-based crawler or a new, *much* faster HTTP-only crawler for simpler tasks.
-*   **Docker Deployment:**  Deploy Crawl4AI as a scalable, self-contained service with built-in API endpoints and optional JWT authentication.
-*   **Command-Line Interface (CLI):**  Interact with Crawl4AI directly from your terminal.  Crawl, configure, and extract data with simple commands.
-*   **LLM Configuration (`LLMConfig`):** A new, unified way to configure LLM providers (OpenAI, Anthropic, Ollama, etc.) for extraction, filtering, and schema generation.  Simplifies API key management and switching between models.
-
-**Minor Updates & Improvements:**
-
-*   **LXML Scraping Mode:** Faster HTML parsing with `LXMLWebScrapingStrategy`.
-*   **Proxy Rotation:** Added `ProxyRotationStrategy` with a `RoundRobinProxyStrategy` implementation.
-*   **PDF Processing:** Extract text, images, and metadata from PDF files.
-*   **URL Redirection Tracking:**  Automatically follows and records redirects.
-*   **Robots.txt Compliance:**  Optionally respect website crawling rules.
-*   **LLM-Powered Schema Generation:**  Automatically create extraction schemas using an LLM.
-*   **`LLMContentFilter`:** Generate high-quality, focused markdown using an LLM.
-*   **Improved Error Handling & Stability:** Numerous bug fixes and performance enhancements.
-*   **Enhanced Documentation:**  Updated guides and examples.
-
-**Breaking Changes & Migration:**
-
-This release includes several breaking changes to improve the library's structure and consistency.  Here's what you need to know:
-
-*   **`arun_many()` Behavior:** Now uses the `MemoryAdaptiveDispatcher` by default.  The return type depends on the `stream` parameter in `CrawlerRunConfig`.  Adjust code that relied on unbounded concurrency.
-*   **`max_depth` Location:** Moved to `CrawlerRunConfig` and now controls *crawl depth*.
-*   **Deep Crawling Imports:**  Import `DeepCrawlStrategy` and related classes from `crawl4ai.deep_crawling`.
-*   **`BrowserContext` API:**  Updated; the old `get_context` method is deprecated.
-*   **Optional Model Fields:** Many data model fields are now optional.  Handle potential `None` values.
-*   **`ScrapingMode` Enum:** Replaced with strategy pattern (`WebScrapingStrategy`, `LXMLWebScrapingStrategy`).
-*   **`content_filter` Parameter:** Removed from `CrawlerRunConfig`. Use extraction strategies or markdown generators with filters.
-*   **Removed Functionality:** The synchronous `WebCrawler`, the old CLI, and docs management tools have been removed.
-*   **Docker:**  Significant changes to deployment.  See the [Docker documentation](../deploy/docker/README.md).
-*   **`ssl_certificate.json`:** This file has been removed.
-* **Config**: FastFilterChain has been replaced with FilterChain
-* **Deep-Crawl**: DeepCrawlStrategy.arun now returns Union[CrawlResultT, List[CrawlResultT], AsyncGenerator[CrawlResultT, None]]
-* **Proxy**: Removed synchronous WebCrawler support and related rate limiting configurations
-*   **LLM Parameters:** Use the new `LLMConfig` object instead of passing `provider`, `api_token`, `base_url`, and `api_base` directly to `LLMExtractionStrategy` and `LLMContentFilter`.
-
-**In short:** Update imports, adjust `arun_many()` usage, check for optional fields, and review the Docker deployment guide.
-
-## License Change
-
-Crawl4AI v0.5.0 updates the license to Apache 2.0 *with a required attribution clause*.  This means you are free to use, modify, and distribute Crawl4AI (even commercially), but you *must* clearly attribute the project in any public use or distribution.  See the updated `LICENSE` file for the full legal text and specific requirements.
-
-**Get Started:**
-
-*   **Installation:** `pip install "crawl4ai[all]"` (or use the Docker image)
-*   **Documentation:** [https://docs.crawl4ai.com](https://docs.crawl4ai.com)
-*   **GitHub:** [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
-
-I'm very excited to see what you build with Crawl4AI v0.5.0!
-
----
-
-### [0.4.2 - Configurable Crawlers, Session Management, and Smarter Screenshots](releases/0.4.2.md)
-*December 12, 2024*
-
-The 0.4.2 update brings massive improvements to configuration, making crawlers and browsers easier to manage with dedicated objects. You can now import/export local storage for seamless session management. Plus, long-page screenshots are faster and cleaner, and full-page PDF exports are now possible. Check out all the new features to make your crawling experience even smoother.
-
-[Read full release notes →](releases/0.4.2.md)
-
----
-
-### [0.4.1 - Smarter Crawling with Lazy-Load Handling, Text-Only Mode, and More](releases/0.4.1.md)
-*December 8, 2024*
-
-This release brings major improvements to handling lazy-loaded images, a blazing-fast Text-Only Mode, full-page scanning for infinite scrolls, dynamic viewport adjustments, and session reuse for efficient crawling. If you're looking to improve speed, reliability, or handle dynamic content with ease, this update has you covered.
-
-[Read full release notes →](releases/0.4.1.md)
-
----
-
-### [0.4.0 - Major Content Filtering Update](releases/0.4.0.md)
-*December 1, 2024*
-
-Introduced significant improvements to content filtering, multi-threaded environment handling, and user-agent generation. This release features the new PruningContentFilter, enhanced thread safety, and improved test coverage.
-
-[Read full release notes →](releases/0.4.0.md)
-
 ## Project History
 
 Curious about how Crawl4AI has evolved? Check out our [complete changelog](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md) for a detailed history of all versions and updates.
@@ -140,5 +44,4 @@ Curious about how Crawl4AI has evolved? Check out our [complete changelog](https
 
 - Star us on [GitHub](https://github.com/unclecode/crawl4ai)
 - Follow [@unclecode](https://twitter.com/unclecode) on Twitter
-- Join our community discussions on GitHub
-
+- Join our community discussions on GitHub

docs/md_v2/blog/index.md.bak

@@ -0,0 +1,144 @@
+# Crawl4AI Blog
+
+Welcome to the Crawl4AI blog! Here you'll find detailed release notes, technical insights, and updates about the project. Whether you're looking for the latest improvements or want to dive deep into web crawling techniques, this is the place.
+
+## Featured Articles
+
+### [When to Stop Crawling: The Art of Knowing "Enough"](articles/adaptive-crawling-revolution.md)
+*January 29, 2025*
+
+Traditional crawlers are like tourists with unlimited time—they'll visit every street, every alley, every dead end. But what if your crawler could think like a researcher with a deadline? Discover how Adaptive Crawling revolutionizes web scraping by knowing when to stop. Learn about the three-layer intelligence system that evaluates coverage, consistency, and saturation to build focused knowledge bases instead of endless page collections.
+
+[Read the full article →](articles/adaptive-crawling-revolution.md)
+
+### [The LLM Context Protocol: Why Your AI Assistant Needs Memory, Reasoning, and Examples](articles/llm-context-revolution.md)
+*January 24, 2025*
+
+Ever wondered why your AI coding assistant struggles with your library despite comprehensive documentation? This article introduces the three-dimensional context protocol that transforms how AI understands code. Learn why memory, reasoning, and examples together create wisdom—not just information.
+
+[Read the full article →](articles/llm-context-revolution.md)
+
+## Latest Release
+
+Here’s the blog index entry for **v0.6.0**, written to match the exact tone and structure of your previous entries:
+
+---
+
+### [Crawl4AI v0.6.0 – World-Aware Crawling, Pre-Warmed Browsers, and the MCP API](releases/0.6.0.md)
+*April 23, 2025*
+
+Crawl4AI v0.6.0 is our most powerful release yet. This update brings major architectural upgrades including world-aware crawling (set geolocation, locale, and timezone), real-time traffic capture, and a memory-efficient crawler pool with pre-warmed pages.  
+
+The Docker server now exposes a full-featured MCP socket + SSE interface, supports streaming, and comes with a new Playground UI. Plus, table extraction is now native, and the new stress-test framework supports crawling 1,000+ URLs.  
+
+Other key changes:  
+
+*   Native support for `result.media["tables"]` to export DataFrames  
+* Full network + console logs and MHTML snapshot per crawl  
+* Browser pooling and pre-warming for faster cold starts  
+* New streaming endpoints via MCP API and Playground  
+* Robots.txt support, proxy rotation, and improved session handling  
+* Deprecated old markdown names, legacy modules cleaned up  
+* Massive repo cleanup: ~36K insertions, ~5K deletions across 121 files
+
+[Read full release notes →](releases/0.6.0.md)
+
+---
+
+Let me know if you want me to auto-update the actual file or just paste this into the markdown.
+
+### [Crawl4AI v0.5.0: Deep Crawling, Scalability, and a New CLI!](releases/0.5.0.md)
+
+My dear friends and crawlers, there you go, this is the release of Crawl4AI v0.5.0! This release brings a wealth of new features, performance improvements, and a more streamlined developer experience.  Here's a breakdown of what's new:
+
+**Major New Features:**
+
+*   **Deep Crawling:** Explore entire websites with configurable strategies (BFS, DFS, Best-First).  Define custom filters and URL scoring for targeted crawls.
+*   **Memory-Adaptive Dispatcher:**  Handle large-scale crawls with ease!  Our new dispatcher dynamically adjusts concurrency based on available memory and includes built-in rate limiting.
+*   **Multiple Crawler Strategies:** Choose between the full-featured Playwright browser-based crawler or a new, *much* faster HTTP-only crawler for simpler tasks.
+*   **Docker Deployment:**  Deploy Crawl4AI as a scalable, self-contained service with built-in API endpoints and optional JWT authentication.
+*   **Command-Line Interface (CLI):**  Interact with Crawl4AI directly from your terminal.  Crawl, configure, and extract data with simple commands.
+*   **LLM Configuration (`LLMConfig`):** A new, unified way to configure LLM providers (OpenAI, Anthropic, Ollama, etc.) for extraction, filtering, and schema generation.  Simplifies API key management and switching between models.
+
+**Minor Updates & Improvements:**
+
+*   **LXML Scraping Mode:** Faster HTML parsing with `LXMLWebScrapingStrategy`.
+*   **Proxy Rotation:** Added `ProxyRotationStrategy` with a `RoundRobinProxyStrategy` implementation.
+*   **PDF Processing:** Extract text, images, and metadata from PDF files.
+*   **URL Redirection Tracking:**  Automatically follows and records redirects.
+*   **Robots.txt Compliance:**  Optionally respect website crawling rules.
+*   **LLM-Powered Schema Generation:**  Automatically create extraction schemas using an LLM.
+*   **`LLMContentFilter`:** Generate high-quality, focused markdown using an LLM.
+*   **Improved Error Handling & Stability:** Numerous bug fixes and performance enhancements.
+*   **Enhanced Documentation:**  Updated guides and examples.
+
+**Breaking Changes & Migration:**
+
+This release includes several breaking changes to improve the library's structure and consistency.  Here's what you need to know:
+
+*   **`arun_many()` Behavior:** Now uses the `MemoryAdaptiveDispatcher` by default.  The return type depends on the `stream` parameter in `CrawlerRunConfig`.  Adjust code that relied on unbounded concurrency.
+*   **`max_depth` Location:** Moved to `CrawlerRunConfig` and now controls *crawl depth*.
+*   **Deep Crawling Imports:**  Import `DeepCrawlStrategy` and related classes from `crawl4ai.deep_crawling`.
+*   **`BrowserContext` API:**  Updated; the old `get_context` method is deprecated.
+*   **Optional Model Fields:** Many data model fields are now optional.  Handle potential `None` values.
+*   **`ScrapingMode` Enum:** Replaced with strategy pattern (`WebScrapingStrategy`, `LXMLWebScrapingStrategy`).
+*   **`content_filter` Parameter:** Removed from `CrawlerRunConfig`. Use extraction strategies or markdown generators with filters.
+*   **Removed Functionality:** The synchronous `WebCrawler`, the old CLI, and docs management tools have been removed.
+*   **Docker:**  Significant changes to deployment.  See the [Docker documentation](../deploy/docker/README.md).
+*   **`ssl_certificate.json`:** This file has been removed.
+* **Config**: FastFilterChain has been replaced with FilterChain
+* **Deep-Crawl**: DeepCrawlStrategy.arun now returns Union[CrawlResultT, List[CrawlResultT], AsyncGenerator[CrawlResultT, None]]
+* **Proxy**: Removed synchronous WebCrawler support and related rate limiting configurations
+*   **LLM Parameters:** Use the new `LLMConfig` object instead of passing `provider`, `api_token`, `base_url`, and `api_base` directly to `LLMExtractionStrategy` and `LLMContentFilter`.
+
+**In short:** Update imports, adjust `arun_many()` usage, check for optional fields, and review the Docker deployment guide.
+
+## License Change
+
+Crawl4AI v0.5.0 updates the license to Apache 2.0 *with a required attribution clause*.  This means you are free to use, modify, and distribute Crawl4AI (even commercially), but you *must* clearly attribute the project in any public use or distribution.  See the updated `LICENSE` file for the full legal text and specific requirements.
+
+**Get Started:**
+
+*   **Installation:** `pip install "crawl4ai[all]"` (or use the Docker image)
+*   **Documentation:** [https://docs.crawl4ai.com](https://docs.crawl4ai.com)
+*   **GitHub:** [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
+
+I'm very excited to see what you build with Crawl4AI v0.5.0!
+
+---
+
+### [0.4.2 - Configurable Crawlers, Session Management, and Smarter Screenshots](releases/0.4.2.md)
+*December 12, 2024*
+
+The 0.4.2 update brings massive improvements to configuration, making crawlers and browsers easier to manage with dedicated objects. You can now import/export local storage for seamless session management. Plus, long-page screenshots are faster and cleaner, and full-page PDF exports are now possible. Check out all the new features to make your crawling experience even smoother.
+
+[Read full release notes →](releases/0.4.2.md)
+
+---
+
+### [0.4.1 - Smarter Crawling with Lazy-Load Handling, Text-Only Mode, and More](releases/0.4.1.md)
+*December 8, 2024*
+
+This release brings major improvements to handling lazy-loaded images, a blazing-fast Text-Only Mode, full-page scanning for infinite scrolls, dynamic viewport adjustments, and session reuse for efficient crawling. If you're looking to improve speed, reliability, or handle dynamic content with ease, this update has you covered.
+
+[Read full release notes →](releases/0.4.1.md)
+
+---
+
+### [0.4.0 - Major Content Filtering Update](releases/0.4.0.md)
+*December 1, 2024*
+
+Introduced significant improvements to content filtering, multi-threaded environment handling, and user-agent generation. This release features the new PruningContentFilter, enhanced thread safety, and improved test coverage.
+
+[Read full release notes →](releases/0.4.0.md)
+
+## Project History
+
+Curious about how Crawl4AI has evolved? Check out our [complete changelog](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md) for a detailed history of all versions and updates.
+
+## Stay Updated
+
+- Star us on [GitHub](https://github.com/unclecode/crawl4ai)
+- Follow [@unclecode](https://twitter.com/unclecode) on Twitter
+- Join our community discussions on GitHub
+

docs/md_v2/blog/releases/0.7.0.md

@@ -0,0 +1,343 @@
+# 🚀 Crawl4AI v0.7.0: The Adaptive Intelligence Update
+
+*January 28, 2025 • 10 min read*
+
+---
+
+Today I'm releasing Crawl4AI v0.7.0—the Adaptive Intelligence Update. This release introduces fundamental improvements in how Crawl4AI handles modern web complexity through adaptive learning, intelligent content discovery, and advanced extraction capabilities.
+
+## 🎯 What's New at a Glance
+
+- **Adaptive Crawling**: Your crawler now learns and adapts to website patterns
+- **Virtual Scroll Support**: Complete content extraction from infinite scroll pages
+- **Link Preview with Intelligent Scoring**: Intelligent link analysis and prioritization
+- **Async URL Seeder**: Discover thousands of URLs in seconds with intelligent filtering
+- **Performance Optimizations**: Significant speed and memory improvements
+
+## 🧠 Adaptive Crawling: Intelligence Through Pattern Learning
+
+**The Problem:** Websites change. Class names shift. IDs disappear. Your carefully crafted selectors break at 3 AM, and you wake up to empty datasets and angry stakeholders.
+
+**My Solution:** I implemented an adaptive learning system that observes patterns, builds confidence scores, and adjusts extraction strategies on the fly. It's like having a junior developer who gets better at their job with every page they scrape.
+
+### Technical Deep-Dive
+
+The Adaptive Crawler maintains a persistent state for each domain, tracking:
+- Pattern success rates
+- Selector stability over time  
+- Content structure variations
+- Extraction confidence scores
+
+```python
+from crawl4ai import AsyncWebCrawler, AdaptiveCrawler, AdaptiveConfig
+import asyncio
+
+async def main():
+    
+    # Configure adaptive crawler
+    config = AdaptiveConfig(
+        strategy="statistical",  # or "embedding" for semantic understanding
+        max_pages=10,
+        confidence_threshold=0.7,  # Stop at 70% confidence
+        top_k_links=3,  # Follow top 3 links per page
+        min_gain_threshold=0.05  # Need 5% information gain to continue
+    )
+    
+    async with AsyncWebCrawler(verbose=False) as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        
+        print("Starting adaptive crawl about Python decorators...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/glossary.html",
+            query="python decorators functions wrapping"
+        )
+        
+        print(f"\n✅ Crawling Complete!")
+        print(f"• Confidence Level: {adaptive.confidence:.0%}")
+        print(f"• Pages Crawled: {len(result.crawled_urls)}")
+        print(f"• Knowledge Base: {len(adaptive.state.knowledge_base)} documents")
+        
+        # Get most relevant content
+        relevant = adaptive.get_relevant_content(top_k=3)
+        print(f"\nMost Relevant Pages:")
+        for i, page in enumerate(relevant, 1):
+            print(f"{i}. {page['url']} (relevance: {page['score']:.2%})")
+
+asyncio.run(main())
+```
+
+**Expected Real-World Impact:**
+- **News Aggregation**: Maintain 95%+ extraction accuracy even as news sites update their templates
+- **E-commerce Monitoring**: Track product changes across hundreds of stores without constant maintenance
+- **Research Data Collection**: Build robust academic datasets that survive website redesigns
+- **Reduced Maintenance**: Cut selector update time by 80% for frequently-changing sites
+
+## 🌊 Virtual Scroll: Complete Content Capture
+
+**The Problem:** Modern web apps only render what's visible. Scroll down, new content appears, old content vanishes into the void. Traditional crawlers capture that first viewport and miss 90% of the content. It's like reading only the first page of every book.
+
+**My Solution:** I built Virtual Scroll support that mimics human browsing behavior, capturing content as it loads and preserving it before the browser's garbage collector strikes.
+
+### Implementation Details
+
+```python
+from crawl4ai import VirtualScrollConfig
+
+# For social media feeds (Twitter/X style)
+twitter_config = VirtualScrollConfig(
+    container_selector="[data-testid='primaryColumn']",
+    scroll_count=20,                    # Number of scrolls
+    scroll_by="container_height",       # Smart scrolling by container size
+    wait_after_scroll=1.0              # Let content load
+)
+
+# For e-commerce product grids (Instagram style)
+grid_config = VirtualScrollConfig(
+    container_selector="main .product-grid",
+    scroll_count=30,
+    scroll_by=800,                     # Fixed pixel scrolling
+    wait_after_scroll=1.5              # Images need time
+)
+
+# For news feeds with lazy loading
+news_config = VirtualScrollConfig(
+    container_selector=".article-feed",
+    scroll_count=50,
+    scroll_by="page_height",           # Viewport-based scrolling
+    wait_after_scroll=0.5              # Wait for content to load
+)
+
+# Use it in your crawl
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        "https://twitter.com/trending",
+        config=CrawlerRunConfig(
+            virtual_scroll_config=twitter_config,
+            # Combine with other features
+            extraction_strategy=JsonCssExtractionStrategy({
+                "tweets": {
+                    "selector": "[data-testid='tweet']",
+                    "fields": {
+                        "text": {"selector": "[data-testid='tweetText']", "type": "text"},
+                        "likes": {"selector": "[data-testid='like']", "type": "text"}
+                    }
+                }
+            })
+        )
+    )
+    
+    print(f"Captured {len(result.extracted_content['tweets'])} tweets")
+```
+
+**Key Capabilities:**
+- **DOM Recycling Awareness**: Detects and handles virtual DOM element recycling
+- **Smart Scroll Physics**: Three modes - container height, page height, or fixed pixels
+- **Content Preservation**: Captures content before it's destroyed
+- **Intelligent Stopping**: Stops when no new content appears
+- **Memory Efficient**: Streams content instead of holding everything in memory
+
+**Expected Real-World Impact:**
+- **Social Media Analysis**: Capture entire Twitter threads with hundreds of replies, not just top 10
+- **E-commerce Scraping**: Extract 500+ products from infinite scroll catalogs vs. 20-50 with traditional methods  
+- **News Aggregation**: Get all articles from modern news sites, not just above-the-fold content
+- **Research Applications**: Complete data extraction from academic databases using virtual pagination
+
+## 🔗 Link Preview: Intelligent Link Analysis and Scoring
+
+**The Problem:** You crawl a page and get 200 links. Which ones matter? Which lead to the content you actually want? Traditional crawlers force you to follow everything or build complex filters.
+
+**My Solution:** I implemented a three-layer scoring system that analyzes links like a human would—considering their position, context, and relevance to your goals.
+
+### Intelligent Link Analysis and Scoring
+
+```python
+import asyncio
+from crawl4ai import CrawlerRunConfig, CacheMode, AsyncWebCrawler
+from crawl4ai.adaptive_crawler import LinkPreviewConfig
+
+async def main():
+    # Configure intelligent link analysis
+    link_config = LinkPreviewConfig(
+        include_internal=True,
+        include_external=False,
+        max_links=10,
+        concurrency=5,
+        query="python tutorial",  # For contextual scoring
+        score_threshold=0.3,
+        verbose=True
+    )
+    # Use in your crawl
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            "https://www.geeksforgeeks.org/",
+            config=CrawlerRunConfig(
+                link_preview_config=link_config,
+                score_links=True,  # Enable intrinsic scoring
+                cache_mode=CacheMode.BYPASS
+            )
+        )
+
+        # Access scored and sorted links
+        if result.success and result.links:
+            for link in result.links.get("internal", []):
+                text = link.get('text', 'No text')[:40]
+                print(
+                    text,
+                    f"{link.get('intrinsic_score', 0):.1f}/10" if link.get('intrinsic_score') is not None else "0.0/10",
+                    f"{link.get('contextual_score', 0):.2f}/1" if link.get('contextual_score') is not None else "0.00/1",
+                    f"{link.get('total_score', 0):.3f}" if link.get('total_score') is not None else "0.000"
+                )
+
+asyncio.run(main())
+```
+
+**Scoring Components:**
+
+1. **Intrinsic Score**: Based on link quality indicators
+   - Position on page (navigation, content, footer)
+   - Link attributes (rel, title, class names)
+   - Anchor text quality and length
+   - URL structure and depth
+
+2. **Contextual Score**: Relevance to your query using BM25 algorithm
+   - Keyword matching in link text and title
+   - Meta description analysis
+   - Content preview scoring
+
+3. **Total Score**: Combined score for final ranking
+
+**Expected Real-World Impact:**
+- **Research Efficiency**: Find relevant papers 10x faster by following only high-score links
+- **Competitive Analysis**: Automatically identify important pages on competitor sites
+- **Content Discovery**: Build topic-focused crawlers that stay on track
+- **SEO Audits**: Identify and prioritize high-value internal linking opportunities
+
+## 🎣 Async URL Seeder: Automated URL Discovery at Scale
+
+**The Problem:** You want to crawl an entire domain but only have the homepage. Or worse, you want specific content types across thousands of pages. Manual URL discovery? That's a job for machines, not humans.
+
+**My Solution:** I built Async URL Seeder—a turbocharged URL discovery engine that combines multiple sources with intelligent filtering and relevance scoring.
+
+### Technical Architecture
+
+```python
+import asyncio
+from crawl4ai import AsyncUrlSeeder, SeedingConfig
+
+async def main():
+    async with AsyncUrlSeeder() as seeder:
+        # Discover Python tutorial URLs
+        config = SeedingConfig(
+            source="sitemap",  # Use sitemap
+            pattern="*python*",  # URL pattern filter
+            extract_head=True,  # Get metadata
+            query="python tutorial",  # For relevance scoring
+            scoring_method="bm25",
+            score_threshold=0.2,
+            max_urls=10
+        )
+        
+        print("Discovering Python async tutorial URLs...")
+        urls = await seeder.urls("https://www.geeksforgeeks.org/", config)
+        
+        print(f"\n✅ Found {len(urls)} relevant URLs:")
+        for i, url_info in enumerate(urls[:5], 1):
+            print(f"\n{i}. {url_info['url']}")
+            if url_info.get('relevance_score'):
+                print(f"   Relevance: {url_info['relevance_score']:.3f}")
+            if url_info.get('head_data', {}).get('title'):
+                print(f"   Title: {url_info['head_data']['title'][:60]}...")
+
+asyncio.run(main())
+```
+
+**Discovery Methods:**
+- **Sitemap Mining**: Parses robots.txt and all linked sitemaps
+- **Common Crawl**: Queries the Common Crawl index for historical URLs
+- **Intelligent Crawling**: Follows links with smart depth control
+- **Pattern Analysis**: Learns URL structures and generates variations
+
+**Expected Real-World Impact:**
+- **Migration Projects**: Discover 10,000+ URLs from legacy sites in under 60 seconds
+- **Market Research**: Map entire competitor ecosystems automatically
+- **Academic Research**: Build comprehensive datasets without manual URL collection
+- **SEO Audits**: Find every indexable page with content scoring
+- **Content Archival**: Ensure no content is left behind during site migrations
+
+## ⚡ Performance Optimizations
+
+This release includes significant performance improvements through optimized resource handling, better concurrency management, and reduced memory footprint.
+
+### What We Optimized
+
+```python
+# Optimized crawling with v0.7.0 improvements
+results = []
+for url in urls:
+    result = await crawler.arun(
+        url,
+        config=CrawlerRunConfig(
+            # Performance optimizations
+            wait_until="domcontentloaded",  # Faster than networkidle
+            cache_mode=CacheMode.ENABLED    # Enable caching
+        )
+    )
+    results.append(result)
+```
+
+**Performance Gains:**
+- **Startup Time**: 70% faster browser initialization
+- **Page Loading**: 40% reduction with smart resource blocking
+- **Extraction**: 3x faster with compiled CSS selectors
+- **Memory Usage**: 60% reduction with streaming processing
+- **Concurrent Crawls**: Handle 5x more parallel requests
+
+
+## 🔧 Important Changes
+
+### Breaking Changes
+- `link_extractor` renamed to `link_preview` (better reflects functionality)
+- Minimum Python version now 3.9
+- `CrawlerConfig` split into `CrawlerRunConfig` and `BrowserConfig`
+
+### Migration Guide
+```python
+# Old (v0.6.x)
+from crawl4ai import CrawlerConfig
+config = CrawlerConfig(timeout=30000)
+
+# New (v0.7.0)
+from crawl4ai import CrawlerRunConfig, BrowserConfig
+browser_config = BrowserConfig(timeout=30000)
+run_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
+```
+
+## 🤖 Coming Soon: Intelligent Web Automation
+
+I'm currently working on bringing advanced automation capabilities to Crawl4AI. This includes:
+
+- **Crawl Agents**: Autonomous crawlers that understand your goals and adapt their strategies
+- **Auto JS Generation**: Automatic JavaScript code generation for complex interactions
+- **Smart Form Handling**: Intelligent form detection and filling
+- **Context-Aware Actions**: Crawlers that understand page context and make decisions
+
+These features are under active development and will revolutionize how we approach web automation. Stay tuned!
+
+## 🚀 Get Started
+
+```bash
+pip install crawl4ai==0.7.0
+```
+
+Check out the [updated documentation](https://docs.crawl4ai.com).
+
+Questions? Issues? I'm always listening:
+- GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
+- Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
+- Twitter: [@unclecode](https://x.com/unclecode)
+
+Happy crawling! 🕷️
+
+---
+
+*P.S. If you're using Crawl4AI in production, I'd love to hear about it. Your use cases inspire the next features.*

docs/md_v2/blog/releases/0.7.1.md

@@ -0,0 +1,43 @@
+# 🛠️ Crawl4AI v0.7.1: Minor Cleanup Update
+
+*July 17, 2025 • 2 min read*
+
+---
+
+A small maintenance release that removes unused code and improves documentation.
+
+## 🎯 What's Changed
+
+- **Removed unused StealthConfig** from `crawl4ai/browser_manager.py`
+- **Updated documentation** with better examples and parameter explanations
+- **Fixed virtual scroll configuration** examples in docs
+
+## 🧹 Code Cleanup
+
+Removed unused `StealthConfig` import and configuration that wasn't being used anywhere in the codebase. The project uses its own custom stealth implementation through JavaScript injection instead.
+
+```python
+# Removed unused code:
+from playwright_stealth import StealthConfig
+stealth_config = StealthConfig(...)  # This was never used
+```
+
+## 📖 Documentation Updates
+
+- Fixed adaptive crawling parameter examples
+- Updated session management documentation
+- Corrected virtual scroll configuration examples
+
+## 🚀 Installation
+
+```bash
+pip install crawl4ai==0.7.1
+```
+
+No breaking changes - upgrade directly from v0.7.0.
+
+---
+
+Questions? Issues? 
+- GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
+- Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)

docs/md_v2/blog/releases/0.7.2.md

@@ -0,0 +1,98 @@
+# 🚀 Crawl4AI v0.7.2: CI/CD & Dependency Optimization Update
+
+*July 25, 2025 • 3 min read*
+
+---
+
+This release introduces automated CI/CD pipelines for seamless releases and optimizes dependencies for a lighter, more efficient package.
+
+## 🎯 What's New
+
+### 🔄 Automated Release Pipeline
+- **GitHub Actions CI/CD**: Automated PyPI and Docker Hub releases on tag push
+- **Multi-platform Docker images**: Support for both AMD64 and ARM64 architectures
+- **Version consistency checks**: Ensures tag, package, and Docker versions align
+- **Automated release notes**: GitHub releases created automatically
+
+### 📦 Dependency Optimization
+- **Moved sentence-transformers to optional dependencies**: Significantly reduces default installation size
+- **Lighter Docker images**: Optimized Dockerfile for faster builds and smaller images
+- **Better dependency management**: Core vs. optional dependencies clearly separated
+
+## 🏗️ CI/CD Pipeline
+
+The new automated release process ensures consistent, reliable releases:
+
+```yaml
+# Trigger releases with a simple tag
+git tag v0.7.2
+git push origin v0.7.2
+
+# Automatically:
+# ✅ Validates version consistency
+# ✅ Builds and publishes to PyPI
+# ✅ Builds multi-platform Docker images
+# ✅ Pushes to Docker Hub with proper tags
+# ✅ Creates GitHub release
+```
+
+## 💾 Lighter Installation
+
+Default installation is now significantly smaller:
+
+```bash
+# Core installation (smaller, faster)
+pip install crawl4ai==0.7.2
+
+# With ML features (includes sentence-transformers)
+pip install crawl4ai[transformer]==0.7.2
+
+# Full installation
+pip install crawl4ai[all]==0.7.2
+```
+
+## 🐳 Docker Improvements
+
+Enhanced Docker support with multi-platform images:
+
+```bash
+# Pull the latest version
+docker pull unclecode/crawl4ai:0.7.2
+docker pull unclecode/crawl4ai:latest
+
+# Available tags:
+# - unclecode/crawl4ai:0.7.2 (specific version)
+# - unclecode/crawl4ai:0.7 (minor version)
+# - unclecode/crawl4ai:0 (major version)
+# - unclecode/crawl4ai:latest
+```
+
+## 🔧 Technical Details
+
+### Dependency Changes
+- `sentence-transformers` moved from required to optional dependencies
+- Reduces default installation by ~500MB
+- No impact on functionality when transformer features aren't needed
+
+### CI/CD Configuration
+- GitHub Actions workflows for automated releases
+- Version validation before publishing
+- Parallel PyPI and Docker Hub deployments
+- Automatic tagging strategy for Docker images
+
+## 🚀 Installation
+
+```bash
+pip install crawl4ai==0.7.2
+```
+
+No breaking changes - direct upgrade from v0.7.0 or v0.7.1.
+
+---
+
+Questions? Issues? 
+- GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
+- Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
+- Twitter: [@unclecode](https://x.com/unclecode)
+
+*P.S. The new CI/CD pipeline will make future releases faster and more reliable. Thanks for your patience as we improve our release process!*

docs/md_v2/blog/releases/0.7.3.md

@@ -0,0 +1,170 @@
+# 🚀 Crawl4AI v0.7.3: The Multi-Config Intelligence Update
+
+*August 6, 2025 • 5 min read*
+
+---
+
+Today I'm releasing Crawl4AI v0.7.3—the Multi-Config Intelligence Update. This release brings smarter URL-specific configurations, flexible Docker deployments, important bug fixes, and documentation improvements that make Crawl4AI more robust and production-ready.
+
+## 🎯 What's New at a Glance
+
+- **Multi-URL Configurations**: Different crawling strategies for different URL patterns in a single batch
+- **Flexible Docker LLM Providers**: Configure LLM providers via environment variables
+- **Bug Fixes**: Resolved several critical issues for better stability
+- **Documentation Updates**: Clearer examples and improved API documentation
+
+## 🎨 Multi-URL Configurations: One Size Doesn't Fit All
+
+**The Problem:** You're crawling a mix of documentation sites, blogs, and API endpoints. Each needs different handling—caching for docs, fresh content for news, structured extraction for APIs. Previously, you'd run separate crawls or write complex conditional logic.
+
+**My Solution:** I implemented URL-specific configurations that let you define different strategies for different URL patterns in a single crawl batch. First match wins, with optional fallback support.
+
+### Technical Implementation
+
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, MatchMode
+
+# Define specialized configs for different content types
+configs = [
+    # Documentation sites - aggressive caching, include links
+    CrawlerRunConfig(
+        url_matcher=["*docs*", "*documentation*"],
+        cache_mode="write",
+        markdown_generator_options={"include_links": True}
+    ),
+    
+    # News/blog sites - fresh content, scroll for lazy loading
+    CrawlerRunConfig(
+        url_matcher=lambda url: 'blog' in url or 'news' in url,
+        cache_mode="bypass",
+        js_code="window.scrollTo(0, document.body.scrollHeight/2);"
+    ),
+    
+    # API endpoints - structured extraction
+    CrawlerRunConfig(
+        url_matcher=["*.json", "*api*"],
+        extraction_strategy=LLMExtractionStrategy(
+            provider="openai/gpt-4o-mini",
+            extraction_type="structured"
+        )
+    ),
+    
+    # Default fallback for everything else
+    CrawlerRunConfig()  # No url_matcher = matches everything
+]
+
+# Crawl multiple URLs with appropriate configs
+async with AsyncWebCrawler() as crawler:
+    results = await crawler.arun_many(
+        urls=[
+            "https://docs.python.org/3/",      # → Uses documentation config
+            "https://blog.python.org/",        # → Uses blog config  
+            "https://api.github.com/users",    # → Uses API config
+            "https://example.com/"             # → Uses default config
+        ],
+        config=configs
+    )
+```
+
+**Matching Capabilities:**
+- **String Patterns**: Wildcards like `"*.pdf"`, `"*/blog/*"`
+- **Function Matchers**: Lambda functions for complex logic
+- **Mixed Matchers**: Combine strings and functions with AND/OR logic
+- **Fallback Support**: Default config when nothing matches
+
+**Expected Real-World Impact:**
+- **Mixed Content Sites**: Handle blogs, docs, and downloads in one crawl
+- **Multi-Domain Crawling**: Different strategies per domain without separate runs
+- **Reduced Complexity**: No more if/else forests in your extraction code
+- **Better Performance**: Each URL gets exactly the processing it needs
+
+## 🐳 Docker: Flexible LLM Provider Configuration
+
+**The Problem:** Hardcoded LLM providers in Docker deployments. Want to switch from OpenAI to Groq? Rebuild and redeploy. Testing different models? Multiple Docker images.
+
+**My Solution:** Configure LLM providers via environment variables. Switch providers without touching code or rebuilding images.
+
+### Deployment Flexibility
+
+```bash
+# Option 1: Direct environment variables
+docker run -d \
+  -e LLM_PROVIDER="groq/llama-3.2-3b-preview" \
+  -e GROQ_API_KEY="your-key" \
+  -p 11235:11235 \
+  unclecode/crawl4ai:latest
+
+# Option 2: Using .llm.env file (recommended for production)
+# Create .llm.env file:
+# LLM_PROVIDER=openai/gpt-4o-mini
+# OPENAI_API_KEY=your-openai-key
+# GROQ_API_KEY=your-groq-key
+
+docker run -d \
+  --env-file .llm.env \
+  -p 11235:11235 \
+  unclecode/crawl4ai:latest
+```
+
+Override per request when needed:
+```python
+# Use default provider from .llm.env
+response = requests.post("http://localhost:11235/crawl", json={
+    "url": "https://example.com",
+    "extraction_strategy": {"type": "llm"}
+})
+
+# Override to use different provider for this specific request
+response = requests.post("http://localhost:11235/crawl", json={
+    "url": "https://complex-page.com",
+    "extraction_strategy": {
+        "type": "llm",
+        "provider": "openai/gpt-4"  # Override default
+    }
+})
+```
+
+**Expected Real-World Impact:**
+- **Cost Optimization**: Use cheaper models for simple tasks, premium for complex
+- **A/B Testing**: Compare provider performance without deployment changes
+- **Fallback Strategies**: Switch providers on-the-fly during outages
+- **Development Flexibility**: Test locally with one provider, deploy with another
+- **Secure Configuration**: Keep API keys in `.llm.env` file, not in commands
+
+## 🔧 Bug Fixes & Improvements
+
+This release includes several important bug fixes that improve stability and reliability:
+
+- **URL Matcher Fallback**: Fixed edge cases in URL pattern matching logic
+- **Memory Management**: Resolved memory leaks in long-running crawl sessions
+- **Sitemap Processing**: Fixed redirect handling in sitemap fetching
+- **Table Extraction**: Improved table detection and extraction accuracy
+- **Error Handling**: Better error messages and recovery from network failures
+
+## 📚 Documentation Enhancements
+
+Based on community feedback, we've updated:
+- Clearer examples for multi-URL configuration
+- Improved CrawlResult documentation with all available fields
+- Fixed typos and inconsistencies across documentation
+- Added real-world URLs in examples for better understanding
+- New comprehensive demo showcasing all v0.7.3 features
+
+## 🙏 Acknowledgments
+
+Thanks to our contributors and the entire community for feedback and bug reports.
+
+## 📚 Resources
+
+- [Full Documentation](https://docs.crawl4ai.com)
+- [GitHub Repository](https://github.com/unclecode/crawl4ai)
+- [Discord Community](https://discord.gg/crawl4ai)
+- [Feature Demo](https://github.com/unclecode/crawl4ai/blob/main/docs/releases_review/demo_v0.7.3.py)
+
+---
+
+*Crawl4AI continues to evolve with your needs. This release makes it smarter, more flexible, and more stable. Try the new multi-config feature and flexible Docker deployment—they're game changers!*
+
+**Happy Crawling! 🕷️**
+
+*- The Crawl4AI Team*

docs/md_v2/core/adaptive-crawling.md

@@ -35,7 +35,7 @@ from crawl4ai import AsyncWebCrawler, AdaptiveCrawler
 
 async def main():
     async with AsyncWebCrawler() as crawler:
-        # Create an adaptive crawler
+        # Create an adaptive crawler (config is optional)
         adaptive = AdaptiveCrawler(crawler)
         
         # Start crawling with a query
@@ -59,13 +59,13 @@ async def main():
 from crawl4ai import AdaptiveConfig
 
 config = AdaptiveConfig(
-    confidence_threshold=0.7,    # Stop when 70% confident (default: 0.8)
-    max_pages=20,               # Maximum pages to crawl (default: 50)
-    top_k_links=3,              # Links to follow per page (default: 5)
+    confidence_threshold=0.8,    # Stop when 80% confident (default: 0.7)
+    max_pages=30,               # Maximum pages to crawl (default: 20)
+    top_k_links=5,              # Links to follow per page (default: 3)
     min_gain_threshold=0.05     # Minimum expected gain to continue (default: 0.1)
 )
 
-adaptive = AdaptiveCrawler(crawler, config=config)
+adaptive = AdaptiveCrawler(crawler, config)
 ```
 
 ## Crawling Strategies
@@ -198,8 +198,8 @@ if result.metrics.get('is_irrelevant', False):
 The confidence score (0-1) indicates how sufficient the gathered information is:
 - **0.0-0.3**: Insufficient information, needs more crawling
 - **0.3-0.6**: Partial information, may answer basic queries
-- **0.6-0.8**: Good coverage, can answer most queries
-- **0.8-1.0**: Excellent coverage, comprehensive information
+- **0.6-0.7**: Good coverage, can answer most queries
+- **0.7-1.0**: Excellent coverage, comprehensive information
 
 ### Statistics Display
 
@@ -257,9 +257,9 @@ new_adaptive.import_knowledge_base("knowledge_base.jsonl")
 - Avoid overly broad queries
 
 ### 2. Threshold Tuning
-- Start with default (0.8) for general use
-- Lower to 0.6-0.7 for exploratory crawling
-- Raise to 0.9+ for exhaustive coverage
+- Start with default (0.7) for general use
+- Lower to 0.5-0.6 for exploratory crawling
+- Raise to 0.8+ for exhaustive coverage
 
 ### 3. Performance Optimization
 - Use appropriate `max_pages` limits

docs/md_v2/core/browser-crawler-config.md

@@ -29,6 +29,7 @@ class BrowserConfig:
         text_mode=False,
         light_mode=False,
         extra_args=None,
+        enable_stealth=False,
         # ... other advanced parameters omitted here
     ):
         ...
@@ -84,6 +85,11 @@ class BrowserConfig:
     - Additional flags for the underlying browser.  
     - E.g. `["--disable-extensions"]`.
 
+11. **`enable_stealth`**:  
+    - If `True`, enables stealth mode using playwright-stealth.  
+    - Modifies browser fingerprints to avoid basic bot detection.  
+    - Default is `False`. Recommended for sites with bot protection.
+
 ### Helper Methods
 
 Both configuration classes provide a `clone()` method to create modified copies:
@@ -209,7 +215,13 @@ class CrawlerRunConfig:
     - The maximum number of concurrent crawl sessions.  
     - Helps prevent overwhelming the system.
 
-14. **`display_mode`**:  
+14. **`url_matcher`** & **`match_mode`**:  
+    - Enable URL-specific configurations when used with `arun_many()`.
+    - Set `url_matcher` to patterns (glob, function, or list) to match specific URLs.
+    - Use `match_mode` (OR/AND) to control how multiple patterns combine.
+    - See [URL-Specific Configurations](../api/arun_many.md#url-specific-configurations) for examples.
+
+15. **`display_mode`**:  
     - The display mode for progress information (`DETAILED`, `BRIEF`, etc.).  
     - Affects how much information is printed during the crawl.
 

docs/md_v2/core/c4a-script.md

@@ -52,11 +52,9 @@ That's it! In just a few lines, you've automated a complete search workflow.
 
 Want to learn by doing? We've got you covered:
 
-**🚀 [Live Demo](https://docs.crawl4ai.com/c4a-script/demo)** - Try C4A-Script in your browser right now!
+**🚀 [Live Demo](https://docs.crawl4ai.com/apps/c4a-script/)** - Try C4A-Script in your browser right now!
 
-**📁 [Tutorial Examples](/examples/c4a_script/)** - Complete examples with source code
-
-**🛠️ [Local Tutorial](/examples/c4a_script/tutorial/)** - Run the interactive tutorial on your machine
+**📁 [Tutorial Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/c4a_script/)** - Complete examples with source code
 
 ### Running the Tutorial Locally
 

docs/md_v2/core/content-selection.md

@@ -350,15 +350,22 @@ if __name__ == "__main__":
 
 ## 6. Scraping Modes
 
-Crawl4AI provides two different scraping strategies for HTML content processing: `WebScrapingStrategy` (BeautifulSoup-based, default) and `LXMLWebScrapingStrategy` (LXML-based). The LXML strategy offers significantly better performance, especially for large HTML documents.
+Crawl4AI uses `LXMLWebScrapingStrategy` (LXML-based) as the default scraping strategy for HTML content processing. This strategy offers excellent performance, especially for large HTML documents.
+
+**Note:** For backward compatibility, `WebScrapingStrategy` is still available as an alias for `LXMLWebScrapingStrategy`.
 
 ```python
 from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LXMLWebScrapingStrategy
 
 async def main():
-    config = CrawlerRunConfig(
-        scraping_strategy=LXMLWebScrapingStrategy()  # Faster alternative to default BeautifulSoup
+    # Default configuration already uses LXMLWebScrapingStrategy
+    config = CrawlerRunConfig()
+    
+    # Or explicitly specify it if desired
+    config_explicit = CrawlerRunConfig(
+        scraping_strategy=LXMLWebScrapingStrategy()
     )
+    
     async with AsyncWebCrawler() as crawler:
         result = await crawler.arun(
             url="https://example.com", 
@@ -417,21 +424,20 @@ class CustomScrapingStrategy(ContentScrapingStrategy):
 
 ### Performance Considerations
 
-The LXML strategy can be up to 10-20x faster than BeautifulSoup strategy, particularly when processing large HTML documents. However, please note:
+The LXML strategy provides excellent performance, particularly when processing large HTML documents, offering up to 10-20x faster processing compared to BeautifulSoup-based approaches.
 
-1. LXML strategy is currently experimental
-2. In some edge cases, the parsing results might differ slightly from BeautifulSoup
-3. If you encounter any inconsistencies between LXML and BeautifulSoup results, please [raise an issue](https://github.com/codeium/crawl4ai/issues) with a reproducible example
+Benefits of LXML strategy:
+- Fast processing of large HTML documents (especially >100KB)
+- Efficient memory usage
+- Good handling of well-formed HTML
+- Robust table detection and extraction
 
-Choose LXML strategy when:
-- Processing large HTML documents (recommended for >100KB)
-- Performance is critical
-- Working with well-formed HTML
+### Backward Compatibility
 
-Stick to BeautifulSoup strategy (default) when:
-- Maximum compatibility is needed
-- Working with malformed HTML
-- Exact parsing behavior is critical
+For users upgrading from earlier versions:
+- `WebScrapingStrategy` is now an alias for `LXMLWebScrapingStrategy`
+- Existing code using `WebScrapingStrategy` will continue to work without modification
+- No changes are required to your existing code
 
 ---
 

docs/md_v2/core/crawler-result.md

@@ -19,13 +19,15 @@ class MarkdownGenerationResult(BaseModel):
 class CrawlResult(BaseModel):
     url: str
     html: str
+    fit_html: Optional[str] = None
     success: bool
     cleaned_html: Optional[str] = None
     media: Dict[str, List[Dict]] = {}
     links: Dict[str, List[Dict]] = {}
     downloaded_files: Optional[List[str]] = None
+    js_execution_result: Optional[Dict[str, Any]] = None
     screenshot: Optional[str] = None
-    pdf : Optional[bytes] = None
+    pdf: Optional[bytes] = None
     mhtml: Optional[str] = None
     markdown: Optional[Union[str, MarkdownGenerationResult]] = None
     extracted_content: Optional[str] = None
@@ -35,6 +37,12 @@ class CrawlResult(BaseModel):
     response_headers: Optional[dict] = None
     status_code: Optional[int] = None
     ssl_certificate: Optional[SSLCertificate] = None
+    dispatch_result: Optional[DispatchResult] = None
+    redirected_url: Optional[str] = None
+    network_requests: Optional[List[Dict[str, Any]]] = None
+    console_messages: Optional[List[Dict[str, Any]]] = None
+    tables: List[Dict] = Field(default_factory=list)
+
     class Config:
         arbitrary_types_allowed = True
 ```
@@ -45,11 +53,13 @@ class CrawlResult(BaseModel):
 |-------------------------------------------|-----------------------------------------------------------------------------------------------------|
 | **url (`str`)**                           | The final or actual URL crawled (in case of redirects).                                             |
 | **html (`str`)**                          | Original, unmodified page HTML. Good for debugging or custom processing.                            |
+| **fit_html (`Optional[str]`)**            | Preprocessed HTML optimized for extraction and content filtering.                                    |
 | **success (`bool`)**                      | `True` if the crawl completed without major errors, else `False`.                                   |
 | **cleaned_html (`Optional[str]`)**        | Sanitized HTML with scripts/styles removed; can exclude tags if configured via `excluded_tags` etc. |
 | **media (`Dict[str, List[Dict]]`)**       | Extracted media info (images, audio, etc.), each with attributes like `src`, `alt`, `score`, etc.   |
 | **links (`Dict[str, List[Dict]]`)**       | Extracted link data, split by `internal` and `external`. Each link usually has `href`, `text`, etc. |
 | **downloaded_files (`Optional[List[str]]`)** | If `accept_downloads=True` in `BrowserConfig`, this lists the filepaths of saved downloads.         |
+| **js_execution_result (`Optional[Dict[str, Any]]`)** | Results from JavaScript execution during crawling. |
 | **screenshot (`Optional[str]`)**          | Screenshot of the page (base64-encoded) if `screenshot=True`.                                       |
 | **pdf (`Optional[bytes]`)**               | PDF of the page if `pdf=True`.                                                                      |
 | **mhtml (`Optional[str]`)**               | MHTML snapshot of the page if `capture_mhtml=True`. Contains the full page with all resources.      |
@@ -61,6 +71,11 @@ class CrawlResult(BaseModel):
 | **response_headers (`Optional[dict]`)**   | HTTP response headers, if captured.                                                                 |
 | **status_code (`Optional[int]`)**         | HTTP status code (e.g., 200 for OK).                                                                |
 | **ssl_certificate (`Optional[SSLCertificate]`)** | SSL certificate info if `fetch_ssl_certificate=True`.                                               |
+| **dispatch_result (`Optional[DispatchResult]`)** | Additional concurrency and resource usage information when crawling URLs in parallel.               |
+| **redirected_url (`Optional[str]`)**      | The URL after any redirects (different from `url` which is the final URL).                          |
+| **network_requests (`Optional[List[Dict[str, Any]]]`)** | List of network requests, responses, and failures captured during the crawl if `capture_network_requests=True`. |
+| **console_messages (`Optional[List[Dict[str, Any]]]`)** | List of browser console messages captured during the crawl if `capture_console_messages=True`.       |
+| **tables (`List[Dict]`)**                 | Table data extracted from HTML tables with structure `[{headers, rows, caption, summary}]`.           |
 
 ---
 
@@ -172,7 +187,7 @@ Here:
 
 ---
 
-## 5. More Fields: Links, Media, and More
+## 5. More Fields: Links, Media, Tables and More
 
 ### 5.1 `links`
 
@@ -192,7 +207,77 @@ for img in images:
     print("Image URL:", img["src"], "Alt:", img.get("alt"))
 ```
 
-### 5.3 `screenshot`, `pdf`, and `mhtml`
+### 5.3 `tables`
+
+The `tables` field contains structured data extracted from HTML tables found on the crawled page. Tables are analyzed based on various criteria to determine if they are actual data tables (as opposed to layout tables), including:
+
+- Presence of thead and tbody sections
+- Use of th elements for headers
+- Column consistency
+- Text density
+- And other factors
+
+Tables that score above the threshold (default: 7) are extracted and stored in result.tables.
+
+### Accessing Table data:
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+
+async def main():
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url="https://www.w3schools.com/html/html_tables.asp",
+            config=CrawlerRunConfig(
+                table_score_threshold=7  # Minimum score for table detection
+            )
+        )
+
+        if result.success and result.tables:
+            print(f"Found {len(result.tables)} tables")
+
+            for i, table in enumerate(result.tables):
+                print(f"\nTable {i+1}:")
+                print(f"Caption: {table.get('caption', 'No caption')}")
+                print(f"Headers: {table['headers']}")
+                print(f"Rows: {len(table['rows'])}")
+
+                # Print first few rows as example
+                for j, row in enumerate(table['rows'][:3]):
+                    print(f"  Row {j+1}: {row}")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+```
+
+### Configuring Table Extraction:
+
+You can adjust the sensitivity of the table detection algorithm with:
+
+```python
+config = CrawlerRunConfig(
+    table_score_threshold=5  # Lower value = more tables detected (default: 7)
+)
+```
+
+Each extracted table contains: 
+
+- `headers`: Column header names 
+- `rows`: List of rows, each containing cell values
+- `caption`: Table caption text (if available) 
+- `summary`: Table summary attribute (if specified)
+
+### Table Extraction Tips
+
+- Not all HTML tables are extracted - only those detected as "data tables" vs. layout tables.
+- Tables with inconsistent cell counts, nested tables, or those used purely for layout may be skipped.
+- If you're missing tables, try adjusting the `table_score_threshold` to a lower value (default is 7).
+
+The table detection algorithm scores tables based on features like consistent columns, presence of headers, text density, and more. Tables scoring above the threshold are considered data tables worth extracting.
+
+
+### 5.4 `screenshot`, `pdf`, and `mhtml`
 
 If you set `screenshot=True`, `pdf=True`, or `capture_mhtml=True` in **`CrawlerRunConfig`**, then:
 
@@ -213,7 +298,7 @@ if result.mhtml:
 
 The MHTML (MIME HTML) format is particularly useful as it captures the entire web page including all of its resources (CSS, images, scripts, etc.) in a single file, making it perfect for archiving or offline viewing.
 
-### 5.4 `ssl_certificate`
+### 5.5 `ssl_certificate`
 
 If `fetch_ssl_certificate=True`, `result.ssl_certificate` holds details about the site’s SSL cert, such as issuer, validity dates, etc.
 

docs/md_v2/core/docker-deployment.md

@@ -58,13 +58,15 @@ Pull and run images directly from Docker Hub without building locally.
 
 #### 1. Pull the Image
 
-Our latest release candidate is `0.6.0-r2`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
+Our latest release is `0.7.3`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
+
+> 💡 **Note**: The `latest` tag points to the stable `0.7.3` version.
 
 ```bash
-# Pull the release candidate (recommended for latest features)
-docker pull unclecode/crawl4ai:0.6.0-r1
+# Pull the latest version
+docker pull unclecode/crawl4ai:0.7.3
 
-# Or pull the latest stable version
+# Or pull using the latest tag
 docker pull unclecode/crawl4ai:latest
 ```
 
@@ -124,7 +126,7 @@ docker stop crawl4ai && docker rm crawl4ai
 #### Docker Hub Versioning Explained
 
 *   **Image Name:** `unclecode/crawl4ai`
-*   **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.6.0-r2`)
+*   **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.7.3`)
     *   `LIBRARY_VERSION`: The semantic version of the core `crawl4ai` Python library
     *   `SUFFIX`: Optional tag for release candidates (``) and revisions (`r1`)
 *   **`latest` Tag:** Points to the most recent stable version
@@ -152,6 +154,30 @@ cp deploy/docker/.llm.env.example .llm.env
 # Now edit .llm.env and add your API keys
 ```
 
+**Flexible LLM Provider Configuration:**
+
+The Docker setup now supports flexible LLM provider configuration through three methods:
+
+1. **Environment Variable** (Highest Priority): Set `LLM_PROVIDER` to override the default
+   ```bash
+   export LLM_PROVIDER="anthropic/claude-3-opus"
+   # Or in your .llm.env file:
+   # LLM_PROVIDER=anthropic/claude-3-opus
+   ```
+
+2. **API Request Parameter**: Specify provider per request
+   ```json
+   {
+     "url": "https://example.com",
+     "f": "llm",
+     "provider": "groq/mixtral-8x7b"
+   }
+   ```
+
+3. **Config File Default**: Falls back to `config.yml` (default: `openai/gpt-4o-mini`)
+
+The system automatically selects the appropriate API key based on the configured `api_key_env` in the config file.
+
 #### 3. Build and Run with Compose
 
 The `docker-compose.yml` file in the project root provides a simplified approach that automatically handles architecture detection using buildx.
@@ -666,7 +692,7 @@ app:
 
 # Default LLM Configuration
 llm:
-  provider: "openai/gpt-4o-mini"
+  provider: "openai/gpt-4o-mini"  # Can be overridden by LLM_PROVIDER env var
   api_key_env: "OPENAI_API_KEY"
   # api_key: sk-...  # If you pass the API key directly then api_key_env will be ignored
 

docs/md_v2/core/examples.md

@@ -28,11 +28,8 @@ This page provides a comprehensive list of example scripts that demonstrate vari
 | Example | Description | Link |
 |---------|-------------|------|
 | Deep Crawling | An extensive tutorial on deep crawling capabilities, demonstrating BFS and BestFirst strategies, stream vs. non-stream execution, filters, scorers, and advanced configurations. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/deepcrawl_example.py) |
-<<<<<<< HEAD
 | Virtual Scroll | Comprehensive examples for handling virtualized scrolling on sites like Twitter, Instagram. Demonstrates different scrolling scenarios with local test server. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/virtual_scroll_example.py) |
-=======
 | Adaptive Crawling | Demonstrates intelligent crawling that automatically determines when sufficient information has been gathered. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/adaptive_crawling/) |
->>>>>>> feature/progressive-crawling
 | Dispatcher | Shows how to use the crawl dispatcher for advanced workload management. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/dispatcher_example.py) |
 | Storage State | Tutorial on managing browser storage state for persistence. | [View Guide](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/storage_state_tutorial.md) |
 | Network Console Capture | Demonstrates how to capture and analyze network requests and console logs. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/network_console_capture_example.py) |
@@ -57,6 +54,16 @@ This page provides a comprehensive list of example scripts that demonstrate vari
 | Crypto Analysis | Demonstrates how to crawl and analyze cryptocurrency data. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/crypto_analysis_example.py) |
 | SERP API | Demonstrates using Crawl4AI with search engine result pages. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/serp_api_project_11_feb.py) |
 
+## Anti-Bot & Stealth Features
+
+| Example | Description | Link |
+|---------|-------------|------|
+| Stealth Mode Quick Start | Five practical examples showing how to use stealth mode for bypassing basic bot detection. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/stealth_mode_quick_start.py) |
+| Stealth Mode Comprehensive | Comprehensive demonstration of stealth mode features with bot detection testing and comparisons. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/stealth_mode_example.py) |
+| Undetected Browser | Simple example showing how to use the undetected browser adapter. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/hello_world_undetected.py) |
+| Undetected Browser Demo | Basic demo comparing regular and undetected browser modes. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/undetected_simple_demo.py) |
+| Undetected Tests | Advanced tests comparing regular vs undetected browsers on various bot detection services. | [View Folder](https://github.com/unclecode/crawl4ai/tree/main/docs/examples/undetectability/) |
+
 ## Customization & Security
 
 | Example | Description | Link |
@@ -117,4 +124,4 @@ Some examples may require:
 
 ## Contributing New Examples
 
-If you've created an interesting example that demonstrates a unique use case or feature of Crawl4AI, we encourage you to contribute it to our examples collection. Please see our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTORS.md) for more information.
+If you've created an interesting example that demonstrates a unique use case or feature of Crawl4AI, we encourage you to contribute it to our examples collection. Please see our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTORS.md) for more information.

docs/md_v2/core/installation.md

@@ -18,7 +18,7 @@ crawl4ai-setup
 ```
 
 **What does it do?**
-- Installs or updates required Playwright browsers (Chromium, Firefox, etc.)
+- Installs or updates required browser dependencies for both regular and undetected modes
 - Performs OS-level checks (e.g., missing libs on Linux)
 - Confirms your environment is ready to crawl
 

docs/md_v2/core/link-media.md

@@ -125,7 +125,7 @@ Here's a full example you can copy, paste, and run immediately:
 ```python
 import asyncio
 from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
-from crawl4ai.async_configs import LinkPreviewConfig
+from crawl4ai import LinkPreviewConfig
 
 async def extract_link_heads_example():
     """
@@ -237,7 +237,7 @@ if __name__ == "__main__":
 The `LinkPreviewConfig` class supports these options:
 
 ```python
-from crawl4ai.async_configs import LinkPreviewConfig
+from crawl4ai import LinkPreviewConfig
 
 link_preview_config = LinkPreviewConfig(
     # BASIC SETTINGS
@@ -520,7 +520,8 @@ This approach is handy when you still want external links but need to block cert
 
 ### 4.1 Accessing `result.media`
 
-By default, Crawl4AI collects images, audio, video URLs, and data tables it finds on the page. These are stored in `result.media`, a dictionary keyed by media type (e.g., `images`, `videos`, `audio`, `tables`).
+By default, Crawl4AI collects images, audio and video URLs it finds on the page. These are stored in `result.media`, a dictionary keyed by media type (e.g., `images`, `videos`, `audio`).
+**Note: Tables have been moved from `result.media["tables"]` to the new `result.tables` format for better organization and direct access.**
 
 **Basic Example**:
 
@@ -534,14 +535,6 @@ if result.success:
         print(f"           Alt text: {img.get('alt', '')}")
         print(f"           Score: {img.get('score')}")
         print(f"           Description: {img.get('desc', '')}\n")
-    
-    # Get tables
-    tables = result.media.get("tables", [])
-    print(f"Found {len(tables)} data tables in total.")
-    for i, table in enumerate(tables):
-        print(f"[Table {i}] Caption: {table.get('caption', 'No caption')}")
-        print(f"           Columns: {len(table.get('headers', []))}")
-        print(f"           Rows: {len(table.get('rows', []))}")
 ```
 
 **Structure Example**:
@@ -568,19 +561,6 @@ result.media = {
   "audio": [
     # Similar structure but with audio-specific fields
   ],
-  "tables": [
-    {
-      "headers": ["Name", "Age", "Location"],
-      "rows": [
-        ["John Doe", "34", "New York"],
-        ["Jane Smith", "28", "San Francisco"],
-        ["Alex Johnson", "42", "Chicago"]
-      ],
-      "caption": "Employee Directory",
-      "summary": "Directory of company employees"
-    },
-    # More tables if present
-  ]
 }
 ```
 
@@ -608,53 +588,7 @@ crawler_cfg = CrawlerRunConfig(
 
 This setting attempts to discard images from outside the primary domain, keeping only those from the site you’re crawling.
 
-### 3.3 Working with Tables
-
-Crawl4AI can detect and extract structured data from HTML tables. Tables are analyzed based on various criteria to determine if they are actual data tables (as opposed to layout tables), including:
-
-- Presence of thead and tbody sections
-- Use of th elements for headers
-- Column consistency
-- Text density
-- And other factors
-
-Tables that score above the threshold (default: 7) are extracted and stored in `result.media.tables`.
-
-**Accessing Table Data**:
-
-```python
-if result.success:
-    tables = result.media.get("tables", [])
-    print(f"Found {len(tables)} data tables on the page")
-    
-    if tables:
-        # Access the first table
-        first_table = tables[0]
-        print(f"Table caption: {first_table.get('caption', 'No caption')}")
-        print(f"Headers: {first_table.get('headers', [])}")
-        
-        # Print the first 3 rows
-        for i, row in enumerate(first_table.get('rows', [])[:3]):
-            print(f"Row {i+1}: {row}")
-```
-
-**Configuring Table Extraction**:
-
-You can adjust the sensitivity of the table detection algorithm with:
-
-```python
-crawler_cfg = CrawlerRunConfig(
-    table_score_threshold=5  # Lower value = more tables detected (default: 7)
-)
-```
-
-Each extracted table contains:
-- `headers`: Column header names
-- `rows`: List of rows, each containing cell values
-- `caption`: Table caption text (if available)
-- `summary`: Table summary attribute (if specified)
-
-### 3.4 Additional Media Config
+### 4.3 Additional Media Config
 
 - **`screenshot`**: Set to `True` if you want a full-page screenshot stored as `base64` in `result.screenshot`.  
 - **`pdf`**: Set to `True` if you want a PDF version of the page in `result.pdf`.  
@@ -695,7 +629,7 @@ The MHTML format is particularly useful because:
 
 ---
 
-## 4. Putting It All Together: Link & Media Filtering
+## 5. Putting It All Together: Link & Media Filtering
 
 Here’s a combined example demonstrating how to filter out external links, skip certain domains, and exclude external images:
 
@@ -743,7 +677,7 @@ if __name__ == "__main__":
 
 ---
 
-## 5. Common Pitfalls & Tips
+## 6. Common Pitfalls & Tips
 
 1. **Conflicting Flags**:  
    - `exclude_external_links=True` but then also specifying `exclude_social_media_links=True` is typically fine, but understand that the first setting already discards *all* external links. The second becomes somewhat redundant.  
@@ -762,10 +696,3 @@ if __name__ == "__main__":
 ---
 
 **That’s it for Link & Media Analysis!** You’re now equipped to filter out unwanted sites and zero in on the images and videos that matter for your project.
-### Table Extraction Tips
-
-- Not all HTML tables are extracted - only those detected as "data tables" vs. layout tables.
-- Tables with inconsistent cell counts, nested tables, or those used purely for layout may be skipped.
-- If you're missing tables, try adjusting the `table_score_threshold` to a lower value (default is 7).
-
-The table detection algorithm scores tables based on features like consistent columns, presence of headers, text density, and more. Tables scoring above the threshold are considered data tables worth extracting.

docs/md_v2/core/simple-crawling.md

@@ -31,9 +31,16 @@ if __name__ == "__main__":
 The `arun()` method returns a `CrawlResult` object with several useful properties. Here's a quick overview (see [CrawlResult](../api/crawl-result.md) for complete details):
 
 ```python
+config = CrawlerRunConfig(
+    markdown_generator=DefaultMarkdownGenerator(
+        content_filter=PruningContentFilter(threshold=0.6),
+        options={"ignore_links": True}
+    )
+)
+
 result = await crawler.arun(
     url="https://example.com",
-    config=CrawlerRunConfig(fit_markdown=True)
+    config=config
 )
 
 # Different content formats

docs/md_v2/core/table_extraction.md

@@ -0,0 +1,807 @@
+# Table Extraction Strategies
+
+## Overview
+
+**New in v0.7.3+**: Table extraction now follows the **Strategy Design Pattern**, providing unprecedented flexibility and power for handling different table structures. Don't worry - **your existing code still works!** We maintain full backward compatibility while offering new capabilities.
+
+### What's Changed?
+- **Architecture**: Table extraction now uses pluggable strategies
+- **Backward Compatible**: Your existing code with `table_score_threshold` continues to work
+- **More Power**: Choose from multiple strategies or create your own
+- **Same Default Behavior**: By default, uses `DefaultTableExtraction` (same as before)
+
+### Key Points
+✅ **Old code still works** - No breaking changes  
+✅ **Same default behavior** - Uses the proven extraction algorithm  
+✅ **New capabilities** - Add LLM extraction or custom strategies when needed  
+✅ **Strategy pattern** - Clean, extensible architecture
+
+## Quick Start
+
+### The Simplest Way (Works Like Before)
+
+If you're already using Crawl4AI, nothing changes:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+
+async def extract_tables():
+    async with AsyncWebCrawler() as crawler:
+        # This works exactly like before - uses DefaultTableExtraction internally
+        result = await crawler.arun("https://example.com/data")
+        
+        # Tables are automatically extracted and available in result.tables
+        for table in result.tables:
+            print(f"Table with {len(table['rows'])} rows and {len(table['headers'])} columns")
+            print(f"Headers: {table['headers']}")
+            print(f"First row: {table['rows'][0] if table['rows'] else 'No data'}")
+
+asyncio.run(extract_tables())
+```
+
+### Using the Old Configuration (Still Supported)
+
+Your existing code with `table_score_threshold` continues to work:
+
+```python
+# This old approach STILL WORKS - we maintain backward compatibility
+config = CrawlerRunConfig(
+    table_score_threshold=7  # Internally creates DefaultTableExtraction(table_score_threshold=7)
+)
+result = await crawler.arun(url, config)
+```
+
+## Table Extraction Strategies
+
+### Understanding the Strategy Pattern
+
+The strategy pattern allows you to choose different table extraction algorithms at runtime. Think of it as having different tools in a toolbox - you pick the right one for the job:
+
+- **No explicit strategy?** → Uses `DefaultTableExtraction` automatically (same as v0.7.2 and earlier)
+- **Need complex table handling?** → Choose `LLMTableExtraction` (costs money, use sparingly)
+- **Want to disable tables?** → Use `NoTableExtraction`
+- **Have special requirements?** → Create a custom strategy
+
+### Available Strategies
+
+| Strategy | Description | Use Case | Cost | When to Use |
+|----------|-------------|----------|------|-------------|
+| `DefaultTableExtraction` | **RECOMMENDED**: Same algorithm as before v0.7.3 | General purpose (default) | Free | **Use this first - handles 95% of cases** |
+| `LLMTableExtraction` | AI-powered extraction for complex tables | Tables with complex rowspan/colspan | **$$$ Per API call** | Only when DefaultTableExtraction fails |
+| `NoTableExtraction` | Disables table extraction | When tables aren't needed | Free | For text-only extraction |
+| Custom strategies | User-defined extraction logic | Specialized requirements | Free | Domain-specific needs |
+
+> **⚠️ CRITICAL COST WARNING for LLMTableExtraction**: 
+> 
+> **DO NOT USE `LLMTableExtraction` UNLESS ABSOLUTELY NECESSARY!**
+> 
+> - **Always try `DefaultTableExtraction` first** - It's free and handles most tables perfectly
+> - LLM extraction **costs money** with every API call
+> - For large tables (100+ rows), LLM extraction can be **very slow**
+> - **For large tables**: If you must use LLM, choose fast providers:
+>   - ✅ **Groq** (fastest inference)
+>   - ✅ **Cerebras** (optimized for speed)
+>   - ⚠️ Avoid: OpenAI, Anthropic for large tables (slower)
+> 
+> **🚧 WORK IN PROGRESS**: 
+> We are actively developing an **advanced non-LLM algorithm** that will handle complex table structures (rowspan, colspan, nested tables) for **FREE**. This will replace the need for costly LLM extraction in most cases. Coming soon!
+
+### DefaultTableExtraction
+
+The default strategy uses a sophisticated scoring system to identify data tables:
+
+```python
+from crawl4ai import DefaultTableExtraction, CrawlerRunConfig
+
+# Customize the default extraction
+table_strategy = DefaultTableExtraction(
+    table_score_threshold=7,  # Scoring threshold (default: 7)
+    min_rows=2,               # Minimum rows required
+    min_cols=2,               # Minimum columns required
+    verbose=True              # Enable detailed logging
+)
+
+config = CrawlerRunConfig(
+    table_extraction=table_strategy
+)
+```
+
+#### Scoring System
+
+The scoring system evaluates multiple factors:
+
+| Factor | Score Impact | Description |
+|--------|--------------|-------------|
+| Has `<thead>` | +2 | Semantic table structure |
+| Has `<tbody>` | +1 | Organized table body |
+| Has `<th>` elements | +2 | Header cells present |
+| Headers in correct position | +1 | Proper semantic structure |
+| Consistent column count | +2 | Regular data structure |
+| Has caption | +2 | Descriptive caption |
+| Has summary | +1 | Summary attribute |
+| High text density | +2 to +3 | Content-rich cells |
+| Data attributes | +0.5 each | Data-* attributes |
+| Nested tables | -3 | Often indicates layout |
+| Role="presentation" | -3 | Explicitly non-data |
+| Too few rows | -2 | Insufficient data |
+
+### LLMTableExtraction (Use Sparingly!)
+
+**⚠️ WARNING**: Only use this when `DefaultTableExtraction` fails with complex tables!
+
+LLMTableExtraction uses AI to understand complex table structures that traditional parsers struggle with. It automatically handles large tables through intelligent chunking and parallel processing:
+
+```python
+from crawl4ai import LLMTableExtraction, LLMConfig, CrawlerRunConfig
+
+# Configure LLM (costs money per call!)
+llm_config = LLMConfig(
+    provider="groq/llama-3.3-70b-versatile",  # Fast provider for large tables
+    api_token="your_api_key",
+    temperature=0.1
+)
+
+# Create LLM extraction strategy with smart chunking
+table_strategy = LLMTableExtraction(
+    llm_config=llm_config,
+    max_tries=3,                      # Retry up to 3 times if extraction fails
+    css_selector="table",             # Optional: focus on specific tables
+    enable_chunking=True,             # Automatically chunk large tables (default: True)
+    chunk_token_threshold=3000,       # Split tables larger than this (default: 3000 tokens)
+    min_rows_per_chunk=10,            # Minimum rows per chunk (default: 10)
+    max_parallel_chunks=5,            # Process up to 5 chunks in parallel (default: 5)
+    verbose=True
+)
+
+config = CrawlerRunConfig(
+    table_extraction=table_strategy
+)
+
+result = await crawler.arun(url, config)
+```
+
+#### When to Use LLMTableExtraction
+
+✅ **Use ONLY when**:
+- Tables have complex merged cells (rowspan/colspan) that break DefaultTableExtraction
+- Nested tables that need semantic understanding
+- Tables with irregular structures
+- You've tried DefaultTableExtraction and it failed
+
+❌ **Never use when**:
+- DefaultTableExtraction works (99% of cases)
+- Tables are simple or well-structured
+- You're processing many pages (costs add up!)
+- Tables have 100+ rows (very slow)
+
+#### How Smart Chunking Works
+
+LLMTableExtraction automatically handles large tables through intelligent chunking:
+
+1. **Automatic Detection**: Tables exceeding the token threshold are automatically split
+2. **Smart Splitting**: Chunks are created at row boundaries, preserving table structure
+3. **Header Preservation**: Each chunk includes the original headers for context
+4. **Parallel Processing**: Multiple chunks are processed simultaneously for speed
+5. **Intelligent Merging**: Results are merged back into a single, complete table
+
+**Chunking Parameters**:
+- `enable_chunking` (default: `True`): Automatically handle large tables
+- `chunk_token_threshold` (default: `3000`): When to split tables
+- `min_rows_per_chunk` (default: `10`): Ensures meaningful chunk sizes
+- `max_parallel_chunks` (default: `5`): Concurrent processing for speed
+
+The chunking is completely transparent - you get the same output format whether the table was processed in one piece or multiple chunks.
+
+#### Performance Optimization for LLMTableExtraction
+
+**Provider Recommendations by Table Size**:
+
+| Table Size | Recommended Providers | Why |
+|------------|----------------------|-----|
+| Small (<50 rows) | Any provider | Fast enough |
+| Medium (50-200 rows) | Groq, Cerebras | Optimized inference |
+| Large (200+ rows) | **Groq** (best), Cerebras | Fastest inference + automatic chunking |
+| Very Large (500+ rows) | Groq with chunking | Parallel processing keeps it fast |
+
+### NoTableExtraction
+
+Disable table extraction for better performance when tables aren't needed:
+
+```python
+from crawl4ai import NoTableExtraction, CrawlerRunConfig
+
+config = CrawlerRunConfig(
+    table_extraction=NoTableExtraction()
+)
+
+# Tables won't be extracted, improving performance
+result = await crawler.arun(url, config)
+assert len(result.tables) == 0
+```
+
+## Extracted Table Structure
+
+Each extracted table contains:
+
+```python
+{
+    "headers": ["Column 1", "Column 2", ...],  # Column headers
+    "rows": [                                   # Data rows
+        ["Row 1 Col 1", "Row 1 Col 2", ...],
+        ["Row 2 Col 1", "Row 2 Col 2", ...],
+    ],
+    "caption": "Table Caption",                # If present
+    "summary": "Table Summary",                # If present
+    "metadata": {
+        "row_count": 10,                       # Number of rows
+        "column_count": 3,                      # Number of columns
+        "has_headers": True,                    # Headers detected
+        "has_caption": True,                    # Caption exists
+        "has_summary": False,                   # Summary exists
+        "id": "data-table-1",                   # Table ID if present
+        "class": "financial-data"               # Table class if present
+    }
+}
+```
+
+## Configuration Options
+
+### Basic Configuration
+
+```python
+config = CrawlerRunConfig(
+    # Table extraction settings
+    table_score_threshold=7,      # Default threshold (backward compatible)
+    table_extraction=strategy,     # Optional: custom strategy
+    
+    # Filter what to process
+    css_selector="main",          # Focus on specific area
+    excluded_tags=["nav", "aside"] # Exclude page sections
+)
+```
+
+### Advanced Configuration
+
+```python
+from crawl4ai import DefaultTableExtraction, CrawlerRunConfig
+
+# Fine-tuned extraction
+strategy = DefaultTableExtraction(
+    table_score_threshold=5,      # Lower = more permissive
+    min_rows=3,                   # Require at least 3 rows
+    min_cols=2,                   # Require at least 2 columns
+    verbose=True                  # Detailed logging
+)
+
+config = CrawlerRunConfig(
+    table_extraction=strategy,
+    css_selector="article.content", # Target specific content
+    exclude_domains=["ads.com"],   # Exclude ad domains
+    cache_mode=CacheMode.BYPASS    # Fresh extraction
+)
+```
+
+## Working with Extracted Tables
+
+### Convert to Pandas DataFrame
+
+```python
+import pandas as pd
+
+async def tables_to_dataframes(url):
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url)
+        
+        dataframes = []
+        for table_data in result.tables:
+            # Create DataFrame
+            if table_data['headers']:
+                df = pd.DataFrame(
+                    table_data['rows'],
+                    columns=table_data['headers']
+                )
+            else:
+                df = pd.DataFrame(table_data['rows'])
+            
+            # Add metadata as DataFrame attributes
+            df.attrs['caption'] = table_data.get('caption', '')
+            df.attrs['metadata'] = table_data.get('metadata', {})
+            
+            dataframes.append(df)
+        
+        return dataframes
+```
+
+### Filter Tables by Criteria
+
+```python
+async def extract_large_tables(url):
+    async with AsyncWebCrawler() as crawler:
+        # Configure minimum size requirements
+        strategy = DefaultTableExtraction(
+            min_rows=10,
+            min_cols=3,
+            table_score_threshold=6
+        )
+        
+        config = CrawlerRunConfig(
+            table_extraction=strategy
+        )
+        
+        result = await crawler.arun(url, config)
+        
+        # Further filter results
+        large_tables = [
+            table for table in result.tables
+            if table['metadata']['row_count'] > 10
+            and table['metadata']['column_count'] > 3
+        ]
+        
+        return large_tables
+```
+
+### Export Tables to Different Formats
+
+```python
+import json
+import csv
+
+async def export_tables(url):
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(url)
+        
+        for i, table in enumerate(result.tables):
+            # Export as JSON
+            with open(f'table_{i}.json', 'w') as f:
+                json.dump(table, f, indent=2)
+            
+            # Export as CSV
+            with open(f'table_{i}.csv', 'w', newline='') as f:
+                writer = csv.writer(f)
+                if table['headers']:
+                    writer.writerow(table['headers'])
+                writer.writerows(table['rows'])
+            
+            # Export as Markdown
+            with open(f'table_{i}.md', 'w') as f:
+                # Write headers
+                if table['headers']:
+                    f.write('| ' + ' | '.join(table['headers']) + ' |\n')
+                    f.write('|' + '---|' * len(table['headers']) + '\n')
+                
+                # Write rows
+                for row in table['rows']:
+                    f.write('| ' + ' | '.join(str(cell) for cell in row) + ' |\n')
+```
+
+## Creating Custom Strategies
+
+Extend `TableExtractionStrategy` to create custom extraction logic:
+
+### Example: Financial Table Extractor
+
+```python
+from crawl4ai import TableExtractionStrategy
+from typing import List, Dict, Any
+import re
+
+class FinancialTableExtractor(TableExtractionStrategy):
+    """Extract tables containing financial data."""
+    
+    def __init__(self, currency_symbols=None, require_numbers=True, **kwargs):
+        super().__init__(**kwargs)
+        self.currency_symbols = currency_symbols or ['$', '€', '£', '¥']
+        self.require_numbers = require_numbers
+        self.number_pattern = re.compile(r'\d+[,.]?\d*')
+    
+    def extract_tables(self, element, **kwargs):
+        tables_data = []
+        
+        for table in element.xpath(".//table"):
+            # Check if table contains financial indicators
+            table_text = ''.join(table.itertext())
+            
+            # Must contain currency symbols
+            has_currency = any(sym in table_text for sym in self.currency_symbols)
+            if not has_currency:
+                continue
+            
+            # Must contain numbers if required
+            if self.require_numbers:
+                numbers = self.number_pattern.findall(table_text)
+                if len(numbers) < 3:  # Arbitrary minimum
+                    continue
+            
+            # Extract the table data
+            table_data = self._extract_financial_data(table)
+            if table_data:
+                tables_data.append(table_data)
+        
+        return tables_data
+    
+    def _extract_financial_data(self, table):
+        """Extract and clean financial data from table."""
+        headers = []
+        rows = []
+        
+        # Extract headers
+        for th in table.xpath(".//thead//th | .//tr[1]//th"):
+            headers.append(th.text_content().strip())
+        
+        # Extract and clean rows
+        for tr in table.xpath(".//tbody//tr | .//tr[position()>1]"):
+            row = []
+            for td in tr.xpath(".//td"):
+                text = td.text_content().strip()
+                # Clean currency formatting
+                text = re.sub(r'[$€£¥,]', '', text)
+                row.append(text)
+            if row:
+                rows.append(row)
+        
+        return {
+            "headers": headers,
+            "rows": rows,
+            "caption": self._get_caption(table),
+            "summary": table.get("summary", ""),
+            "metadata": {
+                "type": "financial",
+                "row_count": len(rows),
+                "column_count": len(headers) or len(rows[0]) if rows else 0
+            }
+        }
+    
+    def _get_caption(self, table):
+        caption = table.xpath(".//caption/text()")
+        return caption[0].strip() if caption else ""
+
+# Usage
+strategy = FinancialTableExtractor(
+    currency_symbols=['$', 'EUR'],
+    require_numbers=True
+)
+
+config = CrawlerRunConfig(
+    table_extraction=strategy
+)
+```
+
+### Example: Specific Table Extractor
+
+```python
+class SpecificTableExtractor(TableExtractionStrategy):
+    """Extract only tables matching specific criteria."""
+    
+    def __init__(self, 
+                 required_headers=None, 
+                 id_pattern=None,
+                 class_pattern=None,
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.required_headers = required_headers or []
+        self.id_pattern = id_pattern
+        self.class_pattern = class_pattern
+    
+    def extract_tables(self, element, **kwargs):
+        tables_data = []
+        
+        for table in element.xpath(".//table"):
+            # Check ID pattern
+            if self.id_pattern:
+                table_id = table.get('id', '')
+                if not re.match(self.id_pattern, table_id):
+                    continue
+            
+            # Check class pattern
+            if self.class_pattern:
+                table_class = table.get('class', '')
+                if not re.match(self.class_pattern, table_class):
+                    continue
+            
+            # Extract headers to check requirements
+            headers = self._extract_headers(table)
+            
+            # Check if required headers are present
+            if self.required_headers:
+                if not all(req in headers for req in self.required_headers):
+                    continue
+            
+            # Extract full table data
+            table_data = self._extract_table_data(table, headers)
+            tables_data.append(table_data)
+        
+        return tables_data
+```
+
+## Combining with Other Strategies
+
+Table extraction works seamlessly with other Crawl4AI strategies:
+
+```python
+from crawl4ai import (
+    AsyncWebCrawler,
+    CrawlerRunConfig,
+    DefaultTableExtraction,
+    LLMExtractionStrategy,
+    JsonCssExtractionStrategy
+)
+
+async def combined_extraction(url):
+    async with AsyncWebCrawler() as crawler:
+        config = CrawlerRunConfig(
+            # Table extraction
+            table_extraction=DefaultTableExtraction(
+                table_score_threshold=6,
+                min_rows=2
+            ),
+            
+            # CSS-based extraction for specific elements
+            extraction_strategy=JsonCssExtractionStrategy({
+                "title": "h1",
+                "summary": "p.summary",
+                "date": "time"
+            }),
+            
+            # Focus on main content
+            css_selector="main.content"
+        )
+        
+        result = await crawler.arun(url, config)
+        
+        # Access different extraction results
+        tables = result.tables  # Table data
+        structured = json.loads(result.extracted_content)  # CSS extraction
+        
+        return {
+            "tables": tables,
+            "structured_data": structured,
+            "markdown": result.markdown
+        }
+```
+
+## Performance Considerations
+
+### Optimization Tips
+
+1. **Disable when not needed**: Use `NoTableExtraction` if tables aren't required
+2. **Target specific areas**: Use `css_selector` to limit processing scope
+3. **Set minimum thresholds**: Filter out small/irrelevant tables early
+4. **Cache results**: Use appropriate cache modes for repeated extractions
+
+```python
+# Optimized configuration for large pages
+config = CrawlerRunConfig(
+    # Only process main content area
+    css_selector="article.main-content",
+    
+    # Exclude navigation and sidebars
+    excluded_tags=["nav", "aside", "footer"],
+    
+    # Higher threshold for stricter filtering
+    table_extraction=DefaultTableExtraction(
+        table_score_threshold=8,
+        min_rows=5,
+        min_cols=3
+    ),
+    
+    # Enable caching for repeated access
+    cache_mode=CacheMode.ENABLED
+)
+```
+
+## Migration Guide
+
+### Important: Your Code Still Works!
+
+**No changes required!** The transition to the strategy pattern is **fully backward compatible**.
+
+### How It Works Internally
+
+#### v0.7.2 and Earlier
+```python
+# Old way - directly passing table_score_threshold
+config = CrawlerRunConfig(
+    table_score_threshold=7
+)
+# Internally: No strategy pattern, direct implementation
+```
+
+#### v0.7.3+ (Current)
+```python
+# Old way STILL WORKS - we handle it internally
+config = CrawlerRunConfig(
+    table_score_threshold=7
+)
+# Internally: Automatically creates DefaultTableExtraction(table_score_threshold=7)
+```
+
+### Taking Advantage of New Features
+
+While your old code works, you can now use the strategy pattern for more control:
+
+```python
+# Option 1: Keep using the old way (perfectly fine!)
+config = CrawlerRunConfig(
+    table_score_threshold=7  # Still supported
+)
+
+# Option 2: Use the new strategy pattern (more flexibility)
+from crawl4ai import DefaultTableExtraction
+
+strategy = DefaultTableExtraction(
+    table_score_threshold=7,
+    min_rows=2,  # New capability!
+    min_cols=2   # New capability!
+)
+
+config = CrawlerRunConfig(
+    table_extraction=strategy
+)
+
+# Option 3: Use advanced strategies when needed
+from crawl4ai import LLMTableExtraction, LLMConfig
+
+# Only for complex tables that DefaultTableExtraction can't handle
+# Automatically handles large tables with smart chunking
+llm_strategy = LLMTableExtraction(
+    llm_config=LLMConfig(
+        provider="groq/llama-3.3-70b-versatile",
+        api_token="your_key"
+    ),
+    max_tries=3,
+    enable_chunking=True,  # Automatically chunk large tables
+    chunk_token_threshold=3000,  # Chunk when exceeding 3000 tokens
+    max_parallel_chunks=5  # Process up to 5 chunks in parallel
+)
+
+config = CrawlerRunConfig(
+    table_extraction=llm_strategy  # Advanced extraction with automatic chunking
+)
+```
+
+### Summary
+
+- ✅ **No breaking changes** - Old code works as-is
+- ✅ **Same defaults** - DefaultTableExtraction is automatically used
+- ✅ **Gradual adoption** - Use new features when you need them
+- ✅ **Full compatibility** - result.tables structure unchanged
+
+## Best Practices
+
+### 1. Choose the Right Strategy (Cost-Conscious Approach)
+
+**Decision Flow**:
+```
+1. Do you need tables? 
+   → No: Use NoTableExtraction
+   → Yes: Continue to #2
+
+2. Try DefaultTableExtraction first (FREE)
+   → Works? Done! ✅
+   → Fails? Continue to #3
+
+3. Is the table critical and complex?
+   → No: Accept DefaultTableExtraction results
+   → Yes: Continue to #4
+
+4. Use LLMTableExtraction (COSTS MONEY)
+   → Small table (<50 rows): Any LLM provider
+   → Large table (50+ rows): Use Groq or Cerebras
+   → Very large (500+ rows): Reconsider - maybe chunk the page
+```
+
+**Strategy Selection Guide**:
+- **DefaultTableExtraction**: Use for 99% of cases - it's free and effective
+- **LLMTableExtraction**: Only for complex tables with merged cells that break DefaultTableExtraction
+- **NoTableExtraction**: When you only need text/markdown content
+- **Custom Strategy**: For specialized requirements (financial, scientific, etc.)
+
+### 2. Validate Extracted Data
+
+```python
+def validate_table(table):
+    """Validate table data quality."""
+    # Check structure
+    if not table.get('rows'):
+        return False
+    
+    # Check consistency
+    if table.get('headers'):
+        expected_cols = len(table['headers'])
+        for row in table['rows']:
+            if len(row) != expected_cols:
+                return False
+    
+    # Check minimum content
+    total_cells = sum(len(row) for row in table['rows'])
+    non_empty = sum(1 for row in table['rows'] 
+                    for cell in row if cell.strip())
+    
+    if non_empty / total_cells < 0.5:  # Less than 50% non-empty
+        return False
+    
+    return True
+
+# Filter valid tables
+valid_tables = [t for t in result.tables if validate_table(t)]
+```
+
+### 3. Handle Edge Cases
+
+```python
+async def robust_table_extraction(url):
+    """Extract tables with error handling."""
+    async with AsyncWebCrawler() as crawler:
+        try:
+            config = CrawlerRunConfig(
+                table_extraction=DefaultTableExtraction(
+                    table_score_threshold=6,
+                    verbose=True
+                )
+            )
+            
+            result = await crawler.arun(url, config)
+            
+            if not result.success:
+                print(f"Crawl failed: {result.error}")
+                return []
+            
+            # Process tables safely
+            processed_tables = []
+            for table in result.tables:
+                try:
+                    # Validate and process
+                    if validate_table(table):
+                        processed_tables.append(table)
+                except Exception as e:
+                    print(f"Error processing table: {e}")
+                    continue
+            
+            return processed_tables
+            
+        except Exception as e:
+            print(f"Extraction error: {e}")
+            return []
+```
+
+## Troubleshooting
+
+### Common Issues and Solutions
+
+| Issue | Cause | Solution |
+|-------|-------|----------|
+| No tables extracted | Score too high | Lower `table_score_threshold` |
+| Layout tables included | Score too low | Increase `table_score_threshold` |
+| Missing tables | CSS selector too specific | Broaden or remove `css_selector` |
+| Incomplete data | Complex table structure | Create custom strategy |
+| Performance issues | Processing entire page | Use `css_selector` to limit scope |
+
+### Debug Logging
+
+Enable verbose logging to understand extraction decisions:
+
+```python
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+
+# Enable verbose mode in strategy
+strategy = DefaultTableExtraction(
+    table_score_threshold=7,
+    verbose=True  # Detailed extraction logs
+)
+
+config = CrawlerRunConfig(
+    table_extraction=strategy,
+    verbose=True  # General crawler logs
+)
+```
+
+## See Also
+
+- [Extraction Strategies](extraction-strategies.md) - Overview of all extraction strategies
+- [Content Selection](content-selection.md) - Using CSS selectors and filters
+- [Performance Optimization](../optimization/performance-tuning.md) - Speed up extraction
+- [Examples](../examples/table_extraction_example.py) - Complete working examples

docs/md_v2/core/url-seeding.md

@@ -137,7 +137,7 @@ async def smart_blog_crawler():
             word_count_threshold=300  # Only substantial articles
         )
         
-        # Extract URLs and stream results as they come
+        # Extract URLs and crawl them
         tutorial_urls = [t["url"] for t in tutorials[:10]]
         results = await crawler.arun_many(tutorial_urls, config=config)
         
@@ -231,7 +231,7 @@ Common Crawl is a massive public dataset that regularly crawls the entire web. I
 
 ```python
 # Use both sources
-config = SeedingConfig(source="cc+sitemap")
+config = SeedingConfig(source="sitemap+cc")
 urls = await seeder.urls("example.com", config)
 ```
 
@@ -241,13 +241,13 @@ The `SeedingConfig` object is your control panel. Here's everything you can conf
 
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `source` | str | "cc" | URL source: "cc" (Common Crawl), "sitemap", or "cc+sitemap" |
+| `source` | str | "sitemap+cc" | URL source: "cc" (Common Crawl), "sitemap", or "sitemap+cc" |
 | `pattern` | str | "*" | URL pattern filter (e.g., "*/blog/*", "*.html") |
 | `extract_head` | bool | False | Extract metadata from page `<head>` |
 | `live_check` | bool | False | Verify URLs are accessible |
 | `max_urls` | int | -1 | Maximum URLs to return (-1 = unlimited) |
 | `concurrency` | int | 10 | Parallel workers for fetching |
-| `hits_per_sec` | int | None | Rate limit for requests |
+| `hits_per_sec` | int | 5 | Rate limit for requests |
 | `force` | bool | False | Bypass cache, fetch fresh data |
 | `verbose` | bool | False | Show detailed progress |
 | `query` | str | None | Search query for BM25 scoring |
@@ -522,7 +522,7 @@ urls = await seeder.urls("docs.example.com", config)
 ```python
 # Find specific products
 config = SeedingConfig(
-    source="cc+sitemap",  # Use both sources
+    source="sitemap+cc",  # Use both sources
     extract_head=True,
     query="wireless headphones noise canceling",
     scoring_method="bm25",
@@ -782,7 +782,7 @@ class ResearchAssistant:
         
         # Step 1: Discover relevant URLs
         config = SeedingConfig(
-            source="cc+sitemap",     # Maximum coverage
+            source="sitemap+cc",     # Maximum coverage
             extract_head=True,       # Get metadata
             query=topic,             # Research topic
             scoring_method="bm25",   # Smart scoring
@@ -832,7 +832,8 @@ class ResearchAssistant:
             # Extract URLs and crawl all articles
             article_urls = [article['url'] for article in top_articles]
             results = []
-            async for result in await crawler.arun_many(article_urls, config=config):
+            crawl_results = await crawler.arun_many(article_urls, config=config)
+            async for result in crawl_results:
                 if result.success:
                     results.append({
                         'url': result.url,
@@ -933,10 +934,10 @@ config = SeedingConfig(concurrency=10, hits_per_sec=5)
 # When crawling many URLs
 async with AsyncWebCrawler() as crawler:
     # Assuming urls is a list of URL strings
-    results = await crawler.arun_many(urls, config=config)
+    crawl_results = await crawler.arun_many(urls, config=config)
     
     # Process as they arrive
-    async for result in results:
+    async for result in crawl_results:
         process_immediately(result)  # Don't wait for all
 ```
 
@@ -1020,7 +1021,7 @@ config = SeedingConfig(
 
 # E-commerce product discovery
 config = SeedingConfig(
-    source="cc+sitemap",
+    source="sitemap+cc",
     pattern="*/product/*",
     extract_head=True,
     live_check=True

docs/md_v2/migration/table_extraction_v073.md

@@ -0,0 +1,376 @@
+# Migration Guide: Table Extraction v0.7.3
+
+## Overview
+
+Version 0.7.3 introduces the **Table Extraction Strategy Pattern**, providing a more flexible and extensible approach to table extraction while maintaining full backward compatibility.
+
+## What's New
+
+### Strategy Pattern Implementation
+
+Table extraction now follows the same strategy pattern used throughout Crawl4AI:
+
+- **Consistent Architecture**: Aligns with extraction, chunking, and markdown strategies
+- **Extensibility**: Easy to create custom table extraction strategies
+- **Better Separation**: Table logic moved from content scraping to dedicated module
+- **Full Control**: Fine-grained control over table detection and extraction
+
+### New Classes
+
+```python
+from crawl4ai import (
+    TableExtractionStrategy,    # Abstract base class
+    DefaultTableExtraction,      # Current implementation (default)
+    NoTableExtraction           # Explicitly disable extraction
+)
+```
+
+## Backward Compatibility
+
+**✅ All existing code continues to work without changes.**
+
+### No Changes Required
+
+If your code looks like this, it will continue to work:
+
+```python
+# This still works exactly the same
+config = CrawlerRunConfig(
+    table_score_threshold=7
+)
+result = await crawler.arun(url, config)
+tables = result.tables  # Same structure, same data
+```
+
+### What Happens Behind the Scenes
+
+When you don't specify a `table_extraction` strategy:
+
+1. `CrawlerRunConfig` automatically creates `DefaultTableExtraction`
+2. It uses your `table_score_threshold` parameter
+3. Tables are extracted exactly as before
+4. Results appear in `result.tables` with the same structure
+
+## New Capabilities
+
+### 1. Explicit Strategy Configuration
+
+You can now explicitly configure table extraction:
+
+```python
+# New: Explicit control
+strategy = DefaultTableExtraction(
+    table_score_threshold=7,
+    min_rows=2,              # New: minimum row filter
+    min_cols=2,              # New: minimum column filter
+    verbose=True             # New: detailed logging
+)
+
+config = CrawlerRunConfig(
+    table_extraction=strategy
+)
+```
+
+### 2. Disable Table Extraction
+
+Improve performance when tables aren't needed:
+
+```python
+# New: Skip table extraction entirely
+config = CrawlerRunConfig(
+    table_extraction=NoTableExtraction()
+)
+# No CPU cycles spent on table detection/extraction
+```
+
+### 3. Custom Extraction Strategies
+
+Create specialized extractors:
+
+```python
+class MyTableExtractor(TableExtractionStrategy):
+    def extract_tables(self, element, **kwargs):
+        # Custom extraction logic
+        return custom_tables
+
+config = CrawlerRunConfig(
+    table_extraction=MyTableExtractor()
+)
+```
+
+## Migration Scenarios
+
+### Scenario 1: Basic Usage (No Changes Needed)
+
+**Before (v0.7.2):**
+```python
+config = CrawlerRunConfig()
+result = await crawler.arun(url, config)
+for table in result.tables:
+    print(table['headers'])
+```
+
+**After (v0.7.3):**
+```python
+# Exactly the same - no changes required
+config = CrawlerRunConfig()
+result = await crawler.arun(url, config)
+for table in result.tables:
+    print(table['headers'])
+```
+
+### Scenario 2: Custom Threshold (No Changes Needed)
+
+**Before (v0.7.2):**
+```python
+config = CrawlerRunConfig(
+    table_score_threshold=5
+)
+```
+
+**After (v0.7.3):**
+```python
+# Still works the same
+config = CrawlerRunConfig(
+    table_score_threshold=5
+)
+
+# Or use new explicit approach for more control
+strategy = DefaultTableExtraction(
+    table_score_threshold=5,
+    min_rows=2  # Additional filtering
+)
+config = CrawlerRunConfig(
+    table_extraction=strategy
+)
+```
+
+### Scenario 3: Advanced Filtering (New Feature)
+
+**Before (v0.7.2):**
+```python
+# Had to filter after extraction
+config = CrawlerRunConfig(
+    table_score_threshold=5
+)
+result = await crawler.arun(url, config)
+
+# Manual filtering
+large_tables = [
+    t for t in result.tables 
+    if len(t['rows']) >= 5 and len(t['headers']) >= 3
+]
+```
+
+**After (v0.7.3):**
+```python
+# Filter during extraction (more efficient)
+strategy = DefaultTableExtraction(
+    table_score_threshold=5,
+    min_rows=5,
+    min_cols=3
+)
+config = CrawlerRunConfig(
+    table_extraction=strategy
+)
+result = await crawler.arun(url, config)
+# result.tables already filtered
+```
+
+## Code Organization Changes
+
+### Module Structure
+
+**Before (v0.7.2):**
+```
+crawl4ai/
+  content_scraping_strategy.py
+    - LXMLWebScrapingStrategy
+      - is_data_table()      # Table detection
+      - extract_table_data() # Table extraction
+```
+
+**After (v0.7.3):**
+```
+crawl4ai/
+  content_scraping_strategy.py
+    - LXMLWebScrapingStrategy
+      # Table methods removed, uses strategy
+  
+  table_extraction.py (NEW)
+    - TableExtractionStrategy    # Base class
+    - DefaultTableExtraction      # Moved logic here
+    - NoTableExtraction          # New option
+```
+
+### Import Changes
+
+**New imports available (optional):**
+```python
+# These are now available but not required for existing code
+from crawl4ai import (
+    TableExtractionStrategy,
+    DefaultTableExtraction,
+    NoTableExtraction
+)
+```
+
+## Performance Implications
+
+### No Performance Impact
+
+For existing code, performance remains identical:
+- Same extraction logic
+- Same scoring algorithm
+- Same processing time
+
+### Performance Improvements Available
+
+New options for better performance:
+
+```python
+# Skip tables entirely (faster)
+config = CrawlerRunConfig(
+    table_extraction=NoTableExtraction()
+)
+
+# Process only specific areas (faster)
+config = CrawlerRunConfig(
+    css_selector="main.content",
+    table_extraction=DefaultTableExtraction(
+        min_rows=5,  # Skip small tables
+        min_cols=3
+    )
+)
+```
+
+## Testing Your Migration
+
+### Verification Script
+
+Run this to verify your extraction still works:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+
+async def verify_extraction():
+    url = "your_url_here"
+    
+    async with AsyncWebCrawler() as crawler:
+        # Test 1: Old approach
+        config_old = CrawlerRunConfig(
+            table_score_threshold=7
+        )
+        result_old = await crawler.arun(url, config_old)
+        
+        # Test 2: New explicit approach
+        from crawl4ai import DefaultTableExtraction
+        config_new = CrawlerRunConfig(
+            table_extraction=DefaultTableExtraction(
+                table_score_threshold=7
+            )
+        )
+        result_new = await crawler.arun(url, config_new)
+        
+        # Compare results
+        assert len(result_old.tables) == len(result_new.tables)
+        print(f"✓ Both approaches extracted {len(result_old.tables)} tables")
+        
+        # Verify structure
+        for old, new in zip(result_old.tables, result_new.tables):
+            assert old['headers'] == new['headers']
+            assert old['rows'] == new['rows']
+        
+        print("✓ Table content identical")
+
+asyncio.run(verify_extraction())
+```
+
+## Deprecation Notes
+
+### No Deprecations
+
+- All existing parameters continue to work
+- `table_score_threshold` in `CrawlerRunConfig` is still supported
+- No breaking changes
+
+### Internal Changes (Transparent to Users)
+
+- `LXMLWebScrapingStrategy.is_data_table()` - Moved to `DefaultTableExtraction`
+- `LXMLWebScrapingStrategy.extract_table_data()` - Moved to `DefaultTableExtraction`
+
+These methods were internal and not part of the public API.
+
+## Benefits of Upgrading
+
+While not required, using the new pattern provides:
+
+1. **Better Control**: Filter tables during extraction, not after
+2. **Performance Options**: Skip extraction when not needed
+3. **Extensibility**: Create custom extractors for specific needs
+4. **Consistency**: Same pattern as other Crawl4AI strategies
+5. **Future-Proof**: Ready for upcoming advanced strategies
+
+## Troubleshooting
+
+### Issue: Different Number of Tables
+
+**Cause**: Threshold or filtering differences
+
+**Solution**: 
+```python
+# Ensure same threshold
+strategy = DefaultTableExtraction(
+    table_score_threshold=7,  # Match your old setting
+    min_rows=0,               # No filtering (default)
+    min_cols=0                # No filtering (default)
+)
+```
+
+### Issue: Import Errors
+
+**Cause**: Using new classes without importing
+
+**Solution**:
+```python
+# Add imports if using new features
+from crawl4ai import (
+    DefaultTableExtraction,
+    NoTableExtraction,
+    TableExtractionStrategy
+)
+```
+
+### Issue: Custom Strategy Not Working
+
+**Cause**: Incorrect method signature
+
+**Solution**:
+```python
+class CustomExtractor(TableExtractionStrategy):
+    def extract_tables(self, element, **kwargs):  # Correct signature
+        # Not: extract_tables(self, html)
+        # Not: extract(self, element)
+        return tables_list
+```
+
+## Getting Help
+
+If you encounter issues:
+
+1. Check your `table_score_threshold` matches previous settings
+2. Verify imports if using new classes
+3. Enable verbose logging: `DefaultTableExtraction(verbose=True)`
+4. Review the [Table Extraction Documentation](../core/table_extraction.md)
+5. Check [examples](../examples/table_extraction_example.py)
+
+## Summary
+
+- ✅ **Full backward compatibility** - No code changes required
+- ✅ **Same results** - Identical extraction behavior by default
+- ✅ **New options** - Additional control when needed
+- ✅ **Better architecture** - Consistent with Crawl4AI patterns
+- ✅ **Ready for future** - Foundation for advanced strategies
+
+The migration to v0.7.3 is seamless with no required changes while providing new capabilities for those who need them.

docs/md_v2/migration/webscraping-strategy-migration.md

@@ -0,0 +1,92 @@
+# WebScrapingStrategy Migration Guide
+
+## Overview
+
+Crawl4AI has simplified its content scraping architecture. The BeautifulSoup-based `WebScrapingStrategy` has been deprecated in favor of the faster LXML-based implementation. However, **no action is required** - your existing code will continue to work.
+
+## What Changed?
+
+1. **`WebScrapingStrategy` is now an alias** for `LXMLWebScrapingStrategy`
+2. **The BeautifulSoup implementation has been removed** (~1000 lines of redundant code)
+3. **`LXMLWebScrapingStrategy` inherits directly** from `ContentScrapingStrategy`
+4. **Performance remains optimal** with LXML as the sole implementation
+
+## Backward Compatibility
+
+**Your existing code continues to work without any changes:**
+
+```python
+# This still works perfectly
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, WebScrapingStrategy
+
+config = CrawlerRunConfig(
+    scraping_strategy=WebScrapingStrategy()  # Works as before
+)
+```
+
+## Migration Options
+
+You have three options:
+
+### Option 1: Do Nothing (Recommended)
+Your code will continue to work. `WebScrapingStrategy` is permanently aliased to `LXMLWebScrapingStrategy`.
+
+### Option 2: Update Imports (Optional)
+For clarity, you can update your imports:
+
+```python
+# Old (still works)
+from crawl4ai import WebScrapingStrategy
+strategy = WebScrapingStrategy()
+
+# New (more explicit)
+from crawl4ai import LXMLWebScrapingStrategy
+strategy = LXMLWebScrapingStrategy()
+```
+
+### Option 3: Use Default Configuration
+Since `LXMLWebScrapingStrategy` is the default, you can omit the strategy parameter:
+
+```python
+# Simplest approach - uses LXMLWebScrapingStrategy by default
+config = CrawlerRunConfig()
+```
+
+## Type Hints
+
+If you use type hints, both work:
+
+```python
+from crawl4ai import WebScrapingStrategy, LXMLWebScrapingStrategy
+
+def process_with_strategy(strategy: WebScrapingStrategy) -> None:
+    # Works with both WebScrapingStrategy and LXMLWebScrapingStrategy
+    pass
+
+# Both are valid
+process_with_strategy(WebScrapingStrategy())
+process_with_strategy(LXMLWebScrapingStrategy())
+```
+
+## Subclassing
+
+If you've subclassed `WebScrapingStrategy`, it continues to work:
+
+```python
+class MyCustomStrategy(WebScrapingStrategy):
+    def __init__(self):
+        super().__init__()
+        # Your custom code
+```
+
+## Performance Benefits
+
+By consolidating to LXML:
+- **10-20x faster** HTML parsing for large documents
+- **Lower memory usage**
+- **Consistent behavior** across all use cases
+- **Simplified maintenance** and bug fixes
+
+## Summary
+
+This change simplifies Crawl4AI's internals while maintaining 100% backward compatibility. Your existing code continues to work, and you get better performance automatically.

docs/releases_review/demo_v0.7.0.py

@@ -0,0 +1,408 @@
+"""
+🚀 Crawl4AI v0.7.0 Release Demo
+================================
+This demo showcases all major features introduced in v0.7.0 release.
+
+Major Features:
+1. ✅ Adaptive Crawling - Intelligent crawling with confidence tracking
+2. ✅ Virtual Scroll Support - Handle infinite scroll pages
+3. ✅ Link Preview - Advanced link analysis with 3-layer scoring
+4. ✅ URL Seeder - Smart URL discovery and filtering
+5. ✅ C4A Script - Domain-specific language for web automation
+6. ✅ Chrome Extension Updates - Click2Crawl and instant schema extraction
+7. ✅ PDF Parsing Support - Extract content from PDF documents
+8. ✅ Nightly Builds - Automated nightly releases
+
+Run this demo to see all features in action!
+"""
+
+import asyncio
+import json
+from typing import List, Dict
+from rich.console import Console
+from rich.table import Table
+from rich.panel import Panel
+from rich import box
+
+from crawl4ai import (
+    AsyncWebCrawler, 
+    CrawlerRunConfig, 
+    BrowserConfig,
+    CacheMode,
+    AdaptiveCrawler, 
+    AdaptiveConfig,
+    AsyncUrlSeeder, 
+    SeedingConfig,
+    c4a_compile,
+    CompilationResult
+)
+from crawl4ai.async_configs import VirtualScrollConfig, LinkPreviewConfig
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+console = Console()
+
+def print_section(title: str, description: str = ""):
+    """Print a section header"""
+    console.print(f"\n[bold cyan]{'=' * 60}[/bold cyan]")
+    console.print(f"[bold yellow]{title}[/bold yellow]")
+    if description:
+        console.print(f"[dim]{description}[/dim]")
+    console.print(f"[bold cyan]{'=' * 60}[/bold cyan]\n")
+
+
+async def demo_1_adaptive_crawling():
+    """Demo 1: Adaptive Crawling - Intelligent content extraction"""
+    print_section(
+        "Demo 1: Adaptive Crawling",
+        "Intelligently learns and adapts to website patterns"
+    )
+    
+    # Create adaptive crawler with custom configuration
+    config = AdaptiveConfig(
+        strategy="statistical",  # or "embedding"
+        confidence_threshold=0.7,
+        max_pages=10,
+        top_k_links=3,
+        min_gain_threshold=0.1
+    )
+    
+    # Example: Learn from a product page
+    console.print("[cyan]Learning from product page patterns...[/cyan]")
+    
+    async with AsyncWebCrawler() as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        
+        # Start adaptive crawl
+        console.print("[cyan]Starting adaptive crawl...[/cyan]")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/",
+            query="python decorators tutorial"
+        )
+        
+        console.print("[green]✓ Adaptive crawl completed[/green]")
+        console.print(f"  - Confidence Level: {adaptive.confidence:.0%}")
+        console.print(f"  - Pages Crawled: {len(result.crawled_urls)}")
+        console.print(f"  - Knowledge Base: {len(adaptive.state.knowledge_base)} documents")
+        
+        # Get most relevant content
+        relevant = adaptive.get_relevant_content(top_k=3)
+        if relevant:
+            console.print("\nMost relevant pages:")
+            for i, page in enumerate(relevant, 1):
+                console.print(f"  {i}. {page['url']} (relevance: {page['score']:.2%})")
+
+
+async def demo_2_virtual_scroll():
+    """Demo 2: Virtual Scroll - Handle infinite scroll pages"""
+    print_section(
+        "Demo 2: Virtual Scroll Support",
+        "Capture content from modern infinite scroll pages"
+    )
+    
+    # Configure virtual scroll - using body as container for example.com
+    scroll_config = VirtualScrollConfig(
+        container_selector="body",  # Using body since example.com has simple structure
+        scroll_count=3,  # Just 3 scrolls for demo
+        scroll_by="container_height",  # or "page_height" or pixel value
+        wait_after_scroll=0.5  # Wait 500ms after each scroll
+    )
+    
+    config = CrawlerRunConfig(
+        virtual_scroll_config=scroll_config,
+        cache_mode=CacheMode.BYPASS,
+        wait_until="networkidle"
+    )
+    
+    console.print("[cyan]Virtual Scroll Configuration:[/cyan]")
+    console.print(f"  - Container: {scroll_config.container_selector}")
+    console.print(f"  - Scroll count: {scroll_config.scroll_count}")
+    console.print(f"  - Scroll by: {scroll_config.scroll_by}")
+    console.print(f"  - Wait after scroll: {scroll_config.wait_after_scroll}s")
+    
+    console.print("\n[dim]Note: Using example.com for demo - in production, use this[/dim]")
+    console.print("[dim]with actual infinite scroll pages like social media feeds.[/dim]\n")
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            "https://example.com",
+            config=config
+        )
+        
+        if result.success:
+            console.print("[green]✓ Virtual scroll executed successfully![/green]")
+            console.print(f"  - Content length: {len(result.markdown)} chars")
+            
+            # Show example of how to use with real infinite scroll sites
+            console.print("\n[yellow]Example for real infinite scroll sites:[/yellow]")
+            console.print("""
+# For Twitter-like feeds:
+scroll_config = VirtualScrollConfig(
+    container_selector="[data-testid='primaryColumn']",
+    scroll_count=20,
+    scroll_by="container_height",
+    wait_after_scroll=1.0
+)
+
+# For Instagram-like grids:
+scroll_config = VirtualScrollConfig(
+    container_selector="main article",
+    scroll_count=15,
+    scroll_by=1000,  # Fixed pixel amount
+    wait_after_scroll=1.5
+)""")
+
+
+async def demo_3_link_preview():
+    """Demo 3: Link Preview with 3-layer scoring"""
+    print_section(
+        "Demo 3: Link Preview & Scoring",
+        "Advanced link analysis with intrinsic, contextual, and total scoring"
+    )
+    
+    # Configure link preview
+    link_config = LinkPreviewConfig(
+        include_internal=True,
+        include_external=False,
+        max_links=10,
+        concurrency=5,
+        query="python tutorial",  # For contextual scoring
+        score_threshold=0.3,
+        verbose=True
+    )
+    
+    config = CrawlerRunConfig(
+        link_preview_config=link_config,
+        score_links=True,  # Enable intrinsic scoring
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    console.print("[cyan]Analyzing links with 3-layer scoring system...[/cyan]")
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://docs.python.org/3/", config=config)
+        
+        if result.success and result.links:
+            # Get scored links
+            internal_links = result.links.get("internal", [])
+            scored_links = [l for l in internal_links if l.get("total_score")]
+            scored_links.sort(key=lambda x: x.get("total_score", 0), reverse=True)
+            
+            # Create a scoring table
+            table = Table(title="Link Scoring Results", box=box.ROUNDED)
+            table.add_column("Link Text", style="cyan", width=40)
+            table.add_column("Intrinsic Score", justify="center")
+            table.add_column("Contextual Score", justify="center")
+            table.add_column("Total Score", justify="center", style="bold green")
+            
+            for link in scored_links[:5]:
+                text = link.get('text', 'No text')[:40]
+                table.add_row(
+                    text,
+                    f"{link.get('intrinsic_score', 0):.1f}/10",
+                    f"{link.get('contextual_score', 0):.2f}/1",
+                    f"{link.get('total_score', 0):.3f}"
+                )
+            
+            console.print(table)
+
+
+async def demo_4_url_seeder():
+    """Demo 4: URL Seeder - Smart URL discovery"""
+    print_section(
+        "Demo 4: URL Seeder",
+        "Intelligent URL discovery and filtering"
+    )
+    
+    # Configure seeding
+    seeding_config = SeedingConfig(
+        source="cc+sitemap",  # or "crawl"
+        pattern="*tutorial*",  # URL pattern filter
+        max_urls=50,
+        extract_head=True,  # Get metadata
+        query="python programming",  # For relevance scoring
+        scoring_method="bm25",
+        score_threshold=0.2,
+        force = True
+    )
+    
+    console.print("[cyan]URL Seeder Configuration:[/cyan]")
+    console.print(f"  - Source: {seeding_config.source}")
+    console.print(f"  - Pattern: {seeding_config.pattern}")
+    console.print(f"  - Max URLs: {seeding_config.max_urls}")
+    console.print(f"  - Query: {seeding_config.query}")
+    console.print(f"  - Scoring: {seeding_config.scoring_method}")
+    
+    # Use URL seeder to discover URLs
+    async with AsyncUrlSeeder() as seeder:
+        console.print("\n[cyan]Discovering URLs from Python docs...[/cyan]")
+        urls = await seeder.urls("docs.python.org", seeding_config)
+        
+        console.print(f"\n[green]✓ Discovered {len(urls)} URLs[/green]")
+        for i, url_info in enumerate(urls[:5], 1):
+            console.print(f"  {i}. {url_info['url']}")
+            if url_info.get('relevance_score'):
+                console.print(f"     Relevance: {url_info['relevance_score']:.3f}")
+
+
+async def demo_5_c4a_script():
+    """Demo 5: C4A Script - Domain-specific language"""
+    print_section(
+        "Demo 5: C4A Script Language",
+        "Domain-specific language for web automation"
+    )
+    
+    # Example C4A script
+    c4a_script = """
+# Simple C4A script example
+WAIT `body` 3
+IF (EXISTS `.cookie-banner`) THEN CLICK `.accept`
+CLICK `.search-button`
+TYPE "python tutorial"
+PRESS Enter
+WAIT `.results` 5
+"""
+    
+    console.print("[cyan]C4A Script Example:[/cyan]")
+    console.print(Panel(c4a_script, title="script.c4a", border_style="blue"))
+    
+    # Compile the script
+    compilation_result = c4a_compile(c4a_script)
+    
+    if compilation_result.success:
+        console.print("[green]✓ Script compiled successfully![/green]")
+        console.print(f"  - Generated {len(compilation_result.js_code)} JavaScript statements")
+        console.print("\nFirst 3 JS statements:")
+        for stmt in compilation_result.js_code[:3]:
+            console.print(f"  • {stmt}")
+    else:
+        console.print("[red]✗ Script compilation failed[/red]")
+        if compilation_result.first_error:
+            error = compilation_result.first_error
+            console.print(f"  Error at line {error.line}: {error.message}")
+
+
+async def demo_6_css_extraction():
+    """Demo 6: Enhanced CSS/JSON extraction"""
+    print_section(
+        "Demo 6: Enhanced Extraction",
+        "Improved CSS selector and JSON extraction"
+    )
+    
+    # Define extraction schema
+    schema = {
+        "name": "Example Page Data",
+        "baseSelector": "body",
+        "fields": [
+            {
+                "name": "title",
+                "selector": "h1",
+                "type": "text"
+            },
+            {
+                "name": "paragraphs",
+                "selector": "p",
+                "type": "list",
+                "fields": [
+                    {"name": "text", "type": "text"}
+                ]
+            }
+        ]
+    }
+    
+    extraction_strategy = JsonCssExtractionStrategy(schema)
+    
+    console.print("[cyan]Extraction Schema:[/cyan]")
+    console.print(json.dumps(schema, indent=2))
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            "https://example.com",
+            config=CrawlerRunConfig(
+                extraction_strategy=extraction_strategy,
+                cache_mode=CacheMode.BYPASS
+            )
+        )
+        
+        if result.success and result.extracted_content:
+            console.print("\n[green]✓ Content extracted successfully![/green]")
+            console.print(f"Extracted: {json.dumps(json.loads(result.extracted_content), indent=2)[:200]}...")
+
+
+async def demo_7_performance_improvements():
+    """Demo 7: Performance improvements"""
+    print_section(
+        "Demo 7: Performance Improvements",
+        "Faster crawling with better resource management"
+    )
+    
+    # Performance-optimized configuration
+    config = CrawlerRunConfig(
+        cache_mode=CacheMode.ENABLED,  # Use caching
+        wait_until="domcontentloaded",  # Faster than networkidle
+        page_timeout=10000,  # 10 second timeout
+        exclude_external_links=True,
+        exclude_social_media_links=True,
+        exclude_external_images=True
+    )
+    
+    console.print("[cyan]Performance Configuration:[/cyan]")
+    console.print("  - Cache: ENABLED")
+    console.print("  - Wait: domcontentloaded (faster)")
+    console.print("  - Timeout: 10s")
+    console.print("  - Excluding: external links, images, social media")
+    
+    # Measure performance
+    import time
+    start_time = time.time()
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://example.com", config=config)
+        
+    elapsed = time.time() - start_time
+    
+    if result.success:
+        console.print(f"\n[green]✓ Page crawled in {elapsed:.2f} seconds[/green]")
+
+
+async def main():
+    """Run all demos"""
+    console.print(Panel(
+        "[bold cyan]Crawl4AI v0.7.0 Release Demo[/bold cyan]\n\n"
+        "This demo showcases all major features introduced in v0.7.0.\n"
+        "Each demo is self-contained and demonstrates a specific feature.",
+        title="Welcome",
+        border_style="blue"
+    ))
+    
+    demos = [
+        demo_1_adaptive_crawling,
+        demo_2_virtual_scroll,
+        demo_3_link_preview,
+        demo_4_url_seeder,
+        demo_5_c4a_script,
+        demo_6_css_extraction,
+        demo_7_performance_improvements
+    ]
+    
+    for i, demo in enumerate(demos, 1):
+        try:
+            await demo()
+            if i < len(demos):
+                console.print("\n[dim]Press Enter to continue to next demo...[/dim]")
+                input()
+        except Exception as e:
+            console.print(f"[red]Error in demo: {e}[/red]")
+            continue
+    
+    console.print(Panel(
+        "[bold green]Demo Complete![/bold green]\n\n"
+        "Thank you for trying Crawl4AI v0.7.0!\n"
+        "For more examples and documentation, visit:\n"
+        "https://github.com/unclecode/crawl4ai",
+        title="Complete",
+        border_style="green"
+    ))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/releases_review/v0_7_0_features_demo.py

@@ -0,0 +1,280 @@
+"""
+🚀 Crawl4AI v0.7.0 Feature Demo
+================================
+This file demonstrates the major features introduced in v0.7.0 with practical examples.
+"""
+
+import asyncio
+import json
+from pathlib import Path
+from crawl4ai import (
+    AsyncWebCrawler,
+    CrawlerRunConfig,
+    BrowserConfig,
+    CacheMode,
+    # New imports for v0.7.0
+    VirtualScrollConfig,
+    LinkPreviewConfig,
+    AdaptiveCrawler,
+    AdaptiveConfig,
+    AsyncUrlSeeder,
+    SeedingConfig,
+    c4a_compile,
+)
+
+
+async def demo_link_preview():
+    """
+    Demo 1: Link Preview with 3-Layer Scoring
+    
+    Shows how to analyze links with intrinsic quality scores,
+    contextual relevance, and combined total scores.
+    """
+    print("\n" + "="*60)
+    print("🔗 DEMO 1: Link Preview & Intelligent Scoring")
+    print("="*60)
+    
+    # Configure link preview with contextual scoring
+    config = CrawlerRunConfig(
+        link_preview_config=LinkPreviewConfig(
+            include_internal=True,
+            include_external=False,
+            max_links=10,
+            concurrency=5,
+            query="machine learning tutorials",  # For contextual scoring
+            score_threshold=0.3,  # Minimum relevance
+            verbose=True
+        ),
+        score_links=True,  # Enable intrinsic scoring
+        cache_mode=CacheMode.BYPASS
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun("https://scikit-learn.org/stable/", config=config)
+        
+        if result.success:
+            # Get scored links
+            internal_links = result.links.get("internal", [])
+            scored_links = [l for l in internal_links if l.get("total_score")]
+            scored_links.sort(key=lambda x: x.get("total_score", 0), reverse=True)
+            
+            print(f"\nTop 5 Most Relevant Links:")
+            for i, link in enumerate(scored_links[:5], 1):
+                print(f"\n{i}. {link.get('text', 'No text')[:50]}...")
+                print(f"   URL: {link['href']}")
+                print(f"   Intrinsic Score: {link.get('intrinsic_score', 0):.2f}/10")
+                print(f"   Contextual Score: {link.get('contextual_score', 0):.3f}")
+                print(f"   Total Score: {link.get('total_score', 0):.3f}")
+                
+                # Show metadata if available
+                if link.get('head_data'):
+                    title = link['head_data'].get('title', 'No title')
+                    print(f"   Title: {title[:60]}...")
+
+
+async def demo_adaptive_crawling():
+    """
+    Demo 2: Adaptive Crawling
+    
+    Shows intelligent crawling that stops when enough information
+    is gathered, with confidence tracking.
+    """
+    print("\n" + "="*60)
+    print("🎯 DEMO 2: Adaptive Crawling with Confidence Tracking")
+    print("="*60)
+    
+    # Configure adaptive crawler
+    config = AdaptiveConfig(
+        strategy="statistical",  # or "embedding" for semantic understanding
+        max_pages=10,
+        confidence_threshold=0.7,  # Stop at 70% confidence
+        top_k_links=3,  # Follow top 3 links per page
+        min_gain_threshold=0.05  # Need 5% information gain to continue
+    )
+    
+    async with AsyncWebCrawler(verbose=False) as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        
+        print("Starting adaptive crawl about Python decorators...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/glossary.html",
+            query="python decorators functions wrapping"
+        )
+        
+        print(f"\n✅ Crawling Complete!")
+        print(f"• Confidence Level: {adaptive.confidence:.0%}")
+        print(f"• Pages Crawled: {len(result.crawled_urls)}")
+        print(f"• Knowledge Base: {len(adaptive.state.knowledge_base)} documents")
+        
+        # Get most relevant content
+        relevant = adaptive.get_relevant_content(top_k=3)
+        print(f"\nMost Relevant Pages:")
+        for i, page in enumerate(relevant, 1):
+            print(f"{i}. {page['url']} (relevance: {page['score']:.2%})")
+
+
+async def demo_virtual_scroll():
+    """
+    Demo 3: Virtual Scroll for Modern Web Pages
+    
+    Shows how to capture content from pages with DOM recycling
+    (Twitter, Instagram, infinite scroll).
+    """
+    print("\n" + "="*60)
+    print("📜 DEMO 3: Virtual Scroll Support")
+    print("="*60)
+    
+    # Configure virtual scroll for a news site
+    virtual_config = VirtualScrollConfig(
+        container_selector="main, article, .content",  # Common containers
+        scroll_count=20,  # Scroll up to 20 times
+        scroll_by="container_height",  # Scroll by container height
+        wait_after_scroll=0.5  # Wait 500ms after each scroll
+    )
+    
+    config = CrawlerRunConfig(
+        virtual_scroll_config=virtual_config,
+        cache_mode=CacheMode.BYPASS,
+        wait_for="css:article"  # Wait for articles to load
+    )
+    
+    # Example with a real news site
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            "https://news.ycombinator.com/",
+            config=config
+        )
+        
+        if result.success:
+            # Count items captured
+            import re
+            items = len(re.findall(r'class="athing"', result.html))
+            print(f"\n✅ Captured {items} news items")
+            print(f"• HTML size: {len(result.html):,} bytes")
+            print(f"• Without virtual scroll, would only capture ~30 items")
+
+
+async def demo_url_seeder():
+    """
+    Demo 4: URL Seeder for Intelligent Discovery
+    
+    Shows how to discover and filter URLs before crawling,
+    with relevance scoring.
+    """
+    print("\n" + "="*60)
+    print("🌱 DEMO 4: URL Seeder - Smart URL Discovery")
+    print("="*60)
+    
+    async with AsyncUrlSeeder() as seeder:
+        # Discover Python tutorial URLs
+        config = SeedingConfig(
+            source="sitemap",  # Use sitemap
+            pattern="*python*",  # URL pattern filter
+            extract_head=True,  # Get metadata
+            query="python tutorial",  # For relevance scoring
+            scoring_method="bm25",
+            score_threshold=0.2,
+            max_urls=10
+        )
+        
+        print("Discovering Python async tutorial URLs...")
+        urls = await seeder.urls("https://www.geeksforgeeks.org/", config)
+        
+        print(f"\n✅ Found {len(urls)} relevant URLs:")
+        for i, url_info in enumerate(urls[:5], 1):
+            print(f"\n{i}. {url_info['url']}")
+            if url_info.get('relevance_score'):
+                print(f"   Relevance: {url_info['relevance_score']:.3f}")
+            if url_info.get('head_data', {}).get('title'):
+                print(f"   Title: {url_info['head_data']['title'][:60]}...")
+
+
+async def demo_c4a_script():
+    """
+    Demo 5: C4A Script Language
+    
+    Shows the domain-specific language for web automation
+    with JavaScript transpilation.
+    """
+    print("\n" + "="*60)
+    print("🎭 DEMO 5: C4A Script - Web Automation Language")
+    print("="*60)
+    
+    # Example C4A script
+    c4a_script = """
+# E-commerce automation script
+WAIT `body` 3
+
+# Handle cookie banner
+IF (EXISTS `.cookie-banner`) THEN CLICK `.accept-cookies`
+
+# Search for product
+CLICK `.search-box`
+TYPE "wireless headphones"
+PRESS Enter
+
+# Wait for results
+WAIT `.product-grid` 10
+
+# Load more products
+REPEAT (SCROLL DOWN 500, `document.querySelectorAll('.product').length < 50`)
+
+# Apply filter
+IF (EXISTS `.price-filter`) THEN CLICK `input[data-max-price="100"]`
+"""
+    
+    # Compile the script
+    print("Compiling C4A script...")
+    result = c4a_compile(c4a_script)
+    
+    if result.success:
+        print(f"✅ Successfully compiled to {len(result.js_code)} JavaScript statements!")
+        print("\nFirst 3 JS statements:")
+        for stmt in result.js_code[:3]:
+            print(f"  • {stmt}")
+        
+        # Use with crawler
+        config = CrawlerRunConfig(
+            c4a_script=c4a_script,  # Pass C4A script directly
+            cache_mode=CacheMode.BYPASS
+        )
+        
+        print("\n✅ Script ready for use with AsyncWebCrawler!")
+    else:
+        print(f"❌ Compilation error: {result.first_error.message}")
+
+
+async def main():
+    """Run all demos"""
+    print("\n🚀 Crawl4AI v0.7.0 Feature Demonstrations")
+    print("=" * 60)
+    
+    demos = [
+        ("Link Preview & Scoring", demo_link_preview),
+        ("Adaptive Crawling", demo_adaptive_crawling),
+        ("Virtual Scroll", demo_virtual_scroll),
+        ("URL Seeder", demo_url_seeder),
+        ("C4A Script", demo_c4a_script),
+    ]
+    
+    for name, demo_func in demos:
+        try:
+            await demo_func()
+        except Exception as e:
+            print(f"\n❌ Error in {name} demo: {str(e)}")
+        
+        # Pause between demos
+        await asyncio.sleep(1)
+    
+    print("\n" + "="*60)
+    print("✅ All demos completed!")
+    print("\nKey Takeaways:")
+    print("• Link Preview: 3-layer scoring for intelligent link analysis")
+    print("• Adaptive Crawling: Stop when you have enough information")
+    print("• Virtual Scroll: Capture all content from modern web pages")
+    print("• URL Seeder: Pre-discover and filter URLs efficiently")
+    print("• C4A Script: Simple language for complex automations")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

mkdocs.yml

@@ -1,4 +1,4 @@
-site_name: Crawl4AI Documentation (v0.6.x)
+site_name: Crawl4AI Documentation (v0.7.x)
 site_favicon: docs/md_v2/favicon.ico
 site_description:  🚀🤖 Crawl4AI, Open-source LLM-Friendly Web Crawler & Scraper
 site_url: https://docs.crawl4ai.com
@@ -25,6 +25,8 @@ nav:
     - "Command Line Interface": "core/cli.md"
     - "Simple Crawling": "core/simple-crawling.md"
     - "Deep Crawling": "core/deep-crawling.md"
+    - "Adaptive Crawling": "core/adaptive-crawling.md"
+    - "URL Seeding": "core/url-seeding.md"
     - "C4A-Script": "core/c4a-script.md"
     - "Crawler Result": "core/crawler-result.md"
     - "Browser, Crawler & LLM Config": "core/browser-crawler-config.md"
@@ -37,11 +39,13 @@ nav:
     - "Link & Media": "core/link-media.md"
   - Advanced:
     - "Overview": "advanced/advanced-features.md"
+    - "Adaptive Strategies": "advanced/adaptive-strategies.md"
     - "Virtual Scroll": "advanced/virtual-scroll.md"
     - "File Downloading": "advanced/file-downloading.md"
     - "Lazy Loading": "advanced/lazy-loading.md"
     - "Hooks & Auth": "advanced/hooks-auth.md"
     - "Proxy & Security": "advanced/proxy-security.md"
+    - "Undetected Browser": "advanced/undetected-browser.md"
     - "Session Management": "advanced/session-management.md"
     - "Multi-URL Crawling": "advanced/multi-url-crawling.md"
     - "Crawl Dispatcher": "advanced/crawl-dispatcher.md"

pyproject.toml

@@ -13,38 +13,37 @@ authors = [
     {name = "Unclecode", email = "unclecode@kidocode.com"}
 ]
 dependencies = [
+    "aiofiles>=24.1.0",
+    "aiohttp>=3.11.11",
     "aiosqlite~=0.20",
+    "anyio>=4.0.0",
     "lxml~=5.3",
     "litellm>=1.53.1",
     "numpy>=1.26.0,<3",
     "pillow>=10.4",
     "playwright>=1.49.0",
+    "patchright>=1.49.0",
     "python-dotenv~=1.0",
     "requests~=2.26",
     "beautifulsoup4~=4.12",
     "tf-playwright-stealth>=1.1.0",
     "xxhash~=3.4",
     "rank-bm25~=0.2",
-    "aiofiles>=24.1.0",
     "snowballstemmer~=2.2",
     "pydantic>=2.10",
     "pyOpenSSL>=24.3.0",
     "psutil>=6.1.1",
+    "PyYAML>=6.0",
     "nltk>=3.9.1",
-    "playwright",
     "rich>=13.9.4",
-    "cssselect>=1.2.0",
     "httpx>=0.27.2",
     "httpx[http2]>=0.27.2",
     "fake-useragent>=2.0.3",
     "click>=8.1.7",
-    "pyperclip>=1.8.2",
     "chardet>=5.2.0",
-    "aiohttp>=3.11.11",
     "brotli>=1.1.0",
     "humanize>=4.10.0",
     "lark>=1.2.2",
-    "sentence-transformers>=2.2.0",
     "alphashape>=1.3.1",
     "shapely>=2.0.0"
 ]
@@ -62,8 +61,8 @@ classifiers = [
 [project.optional-dependencies]
 pdf = ["PyPDF2"]  
 torch = ["torch", "nltk", "scikit-learn"]
-transformer = ["transformers", "tokenizers"]
-cosine = ["torch", "transformers", "nltk"]
+transformer = ["transformers", "tokenizers", "sentence-transformers"]
+cosine = ["torch", "transformers", "nltk", "sentence-transformers"]
 sync = ["selenium"]
 all = [
     "PyPDF2",
@@ -72,8 +71,8 @@ all = [
     "scikit-learn",
     "transformers",
     "tokenizers",
-    "selenium",
-    "PyPDF2"  
+    "sentence-transformers",
+    "selenium"
 ]
 
 [project.scripts]

requirements.txt

@@ -1,30 +1,32 @@
 # Note: These requirements are also specified in pyproject.toml
 # This file is kept for development environment setup and compatibility
+aiofiles>=24.1.0
+aiohttp>=3.11.11
 aiosqlite~=0.20
+anyio>=4.0.0
 lxml~=5.3
 litellm>=1.53.1
 numpy>=1.26.0,<3
 pillow>=10.4
 playwright>=1.49.0
+patchright>=1.49.0
 python-dotenv~=1.0
 requests~=2.26
 beautifulsoup4~=4.12
 tf-playwright-stealth>=1.1.0
 xxhash~=3.4
 rank-bm25~=0.2
-aiofiles>=24.1.0
 colorama~=0.4
 snowballstemmer~=2.2
 pydantic>=2.10
 pyOpenSSL>=24.3.0
 psutil>=6.1.1
+PyYAML>=6.0
 nltk>=3.9.1
 rich>=13.9.4
-cssselect>=1.2.0
 chardet>=5.2.0
 brotli>=1.1.0
 httpx[http2]>=0.27.2
-sentence-transformers>=2.2.0
 alphashape>=1.3.1
 shapely>=2.0.0
 

tests/async/test_content_extraction.py

@@ -91,6 +91,17 @@ async def test_css_selector_extraction():
         assert result.markdown
         assert all(heading in result.markdown for heading in ["#", "##", "###"])
 
+@pytest.mark.asyncio
+async def test_base_tag_link_extraction():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        url = "https://sohamkukreti.github.io/portfolio"
+        result = await crawler.arun(url=url)
+        assert result.success
+        assert result.links
+        assert isinstance(result.links, dict)
+        assert "internal" in result.links
+        assert "external" in result.links
+        assert any("github.com" in x["href"] for x in result.links["external"])
 
 # Entry point for debugging
 if __name__ == "__main__":

tests/async/test_content_scraper_strategy.py

@@ -12,11 +12,8 @@ parent_dir = os.path.dirname(
 sys.path.append(parent_dir)
 __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
 
-from crawl4ai.content_scraping_strategy import WebScrapingStrategy
-from crawl4ai.content_scraping_strategy import (
-    WebScrapingStrategy as WebScrapingStrategyCurrent,
-)
-# from crawl4ai.content_scrapping_strategy_current import WebScrapingStrategy as WebScrapingStrategyCurrent
+from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
+# This test compares the same strategy with itself now since WebScrapingStrategy is deprecated
 
 
 @dataclass
@@ -32,8 +29,8 @@ class TestResult:
 
 class StrategyTester:
     def __init__(self):
-        self.new_scraper = WebScrapingStrategy()
-        self.current_scraper = WebScrapingStrategyCurrent()
+        self.new_scraper = LXMLWebScrapingStrategy()
+        self.current_scraper = LXMLWebScrapingStrategy()  # Same strategy now
         with open(__location__ + "/sample_wikipedia.html", "r", encoding="utf-8") as f:
             self.WIKI_HTML = f.read()
         self.results = {"new": [], "current": []}

tests/browser/test_profiles.py

@@ -10,11 +10,13 @@ import sys
 import uuid
 import shutil
 
+from crawl4ai import BrowserProfiler
+from crawl4ai.browser_manager import BrowserManager
+
 # Add the project root to Python path if running directly
 if __name__ == "__main__":
     sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
 
-from crawl4ai.browser import BrowserManager, BrowserProfileManager
 from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
 from crawl4ai.async_logger import AsyncLogger
 
@@ -25,7 +27,7 @@ async def test_profile_creation():
     """Test creating and managing browser profiles."""
     logger.info("Testing profile creation and management", tag="TEST")
     
-    profile_manager = BrowserProfileManager(logger=logger)
+    profile_manager = BrowserProfiler(logger=logger)
     
     try:
         # List existing profiles
@@ -83,7 +85,7 @@ async def test_profile_with_browser():
     """Test using a profile with a browser."""
     logger.info("Testing using a profile with a browser", tag="TEST")
     
-    profile_manager = BrowserProfileManager(logger=logger)
+    profile_manager = BrowserProfiler(logger=logger)
     test_profile_name = f"test-browser-profile-{uuid.uuid4().hex[:8]}"
     profile_path = None
     
@@ -101,6 +103,8 @@ async def test_profile_with_browser():
         # Now use this profile with a browser
         browser_config = BrowserConfig(
             user_data_dir=profile_path,
+            use_managed_browser=True,
+            use_persistent_context=True,
             headless=True
         )
         

tests/check_dependencies.py

@@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""
+Dependency checker for Crawl4AI
+Analyzes imports in the codebase and shows which files use them
+"""
+
+import ast
+import os
+import sys
+from pathlib import Path
+from typing import Set, Dict, List, Tuple
+from collections import defaultdict
+import re
+import toml
+
+# Standard library modules to ignore
+STDLIB_MODULES = {
+    'abc', 'argparse', 'asyncio', 'base64', 'collections', 'concurrent', 'contextlib',
+    'copy', 'datetime', 'decimal', 'email', 'enum', 'functools', 'glob', 'hashlib',
+    'http', 'importlib', 'io', 'itertools', 'json', 'logging', 'math', 'mimetypes',
+    'multiprocessing', 'os', 'pathlib', 'pickle', 'platform', 'pprint', 'random',
+    're', 'shutil', 'signal', 'socket', 'sqlite3', 'string', 'subprocess', 'sys',
+    'tempfile', 'threading', 'time', 'traceback', 'typing', 'unittest', 'urllib',
+    'uuid', 'warnings', 'weakref', 'xml', 'zipfile', 'dataclasses', 'secrets',
+    'statistics', 'textwrap', 'queue', 'csv', 'gzip', 'tarfile', 'configparser',
+    'inspect', 'operator', 'struct', 'binascii', 'codecs', 'locale', 'gc',
+    'atexit', 'builtins', 'html', 'errno', 'fcntl', 'pwd', 'grp', 'resource',
+    'termios', 'tty', 'pty', 'select', 'selectors', 'ssl', 'zlib', 'bz2',
+    'lzma', 'types', 'copy', 'pydoc', 'profile', 'cProfile', 'timeit',
+    'trace', 'doctest', 'pdb', 'contextvars', 'dataclasses', 'graphlib',
+    'zoneinfo', 'tomllib', 'cgi', 'wsgiref', 'fileinput', 'linecache',
+    'tokenize', 'tabnanny', 'compileall', 'dis', 'pickletools', 'formatter',
+    '__future__', 'array', 'ctypes', 'heapq', 'bisect', 'array', 'weakref',
+    'types', 'copy', 'pprint', 'repr', 'numbers', 'cmath', 'fractions',
+    'statistics', 'itertools', 'functools', 'operator', 'pathlib', 'fileinput',
+    'stat', 'filecmp', 'tempfile', 'glob', 'fnmatch', 'linecache', 'shutil',
+    'pickle', 'copyreg', 'shelve', 'marshal', 'dbm', 'sqlite3', 'zlib', 'gzip',
+    'bz2', 'lzma', 'zipfile', 'tarfile', 'configparser', 'netrc', 'xdrlib',
+    'plistlib', 'hashlib', 'hmac', 'secrets', 'os', 'io', 'time', 'argparse',
+    'getopt', 'logging', 'getpass', 'curses', 'platform', 'errno', 'ctypes',
+    'threading', 'multiprocessing', 'concurrent', 'subprocess', 'sched', 'queue',
+    'contextvars', 'asyncio', 'socket', 'ssl', 'email', 'json', 'mailcap',
+    'mailbox', 'mimetypes', 'base64', 'binhex', 'binascii', 'quopri', 'uu',
+    'html', 'xml', 'webbrowser', 'cgi', 'cgitb', 'wsgiref', 'urllib', 'http',
+    'ftplib', 'poplib', 'imaplib', 'nntplib', 'smtplib', 'smtpd', 'telnetlib',
+    'uuid', 'socketserver', 'xmlrpc', 'ipaddress', 'audioop', 'aifc', 'sunau',
+    'wave', 'chunk', 'colorsys', 'imghdr', 'sndhdr', 'ossaudiodev', 'gettext',
+    'locale', 'turtle', 'cmd', 'shlex', 'tkinter', 'typing', 'pydoc', 'doctest',
+    'unittest', 'test', '2to3', 'distutils', 'venv', 'ensurepip', 'zipapp',
+    'py_compile', 'compileall', 'dis', 'pickletools', 'pdb', 'timeit', 'trace',
+    'tracemalloc', 'warnings', 'faulthandler', 'pdb', 'dataclasses', 'cgi', 
+    'cgitb', 'chunk', 'crypt', 'imghdr', 'mailcap', 'nis', 'nntplib', 'optparse',
+    'ossaudiodev', 'pipes', 'smtpd', 'sndhdr', 'spwd', 'sunau', 'telnetlib',
+    'uu', 'xdrlib', 'msilib', 'pstats', 'rlcompleter', 'tkinter', 'ast'
+}
+
+# Known package name mappings (import name -> package name)
+PACKAGE_MAPPINGS = {
+    'bs4': 'beautifulsoup4',
+    'PIL': 'pillow',
+    'cv2': 'opencv-python',
+    'sklearn': 'scikit-learn',
+    'yaml': 'PyYAML',
+    'OpenSSL': 'pyOpenSSL',
+    'sqlalchemy': 'SQLAlchemy',
+    'playwright': 'playwright',
+    'patchright': 'patchright',
+    'dotenv': 'python-dotenv',
+    'fake_useragent': 'fake-useragent',
+    'playwright_stealth': 'tf-playwright-stealth',
+    'sentence_transformers': 'sentence-transformers',
+    'rank_bm25': 'rank-bm25',
+    'snowballstemmer': 'snowballstemmer',
+    'PyPDF2': 'PyPDF2',
+    'pdf2image': 'pdf2image',
+}
+
+
+class ImportVisitor(ast.NodeVisitor):
+    """AST visitor to extract imports from Python files"""
+    
+    def __init__(self):
+        self.imports = {}  # Changed to dict to store line numbers
+        self.from_imports = {}
+    
+    def visit_Import(self, node):
+        for alias in node.names:
+            module_name = alias.name.split('.')[0]
+            if module_name not in self.imports:
+                self.imports[module_name] = []
+            self.imports[module_name].append(node.lineno)
+    
+    def visit_ImportFrom(self, node):
+        if node.module and node.level == 0:  # absolute imports only
+            module_name = node.module.split('.')[0]
+            if module_name not in self.from_imports:
+                self.from_imports[module_name] = []
+            self.from_imports[module_name].append(node.lineno)
+
+
+def extract_imports_from_file(filepath: Path) -> Dict[str, List[int]]:
+    """Extract all imports from a Python file with line numbers"""
+    all_imports = {}
+    
+    try:
+        with open(filepath, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        tree = ast.parse(content)
+        visitor = ImportVisitor()
+        visitor.visit(tree)
+        
+        # Merge imports and from_imports
+        for module, lines in visitor.imports.items():
+            if module not in all_imports:
+                all_imports[module] = []
+            all_imports[module].extend(lines)
+            
+        for module, lines in visitor.from_imports.items():
+            if module not in all_imports:
+                all_imports[module] = []
+            all_imports[module].extend(lines)
+        
+    except Exception as e:
+        # Silently skip files that can't be parsed
+        pass
+    
+    return all_imports
+
+
+def get_codebase_imports_with_files(root_dir: Path) -> Dict[str, List[Tuple[str, List[int]]]]:
+    """Get all imports from the crawl4ai library and docs folders with file locations and line numbers"""
+    import_to_files = defaultdict(list)
+    
+    # Only scan crawl4ai library folder and docs folder
+    target_dirs = [
+        root_dir / 'crawl4ai',
+        root_dir / 'docs'
+    ]
+    
+    for target_dir in target_dirs:
+        if not target_dir.exists():
+            continue
+            
+        for py_file in target_dir.rglob('*.py'):
+            # Skip __pycache__ directories
+            if '__pycache__' in py_file.parts:
+                continue
+            
+            # Skip setup.py and similar files
+            if py_file.name in ['setup.py', 'setup.cfg', 'conf.py']:
+                continue
+                
+            imports = extract_imports_from_file(py_file)
+            
+            # Map each import to the file and line numbers
+            for imp, line_numbers in imports.items():
+                relative_path = py_file.relative_to(root_dir)
+                import_to_files[imp].append((str(relative_path), sorted(line_numbers)))
+    
+    return dict(import_to_files)
+
+
+def get_declared_dependencies() -> Set[str]:
+    """Get declared dependencies from pyproject.toml and requirements.txt"""
+    declared = set()
+    
+    # Read from pyproject.toml
+    if Path('pyproject.toml').exists():
+        with open('pyproject.toml', 'r') as f:
+            data = toml.load(f)
+        
+        # Get main dependencies
+        deps = data.get('project', {}).get('dependencies', [])
+        for dep in deps:
+            # Parse dependency string (e.g., "numpy>=1.26.0,<3")
+            match = re.match(r'^([a-zA-Z0-9_-]+)', dep)
+            if match:
+                pkg_name = match.group(1).lower()
+                declared.add(pkg_name)
+        
+        # Get optional dependencies
+        optional = data.get('project', {}).get('optional-dependencies', {})
+        for group, deps in optional.items():
+            for dep in deps:
+                match = re.match(r'^([a-zA-Z0-9_-]+)', dep)
+                if match:
+                    pkg_name = match.group(1).lower()
+                    declared.add(pkg_name)
+    
+    # Also check requirements.txt as backup
+    if Path('requirements.txt').exists():
+        with open('requirements.txt', 'r') as f:
+            for line in f:
+                line = line.strip()
+                if line and not line.startswith('#'):
+                    match = re.match(r'^([a-zA-Z0-9_-]+)', line)
+                    if match:
+                        pkg_name = match.group(1).lower()
+                        declared.add(pkg_name)
+    
+    return declared
+
+
+def normalize_package_name(name: str) -> str:
+    """Normalize package name for comparison"""
+    # Handle known mappings first
+    if name in PACKAGE_MAPPINGS:
+        return PACKAGE_MAPPINGS[name].lower()
+    
+    # Basic normalization
+    return name.lower().replace('_', '-')
+
+
+def check_missing_dependencies():
+    """Main function to check for missing dependencies"""
+    print("🔍 Analyzing crawl4ai library and docs folders...\n")
+    
+    # Get all imports with their file locations
+    root_dir = Path('.')
+    import_to_files = get_codebase_imports_with_files(root_dir)
+    
+    # Get declared dependencies
+    declared_deps = get_declared_dependencies()
+    
+    # Normalize declared dependencies
+    normalized_declared = {normalize_package_name(dep) for dep in declared_deps}
+    
+    # Categorize imports
+    external_imports = {}
+    local_imports = {}
+    
+    # Known local packages
+    local_packages = {'crawl4ai'}
+    
+    for imp, file_info in import_to_files.items():
+        # Skip standard library
+        if imp in STDLIB_MODULES:
+            continue
+            
+        # Check if it's a local import
+        if any(imp.startswith(local) for local in local_packages):
+            local_imports[imp] = file_info
+        else:
+            external_imports[imp] = file_info
+    
+    # Check which external imports are not declared
+    not_declared = {}
+    declared_imports = {}
+    
+    for imp, file_info in external_imports.items():
+        normalized_imp = normalize_package_name(imp)
+        
+        # Check if import is covered by declared dependencies
+        found = False
+        for declared in normalized_declared:
+            if normalized_imp == declared or normalized_imp.startswith(declared + '.') or declared.startswith(normalized_imp):
+                found = True
+                break
+        
+        if found:
+            declared_imports[imp] = file_info
+        else:
+            not_declared[imp] = file_info
+    
+    # Print results
+    print(f"📊 Summary:")
+    print(f"  - Total unique imports: {len(import_to_files)}")
+    print(f"  - External imports: {len(external_imports)}")
+    print(f"  - Declared dependencies: {len(declared_deps)}")
+    print(f"  - External imports NOT in dependencies: {len(not_declared)}\n")
+    
+    if not_declared:
+        print("❌ External imports NOT declared in pyproject.toml or requirements.txt:\n")
+        
+        # Sort by import name
+        for imp in sorted(not_declared.keys()):
+            file_info = not_declared[imp]
+            print(f"  📦 {imp}")
+            if imp in PACKAGE_MAPPINGS:
+                print(f"     → Package name: {PACKAGE_MAPPINGS[imp]}")
+            
+            # Show up to 3 files that use this import
+            for i, (file_path, line_numbers) in enumerate(file_info[:3]):
+                # Format line numbers for clickable output
+                if len(line_numbers) == 1:
+                    print(f"     - {file_path}:{line_numbers[0]}")
+                else:
+                    # Show first few line numbers
+                    line_str = ','.join(str(ln) for ln in line_numbers[:3])
+                    if len(line_numbers) > 3:
+                        line_str += f"... ({len(line_numbers)} imports)"
+                    print(f"     - {file_path}: lines {line_str}")
+            
+            if len(file_info) > 3:
+                print(f"     ... and {len(file_info) - 3} more files")
+            print()
+    
+    # Check for potentially unused dependencies
+    print("\n🔎 Checking declared dependencies usage...\n")
+    
+    # Get all used external packages
+    used_packages = set()
+    for imp in external_imports.keys():
+        normalized = normalize_package_name(imp)
+        used_packages.add(normalized)
+    
+    # Find unused
+    unused = []
+    for dep in declared_deps:
+        normalized_dep = normalize_package_name(dep)
+        
+        # Check if any import uses this dependency
+        found_usage = False
+        for used in used_packages:
+            if used == normalized_dep or used.startswith(normalized_dep) or normalized_dep.startswith(used):
+                found_usage = True
+                break
+        
+        if not found_usage:
+            # Some packages are commonly unused directly
+            indirect_deps = {'wheel', 'setuptools', 'pip', 'colorama', 'certifi', 'packaging', 'urllib3'}
+            if normalized_dep not in indirect_deps:
+                unused.append(dep)
+    
+    if unused:
+        print("⚠️  Declared dependencies with NO imports found:")
+        for dep in sorted(unused):
+            print(f"  - {dep}")
+        print("\n  Note: These might be used indirectly or by other dependencies")
+    else:
+        print("✅ All declared dependencies have corresponding imports")
+    
+    print("\n" + "="*60)
+    print("💡 How to use this report:")
+    print("  1. Check each ❌ import to see if it's legitimate")
+    print("  2. If legitimate, add the package to pyproject.toml")
+    print("  3. If it's an internal module or typo, fix the import")
+    print("  4. Review unused dependencies - remove if truly not needed")
+    print("="*60)
+
+
+if __name__ == '__main__':
+    check_missing_dependencies()

tests/docker/simple_api_test.py

@@ -0,0 +1,365 @@
+#!/usr/bin/env python3
+"""
+Simple API Test for Crawl4AI Docker Server v0.7.0
+Uses only built-in Python modules to test all endpoints.
+"""
+
+import urllib.request
+import urllib.parse
+import json
+import time
+import sys
+from typing import Dict, List, Optional
+
+# Configuration
+BASE_URL = "http://localhost:11234"  # Change to your server URL
+TEST_TIMEOUT = 30
+
+class SimpleApiTester:
+    def __init__(self, base_url: str = BASE_URL):
+        self.base_url = base_url
+        self.token = None
+        self.results = []
+        
+    def log(self, message: str):
+        print(f"[INFO] {message}")
+    
+    def test_get_endpoint(self, endpoint: str) -> Dict:
+        """Test a GET endpoint"""
+        url = f"{self.base_url}{endpoint}"
+        start_time = time.time()
+        
+        try:
+            req = urllib.request.Request(url)
+            if self.token:
+                req.add_header('Authorization', f'Bearer {self.token}')
+            
+            with urllib.request.urlopen(req, timeout=TEST_TIMEOUT) as response:
+                response_time = time.time() - start_time
+                status_code = response.getcode()
+                content = response.read().decode('utf-8')
+                
+                # Try to parse JSON
+                try:
+                    data = json.loads(content)
+                except:
+                    data = {"raw_response": content[:200]}
+                
+                return {
+                    "endpoint": endpoint,
+                    "method": "GET",
+                    "status": "PASS" if status_code < 400 else "FAIL",
+                    "status_code": status_code,
+                    "response_time": response_time,
+                    "data": data
+                }
+        except Exception as e:
+            response_time = time.time() - start_time
+            return {
+                "endpoint": endpoint,
+                "method": "GET",
+                "status": "FAIL",
+                "status_code": None,
+                "response_time": response_time,
+                "error": str(e)
+            }
+    
+    def test_post_endpoint(self, endpoint: str, payload: Dict) -> Dict:
+        """Test a POST endpoint"""
+        url = f"{self.base_url}{endpoint}"
+        start_time = time.time()
+        
+        try:
+            data = json.dumps(payload).encode('utf-8')
+            req = urllib.request.Request(url, data=data, method='POST')
+            req.add_header('Content-Type', 'application/json')
+            
+            if self.token:
+                req.add_header('Authorization', f'Bearer {self.token}')
+            
+            with urllib.request.urlopen(req, timeout=TEST_TIMEOUT) as response:
+                response_time = time.time() - start_time
+                status_code = response.getcode()
+                content = response.read().decode('utf-8')
+                
+                # Try to parse JSON
+                try:
+                    data = json.loads(content)
+                except:
+                    data = {"raw_response": content[:200]}
+                
+                return {
+                    "endpoint": endpoint,
+                    "method": "POST",
+                    "status": "PASS" if status_code < 400 else "FAIL",
+                    "status_code": status_code,
+                    "response_time": response_time,
+                    "data": data
+                }
+        except Exception as e:
+            response_time = time.time() - start_time
+            return {
+                "endpoint": endpoint,
+                "method": "POST",
+                "status": "FAIL",
+                "status_code": None,
+                "response_time": response_time,
+                "error": str(e)
+            }
+    
+    def print_result(self, result: Dict):
+        """Print a formatted test result"""
+        status_color = {
+            "PASS": "✅",
+            "FAIL": "❌",
+            "SKIP": "⏭️"
+        }
+        
+        print(f"{status_color[result['status']]} {result['method']} {result['endpoint']} "
+              f"| {result['response_time']:.3f}s | Status: {result['status_code'] or 'N/A'}")
+        
+        if result['status'] == 'FAIL' and 'error' in result:
+            print(f"    Error: {result['error']}")
+        
+        self.results.append(result)
+    
+    def run_all_tests(self):
+        """Run all API tests"""
+        print("🚀 Starting Crawl4AI v0.7.0 API Test Suite")
+        print(f"📡 Testing server at: {self.base_url}")
+        print("=" * 60)
+        
+        # # Test basic endpoints
+        # print("\n=== BASIC ENDPOINTS ===")
+        
+        # # Health check
+        # result = self.test_get_endpoint("/health")
+        # self.print_result(result)
+        
+        
+        # # Schema endpoint
+        # result = self.test_get_endpoint("/schema")
+        # self.print_result(result)
+        
+        # # Metrics endpoint
+        # result = self.test_get_endpoint("/metrics")
+        # self.print_result(result)
+        
+        # # Root redirect
+        # result = self.test_get_endpoint("/")
+        # self.print_result(result)
+        
+        # # Test authentication
+        # print("\n=== AUTHENTICATION ===")
+        
+        # # Get token
+        # token_payload = {"email": "test@example.com"}
+        # result = self.test_post_endpoint("/token", token_payload)
+        # self.print_result(result)
+        
+        # # Extract token if successful
+        # if result['status'] == 'PASS' and 'data' in result:
+        #     token = result['data'].get('access_token')
+        #     if token:
+        #         self.token = token
+        #         self.log(f"Successfully obtained auth token: {token[:20]}...")
+        
+        # Test core APIs
+        print("\n=== CORE APIs ===")
+        
+        test_url = "https://example.com"
+        test_raw_html_url = "raw://<html><body><h1>Hello, World!</h1></body></html>"
+        # Test markdown endpoint
+        md_payload = {
+            "url": test_url,
+            "f": "fit",
+            "q": "test query",
+            "c": "0"
+        }
+        result = self.test_post_endpoint("/md", md_payload)
+        # print(result['data'].get('markdown', ''))
+        self.print_result(result)
+        
+        # Test markdown endpoint with raw HTML
+        raw_md_payload = {
+            "url": test_raw_html_url,
+            "f": "fit",
+            "q": "test query",
+            "c": "0"
+        }
+        result = self.test_post_endpoint("/md", raw_md_payload)
+        self.print_result(result)
+        
+
+        # Test HTML endpoint
+        html_payload = {"url": test_url}
+        result = self.test_post_endpoint("/html", html_payload)
+        self.print_result(result)
+        
+        # Test screenshot endpoint
+        screenshot_payload = {
+            "url": test_url,
+            "screenshot_wait_for": 2
+        }
+        result = self.test_post_endpoint("/screenshot", screenshot_payload)
+        self.print_result(result)
+        
+        # Test PDF endpoint
+        pdf_payload = {"url": test_url}
+        result = self.test_post_endpoint("/pdf", pdf_payload)
+        self.print_result(result)
+        
+        # Test JavaScript execution
+        js_payload = {
+            "url": test_url,
+            "scripts": ["(() => document.title)()"]
+        }
+        result = self.test_post_endpoint("/execute_js", js_payload)
+        self.print_result(result)
+        
+        # Test crawl endpoint
+        crawl_payload = {
+            "urls": [test_url],
+            "browser_config": {},
+            "crawler_config": {}
+        }
+        result = self.test_post_endpoint("/crawl", crawl_payload)
+        self.print_result(result)
+        
+        # Test crawl endpoint with raw HTML
+        crawl_payload = {
+            "urls": [test_raw_html_url],
+            "browser_config": {},
+            "crawler_config": {}
+        }
+        result = self.test_post_endpoint("/crawl", crawl_payload)
+        self.print_result(result)
+
+        # Test config dump
+        config_payload = {"code": "CrawlerRunConfig()"}
+        result = self.test_post_endpoint("/config/dump", config_payload)
+        self.print_result(result)
+        
+        # Test LLM endpoint
+        llm_endpoint = f"/llm/{test_url}?q=Extract%20main%20content"
+        result = self.test_get_endpoint(llm_endpoint)
+        self.print_result(result)
+        
+        # Test ask endpoint
+        ask_endpoint = "/ask?context_type=all&query=crawl4ai&max_results=5"
+        result = self.test_get_endpoint(ask_endpoint)
+        print(result)
+        self.print_result(result)
+        
+        # Test job APIs
+        print("\n=== JOB APIs ===")
+        
+        # Test LLM job
+        llm_job_payload = {
+            "url": test_url,
+            "q": "Extract main content",
+            "cache": False
+        }
+        result = self.test_post_endpoint("/llm/job", llm_job_payload)
+        self.print_result(result)
+        
+        # Test crawl job
+        crawl_job_payload = {
+            "urls": [test_url],
+            "browser_config": {},
+            "crawler_config": {}
+        }
+        result = self.test_post_endpoint("/crawl/job", crawl_job_payload)
+        self.print_result(result)
+        
+        # Test MCP
+        print("\n=== MCP APIs ===")
+        
+        # Test MCP schema
+        result = self.test_get_endpoint("/mcp/schema")
+        self.print_result(result)
+        
+        # Test error handling
+        print("\n=== ERROR HANDLING ===")
+        
+        # Test invalid URL
+        invalid_payload = {"url": "invalid-url", "f": "fit"}
+        result = self.test_post_endpoint("/md", invalid_payload)
+        self.print_result(result)
+        
+        # Test invalid endpoint
+        result = self.test_get_endpoint("/nonexistent")
+        self.print_result(result)
+        
+        # Print summary
+        self.print_summary()
+    
+    def print_summary(self):
+        """Print test results summary"""
+        print("\n" + "=" * 60)
+        print("📊 TEST RESULTS SUMMARY")
+        print("=" * 60)
+        
+        total = len(self.results)
+        passed = sum(1 for r in self.results if r['status'] == 'PASS')
+        failed = sum(1 for r in self.results if r['status'] == 'FAIL')
+        
+        print(f"Total Tests: {total}")
+        print(f"✅ Passed: {passed}")
+        print(f"❌ Failed: {failed}")
+        print(f"📈 Success Rate: {(passed/total)*100:.1f}%")
+        
+        if failed > 0:
+            print("\n❌ FAILED TESTS:")
+            for result in self.results:
+                if result['status'] == 'FAIL':
+                    print(f"  • {result['method']} {result['endpoint']}")
+                    if 'error' in result:
+                        print(f"    Error: {result['error']}")
+        
+        # Performance statistics
+        response_times = [r['response_time'] for r in self.results if r['response_time'] > 0]
+        if response_times:
+            avg_time = sum(response_times) / len(response_times)
+            max_time = max(response_times)
+            print(f"\n⏱️  Average Response Time: {avg_time:.3f}s")
+            print(f"⏱️  Max Response Time: {max_time:.3f}s")
+        
+        # Save detailed report
+        report_file = f"crawl4ai_test_report_{int(time.time())}.json"
+        with open(report_file, 'w') as f:
+            json.dump({
+                "timestamp": time.time(),
+                "server_url": self.base_url,
+                "version": "0.7.0",
+                "summary": {
+                    "total": total,
+                    "passed": passed,
+                    "failed": failed
+                },
+                "results": self.results
+            }, f, indent=2)
+        
+        print(f"\n📄 Detailed report saved to: {report_file}")
+
+def main():
+    """Main test runner"""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description='Crawl4AI v0.7.0 API Test Suite')
+    parser.add_argument('--url', default=BASE_URL, help='Base URL of the server')
+    
+    args = parser.parse_args()
+    
+    tester = SimpleApiTester(args.url)
+    
+    try:
+        tester.run_all_tests()
+    except KeyboardInterrupt:
+        print("\n🛑 Test suite interrupted by user")
+    except Exception as e:
+        print(f"\n💥 Test suite failed with error: {e}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

tests/docker/test_docker.py

@@ -74,7 +74,7 @@ async def test_direct_api():
     # Make direct API call
     async with httpx.AsyncClient() as client:
         response = await client.post(
-            "http://localhost:8000/crawl",
+            "http://localhost:11235/crawl",
             json=request_data,
             timeout=300
         )
@@ -100,13 +100,24 @@ async def test_direct_api():
 
     async with httpx.AsyncClient() as client:
         response = await client.post(
-            "http://localhost:8000/crawl",
+            "http://localhost:11235/crawl",
             json=request_data
         )
         assert response.status_code == 200
         result = response.json()
         print("Structured extraction result:", result["success"])
 
+    # Test 3: Raw HTML
+    request_data["urls"] = ["raw://<html><body><h1>Hello, World!</h1><a href='https://example.com'>Example</a></body></html>"]
+    async with httpx.AsyncClient() as client:
+        response = await client.post(
+            "http://localhost:11235/crawl",
+            json=request_data
+        )
+        assert response.status_code == 200
+        result = response.json()
+        print("Raw HTML result:", result["success"])
+
     # Test 3: Get schema
     # async with httpx.AsyncClient() as client:
     #     response = await client.get("http://localhost:8000/schema")
@@ -118,7 +129,7 @@ async def test_with_client():
     """Test using the Crawl4AI Docker client SDK"""
     print("\n=== Testing Client SDK ===")
     
-    async with Crawl4aiDockerClient(verbose=True) as client:
+    async with Crawl4aiDockerClient(base_url="http://localhost:11235", verbose=True) as client:
         # Test 1: Basic crawl
         browser_config = BrowserConfig(headless=True)
         crawler_config = CrawlerRunConfig(

tests/docker_example.py

@@ -6,28 +6,22 @@ import base64
 import os
 from typing import Dict, Any
 
-
 class Crawl4AiTester:
-    def __init__(self, base_url: str = "http://localhost:11235", api_token: str = None):
+    def __init__(self, base_url: str = "http://localhost:11235"):
         self.base_url = base_url
-        self.api_token = api_token or os.getenv(
-            "CRAWL4AI_API_TOKEN"
-        )  # Check environment variable as fallback
-        self.headers = (
-            {"Authorization": f"Bearer {self.api_token}"} if self.api_token else {}
-        )
+
 
     def submit_and_wait(
         self, request_data: Dict[str, Any], timeout: int = 300
     ) -> Dict[str, Any]:
-        # Submit crawl job
+        # Submit crawl job using async endpoint
         response = requests.post(
-            f"{self.base_url}/crawl", json=request_data, headers=self.headers
+            f"{self.base_url}/crawl/job", json=request_data
         )
-        if response.status_code == 403:
-            raise Exception("API token is invalid or missing")
-        task_id = response.json()["task_id"]
-        print(f"Task ID: {task_id}")
+        response.raise_for_status()
+        job_response = response.json()
+        task_id = job_response["task_id"]
+        print(f"Submitted job with task_id: {task_id}")
 
         # Poll for result
         start_time = time.time()
@@ -38,8 +32,9 @@ class Crawl4AiTester:
                 )
 
             result = requests.get(
-                f"{self.base_url}/task/{task_id}", headers=self.headers
+                f"{self.base_url}/crawl/job/{task_id}"
             )
+            result.raise_for_status()
             status = result.json()
 
             if status["status"] == "failed":
@@ -52,10 +47,10 @@ class Crawl4AiTester:
             time.sleep(2)
 
     def submit_sync(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
+        # Use synchronous crawl endpoint
         response = requests.post(
-            f"{self.base_url}/crawl_sync",
+            f"{self.base_url}/crawl",
             json=request_data,
-            headers=self.headers,
             timeout=60,
         )
         if response.status_code == 408:
@@ -66,9 +61,8 @@ class Crawl4AiTester:
 
 def test_docker_deployment(version="basic"):
     tester = Crawl4AiTester(
-        # base_url="http://localhost:11235" ,
-        base_url="https://crawl4ai-sby74.ondigitalocean.app",
-        api_token="test",
+        base_url="http://localhost:11235",
+        #base_url="https://crawl4ai-sby74.ondigitalocean.app",
     )
     print(f"Testing Crawl4AI Docker {version} version")
 
@@ -88,63 +82,60 @@ def test_docker_deployment(version="basic"):
 
     # Test cases based on version
     test_basic_crawl(tester)
-    test_basic_crawl(tester)
     test_basic_crawl_sync(tester)
 
-    # if version in ["full", "transformer"]:
-    #     test_cosine_extraction(tester)
+    if version in ["full", "transformer"]:
+        test_cosine_extraction(tester)
 
-    # test_js_execution(tester)
-    # test_css_selector(tester)
-    # test_structured_extraction(tester)
-    # test_llm_extraction(tester)
-    # test_llm_with_ollama(tester)
-    # test_screenshot(tester)
+    test_js_execution(tester)
+    test_css_selector(tester)
+    test_structured_extraction(tester)
+    test_llm_extraction(tester)
+    test_llm_with_ollama(tester)
+    test_screenshot(tester)
 
 
 def test_basic_crawl(tester: Crawl4AiTester):
-    print("\n=== Testing Basic Crawl ===")
+    print("\n=== Testing Basic Crawl (Async) ===")
     request = {
         "urls": ["https://www.nbcnews.com/business"],
-        "priority": 10,
-        "session_id": "test",
     }
 
     result = tester.submit_and_wait(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
+    print(f"Basic crawl result count: {len(result['result']['results'])}")
     assert result["result"]["success"]
-    assert len(result["result"]["markdown"]) > 0
+    assert len(result["result"]["results"]) > 0
+    assert len(result["result"]["results"][0]["markdown"]) > 0
 
 
 def test_basic_crawl_sync(tester: Crawl4AiTester):
     print("\n=== Testing Basic Crawl (Sync) ===")
     request = {
         "urls": ["https://www.nbcnews.com/business"],
-        "priority": 10,
-        "session_id": "test",
     }
 
     result = tester.submit_sync(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
-    assert result["status"] == "completed"
-    assert result["result"]["success"]
-    assert len(result["result"]["markdown"]) > 0
+    print(f"Basic crawl result count: {len(result['results'])}")
+    assert result["success"]
+    assert len(result["results"]) > 0
+    assert len(result["results"][0]["markdown"]) > 0
 
 
 def test_js_execution(tester: Crawl4AiTester):
     print("\n=== Testing JS Execution ===")
     request = {
         "urls": ["https://www.nbcnews.com/business"],
-        "priority": 8,
-        "js_code": [
-            "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
-        ],
-        "wait_for": "article.tease-card:nth-child(10)",
-        "crawler_params": {"headless": True},
+        "browser_config": {"headless": True},
+        "crawler_config": {
+            "js_code": [
+                "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); if(loadMoreButton) loadMoreButton.click();"
+            ],
+            "wait_for": "wide-tease-item__wrapper df flex-column flex-row-m flex-nowrap-m enable-new-sports-feed-mobile-design(10)"
+        }
     }
 
     result = tester.submit_and_wait(request)
-    print(f"JS execution result length: {len(result['result']['markdown'])}")
+    print(f"JS execution result count: {len(result['result']['results'])}")
     assert result["result"]["success"]
 
 
@@ -152,51 +143,78 @@ def test_css_selector(tester: Crawl4AiTester):
     print("\n=== Testing CSS Selector ===")
     request = {
         "urls": ["https://www.nbcnews.com/business"],
-        "priority": 7,
-        "css_selector": ".wide-tease-item__description",
-        "crawler_params": {"headless": True},
-        "extra": {"word_count_threshold": 10},
+        "browser_config": {"headless": True},
+        "crawler_config": {
+            "css_selector": ".wide-tease-item__description",
+            "word_count_threshold": 10
+        }
     }
 
     result = tester.submit_and_wait(request)
-    print(f"CSS selector result length: {len(result['result']['markdown'])}")
+    print(f"CSS selector result count: {len(result['result']['results'])}")
     assert result["result"]["success"]
 
 
 def test_structured_extraction(tester: Crawl4AiTester):
     print("\n=== Testing Structured Extraction ===")
     schema = {
-        "name": "Coinbase Crypto Prices",
-        "baseSelector": ".cds-tableRow-t45thuk",
-        "fields": [
-            {
-                "name": "crypto",
-                "selector": "td:nth-child(1) h2",
-                "type": "text",
-            },
-            {
-                "name": "symbol",
-                "selector": "td:nth-child(1) p",
-                "type": "text",
-            },
-            {
-                "name": "price",
-                "selector": "td:nth-child(2)",
-                "type": "text",
-            },
-        ],
+  "name": "Cryptocurrency Prices",
+  "baseSelector": "table[data-testid=\"prices-table\"] tbody tr",
+  "fields": [
+    {
+      "name": "asset_name",
+      "selector": "td:nth-child(2) p.cds-headline-h4steop",
+      "type": "text"
+    },
+    {
+      "name": "asset_symbol",
+      "selector": "td:nth-child(2) p.cds-label2-l1sm09ec",
+      "type": "text"
+    },
+    {
+      "name": "asset_image_url",
+      "selector": "td:nth-child(2) img[alt=\"Asset Symbol\"]",
+      "type": "attribute",
+      "attribute": "src"
+    },
+    {
+      "name": "asset_url",
+      "selector": "td:nth-child(2) a[aria-label^=\"Asset page for\"]",
+      "type": "attribute",
+      "attribute": "href"
+    },
+    {
+      "name": "price",
+      "selector": "td:nth-child(3) div.cds-typographyResets-t6muwls.cds-body-bwup3gq",
+      "type": "text"
+    },
+    {
+      "name": "change",
+      "selector": "td:nth-child(7) p.cds-body-bwup3gq",
+      "type": "text"
     }
+  ]
+}
+
 
     request = {
         "urls": ["https://www.coinbase.com/explore"],
-        "priority": 9,
-        "extraction_config": {"type": "json_css", "params": {"schema": schema}},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
+            "params": {
+                "extraction_strategy": {
+                    "type": "JsonCssExtractionStrategy",
+                    "params": {"schema": schema}
+                }
+            }
+        }
     }
 
     result = tester.submit_and_wait(request)
-    extracted = json.loads(result["result"]["extracted_content"])
+    extracted = json.loads(result["result"]["results"][0]["extracted_content"])
     print(f"Extracted {len(extracted)} items")
-    print("Sample item:", json.dumps(extracted[0], indent=2))
+    if extracted:
+        print("Sample item:", json.dumps(extracted[0], indent=2))
     assert result["result"]["success"]
     assert len(extracted) > 0
 
@@ -206,43 +224,54 @@ def test_llm_extraction(tester: Crawl4AiTester):
     schema = {
         "type": "object",
         "properties": {
-            "model_name": {
+            "asset_name": {
                 "type": "string",
-                "description": "Name of the OpenAI model.",
+                "description": "Name of the asset.",
             },
-            "input_fee": {
+            "price": {
                 "type": "string",
-                "description": "Fee for input token for the OpenAI model.",
+                "description": "Price of the asset.",
             },
-            "output_fee": {
+            "change": {
                 "type": "string",
-                "description": "Fee for output token for the OpenAI model.",
+                "description": "Change in price of the asset.",
             },
         },
-        "required": ["model_name", "input_fee", "output_fee"],
+        "required": ["asset_name", "price", "change"],
     }
 
     request = {
-        "urls": ["https://openai.com/api/pricing"],
-        "priority": 8,
-        "extraction_config": {
-            "type": "llm",
+        "urls": ["https://www.coinbase.com/en-in/explore"],
+        "browser_config": {},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
             "params": {
-                "provider": "openai/gpt-4o-mini",
-                "api_token": os.getenv("OPENAI_API_KEY"),
-                "schema": schema,
-                "extraction_type": "schema",
-                "instruction": """From the crawled content, extract all mentioned model names along with their fees for input and output tokens.""",
-            },
-        },
-        "crawler_params": {"word_count_threshold": 1},
+                "extraction_strategy": {
+                    "type": "LLMExtractionStrategy",
+                    "params": {
+                        "llm_config": {
+                            "type": "LLMConfig",
+                            "params": {
+                                "provider": "gemini/gemini-2.5-flash",
+                                "api_token": os.getenv("GEMINI_API_KEY")
+                            }
+                        },
+                        "schema": schema,
+                        "extraction_type": "schema",
+                        "instruction": "From the crawled content    tioned asset names along with their prices and change in price.",
+                    }
+                },
+                "word_count_threshold": 1
+            }
+        }
     }
 
     try:
         result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
+        extracted = json.loads(result["result"]["results"][0]["extracted_content"])
         print(f"Extracted {len(extracted)} model pricing entries")
-        print("Sample entry:", json.dumps(extracted[0], indent=2))
+        if extracted:
+            print("Sample entry:", json.dumps(extracted[0], indent=2))
         assert result["result"]["success"]
     except Exception as e:
         print(f"LLM extraction test failed (might be due to missing API key): {str(e)}")
@@ -271,23 +300,32 @@ def test_llm_with_ollama(tester: Crawl4AiTester):
 
     request = {
         "urls": ["https://www.nbcnews.com/business"],
-        "priority": 8,
-        "extraction_config": {
-            "type": "llm",
+        "browser_config": {"verbose": True},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
             "params": {
-                "provider": "ollama/llama2",
-                "schema": schema,
-                "extraction_type": "schema",
-                "instruction": "Extract the main article information including title, summary, and main topics.",
-            },
-        },
-        "extra": {"word_count_threshold": 1},
-        "crawler_params": {"verbose": True},
+                "extraction_strategy": {
+                    "type": "LLMExtractionStrategy",
+                    "params": {
+                        "llm_config": {
+                            "type": "LLMConfig",
+                            "params": {
+                                "provider": "ollama/llama3.2:latest",
+                            }
+                        },
+                        "schema": schema,
+                        "extraction_type": "schema",
+                        "instruction": "Extract the main article information including title, summary, and main topics.",
+                    }
+                },
+                "word_count_threshold": 1
+            }
+        }
     }
 
     try:
         result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
+        extracted = json.loads(result["result"]["results"][0]["extracted_content"])
         print("Extracted content:", json.dumps(extracted, indent=2))
         assert result["result"]["success"]
     except Exception as e:
@@ -298,23 +336,29 @@ def test_cosine_extraction(tester: Crawl4AiTester):
     print("\n=== Testing Cosine Extraction ===")
     request = {
         "urls": ["https://www.nbcnews.com/business"],
-        "priority": 8,
-        "extraction_config": {
-            "type": "cosine",
+        "browser_config": {},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
             "params": {
-                "semantic_filter": "business finance economy",
-                "word_count_threshold": 10,
-                "max_dist": 0.2,
-                "top_k": 3,
-            },
-        },
+                "extraction_strategy": {
+                    "type": "CosineStrategy",
+                    "params": {
+                        "semantic_filter": "business finance economy",
+                        "word_count_threshold": 10,
+                        "max_dist": 0.2,
+                        "top_k": 3,
+                    }
+                }
+            }
+        }
     }
 
     try:
         result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
+        extracted = json.loads(result["result"]["results"][0]["extracted_content"])
         print(f"Extracted {len(extracted)} text clusters")
-        print("First cluster tags:", extracted[0]["tags"])
+        if extracted:
+            print("First cluster tags:", extracted[0]["tags"])
         assert result["result"]["success"]
     except Exception as e:
         print(f"Cosine extraction test failed: {str(e)}")
@@ -324,19 +368,24 @@ def test_screenshot(tester: Crawl4AiTester):
     print("\n=== Testing Screenshot ===")
     request = {
         "urls": ["https://www.nbcnews.com/business"],
-        "priority": 5,
-        "screenshot": True,
-        "crawler_params": {"headless": True},
+        "browser_config": {"headless": True},
+        "crawler_config": {
+            "type": "CrawlerRunConfig",
+            "params": {
+                "screenshot": True
+            }
+        }
     }
 
     result = tester.submit_and_wait(request)
-    print("Screenshot captured:", bool(result["result"]["screenshot"]))
+    screenshot_data = result["result"]["results"][0]["screenshot"]
+    print("Screenshot captured:", bool(screenshot_data))
 
-    if result["result"]["screenshot"]:
+    if screenshot_data:
         # Save screenshot
-        screenshot_data = base64.b64decode(result["result"]["screenshot"])
+        screenshot_bytes = base64.b64decode(screenshot_data)
         with open("test_screenshot.jpg", "wb") as f:
-            f.write(screenshot_data)
+            f.write(screenshot_bytes)
         print("Screenshot saved as test_screenshot.jpg")
 
     assert result["result"]["success"]

tests/general/test_persistent_context.py

@@ -0,0 +1,43 @@
+import asyncio
+import os
+from crawl4ai.async_webcrawler import AsyncWebCrawler
+from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig, CacheMode
+
+# Simple concurrency test for persistent context page creation
+# Usage: python scripts/test_persistent_context.py
+
+URLS = [
+    # "https://example.com",
+    "https://httpbin.org/html",
+    "https://www.python.org/",
+    "https://www.rust-lang.org/",
+]
+
+async def main():
+    profile_dir = os.path.join(os.path.expanduser("~"), ".crawl4ai", "profiles", "test-persistent-profile")
+    os.makedirs(profile_dir, exist_ok=True)
+
+    browser_config = BrowserConfig(
+        browser_type="chromium",
+        headless=True,
+        use_persistent_context=True,
+        user_data_dir=profile_dir,
+        use_managed_browser=True,
+        verbose=True,
+    )
+
+    run_cfg = CrawlerRunConfig(
+        cache_mode=CacheMode.BYPASS,
+        stream=False,
+        verbose=True,
+    )
+
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        results = await crawler.arun_many(URLS, config=run_cfg)
+        for r in results:
+            print(r.url, r.success, len(r.markdown.raw_markdown) if r.markdown else 0)
+        # r = await crawler.arun(url=URLS[0], config=run_cfg)
+        # print(r.url, r.success, len(r.markdown.raw_markdown) if r.markdown else 0)
+
+if __name__ == "__main__":
+    asyncio.run(main())