Release prep (#749)

* fix: Update export of URLPatternFilter * chore: Add dependancy for cchardet in requirements * docs: Update example for deep crawl in release note for v0.5 * Docs: update the example for memory dispatcher * docs: updated example for crawl strategies * Refactor: Removed wrapping in if __name__==main block since this is a markdown file. * chore: removed cchardet from dependancy list, since unclecode is planning to remove it * docs: updated the example for proxy rotation to a working example * feat: Introduced ProxyConfig param * Add tutorial for deep crawl & update contributor list for bug fixes in feb alpha-1 * chore: update and test new dependancies * feat:Make PyPDF2 a conditional dependancy * updated tutorial and release note for v0.5 * docs: update docs for deep crawl, and fix a typo in docker-deployment markdown filename * refactor: 1. Deprecate markdown_v2 2. Make markdown backward compatible to behave as a string when needed. 3. Fix LlmConfig usage in cli 4. Deprecate markdown_v2 in cli 5. Update AsyncWebCrawler for changes in CrawlResult * fix: Bug in serialisation of markdown in acache_url * Refactor: Added deprecation errors for fit_html and fit_markdown directly on markdown. Now access them via markdown * fix: remove deprecated markdown_v2 from docker * Refactor: remove deprecated fit_markdown and fit_html from result * refactor: fix cache retrieval for markdown as a string * chore: update all docs, examples and tests with deprecation announcements for markdown_v2, fit_html, fit_markdown
2025-02-28 17:23:35 +05:30
parent 3a87b4e43b
commit a9e24307cc
38 changed files with 2040 additions and 326 deletions
--- a/docs/md_v2/api/async-webcrawler.md
+++ b/docs/md_v2/api/async-webcrawler.md
@@ -200,7 +200,7 @@ Each `arun()` returns a **`CrawlResult`** containing:
 - `url`: Final URL (if redirected).
 - `html`: Original HTML.
 - `cleaned_html`: Sanitized HTML.
- `markdown_v2` (or future `markdown`): Markdown outputs (raw, fit, etc.).
+- `markdown_v2`: Deprecated. Instead just use regular `markdown`
 - `extracted_content`: If an extraction strategy was used (JSON for CSS/LLM strategies).
 - `screenshot`, `pdf`: If screenshots/PDF requested.
 - `media`, `links`: Information about discovered images/links.
--- a/docs/md_v2/api/crawl-result.md
+++ b/docs/md_v2/api/crawl-result.md
@@ -16,9 +16,6 @@ class CrawlResult(BaseModel):
    screenshot: Optional[str] = None
    pdf : Optional[bytes] = None
    markdown: Optional[Union[str, MarkdownGenerationResult]] = None
-    markdown_v2: Optional[MarkdownGenerationResult] = None
-    fit_markdown: Optional[str] = None
-    fit_html: Optional[str] = None
    extracted_content: Optional[str] = None
    metadata: Optional[dict] = None
    error_message: Optional[str] = None
@@ -116,8 +113,8 @@ print(result.cleaned_html[:500])  # Show a snippet
 **When**: This is **only** present if your `markdown_generator` or `content_filter` produces it.  
 **Usage**:
 ```python
-if result.fit_html:
-    print("High-value HTML content:", result.fit_html[:300])
+if result.markdown.fit_html:
+    print("High-value HTML content:", result.markdown.fit_html[:300])
 ```

 ---
@@ -132,8 +129,6 @@ Crawl4AI can convert HTML→Markdown, optionally including:
 - **Links as citations** (with a references section)  
 - **Fit** markdown if a **content filter** is used (like Pruning or BM25)

-### 3.2 **`markdown_v2`** *(Optional[MarkdownGenerationResult])*  
-**What**: The **structured** object holding multiple markdown variants. Soon to be consolidated into `markdown`.  

 **`MarkdownGenerationResult`** includes:
 - **`raw_markdown`** *(str)*: The full HTML→Markdown conversion.  
@@ -144,8 +139,8 @@ Crawl4AI can convert HTML→Markdown, optionally including:

 **Usage**:
 ```python
-if result.markdown_v2:
-    md_res = result.markdown_v2
+if result.markdown:
+    md_res = result.markdown
    print("Raw MD:", md_res.raw_markdown[:300])
    print("Citations MD:", md_res.markdown_with_citations[:300])
    print("References:", md_res.references_markdown)
@@ -153,26 +148,15 @@ if result.markdown_v2:
        print("Pruned text:", md_res.fit_markdown[:300])
 ```

-### 3.3 **`markdown`** *(Optional[Union[str, MarkdownGenerationResult]])*  
-**What**: In future versions, `markdown` will fully replace `markdown_v2`. Right now, it might be a `str` or a `MarkdownGenerationResult`.  
+### 3.2 **`markdown`** *(Optional[Union[str, MarkdownGenerationResult]])*  
+**What**: Holds the `MarkdownGenerationResult`.  
 **Usage**:
 ```python
-# Soon, you might see:
-if isinstance(result.markdown, MarkdownGenerationResult):
-    print(result.markdown.raw_markdown[:200])
-else:
-    print(result.markdown)
+print(result.markdown.raw_markdown[:200])
+print(result.markdown.fit_markdown)
+print(result.markdown.fit_html)
 ```
-
-### 3.4 **`fit_markdown`** *(Optional[str])*  
-**What**: A direct reference to the final filtered markdown (legacy approach).  
-**When**: This is set if a filter or content strategy explicitly writes there. Usually overshadowed by `markdown_v2.fit_markdown`.  
-**Usage**:
-```python
-print(result.fit_markdown)  # Legacy field, prefer result.markdown_v2.fit_markdown
-```
-
-**Important**: “Fit” content (in `fit_markdown`/`fit_html`) only exists if you used a **filter** (like **PruningContentFilter** or **BM25ContentFilter**) within a `MarkdownGenerationStrategy`.
+**Important**: “Fit” content (in `fit_markdown`/`fit_html`) exists in result.markdown, only if you used a **filter** (like **PruningContentFilter** or **BM25ContentFilter**) within a `MarkdownGenerationStrategy`.

 ---

@@ -304,13 +288,11 @@ async def handle_result(result: CrawlResult):
    print("Cleaned HTML size:", len(result.cleaned_html or ""))

    # Markdown output
-    if result.markdown_v2:
-        print("Raw Markdown:", result.markdown_v2.raw_markdown[:300])
-        print("Citations Markdown:", result.markdown_v2.markdown_with_citations[:300])
-        if result.markdown_v2.fit_markdown:
-            print("Fit Markdown:", result.markdown_v2.fit_markdown[:200])
-    else:
-        print("Raw Markdown (legacy):", result.markdown[:200] if result.markdown else "N/A")
+    if result.markdown:
+        print("Raw Markdown:", result.markdown.raw_markdown[:300])
+        print("Citations Markdown:", result.markdown.markdown_with_citations[:300])
+        if result.markdown.fit_markdown:
+            print("Fit Markdown:", result.markdown.fit_markdown[:200])

    # Media & Links
    if "images" in result.media:
@@ -333,12 +315,12 @@ async def handle_result(result: CrawlResult):

 ## 8. Key Points & Future

-1. **`markdown_v2` vs `markdown`**  
-   - Right now, `markdown_v2` is the more robust container (`MarkdownGenerationResult`), providing **raw_markdown**, **markdown_with_citations**, references, plus possible **fit_markdown**.  
-   - In future versions, everything will unify under **`markdown`**. If you rely on advanced features (citations, fit content), check `markdown_v2`.
+1. **Deprecated legacy properties of CrawlResult**  
+   - `markdown_v2` - Deprecated in v0.5. Just use `markdown`. It holds the `MarkdownGenerationResult` now!
+   - `fit_markdown` and `fit_html` - Deprecated in v0.5. They can now be accessed via `MarkdownGenerationResult` in `result.markdown`. eg: `result.markdown.fit_markdown` and `result.markdown.fit_html`

 2. **Fit Content**  
-   - **`fit_markdown`** and **`fit_html`** appear only if you used a content filter (like **PruningContentFilter** or **BM25ContentFilter**) inside your **MarkdownGenerationStrategy** or set them directly.  
+   - **`fit_markdown`** and **`fit_html`** appear in MarkdownGenerationResult, only if you used a content filter (like **PruningContentFilter** or **BM25ContentFilter**) inside your **MarkdownGenerationStrategy** or set them directly.  
   - If no filter is used, they remain `None`.

 3. **References & Citations**  
--- a/docs/md_v2/blog/releases/0.5.0.md
+++ b/docs/md_v2/blog/releases/0.5.0.md
@@ -2,79 +2,151 @@

 **Release Theme: Power, Flexibility, and Scalability**

-Crawl4AI v0.5.0 is a major release focused on significantly enhancing the library's power, flexibility, and scalability.  Key improvements include a new **deep crawling** system, a **memory-adaptive dispatcher** for handling large-scale crawls, **multiple crawling strategies** (including a fast HTTP-only crawler), **Docker** deployment options, and a powerful **command-line interface (CLI)**. This release also includes numerous bug fixes, performance optimizations, and documentation updates.
+Crawl4AI v0.5.0 is a major release focused on significantly enhancing the
+library's power, flexibility, and scalability. Key improvements include a new
+**deep crawling** system, a **memory-adaptive dispatcher** for handling
+large-scale crawls, **multiple crawling strategies** (including a fast HTTP-only
+crawler), **Docker** deployment options, and a powerful **command-line interface
+(CLI)**. This release also includes numerous bug fixes, performance
+optimizations, and documentation updates.

-**Important Note:** This release contains several **breaking changes**.  Please review the "Breaking Changes" section carefully and update your code accordingly.
+**Important Note:** This release contains several **breaking changes**. Please
+review the "Breaking Changes" section carefully and update your code
+accordingly.

 ## Key Features

 ### 1. Deep Crawling

-Crawl4AI now supports deep crawling, allowing you to explore websites beyond the initial URLs.  This is controlled by the `deep_crawl_strategy` parameter in `CrawlerRunConfig`.  Several strategies are available:
+Crawl4AI now supports deep crawling, allowing you to explore websites beyond the
+initial URLs. This is controlled by the `deep_crawl_strategy` parameter in
+`CrawlerRunConfig`. Several strategies are available:

-*   **`BFSDeepCrawlStrategy` (Breadth-First Search):**  Explores the website level by level. (Default)
-*   **`DFSDeepCrawlStrategy` (Depth-First Search):**  Explores each branch as deeply as possible before backtracking.
-*   **`BestFirstCrawlingStrategy`:** Uses a scoring function to prioritize which URLs to crawl next.
+- **`BFSDeepCrawlStrategy` (Breadth-First Search):** Explores the website level
+  by level. (Default)
+- **`DFSDeepCrawlStrategy` (Depth-First Search):** Explores each branch as
+  deeply as possible before backtracking.
+- **`BestFirstCrawlingStrategy`:** Uses a scoring function to prioritize which
+  URLs to crawl next.

 ```python
+import time
 from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, BFSDeepCrawlStrategy
-from crawl4ai.deep_crawling import DomainFilter, ContentTypeFilter, FilterChain
+from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
+from crawl4ai.deep_crawling import DomainFilter, ContentTypeFilter, FilterChain, URLPatternFilter, KeywordRelevanceScorer, BestFirstCrawlingStrategy
+import asyncio

-# Configure a deep crawl with BFS, limiting to a specific domain and content type.
+# Create a filter chain to filter urls based on patterns, domains and content type
 filter_chain = FilterChain(
-    filters=[
-        DomainFilter(allowed_domains=["example.com"]),
-        ContentTypeFilter(allowed_types=["text/html"])
+    [
+        DomainFilter(
+            allowed_domains=["docs.crawl4ai.com"],
+            blocked_domains=["old.docs.crawl4ai.com"],
+        ),
+        URLPatternFilter(patterns=["*core*", "*advanced*"],),
+        ContentTypeFilter(allowed_types=["text/html"]),
    ]
 )
-deep_crawl_config = CrawlerRunConfig(
-    deep_crawl_strategy=BFSDeepCrawlStrategy(max_depth=5, filter_chain=filter_chain),
-    stream=True  # Process results as they arrive
+
+# Create a keyword scorer that prioritises the pages with certain keywords first
+keyword_scorer = KeywordRelevanceScorer(
+    keywords=["crawl", "example", "async", "configuration"], weight=0.7
 )

-async with AsyncWebCrawler() as crawler:
-    async for result in await crawler.arun(url="https://example.com", config=deep_crawl_config):
-        print(f"Crawled: {result.url} (Depth: {result.metadata['depth']})")
+# Set up the configuration
+deep_crawl_config = CrawlerRunConfig(
+    deep_crawl_strategy=BestFirstCrawlingStrategy(
+        max_depth=2,
+        include_external=False,
+        filter_chain=filter_chain,
+        url_scorer=keyword_scorer,
+    ),
+    scraping_strategy=LXMLWebScrapingStrategy(),
+    stream=True,
+    verbose=True,
+)
+
+async def main():
+    async with AsyncWebCrawler() as crawler:
+        start_time = time.perf_counter()
+        results = []
+        async for result in await crawler.arun(url="https://docs.crawl4ai.com", config=deep_crawl_config):
+            print(f"Crawled: {result.url} (Depth: {result.metadata['depth']}), score: {result.metadata['score']:.2f}")
+            results.append(result)
+        duration = time.perf_counter() - start_time
+        print(f"\n✅ Crawled {len(results)} high-value pages in {duration:.2f} seconds")
+
+asyncio.run(main())
 ```

-**Breaking Change:** The `max_depth` parameter is now part of `CrawlerRunConfig` and controls the *depth* of the crawl, not the number of concurrent crawls.  The `arun()` and `arun_many()` methods are now decorated to handle deep crawling strategies.  Imports for deep crawling strategies have changed.  See the [Deep Crawling documentation](../deep_crawling/README.md) for more details.
+**Breaking Change:** The `max_depth` parameter is now part of `CrawlerRunConfig`
+and controls the _depth_ of the crawl, not the number of concurrent crawls. The
+`arun()` and `arun_many()` methods are now decorated to handle deep crawling
+strategies. Imports for deep crawling strategies have changed. See the
+[Deep Crawling documentation](../../core/deep-crawling.md) for more details.

 ### 2. Memory-Adaptive Dispatcher

-The new `MemoryAdaptiveDispatcher` dynamically adjusts concurrency based on available system memory and includes built-in rate limiting. This prevents out-of-memory errors and avoids overwhelming target websites.
+The new `MemoryAdaptiveDispatcher` dynamically adjusts concurrency based on
+available system memory and includes built-in rate limiting. This prevents
+out-of-memory errors and avoids overwhelming target websites.

 ```python
 from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, MemoryAdaptiveDispatcher
+import asyncio

 # Configure the dispatcher (optional, defaults are used if not provided)
 dispatcher = MemoryAdaptiveDispatcher(
    memory_threshold_percent=80.0,  # Pause if memory usage exceeds 80%
-    check_interval=0.5             # Check memory every 0.5 seconds
+    check_interval=0.5,  # Check memory every 0.5 seconds
 )

-async with AsyncWebCrawler() as crawler:
-    results = await crawler.arun_many(
-        urls=["https://example.com/1", "https://example.com/2"],
-        config=CrawlerRunConfig(stream=False),  # Batch mode
-        dispatcher=dispatcher
-    )
-    # OR, for streaming:
-    async for result in await crawler.arun_many(urls, config=CrawlerRunConfig(stream=True), dispatcher=dispatcher):
-    #     ...
+async def batch_mode():
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun_many(
+            urls=["https://docs.crawl4ai.com", "https://github.com/unclecode/crawl4ai"],
+            config=CrawlerRunConfig(stream=False),  # Batch mode
+            dispatcher=dispatcher,
+        )
+        for result in results:
+            print(f"Crawled: {result.url} with status code: {result.status_code}")
+
+async def stream_mode():
+    async with AsyncWebCrawler() as crawler:
+        # OR, for streaming:
+        async for result in await crawler.arun_many(
+            urls=["https://docs.crawl4ai.com", "https://github.com/unclecode/crawl4ai"],
+            config=CrawlerRunConfig(stream=True),
+            dispatcher=dispatcher,
+        ):
+            print(f"Crawled: {result.url} with status code: {result.status_code}")
+
+print("Dispatcher in batch mode:")
+asyncio.run(batch_mode())
+print("-" * 50)
+print("Dispatcher in stream mode:")
+asyncio.run(stream_mode())
 ```

-**Breaking Change:** `AsyncWebCrawler.arun_many()` now uses `MemoryAdaptiveDispatcher` by default.  Existing code that relied on unbounded concurrency may require adjustments.
+**Breaking Change:** `AsyncWebCrawler.arun_many()` now uses
+`MemoryAdaptiveDispatcher` by default. Existing code that relied on unbounded
+concurrency may require adjustments.

 ### 3. Multiple Crawling Strategies (Playwright and HTTP)

 Crawl4AI now offers two crawling strategies:

-*   **`AsyncPlaywrightCrawlerStrategy` (Default):**  Uses Playwright for browser-based crawling, supporting JavaScript rendering and complex interactions.
-*   **`AsyncHTTPCrawlerStrategy`:** A lightweight, fast, and memory-efficient HTTP-only crawler.  Ideal for simple scraping tasks where browser rendering is unnecessary.
+- **`AsyncPlaywrightCrawlerStrategy` (Default):** Uses Playwright for
+  browser-based crawling, supporting JavaScript rendering and complex
+  interactions.
+- **`AsyncHTTPCrawlerStrategy`:** A lightweight, fast, and memory-efficient
+  HTTP-only crawler. Ideal for simple scraping tasks where browser rendering is
+  unnecessary.

 ```python
 from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, HTTPCrawlerConfig
 from crawl4ai.async_crawler_strategy import AsyncHTTPCrawlerStrategy
+import asyncio

 # Use the HTTP crawler strategy
 http_crawler_config = HTTPCrawlerConfig(
@@ -84,15 +156,20 @@ http_crawler_config = HTTPCrawlerConfig(
        verify_ssl=True
 )

-async with AsyncWebCrawler(crawler_strategy=AsyncHTTPCrawlerStrategy(browser_config =http_crawler_config)) as crawler:
-    result = await crawler.arun("https://example.com")
-    print(f"Status code: {result.status_code}")
-    print(f"Content length: {len(result.html)}")
+async def main():
+    async with AsyncWebCrawler(crawler_strategy=AsyncHTTPCrawlerStrategy(browser_config =http_crawler_config)) as crawler:
+        result = await crawler.arun("https://example.com")
+        print(f"Status code: {result.status_code}")
+        print(f"Content length: {len(result.html)}")
+
+asyncio.run(main())
 ```

 ### 4. Docker Deployment

-Crawl4AI can now be easily deployed as a Docker container, providing a consistent and isolated environment.  The Docker image includes a FastAPI server with both streaming and non-streaming endpoints.
+Crawl4AI can now be easily deployed as a Docker container, providing a
+consistent and isolated environment. The Docker image includes a FastAPI server
+with both streaming and non-streaming endpoints.

 ```bash
 # Build the image (from the project root)
@@ -104,26 +181,28 @@ docker run -d -p 8000:8000 --name crawl4ai crawl4ai

 **API Endpoints:**

-*   `/crawl` (POST):  Non-streaming crawl.
-*   `/crawl/stream` (POST): Streaming crawl (NDJSON).
-*   `/health` (GET): Health check.
-*   `/schema` (GET):  Returns configuration schemas.
-*   `/md/{url}` (GET):  Returns markdown content of the URL.
-*   `/llm/{url}` (GET):  Returns LLM extracted content.
-* `/token` (POST): Get JWT token
+- `/crawl` (POST): Non-streaming crawl.
+- `/crawl/stream` (POST): Streaming crawl (NDJSON).
+- `/health` (GET): Health check.
+- `/schema` (GET): Returns configuration schemas.
+- `/md/{url}` (GET): Returns markdown content of the URL.
+- `/llm/{url}` (GET): Returns LLM extracted content.
+- `/token` (POST): Get JWT token

 **Breaking Changes:**

-*   Docker deployment now requires a `.llm.env` file for API keys.
-*   Docker deployment now requires Redis and a new `config.yml` structure.
-*   Server startup now uses `supervisord` instead of direct process management.
-*   Docker server now requires authentication by default (JWT tokens).
+- Docker deployment now requires a `.llm.env` file for API keys.
+- Docker deployment now requires Redis and a new `config.yml` structure.
+- Server startup now uses `supervisord` instead of direct process management.
+- Docker server now requires authentication by default (JWT tokens).

-See the [Docker deployment documentation](../deploy/docker/README.md) for detailed instructions.
+See the [Docker deployment documentation](../../core/docker-deployment.md) for
+detailed instructions.

 ### 5. Command-Line Interface (CLI)

-A new CLI (`crwl`) provides convenient access to Crawl4AI's functionality from the terminal.
+A new CLI (`crwl`) provides convenient access to Crawl4AI's functionality from
+the terminal.

 ```bash
 # Basic crawl
@@ -149,14 +228,21 @@ See the [CLI documentation](../docs/md_v2/core/cli.md) for more details.

 ### 6. LXML Scraping Mode

-Added `LXMLWebScrapingStrategy` for faster HTML parsing using the `lxml` library.  This can significantly improve scraping performance, especially for large or complex pages. Set `scraping_strategy=LXMLWebScrapingStrategy()` in your `CrawlerRunConfig`.
+Added `LXMLWebScrapingStrategy` for faster HTML parsing using the `lxml`
+library. This can significantly improve scraping performance, especially for
+large or complex pages. Set `scraping_strategy=LXMLWebScrapingStrategy()` in
+your `CrawlerRunConfig`.

-**Breaking Change:**  The `ScrapingMode` enum has been replaced with a strategy pattern.  Use `WebScrapingStrategy` (default) or `LXMLWebScrapingStrategy`.
+**Breaking Change:** The `ScrapingMode` enum has been replaced with a strategy
+pattern. Use `WebScrapingStrategy` (default) or `LXMLWebScrapingStrategy`.

 ### 7. Proxy Rotation
-Added `ProxyRotationStrategy` abstract base class with `RoundRobinProxyStrategy` concrete implementation.
+
+Added `ProxyRotationStrategy` abstract base class with `RoundRobinProxyStrategy`
+concrete implementation.

 ```python
+import re
 from crawl4ai import (
    AsyncWebCrawler,
    BrowserConfig,
@@ -164,8 +250,12 @@ from crawl4ai import (
    CacheMode,
    RoundRobinProxyStrategy,
 )
-# Load proxies and create rotation strategy
-    proxies = load_proxies_from_env()
+import asyncio
+from crawl4ai.configs import ProxyConfig
+async def main():
+    # Load proxies and create rotation strategy
+    proxies = ProxyConfig.from_env()
+    #eg: export PROXIES="ip1:port1:username1:password1,ip2:port2:username2:password2"
    if not proxies:
        print("No proxies found in environment. Set PROXIES env variable!")
        return
@@ -178,154 +268,257 @@ from crawl4ai import (
        cache_mode=CacheMode.BYPASS,
        proxy_rotation_strategy=proxy_strategy
    )
+
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        urls = ["https://httpbin.org/ip"] * (len(proxies) * 2)  # Test each proxy twice
+
+        print("\n📈 Initializing crawler with proxy rotation...")
+        async with AsyncWebCrawler(config=browser_config) as crawler:
+            print("\n🚀 Starting batch crawl with proxy rotation...")
+            results = await crawler.arun_many(
+                urls=urls,
+                config=run_config
+            )
+            for result in results:
+                if result.success:
+                    ip_match = re.search(r'(?:[0-9]{1,3}\.){3}[0-9]{1,3}', result.html)
+                    current_proxy = run_config.proxy_config if run_config.proxy_config else None
+                    
+                    if current_proxy and ip_match:
+                        print(f"URL {result.url}")
+                        print(f"Proxy {current_proxy.server} -> Response IP: {ip_match.group(0)}")
+                        verified = ip_match.group(0) == current_proxy.ip
+                        if verified:
+                            print(f"✅ Proxy working! IP matches: {current_proxy.ip}")
+                        else:
+                            print("❌ Proxy failed or IP mismatch!")
+                    print("---")
+
+asyncio.run(main())
 ```

 ## Other Changes and Improvements

-*   **Added: `LLMContentFilter` for intelligent markdown generation.** This new filter uses an LLM to create more focused and relevant markdown output.
+- **Added: `LLMContentFilter` for intelligent markdown generation.** This new
+  filter uses an LLM to create more focused and relevant markdown output.

-    ```python
-    from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, DefaultMarkdownGenerator
-    from crawl4ai.content_filter_strategy import LLMContentFilter
-    from crawl4ai.async_configs import LlmConfig
+```python
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, DefaultMarkdownGenerator
+from crawl4ai.content_filter_strategy import LLMContentFilter
+from crawl4ai.async_configs import LlmConfig
+import asyncio

-    llm_config = LlmConfig(provider="openai/gpt-4o", api_token="YOUR_API_KEY")
+llm_config = LlmConfig(provider="gemini/gemini-1.5-pro", api_token="env:GEMINI_API_KEY")

-    markdown_generator = DefaultMarkdownGenerator(
-        content_filter=LLMContentFilter(llmConfig=llm_config, instruction="Extract key concepts and summaries")
-    )
+markdown_generator = DefaultMarkdownGenerator(
+    content_filter=LLMContentFilter(llmConfig=llm_config, instruction="Extract key concepts and summaries")
+)

-    config = CrawlerRunConfig(markdown_generator=markdown_generator)
+config = CrawlerRunConfig(markdown_generator=markdown_generator)
+async def main():
    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun("https://example.com/article", config=config)
-        print(result.markdown) # Output will be filtered and formatted by the LLM
-    ```
+        result = await crawler.arun("https://docs.crawl4ai.com", config=config)
+        print(result.markdown.fit_markdown)

-*   **Added: URL redirection tracking.** The crawler now automatically follows HTTP redirects (301, 302, 307, 308) and records the final URL in the `redirected_url` field of the `CrawlResult` object.  No code changes are required to enable this; it's automatic.
+asyncio.run(main())
+```

-*   **Added: LLM-powered schema generation utility.**  A new `generate_schema` method has been added to `JsonCssExtractionStrategy` and `JsonXPathExtractionStrategy`. This greatly simplifies creating extraction schemas.
+- **Added: URL redirection tracking.** The crawler now automatically follows
+  HTTP redirects (301, 302, 307, 308) and records the final URL in the
+  `redirected_url` field of the `CrawlResult` object. No code changes are
+  required to enable this; it's automatic.

-    ```python
-    from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-    from crawl4ai.async_configs import LlmConfig
+- **Added: LLM-powered schema generation utility.** A new `generate_schema`
+  method has been added to `JsonCssExtractionStrategy` and
+  `JsonXPathExtractionStrategy`. This greatly simplifies creating extraction
+  schemas.

-    llm_config = LlmConfig(provider="openai/gpt-4o", api_token="YOUR_API_KEY")
+```python
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+from crawl4ai.async_configs import LlmConfig

-    schema = JsonCssExtractionStrategy.generate_schema(
-        html="<div class='product'><h2>Product Name</h2><span class='price'>$99</span></div>",
-        llmConfig = llm_config,
-        query="Extract product name and price"
-    )
-    print(schema)
-    # Expected Output (may vary slightly due to LLM):
-    # {
-    #     "name": "ProductExtractor",
-    #     "baseSelector": "div.product",
-    #     "fields": [
-    #         {"name": "name", "selector": "h2", "type": "text"},
-    #         {"name": "price", "selector": ".price", "type": "text"}
-    #     ]
-    # }
-    ```
+llm_config = LlmConfig(provider="gemini/gemini-1.5-pro", api_token="env:GEMINI_API_KEY")

-*   **Added: robots.txt compliance support.** The crawler can now respect `robots.txt` rules. Enable this by setting `check_robots_txt=True` in `CrawlerRunConfig`.
+schema = JsonCssExtractionStrategy.generate_schema(
+    html="<div class='product'><h2>Product Name</h2><span class='price'>$99</span></div>",
+    llmConfig = llm_config,
+    query="Extract product name and price"
+)
+print(schema)
+```

-    ```python
-    config = CrawlerRunConfig(check_robots_txt=True)
-    ```
+ Expected Output (may vary slightly due to LLM)
+```JSON
+{
+  "name": "ProductExtractor",
+  "baseSelector": "div.product",
+  "fields": [
+      {"name": "name", "selector": "h2", "type": "text"},
+      {"name": "price", "selector": ".price", "type": "text"}
+    ]
+ }
+```

-*   **Added: PDF processing capabilities.**  Crawl4AI can now extract text, images, and metadata from PDF files (both local and remote).  This uses a new `PDFCrawlerStrategy` and `PDFContentScrapingStrategy`.
+- **Added: robots.txt compliance support.** The crawler can now respect
+  `robots.txt` rules. Enable this by setting `check_robots_txt=True` in
+  `CrawlerRunConfig`.

-    ```python
-    from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
-    from crawl4ai.processors.pdf import PDFCrawlerStrategy, PDFContentScrapingStrategy
+  ```python
+  config = CrawlerRunConfig(check_robots_txt=True)
+  ```

-    async with AsyncWebCrawler(crawler_strategy=PDFCrawlerStrategy()) as crawler:
-        result = await crawler.arun(
-            "https://example.com/document.pdf",
-            config=CrawlerRunConfig(
-                scraping_strategy=PDFContentScrapingStrategy()
-            )
-        )
-        print(result.markdown)  # Access extracted text
-        print(result.metadata)  # Access PDF metadata (title, author, etc.)
-    ```
+- **Added: PDF processing capabilities.** Crawl4AI can now extract text, images,
+  and metadata from PDF files (both local and remote). This uses a new
+  `PDFCrawlerStrategy` and `PDFContentScrapingStrategy`.

-*   **Added: Support for frozenset serialization.**  Improves configuration serialization, especially for sets of allowed/blocked domains.  No code changes required.
+  ```python
+  from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+  from crawl4ai.processors.pdf import PDFCrawlerStrategy, PDFContentScrapingStrategy
+  import asyncio

-*  **Added: New `LlmConfig` parameter.** This new parameter can be passed for extraction, filtering, and schema generation tasks. It simplifies passing provider strings, API tokens, and base URLs across all sections where LLM configuration is necessary. It also enables reuse and allows for quick experimentation between different LLM configurations.
+  async def main():
+      async with AsyncWebCrawler(crawler_strategy=PDFCrawlerStrategy()) as crawler:
+          result = await crawler.arun(
+              "https://arxiv.org/pdf/2310.06825.pdf",
+              config=CrawlerRunConfig(
+                  scraping_strategy=PDFContentScrapingStrategy()
+              )
+          )
+          print(result.markdown)  # Access extracted text
+          print(result.metadata)  # Access PDF metadata (title, author, etc.)

-    ```python
-    from crawl4ai.async_configs import LlmConfig
-    from crawl4ai.extraction_strategy import LLMExtractionStrategy
-    from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+  asyncio.run(main())
+  ```

-    # Example of using LlmConfig with LLMExtractionStrategy
-    llm_config = LlmConfig(provider="openai/gpt-4o", api_token="YOUR_API_KEY")
-    strategy = LLMExtractionStrategy(llmConfig=llm_config, schema=...)
+- **Added: Support for frozenset serialization.** Improves configuration
+  serialization, especially for sets of allowed/blocked domains. No code changes
+  required.

-    # Example usage within a crawler
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun(
-            url="https://example.com",
-            config=CrawlerRunConfig(extraction_strategy=strategy)
-        )
-    ```
-    **Breaking Change:** Removed old parameters like `provider`, `api_token`, `base_url`, and `api_base` from `LLMExtractionStrategy` and `LLMContentFilter`. Users should migrate to using the `LlmConfig` object.
+- **Added: New `LlmConfig` parameter.** This new parameter can be passed for
+  extraction, filtering, and schema generation tasks. It simplifies passing
+  provider strings, API tokens, and base URLs across all sections where LLM
+  configuration is necessary. It also enables reuse and allows for quick
+  experimentation between different LLM configurations.

-*   **Changed: Improved browser context management and added shared data support. (Breaking Change:** `BrowserContext` API updated). Browser contexts are now managed more efficiently, reducing resource usage.  A new `shared_data` dictionary is available in the `BrowserContext` to allow passing data between different stages of the crawling process.  **Breaking Change:** The `BrowserContext` API has changed, and the old `get_context` method is deprecated.
+  ```python
+  from crawl4ai.async_configs import LlmConfig
+  from crawl4ai.extraction_strategy import LLMExtractionStrategy
+  from crawl4ai import AsyncWebCrawler, CrawlerRunConfig

-*   **Changed:** Renamed `final_url` to `redirected_url` in `CrawledURL`.  This improves consistency and clarity. Update any code referencing the old field name.
+  # Example of using LlmConfig with LLMExtractionStrategy
+  llm_config = LlmConfig(provider="openai/gpt-4o", api_token="YOUR_API_KEY")
+  strategy = LLMExtractionStrategy(llmConfig=llm_config, schema=...)

-*   **Changed:** Improved type hints and removed unused files. This is an internal improvement and should not require code changes.
+  # Example usage within a crawler
+  async with AsyncWebCrawler() as crawler:
+      result = await crawler.arun(
+          url="https://example.com",
+          config=CrawlerRunConfig(extraction_strategy=strategy)
+      )
+  ```
+  **Breaking Change:** Removed old parameters like `provider`, `api_token`,
+  `base_url`, and `api_base` from `LLMExtractionStrategy` and
+  `LLMContentFilter`. Users should migrate to using the `LlmConfig` object.

-*   **Changed:** Reorganized deep crawling functionality into dedicated module. (**Breaking Change:** Import paths for `DeepCrawlStrategy` and related classes have changed).  This improves code organization. Update imports to use the new `crawl4ai.deep_crawling` module.
+- **Changed: Improved browser context management and added shared data support.
+  (Breaking Change:** `BrowserContext` API updated). Browser contexts are now
+  managed more efficiently, reducing resource usage. A new `shared_data`
+  dictionary is available in the `BrowserContext` to allow passing data between
+  different stages of the crawling process. **Breaking Change:** The
+  `BrowserContext` API has changed, and the old `get_context` method is
+  deprecated.

-*   **Changed:** Improved HTML handling and cleanup codebase. (**Breaking Change:** Removed `ssl_certificate.json` file). This removes an unused file. If you were relying on this file for custom certificate validation, you'll need to implement an alternative approach.
+- **Changed:** Renamed `final_url` to `redirected_url` in `CrawledURL`. This
+  improves consistency and clarity. Update any code referencing the old field
+  name.

-*    **Changed:** Enhanced serialization and config handling. (**Breaking Change:** `FastFilterChain` has been replaced with `FilterChain`). This change simplifies config and improves the serialization.
+- **Changed:** Improved type hints and removed unused files. This is an internal
+  improvement and should not require code changes.

-*   **Added:**  Modified the license to Apache 2.0 *with a required attribution clause*.  See the `LICENSE` file for details.  All users must now clearly attribute the Crawl4AI project when using, distributing, or creating derivative works.
+- **Changed:** Reorganized deep crawling functionality into dedicated module.
+  (**Breaking Change:** Import paths for `DeepCrawlStrategy` and related classes
+  have changed). This improves code organization. Update imports to use the new
+  `crawl4ai.deep_crawling` module.

-*   **Fixed:** Prevent memory leaks by ensuring proper closure of Playwright pages. No code changes required.
+- **Changed:** Improved HTML handling and cleanup codebase. (**Breaking
+  Change:** Removed `ssl_certificate.json` file). This removes an unused file.
+  If you were relying on this file for custom certificate validation, you'll
+  need to implement an alternative approach.

-*   **Fixed:** Make model fields optional with default values (**Breaking Change:** Code relying on all fields being present may need adjustment).  Fields in data models (like `CrawledURL`) are now optional, with default values (usually `None`).  Update code to handle potential `None` values.
+- **Changed:** Enhanced serialization and config handling. (**Breaking Change:**
+  `FastFilterChain` has been replaced with `FilterChain`). This change
+  simplifies config and improves the serialization.

-*   **Fixed:** Adjust memory threshold and fix dispatcher initialization. This is an internal bug fix; no code changes are required.
+- **Added:** Modified the license to Apache 2.0 _with a required attribution
+  clause_. See the `LICENSE` file for details. All users must now clearly
+  attribute the Crawl4AI project when using, distributing, or creating
+  derivative works.

-*   **Fixed:** Ensure proper exit after running doctor command. No code changes are required.
-* **Fixed:** JsonCss selector and crawler improvements.
-* **Fixed:** Not working long page screenshot (#403)
-*   **Documentation:**  Updated documentation URLs to the new domain.
-*   **Documentation:** Added SERP API project example.
-*   **Documentation:** Added clarifying comments for CSS selector behavior.
-*   **Documentation:** Add Code of Conduct for the project (#410)
+- **Fixed:** Prevent memory leaks by ensuring proper closure of Playwright
+  pages. No code changes required.
+
+- **Fixed:** Make model fields optional with default values (**Breaking
+  Change:** Code relying on all fields being present may need adjustment).
+  Fields in data models (like `CrawledURL`) are now optional, with default
+  values (usually `None`). Update code to handle potential `None` values.
+
+- **Fixed:** Adjust memory threshold and fix dispatcher initialization. This is
+  an internal bug fix; no code changes are required.
+
+- **Fixed:** Ensure proper exit after running doctor command. No code changes
+  are required.
+- **Fixed:** JsonCss selector and crawler improvements.
+- **Fixed:** Not working long page screenshot (#403)
+- **Documentation:** Updated documentation URLs to the new domain.
+- **Documentation:** Added SERP API project example.
+- **Documentation:** Added clarifying comments for CSS selector behavior.
+- **Documentation:** Add Code of Conduct for the project (#410)

 ## Breaking Changes Summary

-*   **Dispatcher:**  The `MemoryAdaptiveDispatcher` is now the default for `arun_many()`, changing concurrency behavior. The return type of `arun_many` depends on the `stream` parameter.
-*   **Deep Crawling:**  `max_depth` is now part of `CrawlerRunConfig` and controls crawl depth.  Import paths for deep crawling strategies have changed.
-*   **Browser Context:** The `BrowserContext` API has been updated.
-*   **Models:** Many fields in data models are now optional, with default values.
-*   **Scraping Mode:** `ScrapingMode` enum replaced by strategy pattern (`WebScrapingStrategy`, `LXMLWebScrapingStrategy`).
-*   **Content Filter:** Removed `content_filter` parameter from `CrawlerRunConfig`. Use extraction strategies or markdown generators with filters instead.
-*   **Removed:** Synchronous `WebCrawler`, CLI, and docs management functionality.
-*   **Docker:**  Significant changes to Docker deployment, including new requirements and configuration.
-*   **File Removed**: Removed ssl_certificate.json file which might affect existing certificate validations
-* **Renamed**: final_url to redirected_url for consistency
-* **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
+- **Dispatcher:** The `MemoryAdaptiveDispatcher` is now the default for
+  `arun_many()`, changing concurrency behavior. The return type of `arun_many`
+  depends on the `stream` parameter.
+- **Deep Crawling:** `max_depth` is now part of `CrawlerRunConfig` and controls
+  crawl depth. Import paths for deep crawling strategies have changed.
+- **Browser Context:** The `BrowserContext` API has been updated.
+- **Models:** Many fields in data models are now optional, with default values.
+- **Scraping Mode:** `ScrapingMode` enum replaced by strategy pattern
+  (`WebScrapingStrategy`, `LXMLWebScrapingStrategy`).
+- **Content Filter:** Removed `content_filter` parameter from
+  `CrawlerRunConfig`. Use extraction strategies or markdown generators with
+  filters instead.
+- **Removed:** Synchronous `WebCrawler`, CLI, and docs management functionality.
+- **Docker:** Significant changes to Docker deployment, including new
+  requirements and configuration.
+- **File Removed**: Removed ssl_certificate.json file which might affect
+  existing certificate validations
+- **Renamed**: final_url to redirected_url for consistency
+- **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

 ## Migration Guide

-1.  **Update Imports:**  Adjust imports for `DeepCrawlStrategy`, `BreadthFirstSearchStrategy`, and related classes due to the new `deep_crawling` module structure.
-2.  **`CrawlerRunConfig`:**  Move `max_depth` to `CrawlerRunConfig`.  If using `content_filter`, migrate to an extraction strategy or a markdown generator with a filter.
-3.  **`arun_many()`:**  Adapt code to the new `MemoryAdaptiveDispatcher` behavior and the return type.
-4.  **`BrowserContext`:**  Update code using the `BrowserContext` API.
-5.  **Models:**  Handle potential `None` values for optional fields in data models.
-6.  **Scraping:** Replace `ScrapingMode` enum with `WebScrapingStrategy` or `LXMLWebScrapingStrategy`.
-7.  **Docker:**  Review the updated Docker documentation and adjust your deployment accordingly.
-8.  **CLI:**  Migrate to the new `crwl` command and update any scripts using the old CLI.
-9.  **Proxy:**: Removed synchronous WebCrawler support and related rate limiting configurations.
+1. **Update Imports:** Adjust imports for `DeepCrawlStrategy`,
+   `BreadthFirstSearchStrategy`, and related classes due to the new
+   `deep_crawling` module structure.
+2. **`CrawlerRunConfig`:** Move `max_depth` to `CrawlerRunConfig`. If using
+   `content_filter`, migrate to an extraction strategy or a markdown generator
+   with a filter.
+3. **`arun_many()`:** Adapt code to the new `MemoryAdaptiveDispatcher` behavior
+   and the return type.
+4. **`BrowserContext`:** Update code using the `BrowserContext` API.
+5. **Models:** Handle potential `None` values for optional fields in data
+   models.
+6. **Scraping:** Replace `ScrapingMode` enum with `WebScrapingStrategy` or
+   `LXMLWebScrapingStrategy`.
+7. **Docker:** Review the updated Docker documentation and adjust your
+   deployment accordingly.
+8. **CLI:** Migrate to the new `crwl` command and update any scripts using the
+   old CLI.
+9. **Proxy:**: Removed synchronous WebCrawler support and related rate limiting
+   configurations.
 10. **Config:**: Replace FastFilterChain to FilterChain
--- a/docs/md_v2/core/crawler-result.md
+++ b/docs/md_v2/core/crawler-result.md
@@ -27,7 +27,6 @@ class CrawlResult(BaseModel):
    screenshot: Optional[str] = None
    pdf : Optional[bytes] = None
    markdown: Optional[Union[str, MarkdownGenerationResult]] = None
-    markdown_v2: Optional[MarkdownGenerationResult] = None
    extracted_content: Optional[str] = None
    metadata: Optional[dict] = None
    error_message: Optional[str] = None
@@ -52,8 +51,7 @@ class CrawlResult(BaseModel):
 | **downloaded_files (`Optional[List[str]]`)** | If `accept_downloads=True` in `BrowserConfig`, this lists the filepaths of saved downloads.         |
 | **screenshot (`Optional[str]`)**          | Screenshot of the page (base64-encoded) if `screenshot=True`.                                       |
 | **pdf (`Optional[bytes]`)**               | PDF of the page if `pdf=True`.                                                                      |
-| **markdown (`Optional[str or MarkdownGenerationResult]`)** | For now, `markdown_v2` holds a `MarkdownGenerationResult`. Over time, this will be consolidated into `markdown`. The generator can provide raw markdown, citations, references, and optionally `fit_markdown`. |
-| **markdown_v2 (`Optional[MarkdownGenerationResult]`)** | Legacy field for detailed markdown output. This will be replaced by `markdown` soon.                |
+| **markdown (`Optional[str or MarkdownGenerationResult]`)** | It holds a `MarkdownGenerationResult`. Over time, this will be consolidated into `markdown`. The generator can provide raw markdown, citations, references, and optionally `fit_markdown`. |
 | **extracted_content (`Optional[str]`)**   | The output of a structured extraction (CSS/LLM-based) stored as JSON string or other text.          |
 | **metadata (`Optional[dict]`)**           | Additional info about the crawl or extracted data.                                                  |
 | **error_message (`Optional[str]`)**       | If `success=False`, contains a short description of what went wrong.                                |
@@ -90,10 +88,10 @@ print(result.cleaned_html)  # Freed of forms, header, footer, data-* attributes

 ## 3. Markdown Generation

-### 3.1 `markdown_v2` (Legacy) vs `markdown`
+### 3.1 `markdown`

- **`markdown_v2`**: The current location for detailed markdown output, returning a **`MarkdownGenerationResult`** object.  
- **`markdown`**: Eventually, we’re merging these fields. For now, you might see `result.markdown_v2` used widely in code examples.
+- **`markdown`**: The current location for detailed markdown output, returning a **`MarkdownGenerationResult`** object.  
+- **`markdown_v2`**: Deprecated since v0.5.

 **`MarkdownGenerationResult`** Fields:

@@ -118,7 +116,7 @@ config = CrawlerRunConfig(
 )
 result = await crawler.arun(url="https://example.com", config=config)

-md_res = result.markdown_v2  # or eventually 'result.markdown'
+md_res = result.markdown  # or eventually 'result.markdown'
 print(md_res.raw_markdown[:500])
 print(md_res.markdown_with_citations)
 print(md_res.references_markdown)
@@ -224,15 +222,17 @@ Check any field:
 if result.success:
    print(result.status_code, result.response_headers)
    print("Links found:", len(result.links.get("internal", [])))
-    if result.markdown_v2:
-        print("Markdown snippet:", result.markdown_v2.raw_markdown[:200])
+    if result.markdown:
+        print("Markdown snippet:", result.markdown.raw_markdown[:200])
    if result.extracted_content:
        print("Structured JSON:", result.extracted_content)
 else:
    print("Error:", result.error_message)
 ```

-**Remember**: Use `result.markdown_v2` for now. It will eventually become `result.markdown`.
+**Deprecation**: Since v0.5 `result.markdown_v2`, `result.fit_html`,`result.fit_markdown` are deprecated. Use `result.markdown` instead! It holds `MarkdownGenerationResult`, which includes `fit_html` and `fit_markdown`
+as it's properties.
+

 ---

--- a/docs/md_v2/core/deep-crawling.md
+++ b/docs/md_v2/core/deep-crawling.md
@@ -0,0 +1,436 @@
+# Deep Crawling
+
+One of Crawl4AI's most powerful features is its ability to perform **configurable deep crawling** that can explore websites beyond a single page. With fine-tuned control over crawl depth, domain boundaries, and content filtering, Crawl4AI gives you the tools to extract precisely the content you need.
+
+In this tutorial, you'll learn:
+
+1. How to set up a **Basic Deep Crawler** with BFS strategy  
+2. Understanding the difference between **streamed and non-streamed** output  
+3. Implementing **filters and scorers** to target specific content  
+4. Creating **advanced filtering chains** for sophisticated crawls  
+5. Using **BestFirstCrawling** for intelligent exploration prioritization  
+
+> **Prerequisites**  
+> - You’ve completed or read [AsyncWebCrawler Basics](../core/simple-crawling.md) to understand how to run a simple crawl.  
+> - You know how to configure `CrawlerRunConfig`.
+
+---
+
+## 1. Quick Example
+
+Here's a minimal code snippet that implements a basic deep crawl using the **BFSDeepCrawlStrategy**:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
+from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
+
+async def main():
+    # Configure a 2-level deep crawl
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BFSDeepCrawlStrategy(
+            max_depth=2, 
+            include_external=False
+        ),
+        scraping_strategy=LXMLWebScrapingStrategy(),
+        verbose=True
+    )
+    
+    async with AsyncWebCrawler() as crawler:
+        results = await crawler.arun("https://example.com", config=config)
+        
+        print(f"Crawled {len(results)} pages in total")
+        
+        # Access individual results
+        for result in results[:3]:  # Show first 3 results
+            print(f"URL: {result.url}")
+            print(f"Depth: {result.metadata.get('depth', 0)}")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+**What's happening?**  
+- `BFSDeepCrawlStrategy(max_depth=2, include_external=False)` instructs Crawl4AI to:
+  - Crawl the starting page (depth 0) plus 2 more levels
+  - Stay within the same domain (don't follow external links)
+- Each result contains metadata like the crawl depth
+- Results are returned as a list after all crawling is complete
+
+---
+
+## 2. Understanding Deep Crawling Strategy Options
+
+### 2.1 BFSDeepCrawlStrategy (Breadth-First Search)
+
+The **BFSDeepCrawlStrategy** uses a breadth-first approach, exploring all links at one depth before moving deeper:
+
+```python
+from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
+
+# Basic configuration
+strategy = BFSDeepCrawlStrategy(
+    max_depth=2,               # Crawl initial page + 2 levels deep
+    include_external=False,    # Stay within the same domain
+)
+```
+
+**Key parameters:**
+- **`max_depth`**: Number of levels to crawl beyond the starting page
+- **`include_external`**: Whether to follow links to other domains
+
+### 2.2 DFSDeepCrawlStrategy (Depth-First Search)
+
+The **DFSDeepCrawlStrategy** uses a depth-first approach, explores as far down a branch as possible before backtracking.
+
+```python
+from crawl4ai.deep_crawling import DFSDeepCrawlStrategy
+
+# Basic configuration
+strategy = DFSDeepCrawlStrategy(
+    max_depth=2,               # Crawl initial page + 2 levels deep
+    include_external=False,    # Stay within the same domain
+)
+```
+
+**Key parameters:**
+- **`max_depth`**: Number of levels to crawl beyond the starting page
+- **`include_external`**: Whether to follow links to other domains
+
+### 2.3 BestFirstCrawlingStrategy (⭐️ - Recommended Deep crawl strategy)
+
+For more intelligent crawling, use **BestFirstCrawlingStrategy** with scorers to prioritize the most relevant pages:
+
+```python
+from crawl4ai.deep_crawling import BestFirstCrawlingStrategy
+from crawl4ai.deep_crawling.scorers import KeywordRelevanceScorer
+
+# Create a scorer
+scorer = KeywordRelevanceScorer(
+    keywords=["crawl", "example", "async", "configuration"],
+    weight=0.7
+)
+
+# Configure the strategy
+strategy = BestFirstCrawlingStrategy(
+    max_depth=2,
+    include_external=False,
+    url_scorer=scorer
+)
+```
+
+This crawling approach:
+- Evaluates each discovered URL based on scorer criteria
+- Visits higher-scoring pages first
+- Helps focus crawl resources on the most relevant content
+
+---
+
+## 3. Streaming vs. Non-Streaming Results
+
+Crawl4AI can return results in two modes:
+
+### 3.1 Non-Streaming Mode (Default)
+
+```python
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(max_depth=1),
+    stream=False  # Default behavior
+)
+
+async with AsyncWebCrawler() as crawler:
+    # Wait for ALL results to be collected before returning
+    results = await crawler.arun("https://example.com", config=config)
+    
+    for result in results:
+        process_result(result)
+```
+
+**When to use non-streaming mode:**
+- You need the complete dataset before processing
+- You're performing batch operations on all results together
+- Crawl time isn't a critical factor
+
+### 3.2 Streaming Mode
+
+```python
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(max_depth=1),
+    stream=True  # Enable streaming
+)
+
+async with AsyncWebCrawler() as crawler:
+    # Returns an async iterator
+    async for result in await crawler.arun("https://example.com", config=config):
+        # Process each result as it becomes available
+        process_result(result)
+```
+
+**Benefits of streaming mode:**
+- Process results immediately as they're discovered
+- Start working with early results while crawling continues
+- Better for real-time applications or progressive display
+- Reduces memory pressure when handling many pages
+
+---
+
+## 4. Filtering Content with Filter Chains
+
+Filters help you narrow down which pages to crawl. Combine multiple filters using **FilterChain** for powerful targeting.
+
+### 4.1 Basic URL Pattern Filter
+
+```python
+from crawl4ai.deep_crawling.filters import FilterChain, URLPatternFilter
+
+# Only follow URLs containing "blog" or "docs"
+url_filter = URLPatternFilter(patterns=["*blog*", "*docs*"])
+
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(
+        max_depth=1,
+        filter_chain=FilterChain([url_filter])
+    )
+)
+```
+
+### 4.2 Combining Multiple Filters
+
+```python
+from crawl4ai.deep_crawling.filters import (
+    FilterChain,
+    URLPatternFilter,
+    DomainFilter,
+    ContentTypeFilter
+)
+
+# Create a chain of filters
+filter_chain = FilterChain([
+    # Only follow URLs with specific patterns
+    URLPatternFilter(patterns=["*guide*", "*tutorial*"]),
+    
+    # Only crawl specific domains
+    DomainFilter(
+        allowed_domains=["docs.example.com"],
+        blocked_domains=["old.docs.example.com"]
+    ),
+    
+    # Only include specific content types
+    ContentTypeFilter(allowed_types=["text/html"])
+])
+
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(
+        max_depth=2,
+        filter_chain=filter_chain
+    )
+)
+```
+
+### 4.3 Available Filter Types
+
+Crawl4AI includes several specialized filters:
+
+- **`URLPatternFilter`**: Matches URL patterns using wildcard syntax
+- **`DomainFilter`**: Controls which domains to include or exclude
+- **`ContentTypeFilter`**: Filters based on HTTP Content-Type
+- **`ContentRelevanceFilter`**: Uses similarity to a text query
+- **`SEOFilter`**: Evaluates SEO elements (meta tags, headers, etc.)
+
+---
+
+## 5. Using Scorers for Prioritized Crawling
+
+Scorers assign priority values to discovered URLs, helping the crawler focus on the most relevant content first.
+
+### 5.1 KeywordRelevanceScorer
+
+```python
+from crawl4ai.deep_crawling.scorers import KeywordRelevanceScorer
+from crawl4ai.deep_crawling import BestFirstCrawlingStrategy
+
+# Create a keyword relevance scorer
+keyword_scorer = KeywordRelevanceScorer(
+    keywords=["crawl", "example", "async", "configuration"],
+    weight=0.7  # Importance of this scorer (0.0 to 1.0)
+)
+
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BestFirstCrawlingStrategy(
+        max_depth=2,
+        url_scorer=keyword_scorer
+    ),
+    stream=True  # Recommended with BestFirstCrawling
+)
+
+# Results will come in order of relevance score
+async with AsyncWebCrawler() as crawler:
+    async for result in await crawler.arun("https://example.com", config=config):
+        score = result.metadata.get("score", 0)
+        print(f"Score: {score:.2f} | {result.url}")
+```
+
+**How scorers work:**
+- Evaluate each discovered URL before crawling
+- Calculate relevance based on various signals
+- Help the crawler make intelligent choices about traversal order
+
+---
+
+## 6. Advanced Filtering Techniques
+
+### 6.1 SEO Filter for Quality Assessment
+
+The **SEOFilter** helps you identify pages with strong SEO characteristics:
+
+```python
+from crawl4ai.deep_crawling.filters import FilterChain, SEOFilter
+
+# Create an SEO filter that looks for specific keywords in page metadata
+seo_filter = SEOFilter(
+    threshold=0.5,  # Minimum score (0.0 to 1.0)
+    keywords=["tutorial", "guide", "documentation"]
+)
+
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(
+        max_depth=1,
+        filter_chain=FilterChain([seo_filter])
+    )
+)
+```
+
+### 6.2 Content Relevance Filter
+
+The **ContentRelevanceFilter** analyzes the actual content of pages:
+
+```python
+from crawl4ai.deep_crawling.filters import FilterChain, ContentRelevanceFilter
+
+# Create a content relevance filter
+relevance_filter = ContentRelevanceFilter(
+    query="Web crawling and data extraction with Python",
+    threshold=0.7  # Minimum similarity score (0.0 to 1.0)
+)
+
+config = CrawlerRunConfig(
+    deep_crawl_strategy=BFSDeepCrawlStrategy(
+        max_depth=1,
+        filter_chain=FilterChain([relevance_filter])
+    )
+)
+```
+
+This filter:
+- Measures semantic similarity between query and page content
+- It's a BM25-based relevance filter using head section content
+
+---
+
+## 7. Building a Complete Advanced Crawler
+
+This example combines multiple techniques for a sophisticated crawl:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
+from crawl4ai.deep_crawling import BestFirstCrawlingStrategy
+from crawl4ai.deep_crawling.filters import (
+    FilterChain,
+    DomainFilter,
+    URLPatternFilter,
+    ContentTypeFilter
+)
+from crawl4ai.deep_crawling.scorers import KeywordRelevanceScorer
+
+async def run_advanced_crawler():
+    # Create a sophisticated filter chain
+    filter_chain = FilterChain([
+        # Domain boundaries
+        DomainFilter(
+            allowed_domains=["docs.example.com"],
+            blocked_domains=["old.docs.example.com"]
+        ),
+        
+        # URL patterns to include
+        URLPatternFilter(patterns=["*guide*", "*tutorial*", "*blog*"]),
+        
+        # Content type filtering
+        ContentTypeFilter(allowed_types=["text/html"])
+    ])
+
+    # Create a relevance scorer
+    keyword_scorer = KeywordRelevanceScorer(
+        keywords=["crawl", "example", "async", "configuration"],
+        weight=0.7
+    )
+
+    # Set up the configuration
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=BestFirstCrawlingStrategy(
+            max_depth=2,
+            include_external=False,
+            filter_chain=filter_chain,
+            url_scorer=keyword_scorer
+        ),
+        scraping_strategy=LXMLWebScrapingStrategy(),
+        stream=True,
+        verbose=True
+    )
+
+    # Execute the crawl
+    results = []
+    async with AsyncWebCrawler() as crawler:
+        async for result in await crawler.arun("https://docs.example.com", config=config):
+            results.append(result)
+            score = result.metadata.get("score", 0)
+            depth = result.metadata.get("depth", 0)
+            print(f"Depth: {depth} | Score: {score:.2f} | {result.url}")
+
+    # Analyze the results
+    print(f"Crawled {len(results)} high-value pages")
+    print(f"Average score: {sum(r.metadata.get('score', 0) for r in results) / len(results):.2f}")
+
+    # Group by depth
+    depth_counts = {}
+    for result in results:
+        depth = result.metadata.get("depth", 0)
+        depth_counts[depth] = depth_counts.get(depth, 0) + 1
+
+    print("Pages crawled by depth:")
+    for depth, count in sorted(depth_counts.items()):
+        print(f"  Depth {depth}: {count} pages")
+
+if __name__ == "__main__":
+    asyncio.run(run_advanced_crawler())
+```
+
+---
+
+
+## 8. Common Pitfalls & Tips
+
+1.**Set realistic depth limits.** Be cautious with `max_depth` values > 3, which can exponentially increase crawl size. 
+
+2.**Don't neglect the scoring component.** BestFirstCrawling works best with well-tuned scorers. Experiment with keyword weights for optimal prioritization.
+
+3.**Be a good web citizen.**  Respect robots.txt. (disabled by default)
+  
+
+4.**Handle page errors gracefully.** Not all pages will be accessible. Check `result.success` and `result.error_message` when processing results.
+
+---
+
+## 9. Summary & Next Steps
+
+In this **Deep Crawling with Crawl4AI** tutorial, you learned to:
+
+- Configure **BFSDeepCrawlStrategy** and **BestFirstCrawlingStrategy**
+- Process results in streaming or non-streaming mode
+- Apply filters to target specific content
+- Use scorers to prioritize the most relevant pages
+- Build a complete advanced crawler with combined techniques
+
+With these tools, you can efficiently extract structured data from websites at scale, focusing precisely on the content you need for your specific use case.
--- a/docs/md_v2/core/docker-deployment.md
+++ b/docs/md_v2/core/docker-deployment.md
--- a/docs/md_v2/core/fit-markdown.md
+++ b/docs/md_v2/core/fit-markdown.md
@@ -10,11 +10,10 @@

 In **`CrawlerRunConfig`**, you can specify a **`content_filter`** to shape how content is pruned or ranked before final markdown generation. A filter’s logic is applied **before** or **during** the HTML→Markdown process, producing:

- **`result.markdown_v2.raw_markdown`** (unfiltered)
- **`result.markdown_v2.fit_markdown`** (filtered or “fit” version)
- **`result.markdown_v2.fit_html`** (the corresponding HTML snippet that produced `fit_markdown`)
+- **`result.markdown.raw_markdown`** (unfiltered)
+- **`result.markdown.fit_markdown`** (filtered or “fit” version)
+- **`result.markdown.fit_html`** (the corresponding HTML snippet that produced `fit_markdown`)

-> **Note**: We’re currently storing the result in `markdown_v2`, but eventually we’ll unify it as `result.markdown`.

 ### 1.2 Common Filters

@@ -62,8 +61,8 @@ async def main():
        
        if result.success:
            # 'fit_markdown' is your pruned content, focusing on "denser" text
-            print("Raw Markdown length:", len(result.markdown_v2.raw_markdown))
-            print("Fit Markdown length:", len(result.markdown_v2.fit_markdown))
+            print("Raw Markdown length:", len(result.markdown.raw_markdown))
+            print("Fit Markdown length:", len(result.markdown.fit_markdown))
        else:
            print("Error:", result.error_message)

@@ -123,7 +122,7 @@ async def main():
        )
        if result.success:
            print("Fit Markdown (BM25 query-based):")
-            print(result.markdown_v2.fit_markdown)
+            print(result.markdown.fit_markdown)
        else:
            print("Error:", result.error_message)

@@ -144,11 +143,11 @@ if __name__ == "__main__":

 ## 4. Accessing the “Fit” Output

-After the crawl, your “fit” content is found in **`result.markdown_v2.fit_markdown`**. In future versions, it will be **`result.markdown.fit_markdown`**. Meanwhile:
+After the crawl, your “fit” content is found in **`result.markdown.fit_markdown`**. 

 ```python
-fit_md = result.markdown_v2.fit_markdown
-fit_html = result.markdown_v2.fit_html
+fit_md = result.markdown.fit_markdown
+fit_html = result.markdown.fit_html
 ```

 If the content filter is **BM25**, you might see additional logic or references in `fit_markdown` that highlight relevant segments. If it’s **Pruning**, the text is typically well-cleaned but not necessarily matched to a query.
@@ -167,7 +166,6 @@ prune_filter = PruningContentFilter(
 )
 md_generator = DefaultMarkdownGenerator(content_filter=prune_filter)
 config = CrawlerRunConfig(markdown_generator=md_generator)
-# => result.markdown_v2.fit_markdown
 ```

 ### 5.2 BM25
@@ -179,7 +177,6 @@ bm25_filter = BM25ContentFilter(
 )
 md_generator = DefaultMarkdownGenerator(content_filter=bm25_filter)
 config = CrawlerRunConfig(markdown_generator=md_generator)
-# => result.markdown_v2.fit_markdown
 ```

 ---
@@ -203,7 +200,7 @@ Thus, **multi-level** filtering occurs:

 1. The crawler’s `excluded_tags` are removed from the HTML first.  
 2. The content filter (Pruning, BM25, or custom) prunes or ranks the remaining text blocks.  
-3. The final “fit” content is generated in `result.markdown_v2.fit_markdown`.
+3. The final “fit” content is generated in `result.markdown.fit_markdown`.

 ---

@@ -241,7 +238,7 @@ class MyCustomFilter(RelevantContentFilter):
 - **PruningContentFilter**: Great if you just want the “meatiest” text without a user query.  
 - **BM25ContentFilter**: Perfect for query-based extraction or searching.  
 - Combine with **`excluded_tags`, `exclude_external_links`, `word_count_threshold`** to refine your final “fit” text.  
- Fit markdown ends up in **`result.markdown_v2.fit_markdown`**; eventually **`result.markdown.fit_markdown`** in future versions.
+- Fit markdown ends up in **`result.markdown.fit_markdown`**; eventually **`result.markdown.fit_markdown`** in future versions.

 With these tools, you can **zero in** on the text that truly matters, ignoring spammy or boilerplate content, and produce a concise, relevant “fit markdown” for your AI or data pipelines. Happy pruning and searching!

--- a/docs/md_v2/core/markdown-generation.md
+++ b/docs/md_v2/core/markdown-generation.md
@@ -204,7 +204,7 @@ async def main():

    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun("https://example.com", config=config)
-        print(result.fit_markdown)  # Filtered markdown content
+        print(result.markdown.fit_markdown)  # Filtered markdown content
 ```

 **Key Features:**
@@ -249,14 +249,11 @@ filter = LLMContentFilter(

 ## 5. Using Fit Markdown

-When a content filter is active, the library produces two forms of markdown inside `result.markdown_v2` or (if using the simplified field) `result.markdown`:
+When a content filter is active, the library produces two forms of markdown inside `result.markdown`:

 1. **`raw_markdown`**: The full unfiltered markdown.  
 2. **`fit_markdown`**: A “fit” version where the filter has removed or trimmed noisy segments.

-**Note**:  
-> In earlier examples, you may see references to `result.markdown_v2`. Depending on your library version, you might access `result.markdown`, `result.markdown_v2`, or an object named `MarkdownGenerationResult`. The idea is the same: you’ll have a raw version and a filtered (“fit”) version if a filter is used.
-
 ```python
 import asyncio
 from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
@@ -276,7 +273,7 @@ async def main():
            print("Raw markdown:\n", result.markdown)
            
            # If a filter is used, we also have .fit_markdown:
-            md_object = result.markdown_v2  # or your equivalent
+            md_object = result.markdown  # or your equivalent
            print("Filtered markdown:\n", md_object.fit_markdown)
        else:
            print("Crawl failed:", result.error_message)
@@ -300,7 +297,7 @@ If your library stores detailed markdown output in an object like `MarkdownGener
 **Example**:

 ```python
-md_obj = result.markdown_v2  # your library’s naming may vary
+md_obj = result.markdown  # your library’s naming may vary
 print("RAW:\n", md_obj.raw_markdown)
 print("CITED:\n", md_obj.markdown_with_citations)
 print("REFERENCES:\n", md_obj.references_markdown)
--- a/docs/md_v2/core/quickstart.md
+++ b/docs/md_v2/core/quickstart.md
@@ -296,7 +296,7 @@ async def quick_parallel_example():
        # Stream results as they complete
        async for result in await crawler.arun_many(urls, config=run_conf):
            if result.success:
-                print(f"[OK] {result.url}, length: {len(result.markdown_v2.raw_markdown)}")
+                print(f"[OK] {result.url}, length: {len(result.markdown.raw_markdown)}")
            else:
                print(f"[ERROR] {result.url} => {result.error_message}")

@@ -305,7 +305,7 @@ async def quick_parallel_example():
        results = await crawler.arun_many(urls, config=run_conf)
        for res in results:
            if res.success:
-                print(f"[OK] {res.url}, length: {len(res.markdown_v2.raw_markdown)}")
+                print(f"[OK] {res.url}, length: {len(res.markdown.raw_markdown)}")
            else:
                print(f"[ERROR] {res.url} => {res.error_message}")

--- a/docs/md_v2/core/simple-crawling.md
+++ b/docs/md_v2/core/simple-crawling.md
@@ -39,8 +39,8 @@ result = await crawler.arun(
 # Different content formats
 print(result.html)         # Raw HTML
 print(result.cleaned_html) # Cleaned HTML
-print(result.markdown)     # Markdown version
-print(result.fit_markdown) # Most relevant content in markdown
+print(result.markdown.raw_markdown) # Raw markdown from cleaned html
+print(result.markdown.fit_markdown) # Most relevant content in markdown

 # Check success status
 print(result.success)      # True if crawl succeeded