🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper.

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.

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 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.

• 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

• 🧹 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.

• 🤖 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.

• 🖥️ 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.

• 🖼️ 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.

• 🐳 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.

• 🕶️ 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.

Choose the installation option that best fits your needs:

For basic web crawling and scraping tasks:

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:

   playwright install
   

2. If the above doesn't work, try this more specific command:

   python -m playwright install chromium
   

This second method has proven to be more reliable in some cases.

The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:

pip install crawl4ai[sync]

For contributors who plan to modify the source code:

git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai
pip install -e .                    # Basic installation in editable mode

Install optional features:

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

🚀 Now Available! Our completely redesigned Docker implementation is here! This new solution makes deployment more efficient and seamless than ever.

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

# 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

Run a quick test (works for both Docker options):

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. For advanced configuration, environment variables, and usage examples, see our Docker Deployment Guide.

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())

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())

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())

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)}")

• 🚀 LLMTableExtraction: Revolutionary table extraction with intelligent chunking for massive tables:

  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")
  

• ⚡ 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 →

• 🕵️ Undetected Browser Support: Bypass sophisticated bot detection systems:

  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
  

• 🎨 Multi-URL Configuration: Different strategies for different URL patterns in one batch:

  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
  

• 🧠 Memory Monitoring: Track and optimize memory usage during crawling:

  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
  

• 📊 Enhanced Table Extraction: Direct DataFrame conversion from web tables:

  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")
  

• 💰 GitHub Sponsors: 4-tier sponsorship system for project sustainability

• 🐳 Docker LLM Flexibility: Configure providers via environment variables

Full v0.7.3 Release Notes →

• 🧠 Adaptive Crawling: Your crawler now learns and adapts to website patterns automatically:

  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:

  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:

  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:

  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 or check the CHANGELOG.

Our version numbers follow this pattern: MAJOR.MINOR.PATCH (e.g., 0.4.3)

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

• Regular installation (stable version):

  pip install -U crawl4ai
  

• Install pre-release versions:

  pip install crawl4ai --pre
  

• Install specific version:

  pip install crawl4ai==0.4.3b1
  

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.

• 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

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• 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.

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.