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 (supports both string and function-based APIs).
• 💾 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:

• Real-time Monitoring Dashboard with live system metrics and browser pool visibility
• 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
docker pull unclecode/crawl4ai:latest
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:latest

# Visit the monitoring dashboard at http://localhost:11235/dashboard
# Or 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, monitoring features, and production deployment, see our Self-Hosting Guide.