🚀🤖 Crawl4AI: Crawl Smarter, Faster, Freely. For AI.

Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.

✨ Check out latest update v0.4.2

🎉 Version 0.4.2 is out! Introducing our experimental PruningContentFilter - a powerful new algorithm for smarter Markdown generation. Test it out and share your feedback! Read the release notes →

🧐 Why Crawl4AI?

1. Built for LLMs: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.
2. Lightning Fast: Delivers results 6x faster with real-time, cost-efficient performance.
3. Flexible Browser Control: Offers session management, proxies, and custom hooks for seamless data access.
4. Heuristic Intelligence: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.
5. Open Source & Deployable: Fully open-source with no API keys—ready for Docker and cloud integration.
6. Thriving Community: Actively maintained by a vibrant community and the #1 trending GitHub repository.

🚀 Quick Start

1. Install Crawl4AI:

pip install crawl4ai
crawl4ai-setup # Setup the browser

2. Run a simple web crawl:

import asyncio
from crawl4ai import AsyncWebCrawler, CacheMode

async def main():
    async with AsyncWebCrawler(verbose=True) as crawler:
        result = await crawler.arun(url="https://www.nbcnews.com/business")
        # Soone will be change to result.markdown
        print(result.markdown_v2.raw_markdown) 

if __name__ == "__main__":
    asyncio.run(main())

✨ Features

📝 Markdown Generation

• 🧹 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.

📊 Structured Data Extraction

• 🤖 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.

🌐 Browser Integration

• 🖥️ 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.
• 🔒 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.

🔎 Crawling & Scraping

• 🖼️ 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.

🚀 Deployment

• 🐳 Dockerized Setup: Optimized Docker image with API server for easy deployment.
• 🔄 API Gateway: One-click deployment with secure token authentication for API-based workflows.
• 🌐 Scalable Architecture: Designed for mass-scale production and optimized server performance.
• ⚙️ DigitalOcean Deployment: Ready-to-deploy configurations for DigitalOcean and similar platforms.

🎯 Additional Features

• 🕶️ 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.

Try it Now!

✨ Play around with this

✨ Visit our Documentation Website

Installation 🛠️

Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.

🐍 Using pip

Choose the installation option that best fits your needs:

Basic Installation

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.

---

Installation with Synchronous Version

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

pip install crawl4ai[sync]

---

Development Installation

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

🚀 One-Click Deployment

Deploy your own instance of Crawl4AI with one click:

💡 Recommended specs: 4GB RAM minimum. Select "professional-xs" or higher when deploying for stable operation.

The deploy will:

• Set up a Docker container with Crawl4AI
• Configure Playwright and all dependencies
• Start the FastAPI server on port 11235
• Set up health checks and auto-deployment

🐳 Using Docker

Crawl4AI is available as Docker images for easy deployment. You can either pull directly from Docker Hub (recommended) or build from the repository.

---

🐳 Option 1: Docker Hub (Recommended)

Choose the appropriate image based on your platform and needs:

For AMD64 (Regular Linux/Windows):

# Basic version (recommended)
docker pull unclecode/crawl4ai:basic-amd64
docker run -p 11235:11235 unclecode/crawl4ai:basic-amd64

# Full ML/LLM support
docker pull unclecode/crawl4ai:all-amd64
docker run -p 11235:11235 unclecode/crawl4ai:all-amd64

# With GPU support
docker pull unclecode/crawl4ai:gpu-amd64
docker run -p 11235:11235 unclecode/crawl4ai:gpu-amd64

For ARM64 (M1/M2 Macs, ARM servers):

# Basic version (recommended)
docker pull unclecode/crawl4ai:basic-arm64
docker run -p 11235:11235 unclecode/crawl4ai:basic-arm64

# Full ML/LLM support
docker pull unclecode/crawl4ai:all-arm64
docker run -p 11235:11235 unclecode/crawl4ai:all-arm64

# With GPU support
docker pull unclecode/crawl4ai:gpu-arm64
docker run -p 11235:11235 unclecode/crawl4ai:gpu-arm64

Need more memory? Add --shm-size:

docker run --shm-size=2gb -p 11235:11235 unclecode/crawl4ai:basic-amd64

Test the installation:

curl http://localhost:11235/health

For Raspberry Pi (32-bit) (coming soon):

# Pull and run basic version (recommended for Raspberry Pi)
docker pull unclecode/crawl4ai:basic-armv7
docker run -p 11235:11235 unclecode/crawl4ai:basic-armv7

# With increased shared memory if needed
docker run --shm-size=2gb -p 11235:11235 unclecode/crawl4ai:basic-armv7

Note: Due to hardware constraints, only the basic version is recommended for Raspberry Pi.

🐳 Option 2: Build from Repository

Build the image locally based on your platform:

# Clone the repository
git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai

# For AMD64 (Regular Linux/Windows)
docker build --platform linux/amd64 \
  --tag crawl4ai:local \
  --build-arg INSTALL_TYPE=basic \
  .

# For ARM64 (M1/M2 Macs, ARM servers)
docker build --platform linux/arm64 \
  --tag crawl4ai:local \
  --build-arg INSTALL_TYPE=basic \
  .

Build options:

• INSTALL_TYPE=basic (default): Basic crawling features
• INSTALL_TYPE=all: Full ML/LLM support
• ENABLE_GPU=true: Add GPU support

Example with all options:

docker build --platform linux/amd64 \
  --tag crawl4ai:local \
  --build-arg INSTALL_TYPE=all \
  --build-arg ENABLE_GPU=true \
  .

Run your local build:

# Regular run
docker run -p 11235:11235 crawl4ai:local

# With increased shared memory
docker run --shm-size=2gb -p 11235:11235 crawl4ai:local

Test the installation:

curl http://localhost:11235/health

🐳 Option 3: Using Docker Compose

Docker Compose provides a more structured way to run Crawl4AI, especially when dealing with environment variables and multiple configurations.

# Clone the repository
git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai

For AMD64 (Regular Linux/Windows):

# Build and run locally
docker-compose --profile local-amd64 up

# Run from Docker Hub
VERSION=basic docker-compose --profile hub-amd64 up   # Basic version
VERSION=all docker-compose --profile hub-amd64 up     # Full ML/LLM support
VERSION=gpu docker-compose --profile hub-amd64 up     # GPU support

For ARM64 (M1/M2 Macs, ARM servers):

# Build and run locally
docker-compose --profile local-arm64 up

# Run from Docker Hub
VERSION=basic docker-compose --profile hub-arm64 up   # Basic version
VERSION=all docker-compose --profile hub-arm64 up     # Full ML/LLM support
VERSION=gpu docker-compose --profile hub-arm64 up     # GPU support

Environment variables (optional):

# Create a .env file
CRAWL4AI_API_TOKEN=your_token
OPENAI_API_KEY=your_openai_key
CLAUDE_API_KEY=your_claude_key

The compose file includes:

• Memory management (4GB limit, 1GB reserved)
• Shared memory volume for browser support
• Health checks
• Auto-restart policy
• All necessary port mappings

Test the installation:

curl http://localhost:11235/health

---

Quick Test

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}
)
task_id = response.json()["task_id"]

# Continue polling until the task is complete (status="completed")
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.

🔬 Advanced Usage Examples 🔬

You can check the project structure in the directory https://github.com/unclecode/crawl4ai/docs/examples. Over there, you can find a variety of examples; here, some popular examples are shared.

📝 Heuristic Markdown Generation with Clean and Fit Markdown

import asyncio
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.content_filter_strategy import PruningContentFilter, BM25ContentFilter
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator

async def main():
    async with AsyncWebCrawler(
        headless=True,  
        verbose=True,
    ) as crawler:
        result = await crawler.arun(
            url="https://docs.micronaut.io/4.7.6/guide/",
            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)
            # ),
        )
        print(len(result.markdown))
        print(len(result.fit_markdown))
        print(len(result.markdown_v2.fit_markdown))

if __name__ == "__main__":
    asyncio.run(main())

🖥️ Executing JavaScript & Extract Structured Data without LLMs

import asyncio
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.extraction_strategy 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)

    async with AsyncWebCrawler(
        headless=False,
        verbose=True
    ) as crawler:
        
        # Create the JavaScript that handles clicking multiple times
        js_click_tabs = """
        (async () => {
            const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");
            
            for(let tab of tabs) {
                // scroll to the tab
                tab.scrollIntoView();
                tab.click();
                // Wait for content to load and animations to complete
                await new Promise(r => setTimeout(r, 500));
            }
        })();
        """     

        result = await crawler.arun(
            url="https://www.kidocode.com/degrees/technology",
            extraction_strategy=JsonCssExtractionStrategy(schema, verbose=True),
            js_code=[js_click_tabs],
            cache_mode=CacheMode.BYPASS
        )

        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())

📚 Extracting Structured Data with LLMs

import os
import asyncio
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.extraction_strategy 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():
    async with AsyncWebCrawler(verbose=True) as crawler:
        result = await crawler.arun(
            url='https://openai.com/api/pricing/',
            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", 
                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,
        )
        print(result.extracted_content)

if __name__ == "__main__":
    asyncio.run(main())

🤖 Using You own Browswer with Custome User Profile

import os, sys
from pathlib import Path
import asyncio, time
from crawl4ai import AsyncWebCrawler

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)

    async with AsyncWebCrawler(
        verbose=True,
        headless=True,
        user_data_dir=user_data_dir,
        use_persistent_context=True,
        headers={
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
            "Accept-Language": "en-US,en;q=0.5",
            "Accept-Encoding": "gzip, deflate, br",
            "DNT": "1",
            "Connection": "keep-alive",
            "Upgrade-Insecure-Requests": "1",
            "Sec-Fetch-Dest": "document",
            "Sec-Fetch-Mode": "navigate",
            "Sec-Fetch-Site": "none",
            "Sec-Fetch-User": "?1",
            "Cache-Control": "max-age=0",
        }
    ) as crawler:
        url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
        
        result = await crawler.arun(
            url,
            cache_mode=CacheMode.BYPASS,
            magic=True,
        )
        
        print(f"Successfully crawled {url}")
        print(f"Content length: {len(result.markdown)}")

✨ Recent Updates

• 🔧 Configurable Crawlers and Browsers: Simplified crawling with BrowserConfig and CrawlerRunConfig, making setups cleaner and more scalable.
• 🔐 Session Management Enhancements: Import/export local storage for personalized crawling with seamless session reuse.
• 📸 Supercharged Screenshots: Take lightning-fast, full-page screenshots of very long pages.
• 📜 Full-Page PDF Export: Convert any web page into a PDF for easy sharing or archiving.
• 🖼️ Lazy Load Handling: Improved support for websites with lazy-loaded images. The crawler now waits for all images to fully load, ensuring no content is missed.
• ⚡ Text-Only Mode: New mode for fast, lightweight crawling. Disables images, JavaScript, and GPU rendering, improving speed by 3-4x for text-focused crawls.
• 📐 Dynamic Viewport Adjustment: Automatically adjusts the browser viewport to fit page content, ensuring accurate rendering and capturing of all elements.
• 🔄 Full-Page Scanning: Added scrolling support for pages with infinite scroll or dynamic content loading. Ensures every part of the page is captured.
• 🧑‍💻 Session Reuse: Introduced create_session for efficient crawling by reusing the same browser session across multiple requests.
• 🌟 Light Mode: Optimized browser performance by disabling unnecessary features like extensions, background timers, and sync processes.

Read the full details of this release in our 0.4.2 Release Notes.

📖 Documentation & Roadmap

🚨 Documentation Update Alert: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!

For current documentation, including installation instructions, advanced features, and API reference, visit our Documentation Website.

To check our development plans and upcoming features, visit our Roadmap.

📈 Development TODOs

• 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

🤝 Contributing

We welcome contributions from the open-source community. Check out our contribution guidelines for more information.

📄 License

Crawl4AI is released under the Apache 2.0 License.

📧 Contact

For questions, suggestions, or feedback, feel free to reach out:

• GitHub: unclecode
• Twitter: @unclecode
• Website: crawl4ai.com

Happy Crawling! 🕸️🚀

🗾 Mission

Our mission is to unlock the value of personal and enterprise data by transforming digital footprints into structured, tradeable assets. Crawl4AI empowers individuals and organizations with open-source tools to extract and structure data, fostering a shared data economy.

We envision a future where AI is powered by real human knowledge, ensuring data creators directly benefit from their contributions. By democratizing data and enabling ethical sharing, we are laying the foundation for authentic AI advancement.

🔑 Key Opportunities

• 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.

🚀 Development Pathway

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.

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