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 v0.4.24x
🎉 Version 0.4.24x is out! Major improvements in extraction strategies with enhanced JSON handling, SSL security, and Amazon product extraction. Plus, a completely revamped content filtering system! Read the release notes →
- Built for LLMs: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.
- Lightning Fast: Delivers results 6x faster with real-time, cost-efficient performance.
- Flexible Browser Control: Offers session management, proxies, and custom hooks for seamless data access.
- Heuristic Intelligence: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.
- Open Source & Deployable: Fully open-source with no API keys—ready for Docker and cloud integration.
- Thriving Community: Actively maintained by a vibrant community and the #1 trending GitHub repository.
- Install Crawl4AI:
# Install the package
pip install -U crawl4ai
# Run post-installation setup
crawl4ai-setup
# Verify your installation
crawl4ai-doctorIf you encounter any browser-related issues, you can install them manually:
python -m playwright install --with-deps chromium- Run a simple web crawl:
import asyncio
from crawl4ai import *
async def main():
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business",
)
print(result.markdown)
if __name__ == "__main__":
asyncio.run(main())📝 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
srcsetandpicture. - 🚀 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.
✨ Play around with this
✨ Visit our Documentation Website
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:
For basic web crawling and scraping tasks:
pip install crawl4ai
crawl4ai-setup # Setup the browserBy 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:
-
Through the command line:
playwright install
-
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 modeInstall 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🐳 Docker Deployment
🚀 Major Changes Coming! We're developing a completely new Docker implementation that will make deployment even more efficient and seamless. The current Docker setup is being deprecated in favor of this new solution.
The existing Docker implementation is being deprecated and will be replaced soon. If you still need to use Docker with the current version:
- 📚 Deprecated Docker Setup - Instructions for the current Docker implementation
⚠️ Note: This setup will be replaced in the next major release
Our new Docker implementation will bring:
- Improved performance and resource efficiency
- Streamlined deployment process
- Better integration with Crawl4AI features
- Enhanced scalability options
Stay connected with our GitHub repository for updates!
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.
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, 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))
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, BrowserConfig, CrawlerRunConfig, 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)
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())📚 Extracting Structured Data with LLMs
import os
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, 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():
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",
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())🤖 Using You own Browswer with Custome User Profile
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)}")- 🔒 Enhanced SSL & Security: New SSL certificate handling with custom paths and validation options for secure crawling
- 🔍 Smart Content Filtering: Advanced filtering system with regex support and efficient chunking strategies
- 📦 Improved JSON Extraction: Support for complex JSONPath, JSON-CSS, and Microdata extraction
- 🏗️ New Field Types: Added
computed,conditional,aggregate, andtemplatefield types - ⚡ Performance Boost: Optimized caching, parallel processing, and memory management
- 🐛 Better Error Handling: Enhanced debugging capabilities with detailed error tracking
- 🔐 Security Features: Improved input validation and safe expression evaluation
Read the full details of this release in our 0.4.24 Release Notes.
🚨 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
We welcome contributions from the open-source community. Check out our contribution guidelines for more information.
Crawl4AI is released under the Apache 2.0 License.
For questions, suggestions, or feedback, feel free to reach out:
- GitHub: unclecode
- Twitter: @unclecode
- Website: crawl4ai.com
Happy Crawling! 🕸️🚀
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
- Open-Source Tools: Community-driven platforms for transparent data extraction.
- Digital Asset Structuring: Tools to organize and value digital knowledge.
- Ethical Data Marketplace: A secure, fair platform for exchanging structured data.
For more details, see our full mission statement.
