Firecrawl
LLM-Ready Web Scraping & Data Extraction
Turn any website into clean, structured markdown for LLM consumption. JavaScript rendering, semantic chunking, and AI-optimized output.
Core Capabilities
Smart Scraping
- •JavaScript rendering
- •Dynamic content handling
- •Anti-bot bypass
- •Proxy rotation
- •Rate limiting
LLM-Ready Output
- •Clean markdown format
- •Semantic HTML parsing
- •Content extraction
- •Noise removal
- •Structured data
Crawl Modes
- •Single page scrape
- •Full site crawl
- •Map discovery
- •Sitemap parsing
- •Depth control
AI Integration
- •RAG-ready chunks
- •Embedding optimization
- •Context preservation
- •MCP server support
- •Agent integration
Data Extraction
- •Schema-based extraction
- •JSON output
- •Custom selectors
- •Table parsing
- •Link harvesting
Developer API
- •REST API
- •Python SDK
- •Node.js SDK
- •Async operations
- •Webhook callbacks
Why We Deploy Firecrawl
LLM-Optimized
Output specifically formatted for language model consumption. Clean markdown preserves context while removing noise that confuses AI.
JavaScript Support
Full browser rendering handles SPAs, dynamic content, and modern web apps that traditional scrapers can't process.
RAG Pipeline Ready
Semantic chunking and structured output designed for direct ingestion into vector databases and RAG systems.
Open Source
Self-hostable with AGPL license. Run your own instance for complete data control and unlimited usage.
Common Use Cases
Firecrawl powers AI data pipelines across industries.
Ready for AI-Optimized Web Data?
We can help you deploy Firecrawl for building LLM-ready knowledge bases and RAG pipelines.