Firecrawl transforms web content into clean, structured data optimized for AI consumption. It handles the complexity of modern web scraping including JavaScript rendering, anti-bot measures, and content cleaning to deliver consistent results.
Core Capabilities
Single-page extraction — Scrape individual URLs with advanced options for content filtering, format control, and metadata extraction. Returns clean markdown with optional structured data extraction using LLM processing.
Multi-page crawling — Launch crawl jobs that systematically extract content from entire websites or sections. Jobs run asynchronously with status checking and result retrieval.
Site discovery — Map websites to discover all indexed URLs, helping you understand site structure before crawling or identify specific pages to target.
Web search integration — Search the web and optionally extract content from results in a single operation, combining discovery and extraction workflows.
Interactive browsing — Scrape pages and then interact with them in live browser sessions (click elements, fill forms, navigate). Sessions must be explicitly stopped to free resources.
Autonomous research — Deploy AI agents that independently browse, search, and extract information based on research objectives. Separate agent jobs run asynchronously.
Domain Model
Content extraction supports multiple output formats (markdown, structured data, raw HTML) with configurable options for headers, links, media handling, and content filtering. Crawl and agent jobs follow async patterns requiring status polling until completion.
Key Considerations
Interactive browsing sessions consume server resources — always stop sessions when finished. Some tools are deprecated in favor of the scrape + interact workflow. Large crawl jobs may take time to complete, so implement appropriate polling intervals when checking status.
