Firecrawl

API for crawling and extracting clean markdown from websites for RAG and agents.

Overview

Firecrawl is a context API that transforms the live web into structured, machine-usable data for AI systems. It provides three core capabilities—search, scrape, and interact—enabling developers to reliably extract information from any website and feed it to language models, agents, and AI applications.

The platform handles the infrastructure complexity that typically requires duct-taping together proxies, scrapers, and post-processing logic. With support for JavaScript rendering, smart content waiting, media parsing, and browser automation, Firecrawl delivers clean markdown and JSON output with 93% fewer tokens than raw HTML. It's open-source with over 150K GitHub stars and serves over 5 billion requests monthly for companies ranging from startups to enterprises like Apple and Canva.

Key features

Web search with full page content
Website scraping to markdown/JSON/screenshots
Browser interaction and automation
JavaScript rendering and smart content waiting
Media parsing (PDF, DOCX, HTML)
Token-efficient output (93% fewer tokens)
Open-source with 150K+ GitHub stars
MCP server integration for AI agents

Pros

Handles complex web infrastructure automatically
Fast performance (P95 latency 3.4s)
High reliability covering 96% of the web
Token-efficient output reduces LLM costs
Open-source and transparent development
Integrates with AI agents and MCP clients
Comprehensive API with search, scrape, interact, crawl, map

Cons

Credits do not roll over on self-serve plans
No pay-per-use option available
Advanced features cost additional credits
Interact feature limited to hosted version

Use this if

You need to extract structured data from websites for AI applications, build web-powered agents, create RAG pipelines with live web content, or automate lead enrichment and research tasks.

Skip this if

You only need simple HTML parsing without JavaScript rendering, or you require a strict pay-per-use model without monthly commitments.

Best for

AI agents needing live web contextRAG pipelines and knowledge basesLead enrichment and competitive intelligenceDeep research automationWeb data extraction at scaleBuilding AI-native applications