Open-source web data API that turns any site into clean, LLM-ready Markdown or JSON for RAG pipelines and AI agents. One API covers scrape, crawl, site

Overview

Firecrawl is a web data API built specifically for the LLM era. Traditional scrapers hand you raw HTML and leave the cleanup to you; Firecrawl returns Markdown, structured JSON, screenshots or filtered HTML that a model can ingest directly. It exposes four primitives - scrape a single page, crawl an entire site, map a domain’s URL structure, and search the web - behind one key, and handles the infrastructure nobody wants to own: rotating proxies, JavaScript rendering, rate limiting, retries and queueing. The team reports coverage of roughly 96% of the web including JS-heavy single-page apps, with P95 latency around 3.4 seconds. Beyond plain pages it parses web-hosted PDFs and DOCX files, supports interactive Actions (click, scroll, type, wait) before extraction, and offers selective caching so repeat fetches are cheap. It is open source and self-hostable, with official Python and Node SDKs plus native integrations for LangChain, LlamaIndex, n8n, Dify and Lovable. Firecrawl also positions itself as an ethical scraper: it respects robots.txt, throttles requests, and has partnered with publishers such as Wikipedia rather than working around them. The practical audience is anyone building retrieval pipelines, research agents or monitoring tools who wants web context without maintaining a scraping stack.

Key Features

  • Scrape, crawl, map and search endpoints behind a single API key
  • LLM-ready output formats: Markdown, structured JSON, filtered HTML and screenshots
  • Automatic JavaScript rendering, proxy rotation and rate-limit handling
  • Actions that click, scroll, type or wait before extraction for gated content
  • Parses web-hosted PDF, DOCX and other document formats
  • Python and Node SDKs plus LangChain, LlamaIndex, n8n and Dify integrations
  • Open source and self-hostable if you need to run it in your own VPC

Pricing

PlanPriceFor
Free$0 (500 one-time credits)Testing and prototypes
Hobby$16/mo billed yearly (3,000 credits/mo)Side projects, 5 concurrent requests
Standard$83/mo billed yearly (100,000 credits/mo)Production apps, 50 concurrent requests
Growth$333/mo billed yearly (500,000 credits/mo)High-volume crawling
Scale$599/mo (1,000,000 credits/mo)Team data pipelines, priority support

Comparison

Compared to Browse AI, Firecrawl is API-first rather than a no-code robot trainer - Browse AI wins on point-and-click setup and change monitoring, Firecrawl wins when extraction has to run inside your own code. Compared to Exa, the two solve adjacent problems: Exa is a neural search index that finds relevant pages, while Firecrawl fetches and cleans whatever URLs you point it at, and many teams run both. MultiOn drives a browser to complete tasks end to end; Firecrawl deliberately stops at reliable data extraction.

Compare alternatives

Side-by-side with the 3 closest alternatives.

ToolCategoryPricingVisit
Firecrawl (this) code, search, agentsFree $0 (500 one-time credits) · From $16/mo Site ↗
Browse AIproductivity, agentsFree $0 · From $19/mo Site ↗
ExasearchFree $0 Site ↗
MultiOnagentsFree $0 Site ↗
Firecrawl Current

Open-source web data API that turns any site into clean, LLM-ready Markdown or JSON for RAG pipelines and AI agents. One API covers scrape, crawl, site

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Editor’s Review
4.5/5
Pros
  • +One API covers scrape, crawl, site map and search, so most projects never need a second data vendor
  • +Output arrives as clean Markdown or schema-validated JSON, removing an entire preprocessing stage from RAG pipelines
  • +Open source and self-hostable, so you can move off the managed tier without rewriting your integration
Cons
  • Credit-based billing is hard to forecast once crawls run across large sites
  • There is no pure pay-as-you-go option; you have to sit on a monthly subscription tier
  • Crawl jobs pre-authorize credits against the limit parameter, so omitting it triggers surprise 402 errors

Firecrawl has quietly become the default extraction layer for AI apps, and the reason is unglamorous: the output is genuinely clean, so you stop writing HTML cleanup code. Billing is the weak spot - credits disappear faster than you expect on big crawls, and there is no metered tier to soften the landing.

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Last updated: 2026-08-04

When to use it

  • Use it when you need one API covers scrape, crawl, site map and search, so most projects never need a second data vendor
  • Use it when you need output arrives as clean Markdown or schema-validated JSON, removing an entire preprocessing stage from RAG pipelines
  • Use it when you need open source and self-hostable, so you can move off the managed tier without rewriting your integration

When to skip it

  • Avoid it if credit-based billing is hard to forecast once crawls run across large sites
  • Avoid it if there is no pure pay-as-you-go option; you have to sit on a monthly subscription tier
  • Avoid it if crawl jobs pre-authorize credits against the limit parameter, so omitting it triggers surprise 402 errors

Alternatives to consider