Firecrawl is a web data API for AI that turns websites into LLM-ready content. Point it at a URL to scrape clean Markdown/JSON (with optional screenshots), or crawl whole sites to fetch every subpage. Use Search to get results plus full page content, Map to list URLs, and Extract to structure fields with AI. It handles JS-heavy pages, PDFs, rate limits, and blockers—no proxies or headless browser scripts—via simple Python/Node/HTTP calls and an MCP server for agent frameworks.
Give Firecrawl a single URL and get content normalized for LLMs. The service renders JS when needed, strips boilerplate, and returns Markdown, structured JSON, HTML, and optional screenshots. This replaces brittle DIY selectors with consistent outputs you can chunk, embed, or feed to agents. It also catches common pitfalls like redirects and lazy content so downstream prompts see what users would.
Start from any page and recursively gather all accessible subpages. Firecrawl analyzes links, follows them across the domain/subdomains, respects rate limits, and converts each page into clean Markdown or JSON. Use it to build corpora for RAG, compliance reviews, or competitive monitoring without maintaining custom spiders. Output stays uniform, so evals and refresh jobs don’t break when layouts change.
Run a query and get curated results plus the complete page content for each hit—ready for grounding or analysis. Instead of stitching a search API to a separate scraper, Firecrawl returns both in one call, reducing glue code and latency for agents. You can steer freshness and depth, then pair results with Extract to produce table-ready facts from many links in one pass.
Use Extract to pull entities/fields (prices, specs, contacts) from one page, many pages, or entire sites using AI. Map lists all URLs on a domain quickly so you can plan coverage or seed crawls. Together, they turn messy web data into rows with provenance, making it practical to build product trackers, documentation mirrors, or evaluation datasets with clear citations and repeatable runs.
Get started with Python, Node, or raw HTTP in minutes. The Model Context Protocol server lets agent frameworks call scraping/crawling tools natively. Built-in handling for proxies, caching, and rate limits removes ops headaches, and streaming HTTP keeps long jobs responsive. The design targets >90% web coverage, including JS-heavy and protected pages, so teams avoid puppet farms and fragile browser automations.
AI teams building RAG, agents, monitoring, and eval pipelines that need reliable, cited web content. Ideal for startups replacing ad-hoc scrapers, enterprises standardizing ingestion for governance, and researchers assembling refreshable corpora. Also great for product docs mirroring, competitive tracking, and structured extraction jobs where uniform Markdown/JSON beats brittle, one-off crawlers.
Replaces proxy juggling, headless browser scripts, and glue between search + scrape with a single API. It solves coverage gaps on JS/PDF pages, inconsistent output formats, and crawler maintenance as sites change. With scrape/crawl/search/extract in one place and LLM-ready Markdown/JSON, teams ship grounded features faster, keep datasets reproducible, and cut the operational cost of running bespoke spiders and proxy fleets.
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