Product overview
Firecrawl supplies the web-data layer of an AI application: it retrieves pages and converts their contents into Markdown or structured JSON that another system can process. A useful example is preparing a company documentation site for a retrieval-augmented generation (RAG) knowledge base. The application still needs its own retrieval, model and answer-verification logic; acquiring a page does not by itself produce a reliable answer.
Select the endpoint to match the input. Scrape handles a known page, Map discovers candidate URLs, Crawl visits a bounded group of site pages, and Search starts from a query and can also retrieve result contents. For a large site, finding and selecting relevant URLs before fetching their bodies gives you more control over scope, duplicated content and credit use.
What Firecrawl offers
- 01Retrieve page text as Markdown, or request structured JSON using a schema and extraction instructions.
- 02Discover site URLs with Map, then filter the links relevant to your collection.
- 03Crawl selected site sections with path and depth limits instead of collecting everything indiscriminately.
- 04Search for relevant web pages and optionally fetch their contents in the same workflow.
A practical workflow
- 01Start with representative URLs and specify the required fields, output format and acceptable omissions.
- 02Choose Scrape for known pages, Map to select URLs, Crawl for a bounded site section, or Search when starting from a question.
- 03Check the target page status and compare extracted text or JSON fields with the original pages.
- 04Store source URLs with the results, remove duplicates and add an update process before connecting the data to retrieval or an agent.
Summary based on source material; product claims have not been independently tested.
What makes it different
Its practical role is preparing web content for downstream software. Unlike maintaining a custom HTML parser for each site, you use a common extraction API with rendering and output-format options. This does not establish perfect coverage or extraction accuracy: check the exact sites, fields and page states your application needs.
Who should consider it
- Developers building web-grounded assistants or RAG knowledge bases.
- Teams collecting public documentation or product information in a consistent format.
- Engineers connecting web-data retrieval to an agent or existing data pipeline.
What to verify
- Free credits and paid plans have different allowances; inspect current pricing and the cost of extra extraction formats.
- Distinguish request-rate limits from concurrent browser limits, and handle 429 responses with backoff.
- An API success response can contain a target page error such as 403 or 404. Inspect the page status in the metadata.
- Do not assume that login-required pages or missing fields will be accessible or complete; validate the result before using it.
Frequently asked questions
What is the difference between Scrape, Map and Crawl?
Scrape retrieves the content of a known URL. Map primarily helps find URLs on a site. Crawl discovers and retrieves multiple pages under the scope you configure. Map results are not equivalent to having collected all page bodies.
Does Firecrawl replace an AI model or a complete RAG system?
No. It supplies web content that can feed a model or knowledge pipeline. Selecting documents, managing retrieval and checking the generated answer remain application responsibilities.
Does HTTP 200 mean the target page was fetched successfully?
Not necessarily. The API request status and the target page status are separate. The Scrape documentation explains that a successful API request may still return a target page error; inspect data.metadata.statusCode and the extracted content.
Product facts were checked against the official product website or its first-party documentation on Oct 26, 2026.
