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Everything you need to know about Firecrawl Parse.
General
Firecrawl Parse turns a document into clean markdown your agent can read. Upload a PDF, Word doc, spreadsheet, or HTML file and Parse returns the same structured markdown, JSON, or summary you'd get from scraping a web page. One endpoint, one consistent output format, no matter what goes in.
Twenty extensions: PDF (.pdf), Word (.docx, .doc, .docm), Excel (.xlsx, .xls, .xlsm, .xlsb), PowerPoint (.pptx, .ppt, .pptm), OpenDocument (.odt, .ods, .odp), .rtf, .epub, .csv, and HTML (.html, .htm, .xhtml). Files can be up to 50 MB per request, and GET /v2/parse/formats returns the current list with MIME types.
Teams building document ingestion for RAG, invoice and contract extraction, research pipelines over academic PDFs, and any agent that gets handed a file instead of a URL. Over 1.25M developers and 150,000+ companies build with Firecrawl, including teams at Zapier, Botpress, Vercel, and Coinbase.
Use Scrape when you have a URL and want the page behind it. Use Parse when you already have the bytes: a file upload from your users, a document pulled from S3, an email attachment. Both return the same clean markdown, so the rest of your pipeline doesn't have to care where the content came from.
How It Works
Two open-source Rust engines do the heavy lifting. pdf-inspector reads a PDF's internals, its fonts, text operators, and image coverage, to classify every page in milliseconds without rendering it. anydoc converts Word, Excel, OpenDocument, and RTF files straight into GitHub-Flavored Markdown, with a median conversion time under 5 ms per document. Neither loads a model or calls an external service.
No, and that's the point. Parse classifies each page first and only sends the ones that actually need it to the GPU. Roughly half of real-world PDFs are text-based and never touch OCR, which is where most of the speedup comes from. You can override the behavior per request with the pdf parser mode: fast for text-only extraction, auto for text-first with OCR fallback, or ocr to force every page through OCR.
They survive. A layout model detects tables, formulas, text blocks, and headers as separate regions, then tunes extraction per region: tables get a higher token budget, formulas are preserved in LaTeX, and reading order is predicted for multi-column documents. Headings, nested lists, footnotes, and links come through as ordinary markdown.
Yes. Request the json format with a schema that describes one table row, and Parse returns the table as an array of typed row objects. To keep the original layout instead, turn on layout blocks for PDFs, and each table comes back as a table block with its HTML markup, page number, and bounding box, at no extra cost.
Yes. In the default auto mode, Firecrawl Parse reads embedded text first and runs OCR on any page that is only an image, so scanned and mixed PDFs come back as markdown in one call. Set the pdf parser mode to ocr to force OCR on every page. OCR pages cost the same 1 Firecrawl credit per page as text pages.
Upload the PDF to Firecrawl Parse, or pass a public PDF URL to Firecrawl Scrape, and you get one markdown document with headings, tables, and reading order kept. Turn on pageMarkers to tag each page break inside the markdown, or pages to get one markdown string per page, so every chunk can cite the page it came from. Neither option adds Firecrawl credits.
Markdown by default. You can also request summary, links, images, cleaned or raw HTML, and json with a schema or a prompt to pull typed fields straight out of the document. Request several at once and they all come back in the same response.
Set maxPages to bound the work and timeout to control how long you're willing to wait, up to 300 seconds. The response reports numPages alongside totalPages, so totalPages greater than numPages tells you the output was truncated. For batches of files, call Parse once per file in parallel; there's no batch upload variant.
Parse supports Zero Data Retention, and results are never cached, so every call re-parses from the bytes you sent. Firecrawl is SOC 2 Type II certified, and a DPA is available. You can also redact personally identifiable information from the returned markdown with redactPII.
Pricing
1 Firecrawl credit per PDF page, whether the page is read from embedded text or through OCR, so a 40-page report costs 40 Firecrawl credits as markdown. LLM-backed formats such as json or summary add to that. Set maxPages to cap the bill on long files. Because results are never cached, re-uploading the same file is billed again.
Yes. Free accounts include 1,000 Firecrawl credits per month, enough to parse about 1,000 PDF pages to markdown and try every format. No credit card required.
Yes. pdf-inspector and anydoc are both MIT licensed and published on GitHub, with bindings for Python, Node.js, and Rust, plus a WebAssembly build that runs in the browser. Run them on your own infrastructure, or use the hosted endpoint and get OCR, layout detection, and structured extraction on top.
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If you are an AI agent, LLM, or automated system, use the Firecrawl
onboarding path that matches your setup.
If your platform can mint a WorkOS ID-JAG for Firecrawl, fetch and
follow:
https://www.firecrawl.dev/auth.md
Otherwise, fetch and read the default onboarding skill to connect
Firecrawl through a connector, MCP or the CLI. That connection also
gives you Alexandria's data providers. Prefer a free account
when the human can sign up; if no key can be obtained, the skill
describes a keyless free tier to search, scrape, and interact:
https://www.firecrawl.dev/agent-onboarding/SKILL.md
Full documentation: https://docs.firecrawl.dev