firecrawl/anydoc: Trending on GitHub
anydoc
Fast Rust library that converts documents (Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF) into clean GitHub-Flavored Markdown. Includes bindings for Node.js and Python.
Built by Firecrawl to turn any office document into LLM-ready Markdown in single-digit milliseconds, with one consistent output no matter which format goes in. It powers Firecrawl Parse, so if you'd rather not run it yourself, the hosted API gives you the same conversion plus our OCR models for the scanned pages anydoc can't read on its own.
Quick start
Agent skill
anydoc ships as an Agent Skill, so your agent can read any document it runs into:
npx skills add firecrawl/anydoc
The skill teaches the agent to convert documents with the anydoc CLI. Works with Claude Code, Codex, Cursor, OpenCode, and any other compatible agent.
CLI
npx @firecrawl/anydoc report.docx # Markdown to stdout npx @firecrawl/anydoc slides.pptx -o slides.md # or to a file npx @firecrawl/anydoc - --format csv < data.csv # read stdin
npx downloads the prebuilt binary for your platform on first run. For a permanent anydoc command, install globally with npm install -g @firecrawl/anydoc. Run anydoc --help for all options.
Node.js
npm install @firecrawl/anydoc
import { toDocument, toMarkdown, toMarkdownBytes } from '@firecrawl/anydoc';
// From a file path: const markdown = await toMarkdown('report.docx');
// From bytes, with the format detected from the content: const fromBytes = await toMarkdownBytes(bytes);
// Or name it, which signature-less formats (CSV) need: const fromCsv = await toMarkdownBytes(bytes, 'csv');
// Or stop at the document model, which also carries embedded assets: const document = await toDocument(bytes);
Full API reference: node/README.md
Python
pip install firecrawl-anydoc
import anydoc
From a file path:
markdown = anydoc.to_markdown("report.docx")
From bytes, with the format detected from the content:
markdown = anydoc.to_markdown_bytes(data)
Or name it, which signature-less formats (CSV) need:
markdown = anydoc.to_markdown_bytes(data, "csv")
Or stop at the document model, which also carries embedded assets:
document = anydoc.to_document(data)
Full API reference: python/README.md
Rust
cargo add anydoc
// From a file path: let markdown = anydoc::to_markdown("report.docx")?;
// From bytes, with the format detected from the content: let markdown = anydoc::to_markdown_bytes(&bytes, None)?;
// Or name it, which signature-less formats (CSV) need: let markdown = anydoc::to_markdown_bytes(&bytes, anydoc::Format::Csv)?;
// Or stop at the document model, which also carries embedded assets: let document = anydoc::to_document(&bytes, None)?;
Features
One output for every format. Each format parses into a shared document model and renders through a single Markdown serializer, so escaping, tables, heading anchors, and footnotes behave identically whether the input was a .doc from 2003 or a .pptx from yesterday.
Full document structure. Headings with anchors, bold/italic/strikethrough, inline code and code blocks, links and internal cross-references, bulleted/numbered/nested/task lists with the source's own numbering, tables with merged cells and header rows, block quotes, footnotes and endnotes, and speaker notes.
Embedded assets. Images and embedded objects render as their alt text in the Markdown, and the raw bytes stay available on the document model, tagged with their media type. Images with an external URL become ordinary Markdown images.
Content-based format detection. The format is read from the bytes themselves (PDF header, RTF open group, OLE stream names, ZIP package mimetype), so mislabeled files still convert correctly.
Fast. Pure Rust, no ML models, no external services. Median conversion time is under 5ms per document.
Bindings that stay out of the way. Node.js conversion runs on the libuv thread pool and never blocks the event loop; Python releases the GIL so other threads keep running. TypeScript types and Python stubs ship with the packages.
PDF support built in. Text-based PDFs convert locally through pdf-inspector, no OCR service required.
Agent ready. Ships as an Agent Skill: one npx skills add firecrawl/anydoc and any agent can read office documents.
Supported formats
Format Extensions
Word .doc, .docx, .docm
PowerPoint .ppt, .pps, .pot, .pptx, .pptm, .ppsx, .ppsm
Excel .xls, .xlsx, .xlsm, .xlsb
OpenDocument .odt, .ods, .odp
Rich Text Format .rtf
EPUB .epub
CSV .csv
PDF .pdf
Benchmark
anydoc is measured against six other converters on 100 real-world documents spanning fourteen formats. Scores run from 0 to 100, higher is better; speed is the median time to convert one document.
tool formats median ms docs judged score completeness structure formatting cleanliness
anydoc 14/14 4.7 94 80 88 78 77 79
libreoffice 12/14 1129.5 87 40 59 43 43 24
unstructured 8/14 572.9 58 65 76 62 52 67
markitdown 6/14 134.8 33 65 80 67 61 53
pandoc 5/14 102.1 34 57 75 57 58 39
docling 4/14 513.6 21 57 63 59 57 52
mammoth 1/14 52.5 8 70 85 68 74 55
Per format, like for like:
format anydoc libreoffice unstructured markitdown pandoc docling mammoth
doc 88 58 68
docm 82 49
docx 86 53 56 72 68 68 70
epub 74
74 77 53
odp 87 22
ods 82 42
odt 80 52 70
61
ppt 80 25
pptx 76 22
59
50
rtf 89 58 48
46
xls 77 40 68 64
xlsm 70 30
xlsx 70 31 69 55
51
How quality was scored: an LLM judge (Claude Sonnet 5) compares two tools' outputs blind against ground truth: the document's first six pages, rendered to images by LibreOffice. Each output is scored on completeness, structure, formatting, and cleanliness. Every pair is judged twice with the outputs swapped to cancel position bias, for 479 verdicts in total. Each tool's score averages its per-format scores over the formats it supports, so a corpus heavy in one format can't skew it. It also means each row averages a different set of formats (mammoth's 70 is docx alone, while anydoc's 80 spans all fourteen), so the per-format table is the fair comparison.
Speed is one warm conversion per document on a Ryzen 9 9950X3D (Windows 11, 64 GB DDR5-6400). anydoc and the Python libraries are timed with process spawn excluded; the CLI tools include it, since that is how they are used. The harness lives in bench/; the corpus is not redistributable and is not in the repo.
Best fit: pipelines that receive a mixed bag of office documents and need one consistent, structured Markdown output. In this comparison, anydoc was the only tool to cover all fourteen formats, scored highest on every judged format except EPUB, and converted documents an order of magnitude faster than the next-fastest tool.
Format detection
The format is read from the file content, using the marker its specification designates: the PDF header, the RTF open group, OLE stream names, the ZIP package mimetype and content types. CSV has no such marker, so the extension or an explicit format names it instead.
Format::from_bytes(&bytes); // Some(Format::Docx), or None when nothing matches Format::from_extension("pptm"); // Some(Format::Pptx) Format::from_path(Path::new("report.odt")); // Some(Format::Odt)
The same three functions exist in Node (formatFromBytes, ...) and Python (anydoc.format_from_bytes, ...).
How it works
document bytes │ ├─► format detection → content markers, not the extension │ ├─► format parser → one per format (doc, docx, ppt, pptx, xls, │ xlsx, odt/ods/odp, rtf, epub, csv) │ │ │ └─► Document → shared model: blocks, inlines, tables, │ footnotes, assets │ │ │ └─► GFM serializer → Markdown │ └─► PDF → pdf-inspector → Markdown directly
Because every format funnels through the same document model and serializer, output quirks get fixed once. A table-escaping fix for docx is automatically a table-escaping fix for rtf, odt, and everything else.
Development
cargo test cd node && npm install && npm run build && npm test cd python && pip install maturin && maturin develop && python -m unittest discover -s tests
A committed fixture corpus under tests/fixtures/ is snapshot-tested, tests/robustness.rs mutation-tests every fixture, and fuzz/ carries cargo-fuzz targets per format. The speed and quality benchmark lives in bench/.
Releases are tagged v
Cargo.toml: the crate
node/package.json: the npm package
python/Cargo.toml: the wheel (python/pyproject.toml reads it)
License
MIT
