firecrawl/anydoc

Convert Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF to clean Markdown. Built in Rust, with Node.js and Python bindings.

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Summary Information

Updated 34 minutes ago
Added to GitGenius on September 1st, 2026
Created on August 3rd, 2026
Open Issues & Pull Requests: 88 (+0)
GitHub issues: Enabled
Number of forks: 1,260
Total Stargazers: 20,648 (+0)
Total Subscribers: 55 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 34.8 hours
Mean response time: 3.2 days
90th percentile: 8.4 days
Tracked items: 50

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 9% of issues opened in the past year have been closed. Three people close 97% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 37
New in 7 days: 6
Closed in 7 days: 0
Avg open age: 17 days
Stale 30+ days: 5
Stale 90+ days: 0

Recent activity

Opened in 7 days: 6
Closed in 7 days: 0
Comments in 7 days: 2
Events in 7 days: 4

Top labels

  • bug (16)
  • enhancement (16)
  • P2 (15)
  • P3 (15)
  • P1 (6)
  • question (5)
  • duplicate (3)
  • needs-repro (3)

Detailed Description

anydoc is a document conversion library that transforms Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF files into clean GitHub-Flavored Markdown.

The tool solves the problem of preparing office documents for consumption by language models and other text-processing systems. It converts diverse document formats into a consistent Markdown output in single-digit milliseconds, eliminating the need to handle multiple format parsers. The library is built in Rust for performance and provides bindings for Node.js, Python, and the browser via WebAssembly, so developers can integrate it into their preferred environment. For PDFs containing scanned or image-only pages that require OCR, the tool can optionally delegate to a hosted service, with only documents needing OCR leaving the user's machine.

Developers should choose this tool if they need to ingest office documents as part of a pipeline feeding language models or other text systems. It suits projects that process diverse document types and require predictable, clean output regardless of input format. The tool includes an Agent Skill integration, allowing AI agents to read documents directly. A browser-based demo lets you test conversions locally before committing to integration. For teams that prefer not to run the library themselves, the creators offer a hosted API version with additional OCR capabilities for scanned documents.

The project maintains active development across multiple language bindings with regular updates to support new document formats and improve conversion quality. The codebase includes comprehensive documentation for each binding and clear examples for common use cases. The team provides a command-line interface that downloads prebuilt binaries automatically, lowering the barrier to trying the tool. The project demonstrates responsiveness to user needs through its optional hosted OCR fallback, which gracefully handles documents the local library cannot process.