anydoc vs Docling
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Converts Word, PowerPoint, Excel, PDF and other documents into clean Markdown, built in Rust.
Open source · MIT
Python library that converts PDFs and office files into structured text for AI use.
The short answer
Choose anydoc if you need…
- Developers who need document text in Markdown for an app or pipeline
- People building AI agents that must read office files
- Teams converting many Word or PowerPoint files into one consistent format
- Anyone who wants to convert files locally in the browser without uploading them
Choose Docling if you need…
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
Side-by-side
| anydoc | Docling | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 23.3k | 70k |
| Setup difficulty | Beginner | Beginner |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Rust | Python |
| Last code update | 14 days ago | 5 days ago |
What each one is
anydoc
anydoc is a library that reads office and document files such as Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV and PDF and turns them into GitHub-flavored Markdown. The README says it is written in Rust with bindings for Node.js, Python and the browser via WebAssembly, and it can also be used as a command-line tool. It is built by Firecrawl, and the README describes its output as LLM-ready Markdown.
Docling
Docling parses many document formats, including PDF, Word, PowerPoint, Excel, HTML and email, and turns them into a structured representation. It exports that content to formats such as Markdown, HTML and JSON. People use it to prepare documents for generative AI and retrieval pipelines, and it can run locally for sensitive data.
Best for
anydoc
- Developers who need document text in Markdown for an app or pipeline
- People building AI agents that must read office files
- Teams converting many Word or PowerPoint files into one consistent format
- Anyone who wants to convert files locally in the browser without uploading them
Docling
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
Strengths
anydoc
- The README says one shared document model and serializer give the same Markdown style across formats
- Content-based format detection reads the file bytes, so mislabeled files can still convert
- Runs locally for text-based files, with no ML models or external services needed for that path
Docling
- Supports a wide range of input formats in one library, per the README feature list
- Can run locally, which the README highlights for sensitive data and air-gapped setups
- Includes a CLI, a Python API, an MCP server and an API server option
Not ideal for
anydoc
- Scanned or image-only PDFs without OCR, since the README says anydoc does no OCR itself and such pages fail with NeedsOcr unless you opt into the hosted Parse service
- People who need pixel-perfect layout preservation, which the README does not claim
- Users who want a point-and-click desktop app; the README focuses on CLI and code bindings
Docling
- Users who need a no-code desktop app, since the README centers on pip install and CLI or Python use
- Projects that need metadata extraction such as title and authors, which the README lists as coming soon
- Chemistry-heavy work, since molecular structure understanding is also listed as coming soon
Trade-offs
anydoc
- Scanned pages need the hosted Parse service for OCR, which sends the whole document off the machine and may need an API key for higher limits
- The README reports performance numbers and a benchmark against other converters, but the benchmark table is cut off in the provided text, so comparisons cannot be verified here
- Output is Markdown, so complex layouts and formatting may be simplified
Docling
- Advanced features such as VLM pipelines and audio or video parsing may need extra setup or models; the README does not detail requirements for each
- Some features are newer, as shown in the What's new section, so behavior may change between releases
Getting started
anydoc
Node.js package
npm install @firecrawl/anydocPython package
pip install firecrawl-anydocFrequently asked questions
What is the difference between anydoc and Docling?
anydoc: Converts Word, PowerPoint, Excel, PDF and other documents into clean Markdown, built in Rust. Docling: Python library that converts PDFs and office files into structured text for AI use. anydoc suits developers who need document text in markdown for an app or pipeline, while Docling suits python developers building retrieval or rag pipelines over pdfs and office files.
Are anydoc and Docling free?
anydoc: Free and open source. Docling: Free and open source.
Is anydoc or Docling more popular?
Docling has more GitHub stars (70k) than anydoc (23.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to anydoc and Docling?
Other options people compare them with include MarkItDown, pdf-inspector, Rich, Showdown, remark.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.