Morgan Dutemple
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ApplicationData & analyticsModel-agnostic

MarkItDown

MarkItDown converts Word, PowerPoint, Excel, PDF and other formats into clean Markdown, a format language models handle far better than a raw office binary, making it a useful step ahead of any RAG or document-analysis pipeline.

MarkItDown's GitHub repository

Key advantages

  • Maintained by Microsoft
  • Covers a wide range of office formats
  • Clean Markdown output, directly usable by an LLM

Who it's for, and why

Teams needing to feed existing office documents into an analysis or RAG pipeline.

  • Converting office reports before analysis by an agent
  • Preparing documents for a RAG pipeline
  • Cleaning up content before indexing

Getting started

  1. 1Install with all conversion dependencies: `pip install 'markitdown[all]'`.
  2. 2Command-line usage: `markitdown path-to-file.pdf > document.md` (or `-o` to specify output).
  3. 3Also usable in a pipe: `cat file.pdf | markitdown`.

Things to watch out for

  • The project itself warns that MarkItDown performs I/O with the privileges of the current process: on untrusted files, restrict usage to the narrowest conversion functions possible and sanitize inputs.
  • Conversion quality depends on the source format: complex documents (rich layout, nested tables) can lose fidelity once converted to Markdown.

Frequently asked questions

Is MarkItDown safe on files from untrusted sources?

The project itself warns about this: it accesses resources with the same privileges as the process running it. On untrusted inputs, the documentation recommends sanitizing files and using the most restrictive conversion functions possible.

Which formats are supported?

PDF, Word, PowerPoint, Excel, images (with OCR), audio (with transcription), HTML, CSV/JSON/XML, ZIP files, EPUB and YouTube URLs, among others.

Why convert to Markdown rather than keep the original format?

Language models handle Markdown natively far better than a raw office binary, and the format is more token-efficient, making it a useful preparation step ahead of any RAG pipeline.