microsoft/markitdown
Python tool for converting files and office documents to Markdown.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #4 among LLM repositories tracked this week, and #1 over the last 30 days; #47 of 2,841 repositories GStars tracks this week (top 2%).
4 releases in the last 90 days, the latest 16 days ago, with 133 contributors on GitHub.
Peaked at #6 in the weekly ranking the week of Jun 28, its best in the past year.
Use Cases & Benefits
- Converts various file formats and office documents into Markdown for use with large language models and text analysis pipelines.
- Preserves important document structure like headings, lists, tables, and links in Markdown, optimizing for token-efficient LLM consumption.
- Use for extracting structured Markdown content from PDFs, Word, Excel, and PowerPoint files to feed into AI text analysis workflows.
- Use for integrating document conversion into Python applications or LLM pipelines with optional plugin and Azure Document Intelligence support.
- Use for automating transcription and metadata extraction from audio, images, and YouTube videos into Markdown for downstream processing.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to textract, but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption.
Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient.
MarkItDown requires Python 3.10 or higher. It is recommended to use a virtual environment to avoid dependency conflicts.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+23,855 stars over 87 days across 31 snapshot days; best day +1,661 on Sep 8.
Forks
14.0K+2.2K
Open issues
708-128
Compared with peers in LLM
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 4, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| DietrichGebert/ponytail | 162.8K | +7.3K | 75 | today |
| affaan-m/ECC | 278.6K | +4.2K | 64 | 12 days ago |
| thedotmack/claude-mem | 100.4K | +3.7K | 75 | today |
| rohitg00/ai-engineering-from-scratch | 68.7K | +3.5K | 75 | 3 days ago |
| tashfeenahmed/freellmapi | 34.7K | +2.4K | 75 | today |
| microsoft/markitdown | 189.1K | +0 | 75 | 16 days ago |
| Category median | 100.4K | +3.7K | 75 | — |
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