MarkItDown: 80K Stars on GitHub — Is It Actually Any Good?

You’ve probably seen MarkItDown recommended in a group chat, social media feed, or newsletter. The typical share goes something like: “Microsoft built a tool that converts almost any file format to Markdown. No custom parsers. No broken layouts. No garbled text.”

If that piqued your interest — whether you want to know how the tool actually performs, or you need to convert some files to plain text/Markdown and aren’t sure what to use — this article will save you the time of finding out the hard way.

The short version: MarkItDown’s quality varies dramatically by format. Word, Excel, and PowerPoint conversions are genuinely decent, and the API is clean enough. But its PDF conversion ranks near the bottom among comparable tools — headings and tables are almost entirely lost. If you’re mainly dealing with Office documents, it’s a reasonable choice; if you need to process PDFs, use something else.

An Unexpected Timeline

Many people assume MarkItDown is a brand-new tool. In reality, its GitHub repo was created on November 13, 2024, and the first PyPI pre-release landed the same day. By December 2024, it had already gone through its first wave of social media virality, picking up 25,000 stars in two weeks. As of April 2026, the cumulative star count exceeds 87,000.

In other words, this tool has been around for about a year and a half. It’s not something that just shipped — it’s a project that keeps getting “rediscovered” through periodic cycles of social media sharing. This fact alone is telling: its reach comes from Microsoft’s brand and the appeal of a “universal converter” narrative, not from sustained recommendation within the technical community. Among developers who actually work on document processing, the go-to options in selection discussions have long been Docling and Marker.

Breaking It Down by Format: What Works and What Doesn’t

Word Documents: Its Strongest Format

MarkItDown does a genuinely good job converting .docx files to Markdown. Heading hierarchy, lists, bold/italic, and links are all accurately preserved. Real Python’s review said outright: “MarkItDown did a great job converting the document to Markdown.”

Under the hood it uses mammoth, converting to HTML first and then to Markdown. mammoth is a mature Word processing library whose design philosophy prioritizes preserving semantic structure. MarkItDown’s contribution here is further cleaning mammoth’s output into well-formed Markdown.

If you’re mainly working with Word documents, MarkItDown is a reasonable choice.

Excel: Acceptable

It reads data via pandas + openpyxl and outputs Markdown tables. Multiple sheets are handled. For well-structured spreadsheets, the results are acceptable. A Hacker News user shared their real-world experience using uvx markitdown my-excel.xlsx to convert Excel files into GitLab-renderable tables.

PowerPoint: Text Extraction Is Fine, Don’t Expect Perfect Reproduction

It can extract slide titles and bullet-point text, separated by slide number. But what it extracts is textual content, not visual layout. If your goal is “let an AI understand what this PPT is about,” it’s sufficient. If you expect a pixel-accurate reproduction of the slide design, that’s not what it’s built for.

As a side note, both MarkItDown and the underlying python-pptx only solve the “read” problem. If you also need AI to modify a PPT (edit text, swap images, add slides) and render it to images for verification, we built a pptx_skill that wraps read, write, and render into a CLI + Python library — the AI can render and check its own edits without a human opening PowerPoint to take screenshots.

PDF: The Core Use Case, but Worst-in-Class Quality

This is the most critical part, and where the gap between the shared blurb — “no broken layouts, no garbled text” — and reality is the widest.

The OpenDataLoader Benchmark quantitatively evaluated 12 PDF-to-Markdown engines. MarkItDown ranked second to last with an overall score of 0.589 (out of 1.0). For comparison, the top-ranked Docling scored 0.882.

A few key sub-scores tell the story more clearly:

A heading hierarchy score of 0.000 means MarkItDown makes no distinction between headings and body text in a PDF — all text is output at the same level as plain text. A table fidelity score of 0.273 means table structure is essentially destroyed.

This isn’t a benchmark-only finding. systenics.ai ran a comparison test using a real bank statement PDF, and MarkItDown’s output was described as “a long, jumbled list of text.” Transaction tables were split by column — all dates listed first, then all amounts — with data associations completely lost. The same document converted with Docling preserved the tables perfectly.

GitHub issues corroborate this extensively. Issue #296 states: “PDF isn’t supported. Not really, because it fails most relevant tests: recognizing headings, footers, tables, and more is not possible.” Issue #41 is a user who tried converting an SEC 10-K filing and got “no structure.”

The reason is straightforward: MarkItDown’s default PDF backend is pdfminer.six, which only does text stream extraction — no layout analysis, no OCR, no table recognition. You can plug in Azure Document Intelligence (Microsoft’s own paid cloud service providing layout analysis + table extraction + OCR) to improve quality, but that’s no longer “out of the box,” and it involves additional cost and configuration.

Images and Audio

Images by default only yield EXIF metadata extraction. If you want textual descriptions of images, you need to configure an LLM backend like OpenAI GPT-4o. Audio transcription uses Google’s SpeechRecognition API. These features exist but depend on external services — they’re not self-contained local capabilities.

It’s Essentially a Glue Layer

InfoWorld noted in its analysis that MarkItDown “functions largely as a wrapper around existing third-party libraries rather than offering novel conversion capabilities.” That assessment is largely accurate.

Word conversion relies on mammoth, Excel on pandas + openpyxl, PowerPoint on python-pptx, PDF on pdfminer.six, and HTML on BeautifulSoup + markdownify. MarkItDown’s own technical contribution is wrapping these libraries behind a unified convert() interface so you don’t have to care about what’s underneath.

Does that mean it has no value? No. A unified interface is itself valuable — if you deal with a variety of file formats (Word today, Excel tomorrow, HTML the day after), a single entry point is more convenient than calling five different libraries separately. MarkItDown also provides a plugin mechanism and an MCP server (enabling direct integration with AI tools like Claude Desktop and Cursor), which the underlying libraries don’t offer.

But if you primarily process one format (say, PDF), using a purpose-built, specifically optimized tool will serve you much better. MarkItDown’s value lies in breadth and convenience, not in depth on any single format.

If Not MarkItDown, Then What?

That depends on your actual needs. Here’s a concise selection guide:

Primarily processing PDFs, prioritizing conversion quality: Use Docling (developed by IBM Research). It has the highest overall score in the OpenDataLoader benchmark (0.882), with best-in-class table and heading preservation. MIT license; LangChain and LlamaIndex both have native integrations. Requires PyTorch installation (~1 GB); 0.49 sec/page with GPU, also runs on CPU only (3.1 sec/page). Red Hat has integrated it into RHEL AI — currently the most widely adopted solution in enterprise settings.

Bulk PDF processing, speed is the priority: Consider MinerU (OpenDataLab). At 0.21 sec/page on GPU, it’s the fastest. Table recognition is also strong (0.873). However, its AGPL-3.0 license restricts commercial use, and it doesn’t support macOS.

Primarily processing Office documents and mixed formats: MarkItDown’s format breadth and zero GPU requirement are genuine advantages in this scenario. Simple installation (pip install 'markitdown[all]', ~251 MB), no PyTorch needed.

Need an ultra-lightweight solution: Kreuzberg (71 MB, Apache-2.0, Rust core) or PyMuPDF4LLM (fastest at 0.091 sec/page, but AGPL license).

License considerations for commercial use: MarkItDown (MIT) and Docling (MIT) have no restrictions on commercial use. Marker uses GPL — commercial use requires purchasing a commercial license from the author. MinerU and PyMuPDF4LLM are both AGPL, meaning services built on them must be open-sourced or a commercial license must be purchased.

Conclusion

MarkItDown is a tool with a clear scope: Office document conversion is solid, the API is clean, format coverage is broad, no GPU is needed, and the MIT license is friendly. As a glue layer for “converting miscellaneous formats into text,” it has its place.

But it’s not the “universal converter” that the viral blurb implies. Its conversion quality is uneven across formats: Office documents are reliable, while PDF ranks second to last among 12 comparable tools (zero heading recognition, table structure essentially destroyed). Understanding this gap is what lets you decide whether it fits your specific needs. If you do need to process PDFs, Docling is currently the best overall open-source option.

87,000 stars show the project tells a compelling story — not that it excels at every format. Choosing tools is like choosing anything else: the best one isn’t the most popular one, but the one that reliably gets the job done in your actual use case.

Data sources: OpenDataLoader Benchmark, systenics.ai real-world test, GitHub Issues, Real Python, InfoWorld, PyPI