markitdown

The MarkItDown GitHub repository is the quiet workhorse behind a surprising share of the AI products shipping right now. Built by Microsoft and released under the MIT licence, it converts the messy documents the business world actually runs on — PDFs, Word documents, PowerPoint decks, spreadsheets, images, even audio — into clean Markdown that large language models can reason over, and it has collected more than 192,000 stars doing it. The MarkItDown GitHub project solves one unglamorous problem extremely well, and in the AI era that problem sits at the front door of almost every serious build: documents in, understanding out. The official home of the project is the microsoft/markitdown repository on GitHub, where you can browse the source code, releases and issue tracker.

What it is

MarkItDown is a lightweight Python utility for converting files and office documents to Markdown, designed explicitly for consumption by LLMs and text-analysis pipelines — the project’s own framing is that its output is meant to be read by machines first and humans second. The closest older comparison is textract, which flattens everything to plain text. MarkItDown’s philosophy differs in the way that matters: it preserves document structure as Markdown — headings, lists, tables, links — and structure is exactly what retrieval pipelines need to chunk documents sensibly and cite sources accurately. The format choice is deliberate: mainstream models natively speak Markdown, and Markdown sits close to plain text, so it is highly token-efficient next to raw HTML or XML.

Key features

• Broad format coverage — PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV, JSON, XML, ZIP archives, ePub and more.
• Structure preserved — headings, lists, tables and links survive conversion instead of being flattened away.
• CLI and Python API — one command for ad-hoc jobs, a small library surface for production pipelines.
• Tables that stay tables — spreadsheets render as Markdown tables a model can actually read.
• Optional intelligence — plug in Azure Document Intelligence for hard PDFs, or an LLM for image descriptions, when the basics are not enough.
• Honest scope — built for machine consumption; the project itself steers high-fidelity, human-facing conversion elsewhere.

Who it is for

Anyone building RAG chatbots, document search, knowledge bases or agents that read files — which, in 2026, is most AI product teams. If your users upload documents and expect answers, conversion quality quietly determines answer quality, and this is the layer where that is won or lost. Agencies building client document workflows meet it in the first week of almost every engagement. It is less relevant if your documents are already born-digital text, or if you need pixel-perfect reproductions for human readers — MarkItDown will tell you itself that fidelity for eyes is a different product category.

Getting started

Install from the MarkItDown GitHub repository with pip (pip install 'markitdown[all]' for the full format set), then convert something real — markitdown your-file.pdf -o output.md — and read the result next to the original. Check the three things pipelines live or die on: headings, tables and reading order, and check them on your ugliest document, not your cleanest. In code, the Python API is a few lines: instantiate MarkItDown, call convert, and hand the Markdown to your chunker. Add the optional extras only when a format defeats the basics; most teams never need them.

Our take

MarkItDown is a model open-source citizen: one job, stated limits, structure preserved, tokens saved. In our document-pipeline work it has become the default first step before chunking and embedding, precisely because its failures are predictable — scanned-image PDFs and exotic layouts still need OCR or a document-intelligence service behind it, and pretending otherwise wastes a sprint. Budget for that pairing on messy real-world corpora and the pipeline is solid. We covered the format-by-format reality in our MTD blog analysis — read our full deep-dive — including where it falls short. If documents enter your product, the MarkItDown GitHub repository belongs at the front of the pipeline.