MarkItDown: Microsoft's Open-Source Document-to-Markdown Tool — 91K Stars, Built for RAG

Created

MarkItDown is a lightweight open-source Python library from Microsoft Research, purpose-built to convert a wide range of document formats into clean, structured Markdown — optimized for LLMs and RAG pipelines. Originally created as part of the AutoGen framework to support AI agents in the GAIA benchmark, it was open-sourced in late 2024 and quickly gained traction. Today it has over 91,000 GitHub stars, making it one of Microsoft’s most popular Python projects.

If you work with AI applications, you’ve hit this wall: your data lives inside Word files, PowerPoint decks, Excel spreadsheets, and PDFs — formats that are friendly for humans but full of meaningless XML tags and layout metadata for LLMs. You need an intermediate format that preserves structure while remaining AI-friendly. That’s exactly what MarkItDown does.

Supported Formats

MarkItDown converts the following file types:

Format Extensions Notes
📄 Office Suite DOCX / PPTX / XLSX / XLS Word, PowerPoint, Excel
📰 PDF .pdf Text extraction included
🖼️ Images .jpg / .png etc. EXIF metadata + OCR
🔊 Audio .mp3 / .wav Speech transcription (requires audio-transcription)
🌐 Web Pages .html HTML to Markdown
📦 Other Text .csv / .json / .xml Structured text formats
📚 eBooks .epub EPUB format
🎬 YouTube Video URL Auto-fetches subtitles as Markdown
📁 ZIP .zip Auto-extracts and batch-converts contents

Quick Start

Installation

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# Install all format support
pip install 'markitdown[all]'

# Or install only what you need (smaller dependency footprint)
pip install 'markitdown[pdf,docx,pptx]'

💡 Tip: MarkItDown requires Python 3.10 or higher. Using a virtual environment is strongly recommended.

CLI Conversion

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# Output to stdout
markitdown path-to-file.pdf

# Specify output file
markitdown path-to-file.pdf -o document.md

# Pipe input
cat path-to-file.pdf | markitdown

Python Usage

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from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("report.docx")
print(result.text_content)

LLM Image Descriptions (PowerPoint / Images)

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from markitdown import MarkItDown
from openai import OpenAI

client = OpenAI()
md = MarkItDown(
    llm_client=client,
    llm_model="gpt-4o"
)
result = md.convert("slides.pptx")
print(result.text_content)

OCR Plugin (Scanned Document OCR)

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pip install markitdown-ocr openai
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from markitdown import MarkItDown
from openai import OpenAI

md = MarkItDown(
    enable_plugins=True,
    llm_client=OpenAI(),
    llm_model="gpt-4o"
)
result = md.convert("scanned.pdf")
print(result.text_content)

Docker

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docker build -t markitdown:latest .
docker run --rm -i markitdown:latest < ~/your-file.pdf > output.md

Why Choose MarkItDown?

Token Efficiency Far Exceeds HTML / XML

LLMs natively “speak” Markdown. GPT-4o and Claude were both trained extensively on Markdown-formatted text from GitHub technical docs, Stack Overflow, and README files — giving them deep fluency in Markdown syntax. In RAG pipelines, Markdown documents consume far fewer tokens than raw HTML or XML, leaving more context window space for actual content.

Take a 5,000-word technical document as an example: raw HTML format consumes roughly 18,000 tokens, while the same document in Markdown uses only about 7,000 tokens — a 60%+ savings in context space. This means you can fit more documents in the same context window, or process longer documents with the same budget.

A concrete comparison: <h1 class="title"> costs 23 tokens; Markdown’s # is just 3 — a single heading saves 87% of token overhead.

Preserves Structure, Ideal for RAG Chunking

Markdown provides a natural semantic hierarchy through # headers, ## sub-headers, tables, and lists. RAG frameworks like LangChain and LlamaIndex can chunk Markdown documents by logical sections rather than arbitrary token blocks — producing more precise vector embeddings and fewer hallucinations in generated responses.

Active Community with Plugin Ecosystem

MarkItDown supports third-party plugins. Search GitHub for #markitdown-plugin to find community-contributed plugins. The official markitdown-sample-plugin template makes it easy to build your own extensions.

Key Takeaways

📌 Three things you should know before using MarkItDown:

  1. Structured Markdown output: Unlike textract and other general text extractors, MarkItDown preserves full document structure (headings, tables, lists, links) — ideal for AI pipelines
  2. Install only what you need: Don’t install all optional dependencies. Use [pdf,docx,pptx] to keep Docker images small and avoid unnecessary bloat
  3. Security first: MarkItDown runs with the current process’s I/O privileges. Never pass untrusted input directly — use convert_local() instead of convert() in hosted environments

Conclusion

MarkItDown is a document conversion tool built for the AI era. With one command or a few lines of Python, you can turn scattered Word files, PDFs, PowerPoints, and Excel sheets into AI-ready Markdown. If you’re building a RAG pipeline, setting up a knowledge base, or just want LLMs to better understand your documents, MarkItDown is worth a try.

👉 GitHub: https://github.com/microsoft/markitdown