Microsoft MarkItDown Tutorial: Convert Documents to Markdown for AI Knowledge Bases

Microsoft’s microsoft/markitdown is a Python tool for converting many kinds of files into Markdown. Its goal is not high-fidelity layout preservation. Instead, it turns documents, spreadsheets, web pages, images, audio, and other materials into Markdown that is easier to use in LLM, RAG, search indexing, and text-processing workflows.

Project repository:

https://github.com/microsoft/markitdown

If you often need to send PDFs, Word files, Excel spreadsheets, PowerPoint decks, web pages, images, or archives to an AI model for analysis, MarkItDown is a good fit for the preprocessing step. Its output is plain Markdown, which is easier to debug, chunk, search, and archive than raw binary files.

What MarkItDown Supports

According to the official README, MarkItDown supports input types including:

• PDF
• PowerPoint
• Word
• Excel
• Images, including EXIF metadata and OCR
• Audio, including EXIF metadata and speech transcription
• HTML
• CSV, JSON, XML
• ZIP files, with traversal of the contents
• YouTube URLs
• EPUB

Its positioning is clear: convert files into Markdown for downstream LLM and text workflows. Do not treat it as a strict layout-preserving document converter. For resumes, contracts, scanned documents, product materials, meeting recordings, or web snapshots, the important part is usually not pixel-level fidelity, but extracting text structure that a model can understand reliably.

Installation Commands

The simplest installation method is pip:

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pip install markitdown

If you need all optional features, install the all extra:

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pip install 'markitdown[all]'

If you only need support for specific formats, install selected extras. For example, PDF, DOCX, and PPTX:

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pip install 'markitdown[pdf,docx,pptx]'

To install from source:

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git clone https://github.com/microsoft/markitdown.git
cd markitdown
pip install -e 'packages/markitdown[all]'

Command-Line Usage

After installation, you can use the markitdown command directly. The most basic usage converts a file to standard output:

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markitdown path-to-file.pdf

Save the result as a Markdown file:

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markitdown path-to-file.pdf -o output.md

Convert Office documents:

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markitdown report.docx -o report.md
markitdown slides.pptx -o slides.md
markitdown data.xlsx -o data.md

Convert a web page:

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markitdown https://example.com -o page.md

Convert a YouTube URL:

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markitdown https://www.youtube.com/watch?v=VIDEO_ID -o video.md

Process a ZIP file:

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markitdown archive.zip -o archive.md

These commands are useful for manual testing first. Once the output structure looks right, you can put MarkItDown into automation scripts, knowledge-base import workflows, or a RAG pipeline.

Python API Usage

MarkItDown can also be used as a Python library. The official README shows the basic pattern: create a MarkItDown instance and call convert:

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from markitdown import MarkItDown

md = MarkItDown(enable_plugins=False)
result = md.convert("test.xlsx")
print(result.text_content)

If you want to connect an OpenAI client for image descriptions, pass the client to MarkItDown:

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from openai import OpenAI
from markitdown import MarkItDown

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

This is useful for image-based materials such as screenshots, scanned images, or pages with charts. In real use, pay attention to cost, privacy, and network requests, especially when processing internal documents.

Docker Usage

If you do not want to change your local Python environment, you can run it with Docker. The official README provides this image build command:

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docker build -t markitdown:latest .

Then mount the current directory into the container and run the conversion:

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docker run --rm -v .:/workdir markitdown:latest path-to-file.pdf -o output.md

This works well on servers, in CI, or for temporary batch processing. The downside is that dependency debugging can be a little slower than in a local Python environment.

Plugin System

MarkItDown supports plugins, but third-party plugins are not enabled by default. List plugins:

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markitdown --list-plugins

Enable plugins:

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markitdown --use-plugins path-to-file.foo -o output.md

Enable plugins in Python:

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from markitdown import MarkItDown

md = MarkItDown(enable_plugins=True)
result = md.convert("example.foo")
print(result.text_content)

Plugins are useful for custom formats, internal document formats, or files exported by specific business systems. Remember that plugins run additional code, so do not enable plugins from untrusted sources casually.

Where It Fits

I would use MarkItDown in scenarios like these:

1. Convert PDF, DOCX, PPTX, and XLSX files into Markdown before importing them into a knowledge base.
2. Turn web pages, YouTube pages, and EPUB files into searchable text.
3. Prepare documents for RAG by producing Markdown that is easier to chunk.
4. Send converted materials to Codex, Claude Code, Cursor, or similar tools for reading.
5. Batch-organize historical documents into a unified .md intermediate format.

A simple workflow can look like this:

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mkdir -p markdown
markitdown input.pdf -o markdown/input.md

For batch processing, enumerate files with a script and call markitdown for each one. After conversion, manually inspect several outputs, especially tables, scanned documents, complex-layout PDFs, and multi-column files.

Limitations

MarkItDown is useful, but there are a few limits to keep in mind:

1. The reading order of complex PDFs may not be perfect, especially with multi-column pages, footnotes, headers, and footers.
2. OCR and image descriptions depend on extra components or LLMs, so quality and cost need separate evaluation.
3. Markdown is good for text structure, but not for preserving exact layout.
4. When plugins or LLMs process internal files, pay attention to data security.
5. Treat converted output as an intermediate artifact, not as a guaranteed equivalent of the original file.

If the goal is to help a model understand your materials, MarkItDown is heading in the right direction: convert many kinds of files into Markdown first, then clean, chunk, index, and query them. It is not a universal converter, but as an LLM document-ingestion tool, it is clear, lightweight, and easy to connect to an existing Python workflow.