markitdown Documentation

1. Project Overview & Quickstart (microsoft/markitdown)

File: README.md

MarkItDown

[!IMPORTANT]
MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert_* function needed for your use case (e.g., convert_stream(), or convert_local()). See the Security Considerations section of the documentation for more information.

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.

MarkItDown currently supports the conversion from:

PDF
PowerPoint
Word
Excel
Images (EXIF metadata and OCR)
Audio (EXIF metadata and speech transcription)
HTML
Text-based formats (CSV, JSON, XML)
ZIP files (iterates over contents)
YouTube URLs
EPubs
... and more!

Why Markdown?

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.

Prerequisites

MarkItDown requires Python 3.10 or higher. It is recommended to use a virtual environment to avoid dependency conflicts.

With the standard Python installation, you can create and activate a virtual environment using the following commands:

bash

python -m venv .venv
source .venv/bin/activate

If using uv, you can create a virtual environment with:

bash

uv venv --python=3.12 .venv
source .venv/bin/activate
# NOTE: Be sure to use 'uv pip install' rather than just 'pip install' to install packages in this virtual environment

If you are using Anaconda, you can create a virtual environment with:

bash

conda create -n markitdown python=3.12
conda activate markitdown

Installation

To install MarkItDown, use pip: pip install 'markitdown[all]'. Alternatively, you can install it from the source:

bash

git clone [email protected]:microsoft/markitdown.git
cd markitdown
pip install -e 'packages/markitdown[all]'

Usage

Command-Line

bash

markitdown path-to-file.pdf > document.md

Or use -o to specify the output file:

bash

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

You can also pipe content:

bash

cat path-to-file.pdf | markitdown

Optional Dependencies

MarkItDown has optional dependencies for activating various file formats. Earlier in this document, we installed all optional dependencies with the [all] option. However, you can also install them individually for more control. For example:

bash

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

will install only the dependencies for PDF, DOCX, and PPTX files.

At the moment, the following optional dependencies are available:

[all] Installs all optional dependencies
[pptx] Installs dependencies for PowerPoint files
[docx] Installs dependencies for Word files
[xlsx] Installs dependencies for Excel files
[xls] Installs dependencies for older Excel files
[pdf] Installs dependencies for PDF files
[outlook] Installs dependencies for Outlook messages
[az-doc-intel] Installs dependencies for Azure Document Intelligence
[az-content-understanding] Installs dependencies for Azure Content Understanding
[audio-transcription] Installs dependencies for audio transcription of wav and mp3 files
[youtube-transcription] Installs dependencies for fetching YouTube video transcription

Plugins

MarkItDown also supports 3rd-party plugins. Plugins are disabled by default. To list installed plugins:

bash

markitdown --list-plugins

To enable plugins use:

bash

markitdown --use-plugins path-to-file.pdf

To find available plugins, search GitHub for the hashtag #markitdown-plugin. To develop a plugin, see packages/markitdown-sample-plugin.

markitdown-ocr Plugin

The markitdown-ocr plugin adds OCR support to PDF, DOCX, PPTX, and XLSX converters, extracting text from embedded images using LLM Vision — the same llm_client / llm_model pattern that MarkItDown already uses for image descriptions. No new ML libraries or binary dependencies required.

Installation:

bash

pip install markitdown-ocr
pip install openai  # or any OpenAI-compatible client

Usage:

Pass the same llm_client and llm_model you would use for image descriptions:

python

from markitdown import MarkItDown
from openai import OpenAI

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

If no llm_client is provided the plugin still loads, but OCR is silently skipped and the standard built-in converter is used instead.

See packages/markitdown-ocr/README.md for detailed documentation.

Azure Content Understanding

Azure Content Understanding provides higher-quality conversion with structured field extraction (YAML front matter), multi-modal support (documents, images, audio, video), and configurable analyzers.

Install: pip install 'markitdown[az-content-understanding]'

When to use Content Understanding

Content Understanding is ideal when you need capabilities beyond what built-in or Document Intelligence converters provide:

Audio and video files — CU is the only option for video, and the higher-quality cloud option for audio. Built-in converters have no video support and only basic audio transcription.
Structured field extraction — Prebuilt or custom-built analyzers extract domain-specific fields (invoice amounts, receipt dates, contract clauses) serialized as YAML front matter. Neither built-in nor Doc Intel integration exposes fields.
Higher-quality document extraction — Cloud-based layout analysis and OCR for scanned PDFs, complex tables, and multi-page documents.
Single API for all modalities — One cu_endpoint handles documents, images, audio, and video with automatic analyzer routing.

Capability	Built-in converters	Azure Document Intelligence	Azure Content Understanding
Document conversion	Offline, format-specific extraction	Cloud layout extraction	Cloud multimodal extraction
Structured fields	Not available	Not exposed by this integration	YAML front matter from analyzer fields
Custom analyzers	Not available	Not configurable in this integration	Supported with `cu_analyzer_id`
Audio and video	Basic audio, no video	Not supported	Audio and video analyzers
Cost	Local compute only	Billable Azure API calls	Billable Azure API calls

CLI:

bash

markitdown path-to-file.pdf --use-cu --cu-endpoint "<content_understanding_endpoint>"

Python API:

python

from markitdown import MarkItDown

# Zero-config — auto-selects analyzer per file type
md = MarkItDown(cu_endpoint="<content_understanding_endpoint>")
result = md.convert("report.pdf")   # documents → prebuilt-documentSearch
result = md.convert("meeting.mp4")  # video → prebuilt-videoSearch
result = md.convert("call.wav")     # audio → prebuilt-audioSearch
print(result.markdown)

With a custom analyzer (for domain-specific field extraction):

python

md = MarkItDown(
    cu_endpoint="<content_understanding_endpoint>",
    cu_analyzer_id="my-invoice-analyzer",
)
result = md.convert("invoice.pdf")
print(result.markdown)
# Output includes YAML front matter with extracted fields:
# ---
# contentType: document
# fields:
#   VendorName: CONTOSO LTD.
#   InvoiceDate: '2019-11-15'
# ---
# 
# ...

When cu_analyzer_id is set, the converter automatically scopes it to compatible file types based on the analyzer's modality. Incompatible types (e.g., audio files with a document analyzer) auto-route to default prebuilt analyzers.

Cost note: Each convert() call for a CU-routed format is a billable Azure API call. Use cu_file_types to restrict which formats route to CU:

python

from markitdown.converters import ContentUnderstandingFileType

md = MarkItDown(
    cu_endpoint="<content_understanding_endpoint>",
    cu_file_types=[ContentUnderstandingFileType.PDF],  # only PDFs use CU
)

More information about Azure Content Understanding can be found here.

Azure Document Intelligence

To use Microsoft Document Intelligence for conversion:

bash

markitdown path-to-file.pdf -o document.md -d -e "<document_intelligence_endpoint>"

More information about how to set up an Azure Document Intelligence Resource can be found here

Python API

Basic usage in Python:

python

from markitdown import MarkItDown

md = MarkItDown(enable_plugins=False) # Set to True to enable plugins
result = md.convert("test.xlsx")
print(result.text_content)

Document Intelligence conversion in Python:

python

from markitdown import MarkItDown

md = MarkItDown(docintel_endpoint="<document_intelligence_endpoint>")
result = md.convert("test.pdf")
print(result.text_content)

To use Large Language Models for image descriptions (currently only for pptx and image files), provide llm_client and llm_model:

python

from markitdown import MarkItDown
from openai import OpenAI

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

Docker

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

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact [email protected] with any additional questions or comments.

How to Contribute

You can help by looking at issues or helping review PRs. Any issue or PR is welcome, but we have also marked some as 'open for contribution' and 'open for reviewing' to help facilitate community contributions. These are of course just suggestions and you are welcome to contribute in any way you like.

Running Tests and Checks

Navigate to the MarkItDown package:
sh
```
cd packages/markitdown
```

Install hatch in your environment and run tests:

pip install hatch  # Other ways of installing hatch: https://hatch.pypa.io/dev/install/
hatch shell
hatch test

(Alternative) Use the Devcontainer which has all the dependencies installed:

# Reopen the project in Devcontainer and run:
hatch test

Run pre-commit checks before submitting a PR: pre-commit run --all-files

Security Considerations

MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access.

Sanitize your inputs: Do not pass untrusted input directly to MarkItDown. If any part of the input may be controlled by an untrusted user or system, such as in hosted or server-side applications, it must be validated and restricted before calling MarkItDown. Depending on your environment, this may include restricting file paths, limiting URI schemes and network destinations, and blocking access to private, loopback, link-local, or metadata-service addresses.

Call only the conversion method you need: Prefer the narrowest conversion API that fits your use case. MarkItDown's convert() method is intentionally permissive and can handle local files, remote URIs, and byte streams. If your application only needs to read local files, call convert_local() instead. If you need more control over URI fetching, call requests.get() yourself and pass the response object to convert_response(). For maximum control, open a stream to the input you want converted and call convert_stream().

Contributing 3rd-party Plugins

You can also contribute by creating and sharing 3rd party plugins. See packages/markitdown-sample-plugin for more details.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.

File: packages/markitdown-sample-plugin/README.md

MarkItDown Sample Plugin

This project shows how to create a sample plugin for MarkItDown. The most important parts are as follows:

First, implement your custom DocumentConverter:

python

from typing import BinaryIO, Any
from markitdown import MarkItDown, DocumentConverter, DocumentConverterResult, StreamInfo, PRIORITY_SPECIFIC_FILE_FORMAT

class RtfConverter(DocumentConverter):

    def __init__(
        self, priority: float = PRIORITY_SPECIFIC_FILE_FORMAT
    ):
        super().__init__(priority=priority)

    def accepts(
        self,
        file_stream: BinaryIO,
        stream_info: StreamInfo,
        **kwargs: Any,
    ) -> bool:

        # Implement logic to check if the file stream is an RTF file
        # ...
        raise NotImplementedError()


    def convert(
        self,
        file_stream: BinaryIO,
        stream_info: StreamInfo,
        **kwargs: Any,
    ) -> DocumentConverterResult:

        # Implement logic to convert the file stream to Markdown
        # ...
        raise NotImplementedError()

Next, make sure your package implements and exports the following:

python

# The version of the plugin interface that this plugin uses.
# The only supported version is 1 for now.
__plugin_interface_version__ = 1

# The main entrypoint for the plugin. This is called each time MarkItDown instances are created.
def register_converters(markitdown: MarkItDown, **kwargs):
    """
    Called during construction of MarkItDown instances to register converters provided by plugins.
    """

    # Simply create and attach an RtfConverter instance
    markitdown.register_converter(RtfConverter())

Finally, create an entrypoint in the pyproject.toml file:

toml

[project.entry-points."markitdown.plugin"]
sample_plugin = "markitdown_sample_plugin"

Here, the value of sample_plugin can be any key, but should ideally be the name of the plugin. The value is the fully qualified name of the package implementing the plugin.

Installation

To use the plugin with MarkItDown, it must be installed. To install the plugin from the current directory use:

bash

pip install -e .

Once the plugin package is installed, verify that it is available to MarkItDown by running:

bash

markitdown --list-plugins

To use the plugin for a conversion use the --use-plugins flag. For example, to convert an RTF file:

bash

markitdown --use-plugins path-to-file.rtf

In Python, plugins can be enabled as follows:

python

from markitdown import MarkItDown

md = MarkItDown(enable_plugins=True)
result = md.convert("path-to-file.rtf")
print(result.text_content)

Trademarks

File: packages/markitdown-mcp/README.md

MarkItDown-MCP

[!IMPORTANT]
The MarkItDown-MCP package is meant for local use, with local trusted agents. In particular, when running the MCP server with Streamable HTTP or SSE, it binds to localhost by default, and is not exposed to other machines on the network or Internet. In this configuration, it is meant to be a direct alternative to the STDIO transport, which may be more convenient in some cases. DO NOT bind the server to other interfaces unless you understand the security implications of doing so.

The markitdown-mcp package provides a lightweight STDIO, Streamable HTTP, and SSE MCP server for calling MarkItDown.

It exposes one tool: convert_to_markdown(uri), where uri can be any http:, https:, file:, or data: URI.

Installation

To install the package, use pip:

bash

pip install markitdown-mcp

Usage

To run the MCP server, using STDIO (default), use the following command:

bash

markitdown-mcp

To run the MCP server, using Streamable HTTP and SSE, use the following command:

bash

markitdown-mcp --http --host 127.0.0.1 --port 3001

Running in Docker

To run markitdown-mcp in Docker, build the Docker image using the provided Dockerfile:

bash

docker build -t markitdown-mcp:latest .

And run it using:

bash

docker run -it --rm markitdown-mcp:latest

This will be sufficient for remote URIs. To access local files, you need to mount the local directory into the container. For example, if you want to access files in /home/user/data, you can run:

bash

docker run -it --rm -v /home/user/data:/workdir markitdown-mcp:latest

Once mounted, all files under data will be accessible under /workdir in the container. For example, if you have a file example.txt in /home/user/data, it will be accessible in the container at /workdir/example.txt.

Accessing from Claude Desktop

It is recommended to use the Docker image when running the MCP server for Claude Desktop.

Follow these instructions to access Claude's claude_desktop_config.json file.

Edit it to include the following JSON entry:

json

{
  "mcpServers": {
    "markitdown": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "markitdown-mcp:latest"
      ]
    }
  }
}

If you want to mount a directory, adjust it accordingly:

json

{
  "mcpServers": {
    "markitdown": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-v",
        "/home/user/data:/workdir",
        "markitdown-mcp:latest"
      ]
    }
  }
}

Debugging

To debug the MCP server you can use the MCP Inspector tool.

bash

npx @modelcontextprotocol/inspector

You can then connect to the inspector through the specified host and port (e.g., http://localhost:5173/).

If using STDIO:

select STDIO as the transport type,
input markitdown-mcp as the command, and
click Connect

If using Streamable HTTP:

select Streamable HTTP as the transport type,
input http://127.0.0.1:3001/mcp as the URL, and
click Connect

If using SSE:

select SSE as the transport type,
input http://127.0.0.1:3001/sse as the URL, and
click Connect

Finally:

click the Tools tab,
click List Tools,
click convert_to_markdown, and
run the tool on any valid URI.

Security Considerations

The server does not support authentication, and runs with the privileges of the user running it. For this reason, when running in SSE or Streamable HTTP mode, the server binds by default to localhost. Even still, it is important to recognize that the server can be accessed by any process or users on the same local machine, and that the convert_to_markdown tool can be used to read any file that the server's user has access to, or any data from the network. If you require additional security, consider running the server in a sandboxed environment, such as a virtual machine or container, and ensure that the user permissions are properly configured to limit access to sensitive files and network segments. Above all, DO NOT bind the server to other interfaces (non-localhost) unless you understand the security implications of doing so.

Trademarks

File: packages/markitdown/README.md

MarkItDown

[!TIP]
MarkItDown is a Python package and command-line utility for converting various files to Markdown (e.g., for indexing, text analysis, etc).

For more information, and full documentation, see the project README.md on GitHub.

[!IMPORTANT]
MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert_* function needed for your use case (e.g., convert_stream(), or convert_local()). See the Security Considerations section of the documentation for more information.

Installation

From PyPI:

bash

pip install 'markitdown[all]'

From source:

bash

git clone [email protected]:microsoft/markitdown.git
cd markitdown
pip install -e 'packages/markitdown[all]'

Usage

Command-Line

bash

markitdown path-to-file.pdf > document.md

Python API

python

from markitdown import MarkItDown

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

More Information

For more information, and full documentation, see the project README.md on GitHub.

Trademarks

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1. Project Overview & Quickstart (microsoft/markitdown) Copy Chapter

File: README.md Copy Chapter

MarkItDown

Why Markdown? Copy Chapter

Prerequisites Copy Chapter

Installation Copy Chapter

Usage Copy Chapter

Command-Line Copy Section

Optional Dependencies Copy Section

Plugins Copy Section

markitdown-ocr Plugin

Azure Content Understanding Copy Section

When to use Content Understanding

Azure Document Intelligence Copy Section

Python API Copy Section

Docker Copy Section

Contributing Copy Chapter

How to Contribute Copy Section

Running Tests and Checks Copy Section

Security Considerations Copy Section

Contributing 3rd-party Plugins Copy Section

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File: packages/markitdown-sample-plugin/README.md Copy Chapter

MarkItDown Sample Plugin

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MarkItDown-MCP

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Running in Docker Copy Chapter

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MarkItDown

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Explore Related Technologies & Topics

1. Project Overview & Quickstart (microsoft/markitdown)

File: README.md

Why Markdown?

Prerequisites

Installation

Usage

Command-Line

Optional Dependencies

Plugins

Azure Content Understanding

Azure Document Intelligence

Python API

Docker

Contributing

How to Contribute

Running Tests and Checks

Security Considerations

Contributing 3rd-party Plugins

Trademarks

File: packages/markitdown-sample-plugin/README.md

Installation

Trademarks

File: packages/markitdown-mcp/README.md

Installation

Usage

Running in Docker

Accessing from Claude Desktop

Debugging

Security Considerations

Trademarks

File: packages/markitdown/README.md

Installation

Usage

Command-Line

Python API

More Information

Trademarks

2. Official Technical Reference & Guides (microsoft/microsoft.github.io)

Microsoft GitHub resources

Microsoft open source resources

Resources for Microsoft employees

How to identify an official Microsoft organization on GitHub