markitdown

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: If using `uv`, you can create a virtual environment with: If you are using Anaconda, you can create a virtual environment with: Installation To install MarkItDown, use pip: `pip install 'markitdown[all]'`. Alternatively, you can install it from the source: Usage Command-Line Or use `-o` to specify the output file: You can also pipe content: 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: 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: To enable plugins use: 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:** **Usage:** Pass the same `llm_client` and `llm_model` you would use for image descriptions: 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)