MarkItDown

// Overview

MarkItDown is a lightweight Python utility from Microsoft that converts many file types into Markdown. It supports PDF, PowerPoint, Word, Excel, images, audio, HTML, text formats like CSV/JSON/XML, ZIP archives, EPubs, and YouTube URLs, preserving structure such as headings, lists, tables, and links.

It is built for feeding documents into LLMs and text-analysis pipelines, not for high-fidelity conversions meant for human reading. Markdown stays close to plain text while keeping document structure, and mainstream models handle it well and token-efficiently. The project compares itself to textract, with a stronger focus on retaining structure.

It fits the web scraping and data extraction space as a normalization step: take mixed source files and turn them into one consistent text format your prompts and pipelines can consume. It runs as a command-line tool or as a Python library.

// What it does

• Converts PDF, Word, PowerPoint, Excel, HTML, and text formats (CSV, JSON, XML) into Markdown
• Handles images (EXIF metadata and OCR), audio (metadata and speech transcription), EPubs, ZIP archives, and YouTube URLs
• Optional dependency groups (e.g. [pdf], [docx], [pptx]) let you install only the formats you need
• Command-line interface with file, -o output, and piped stdin input
• Supports third-party plugins, including markitdown-ocr for LLM-based OCR of embedded images
• Optional Azure Document Intelligence and Azure Content Understanding backends for higher-quality and structured extraction

// Getting started

MarkItDown requires Python 3.10 or higher; a virtual environment is recommended. Install it with pip, then convert files from the command line or in Python.

pip install 'markitdown[all]'

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

from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("document_with_images.pdf")
print(result.text_content)

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Preparing PDFs, Word, and PowerPoint documents as Markdown context for LLM prompts or RAG pipelines
• Normalizing a folder of mixed file types into one consistent text format for analysis
• Extracting text and structure from spreadsheets, HTML pages, or EPubs in a data pipeline
• Transcribing audio or pulling YouTube transcripts into Markdown for downstream processing

// How MarkItDown compares

MarkItDown alongside other open-source parsing & ingestion tools AI/TLDR tracks, ranked by GitHub stars.