| name | markitdown |
| description | Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription. |
MarkItDown
Overview
MarkItDown is a Python utility that converts various file formats into Markdown format, optimized for use with large language models and text analysis pipelines. It preserves document structure (headings, lists, tables, hyperlinks) while producing clean, token-efficient Markdown output.
When to Use This Skill
Use this skill when users request:
- Converting documents to Markdown format
- Extracting text from PDF, Word, PowerPoint, or Excel files
- Performing OCR on images to extract text
- Transcribing audio files to text
- Extracting YouTube video transcripts
- Processing HTML, EPUB, or web content to Markdown
- Converting structured data (CSV, JSON, XML) to readable Markdown
- Batch converting multiple files or ZIP archives
- Preparing documents for LLM analysis or RAG systems
Core Capabilities
1. Document Conversion
Convert Office documents and PDFs to Markdown while preserving structure.
Supported formats:
- PDF files (with optional Azure Document Intelligence integration)
- Word documents (DOCX)
- PowerPoint presentations (PPTX)
- Excel spreadsheets (XLSX, XLS)
Basic usage:
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("document.pdf")
print(result.text_content)
Command-line:
markitdown document.pdf -o output.md
See references/document_conversion.md for detailed documentation on document-specific features.
2. Media Processing
Extract text from images using OCR and transcribe audio files to text.
Supported formats:
- Images (JPEG, PNG, GIF, etc.) with EXIF metadata extraction
- Audio files with speech transcription (requires speech_recognition)
Image with OCR:
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("image.jpg")
print(result.text_content)
Audio transcription:
result = md.convert("audio.wav")
print(result.text_content)
See references/media_processing.md for advanced media handling options.
3. Web Content Extraction
Convert web-based content and e-books to Markdown.
Supported formats:
- HTML files and web pages
- YouTube video transcripts (via URL)
- EPUB books
- RSS feeds
YouTube transcript:
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("https://youtube.com/watch?v=VIDEO_ID")
print(result.text_content)
See references/web_content.md for web extraction details.
4. Structured Data Handling
Convert structured data formats to readable Markdown tables.
Supported formats:
- CSV files
- JSON files
- XML files
CSV to Markdown table:
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("data.csv")
print(result.text_content)
See references/structured_data.md for format-specific options.
5. Advanced Integrations
Enhance conversion quality with AI-powered features.
Azure Document Intelligence:
For enhanced PDF processing with better table extraction and layout analysis:
from markitdown import MarkItDown
md = MarkItDown(docintel_endpoint="<endpoint>", docintel_key="<key>")
result = md.convert("complex.pdf")
LLM-Powered Image Descriptions:
Generate detailed image descriptions using GPT-4o:
from markitdown import MarkItDown
from openai import OpenAI
client = OpenAI()
md = MarkItDown(llm_client=client, llm_model="gpt-4o")
result = md.convert("presentation.pptx")
See references/advanced_integrations.md for integration details.
6. Batch Processing
Process multiple files or entire ZIP archives at once.
ZIP file processing:
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("archive.zip")
print(result.text_content)
Batch script:
Use the provided batch processing script for directory conversion:
python scripts/batch_convert.py /path/to/documents /path/to/output
See scripts/batch_convert.py for implementation details.
Installation
Full installation (all features):
uv pip install 'markitdown[all]'
Modular installation (specific features):
uv pip install 'markitdown[pdf]'
uv pip install 'markitdown[docx]'
uv pip install 'markitdown[pptx]'
uv pip install 'markitdown[xlsx]'
uv pip install 'markitdown[audio]'
uv pip install 'markitdown[youtube]'
Requirements:
Output Format
MarkItDown produces clean, token-efficient Markdown optimized for LLM consumption:
- Preserves headings, lists, and tables
- Maintains hyperlinks and formatting
- Includes metadata where relevant (EXIF, document properties)
- No temporary files created (streaming approach)
Common Workflows
Preparing documents for RAG:
from markitdown import MarkItDown
md = MarkItDown()
docs = ["manual.pdf", "guide.docx", "faq.html"]
markdown_content = []
for doc in docs:
result = md.convert(doc)
markdown_content.append(result.text_content)
Document analysis pipeline:
for file in documents/*.pdf; do
markitdown "$file" -o "markdown/$(basename "$file" .pdf).md"
done
Plugin System
MarkItDown supports extensible plugins for custom conversion logic. Plugins are disabled by default for security:
from markitdown import MarkItDown
md = MarkItDown(enable_plugins=True)
Resources
This skill includes comprehensive reference documentation for each capability:
- references/document_conversion.md - Detailed PDF, DOCX, PPTX, XLSX conversion options
- references/media_processing.md - Image OCR and audio transcription details
- references/web_content.md - HTML, YouTube, and EPUB extraction
- references/structured_data.md - CSV, JSON, XML conversion formats
- references/advanced_integrations.md - Azure Document Intelligence and LLM integration
- scripts/batch_convert.py - Batch processing utility for directories
