MarkItDown - File to Markdown Conversion Overview

MarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown. It's particularly useful for converting documents into LLM-friendly text format, as Markdown is token-efficient and well-understood by modern language models.

Key Benefits:

Convert documents to clean, structured Markdown Token-efficient format for LLM processing Supports 15+ file formats Optional AI-enhanced image descriptions OCR for images and scanned documents Speech transcription for audio files Visual Enhancement with Scientific Schematics

When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.

If your document does not already contain schematics or diagrams:

Use the scientific-schematics skill to generate AI-powered publication-quality diagrams Simply describe your desired diagram in natural language Nano Banana Pro will automatically generate, review, and refine the schematic

For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

How to generate schematics:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

Create publication-quality images with proper formatting Review and refine through multiple iterations Ensure accessibility (colorblind-friendly, high contrast) Save outputs in the figures/ directory

When to add schematics:

Document conversion workflow diagrams File format architecture illustrations OCR processing pipeline diagrams Integration workflow visualizations System architecture diagrams Data flow diagrams Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

Supported Formats Format Description Notes PDF Portable Document Format Full text extraction DOCX Microsoft Word Tables, formatting preserved PPTX PowerPoint Slides with notes XLSX Excel spreadsheets Tables and data Images JPEG, PNG, GIF, WebP EXIF metadata + OCR Audio WAV, MP3 Metadata + transcription HTML Web pages Clean conversion CSV Comma-separated values Table format JSON JSON data Structured representation XML XML documents Structured format ZIP Archive files Iterates contents EPUB E-books Full text extraction YouTube Video URLs Fetch transcriptions Quick Start Installation

Install with all features

pip install 'markitdown[all]'

Or from source

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

Command-Line Usage

Basic conversion

markitdown document.pdf > output.md

Specify output file

markitdown document.pdf -o output.md

Pipe content

cat document.pdf | markitdown > output.md

Enable plugins

markitdown --list-plugins # List available plugins markitdown --use-plugins document.pdf -o output.md

Python API from markitdown import MarkItDown

Basic usage

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

Convert from stream

with open("document.pdf", "rb") as f: result = md.convert_stream(f, file_extension=".pdf") print(result.text_content)

Advanced Features 1. AI-Enhanced Image Descriptions

Use LLMs via OpenRouter to generate detailed image descriptions (for PPTX and image files):

from markitdown import MarkItDown from openai import OpenAI

Initialize OpenRouter client (OpenAI-compatible API)

client = OpenAI( api_key="your-openrouter-api-key", base_url="https://openrouter.ai/api/v1" )

md = MarkItDown( llm_client=client, llm_model="anthropic/claude-sonnet-4.5", # recommended for scientific vision llm_prompt="Describe this image in detail for scientific documentation" )

result = md.convert("presentation.pptx") print(result.text_content)

Azure Document Intelligence

For enhanced PDF conversion with Microsoft Document Intelligence:

Command line

markitdown document.pdf -o output.md -d -e ""

Python API

from markitdown import MarkItDown

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

Plugin System

MarkItDown supports 3rd-party plugins for extending functionality:

List installed plugins

markitdown --list-plugins

Enable plugins

markitdown --use-plugins file.pdf -o output.md

Find plugins on GitHub with hashtag: #markitdown-plugin

Optional Dependencies

Control which file formats you support:

Install specific formats

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

All available options:

[all] - All optional dependencies

[pptx] - PowerPoint files

[docx] - Word documents

[xlsx] - Excel spreadsheets

[xls] - Older Excel files

[pdf] - PDF documents

[outlook] - Outlook messages

[az-doc-intel] - Azure Document Intelligence

[audio-transcription] - WAV and MP3 transcription

[youtube-transcription] - YouTube video transcription

Common Use Cases 1. Convert Scientific Papers to Markdown from markitdown import MarkItDown

md = MarkItDown()

Convert PDF paper

result = md.convert("research_paper.pdf") with open("paper.md", "w") as f: f.write(result.text_content)

Extract Data from Excel for Analysis from markitdown import MarkItDown

md = MarkItDown() result = md.convert("data.xlsx")

Result will be in Markdown table format

print(result.text_content)

Process Multiple Documents from markitdown import MarkItDown import os from pathlib import Path

md = MarkItDown()

Process all PDFs in a directory

pdf_dir = Path("papers/") output_dir = Path("markdown_output/") output_dir.mkdir(exist_ok=True)

for pdf_file in pdf_dir.glob("*.pdf"): result = md.convert(str(pdf_file)) output_file = output_dir / f"{pdf_file.stem}.md" output_file.write_text(result.text_content) print(f"Converted: {pdf_file.name}")

Convert PowerPoint with AI Descriptions from markitdown import MarkItDown from openai import OpenAI

Use OpenRouter for access to multiple AI models

client = OpenAI( api_key="your-openrouter-api-key", base_url="https://openrouter.ai/api/v1" )

md = MarkItDown( llm_client=client, llm_model="anthropic/claude-sonnet-4.5", # recommended for presentations llm_prompt="Describe this slide image in detail, focusing on key visual elements and data" )

result = md.convert("presentation.pptx") with open("presentation.md", "w") as f: f.write(result.text_content)

Batch Convert with Different Formats from markitdown import MarkItDown from pathlib import Path

md = MarkItDown()

Files to convert

files = [ "document.pdf", "spreadsheet.xlsx", "presentation.pptx", "notes.docx" ]

for file in files: try: result = md.convert(file) output = Path(file).stem + ".md" with open(output, "w") as f: f.write(result.text_content) print(f"✓ Converted {file}") except Exception as e: print(f"✗ Error converting {file}: {e}")

Extract YouTube Video Transcription from markitdown import MarkItDown

md = MarkItDown()

Convert YouTube video to transcript

result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID") print(result.text_content)

Docker Usage

Build image

docker build -t markitdown:latest .

Run conversion

docker run --rm -i markitdown:latest < ~/document.pdf > output.md

Best Practices 1. Choose the Right Conversion Method Simple documents: Use basic MarkItDown() Complex PDFs: Use Azure Document Intelligence Visual content: Enable AI image descriptions Scanned documents: Ensure OCR dependencies are installed 2. Handle Errors Gracefully from markitdown import MarkItDown

md = MarkItDown()

try: result = md.convert("document.pdf") print(result.text_content) except FileNotFoundError: print("File not found") except Exception as e: print(f"Conversion error: {e}")

Process Large Files Efficiently from markitdown import MarkItDown

md = MarkItDown()

For large files, use streaming

with open("large_file.pdf", "rb") as f: result = md.convert_stream(f, file_extension=".pdf")

# Process in chunks or save directly
with open("output.md", "w") as out:
    out.write(result.text_content)

Optimize for Token Efficiency

Markdown output is already token-efficient, but you can:

Remove excessive whitespace Consolidate similar sections Strip metadata if not needed from markitdown import MarkItDown import re

md = MarkItDown() result = md.convert("document.pdf")

Clean up extra whitespace

clean_text = re.sub(r'\n{3,}', '\n\n', result.text_content) clean_text = clean_text.strip()

print(clean_text)

Integration with Scientific Workflows Convert Literature for Review from markitdown import MarkItDown from pathlib import Path

md = MarkItDown()

Convert all papers in literature folder

papers_dir = Path("literature/pdfs") output_dir = Path("literature/markdown") output_dir.mkdir(exist_ok=True)

for paper in papers_dir.glob("*.pdf"): result = md.convert(str(paper))

# Save with metadata
output_file = output_dir / f"{paper.stem}.md"
content = f"# {paper.stem}\n\n"
content += f"**Source**: {paper.name}\n\n"
content += "---\n\n"
content += result.text_content

output_file.write_text(content)

For AI-enhanced conversion with figures

from openai import OpenAI

client = OpenAI( api_key="your-openrouter-api-key", base_url="https://openrouter.ai/api/v1" )

md_ai = MarkItDown( llm_client=client, llm_model="anthropic/claude-sonnet-4.5", llm_prompt="Describe scientific figures with technical precision" )

Extract Tables for Analysis from markitdown import MarkItDown import re

md = MarkItDown() result = md.convert("data_tables.xlsx")

Markdown tables can be parsed or used directly

print(result.text_content)

Troubleshooting Common Issues

Missing dependencies: Install feature-specific packages

pip install 'markitdown[pdf]' # For PDF support

Binary file errors: Ensure files are opened in binary mode

with open("file.pdf", "rb") as f: # Note the "rb" result = md.convert_stream(f, file_extension=".pdf")

OCR not working: Install tesseract

macOS

brew install tesseract

Ubuntu

sudo apt-get install tesseract-ocr

Performance Considerations PDF files: Large PDFs may take time; consider page ranges if supported Image OCR: OCR processing is CPU-intensive Audio transcription: Requires additional compute resources AI image descriptions: Requires API calls (costs may apply) Next Steps See references/api_reference.md for complete API documentation Check references/file_formats.md for format-specific details Review scripts/batch_convert.py for automation examples Explore scripts/convert_with_ai.py for AI-enhanced conversions Resources MarkItDown GitHub: https://github.com/microsoft/markitdown PyPI: https://pypi.org/project/markitdown/ OpenRouter: https://openrouter.ai (for AI-enhanced conversions) OpenRouter API Keys: https://openrouter.ai/keys OpenRouter Models: https://openrouter.ai/models MCP Server: markitdown-mcp (for Claude Desktop integration) Plugin Development: See packages/markitdown-sample-plugin

安装

Install with all features

Or from source

Basic conversion

Specify output file

Pipe content

Enable plugins

Basic usage

Convert from stream

Initialize OpenRouter client (OpenAI-compatible API)

Command line

Python API

List installed plugins

Enable plugins

Install specific formats

All available options:

[all] - All optional dependencies

[pptx] - PowerPoint files

[docx] - Word documents

[xlsx] - Excel spreadsheets

[xls] - Older Excel files

[pdf] - PDF documents

[outlook] - Outlook messages

[az-doc-intel] - Azure Document Intelligence

[audio-transcription] - WAV and MP3 transcription

[youtube-transcription] - YouTube video transcription

Convert PDF paper

Result will be in Markdown table format

Process all PDFs in a directory

Use OpenRouter for access to multiple AI models

Files to convert

Convert YouTube video to transcript

Build image

Run conversion

For large files, use streaming

Clean up extra whitespace

Convert all papers in literature folder

For AI-enhanced conversion with figures

Markdown tables can be parsed or used directly

macOS

Ubuntu