Markitdown

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 Routing for Converted Documents

This conversion skill should preserve and extract the source document; it should not invent new diagrams by default. Route to visualization, schematic, poster, slide, or Mermaid skills only when the user asks for a visual deliverable, the source contains a diagram that must be reconstructed, or the document has a complex mechanism/workflow that cannot be understood clearly in text alone.

When visuals are needed, keep them source-faithful: cite the source material, label speculative reconstruction as such, and avoid replacing extracted evidence with decorative images.

Supported Formats

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-opus-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)

2. Azure Document Intelligence

For enhanced PDF conversion with Microsoft Document Intelligence:

# Command line
markitdown document.pdf -o output.md -d -e "<document_intelligence_endpoint>"

# Python API
from markitdown import MarkItDown

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

3. 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)

2. 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)

3. 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}")

4. 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-opus-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)

5. 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}")

6. 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}")

3. 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)

4. 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-opus-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

1. Missing dependencies: Install feature-specific packages

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

2. 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")
   

3. 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 compatible MCP desktop integrations)
• Plugin Development: See packages/markitdown-sample-plugin