MarkItDown — Convert Any File to Markdown for LLMs

What it is

MarkItDown is a Python library by Microsoft that converts a wide range of file formats into clean Markdown text. It handles PDF, DOCX, PPTX, XLSX, images (via OCR), audio (via transcription), and HTML. The output is structured Markdown suitable for feeding into LLM context windows.

It targets developers building RAG pipelines, document processing systems, or any application that needs to ingest diverse file formats into an LLM-friendly text representation.

How it saves time or tokens

MarkItDown produces clean, structured Markdown without the noise of raw extraction tools. Tables stay as Markdown tables, headings preserve hierarchy, and images get OCR text. This means fewer tokens wasted on formatting artifacts. Estimated token usage is around 2,400 tokens.

How to use

1. Install MarkItDown:

pip install markitdown

1. Convert any file:

from markitdown import MarkItDown

md = MarkItDown()
result = md.convert('report.pdf')
print(result.text_content)

1. Use the output as LLM context or store it in a vector database.

Example

from markitdown import MarkItDown

md = MarkItDown()

# Convert PDF
pdf_result = md.convert('quarterly-report.pdf')
print(pdf_result.text_content[:500])

# Convert DOCX
doc_result = md.convert('proposal.docx')
print(doc_result.text_content[:500])

# Convert PPTX
slides = md.convert('deck.pptx')
print(slides.text_content[:500])

Related on TokRepo

• AI Tools for Documents — Document parsing and processing tools
• AI Tools for RAG — RAG pipeline tools and frameworks

Key considerations

When evaluating MarkItDown for your workflow, consider the following factors. First, assess whether your team has the technical prerequisites to adopt this tool effectively. Second, evaluate the maintenance burden against the productivity gains. Third, check community activity and documentation quality to ensure long-term viability. Integration with your existing toolchain matters more than feature count alone. Start with a small pilot project before rolling out across the organization. Monitor resource usage during the initial adoption phase to identify bottlenecks early. Document your configuration decisions so team members can onboard independently.

Common pitfalls

• Scanned PDFs without embedded text require OCR; install optional OCR dependencies for these files.
• Audio transcription requires additional dependencies (speech recognition libraries); the base install handles text-based formats only.
• Very large files produce extensive Markdown that may exceed LLM context limits; chunk the output before feeding it to a model.