Why Convert Docs to Markdown for LLMs? Meet the Open‑Source MarkItDown Tool

Why Convert to Markdown

Feeding raw PDFs directly to LLMs often produces garbled text, broken formatting, and tables that lose column structure, preventing the model from interpreting the data correctly.

LLMs are trained extensively on Markdown; when structural information such as headings, table relationships, and nested lists is lost, comprehension quality drops.

Supported Input Formats

Office documents : PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx)

Web/structured : HTML, CSV, JSON, XML

Media : Images (with OCR), Audio (with speech‑to‑text)

Online content : YouTube URLs (auto‑fetch subtitles)

Other : ZIP archives (recursive processing), EPub

Image OCR extracts embedded text, audio is transcribed, and YouTube links pull subtitles, covering the preprocessing steps needed before AI ingestion.

Installation and Basic Usage

pip install 'markitdown[all]'

Command‑line conversion: markitdown path-to-file.pdf > document.md Python API (three lines):

from markitdown import MarkItDown
md = MarkItDown(enable_plugins=False)
result = md.convert("report.xlsx")
print(result.text_content)

When using Claude Desktop, MarkItDown can run as an MCP server for direct integration.

Recent Enhancements

OCR plugin : The markitdown-ocr plugin uses LLM Vision to extract text from images embedded in PDFs or PPTs, sharing the existing llm_client / llm_model interface. Installation:

Table extraction : Recent pull requests improve handling of wide and complex tables in PDFs, a long‑standing challenge for PDF parsers.

Azure Document Intelligence integration : For enterprise‑grade scanned documents, processing can be routed through Azure’s Document Intelligence service for higher accuracy:

Adoption and Community Feedback

Since its November 2024 launch, MarkItDown has accumulated nearly 97 k stars on GitHub, reflecting strong interest from developers building Retrieval‑Augmented Generation pipelines.

Obsidian founder @kepano criticized the project as “large and messy,” arguing that Microsoft should focus on high‑fidelity conversion of its own Office formats rather than a catch‑all library. The criticism aligns with MarkItDown’s intended role as a preprocessing tool rather than a perfect visual conversion solution.

The tool is suited for scenarios where the primary concern is whether an LLM can accurately read the information, not the aesthetic quality of the generated Markdown.