---
name: markitdown-4b1d2b3a25e7
description: Convert files and Office documents into clean Markdown when you need LLM-friendly, token-efficient text (e.g., for summarization, search, RAG ingestion, or dataset preparation).
license: MIT
author: AIPOCH
---
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

- Converting research papers or reports (PDF/DOCX/EPUB/HTML) into Markdown for LLM summarization, Q&A, or RAG indexing.
- Extracting tables and structured content from spreadsheets (XLSX/CSV) into Markdown for analysis or documentation.
- Turning slide decks (PPTX) into Markdown notes, including speaker notes and (optionally) AI-generated image descriptions.
- Processing images or scanned documents with OCR to obtain searchable, editable Markdown text.
- Transcribing audio (WAV/MP3) or pulling YouTube transcripts into Markdown for meeting notes, content analysis, or knowledge bases.

## Key Features

- Converts many formats to structured Markdown (PDF, DOCX, PPTX, XLSX, images, audio, HTML, CSV, JSON, XML, ZIP, EPUB, YouTube URLs, etc.).
- Produces token-efficient output suitable for LLM pipelines (summarization, chunking, embedding).
- OCR support for images/scans (when OCR dependencies are installed).
- Audio transcription support (when transcription dependencies are installed).
- Optional AI-enhanced image/slide descriptions via an OpenAI-compatible client (e.g., OpenRouter).
- Plugin system to extend format support and custom behaviors.
- Stream-based conversion API for large files.

## Dependencies

- Python: `>=3.9` (recommended)
- Package:
  - `markitdown[all]` (installs all optional format handlers)

Optional system dependencies (feature-dependent):
- Tesseract OCR: `tesseract-ocr` (for image/scanned-text OCR)

Optional external services (feature-dependent):
- Azure Document Intelligence endpoint (for enhanced PDF extraction)
- OpenAI-compatible LLM endpoint (e.g., OpenRouter) for AI image descriptions

## Example Usage

### Install

```bash
pip install 'markitdown[all]'
```

### CLI: Convert a PDF to Markdown

```bash
markitdown document.pdf -o output.md
```

### Python: Convert multiple formats (PDF/XLSX/PPTX/DOCX) and save outputs

```python
from pathlib import Path
from markitdown import MarkItDown

md = MarkItDown()

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

for path in files:
    result = md.convert(path)
    out = Path(path).with_suffix(".md")
    out.write_text(result.text_content, encoding="utf-8")
    print(f"Converted {path} -> {out}")
```

### Python: Stream conversion (useful for large files)

```python
from markitdown import MarkItDown

md = MarkItDown()

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

with open("large_file.md", "w", encoding="utf-8") as out:
    out.write(result.text_content)
```

### Python: AI-enhanced image/slide descriptions (OpenAI-compatible, e.g., OpenRouter)

```python
from markitdown import MarkItDown
from openai import OpenAI

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",
    llm_prompt="Describe this image in detail for scientific documentation.",
)

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

## Implementation Details

- **Conversion entry points**
  - `MarkItDown().convert(path)` converts a file by path/URL and returns an object whose primary payload is `result.text_content` (Markdown).
  - `MarkItDown().convert_stream(stream, file_extension=".pdf")` converts from a binary stream; use this for large files or when data is not on disk.

- **Format handling**
  - Format support is provided by optional extras (e.g., `pdf`, `docx`, `pptx`, `xlsx`, `audio-transcription`, `youtube-transcription`) or `all`.
  - ZIP inputs are typically processed by iterating through contained files and converting each supported entry.

- **OCR**
  - For images/scanned documents, OCR is enabled when OCR tooling is available (commonly Tesseract). Ensure the OS-level OCR binary is installed and accessible in `PATH`.

- **AI image descriptions**
  - When `llm_client`, `llm_model`, and `llm_prompt` are provided, MarkItDown can request model-generated descriptions for images (including slide images), then inject those descriptions into the Markdown output.
  - Any OpenAI-compatible client can be used (e.g., OpenRouter) by setting `base_url` and `api_key`.

- **Enhanced PDF extraction (Azure Document Intelligence)**
  - When configured with a Document Intelligence endpoint, PDF extraction can be improved for complex layouts (tables, multi-column text, scanned PDFs), producing more faithful Markdown structure.

- **Plugins**
  - Plugins can be listed and enabled from the CLI (e.g., `--list-plugins`, `--use-plugins`) to extend conversion behavior or add new format handlers.

---
Source attribution: aipoch
Original project: https://github.com/aipoch/medical-research-skills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: aipoch
Original project: https://github.com/aipoch/medical-research-skills
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Content & Media, Data & Analytics, Education & Research, Marketing
Roles: Content Marketer, Copywriter, Data Analyst, Marketing Analyst, Researcher
Specialties: API
Tools: Azure, OpenAI API, Tesseract Ocr
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 916c28def7d6436eae3008d348542a72fa42f210c906034b9d6c1576a4621c17