QuivrHQ/MegaParse

Project overview

MegaParse is a document parsing library that converts PDFs, Office files, and images into LLM-optimized formats while focusing on retaining all original information. It requires Python knowledge and system dependency management, with optional integration of OpenAI or Anthropic models for vision-based parsing.

Project type

RAG · Data Processing

Use cases

Knowledge Q&A

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• Developers who need a Python library to convert complex documents into LLM-ready formats without losing structural data.

Key capabilities

• Retains all information from the source document during the parsing process.
• Processes and handles tables, tables of contents, headers, footers, and images.
• Uses multimodal models to convert and parse documents visually.
• Can be run as an API service using a MakeFile.

Limitations and risks

• MegaParse Vision only supports specific multimodal models, including Claude 3.5, Claude 4, GPT-4o, and GPT-4.
• Basic setup instructions indicate the need to add an OpenAI or Anthropic API key for model interactions.

Getting started

• Install system dependencies such as poppler, tesseract, and libmagic. Ensure Python version 3.11 or higher is installed, install the package using pip, configure your .env file with the required LLM API keys, and run the parsing script.

Evidence and sources

• GitHub project description: File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.
• README: - **No Information Loss**: Focus on having no information loss during parsing. - **Fast and Efficient**: Designed with speed and efficiency at its core. - **Wide File Compatibilit…
• Release: megaparse: v0.0.55
• README: - Files: ✅ PDF ✅ Powerpoint ✅ Word - Content: ✅ Tables ✅ TOC ✅ Headers ✅ Footers ✅ Images
• README: required python version >= 3.11 ```bash pip install megaparse ```

Reviewed at: 2026-07-20T15:12:14Z