QuivrHQ/MegaParse

Health v2 · maintenance only

Adoption not part of the score

7414 stars · 418 forks observed · 2026-09-30

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MegaParse is a Python library that parses PDFs, Word, PowerPoint, Excel, CSV, and text documents into LLM-friendly formats with a focus on zero information loss, including tables, headers, footers, and images. It offers both standard parsing and a vision-based mode using multimodal models like GPT-4o and Claude, and can also be run as an API service.

Use cases

• parse pdfs for llm ingestion
• convert docx to markdown for rag
• extract tables from pdf without losing data
• prepare powerpoint slides for a chatbot knowledge base
• build a document ingestion pipeline for a vector database
• run a self-hosted document parsing api

When to choose

• you need high-fidelity document parsing for RAG or LLM pipelines
• your documents contain tables, headers, footers, or images that naive parsers drop
• you want a benchmarked parser with vision-model support
• you prefer an open-source, self-hostable alternative to paid parsing APIs

When to avoid

• you only need plain-text extraction from simple text-based PDFs
• you cannot provide an OpenAI or Anthropic API key for the vision parser
• you need a fully offline solution without installing poppler and tesseract
• your environment runs Python below 3.11

Facets

library · maturity active

parser pdf ocr rag llm-inference pdf files developer-tools python cross-platform self-hosted document-parsing pdf-to-markdown docx pptx llm-ingestion data-preparation natural-language-processing retrieval-augmented-generation docker

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