MegaParse

MegaParse is an open-source document parser built specifically for LLM ingestion: it converts PDF, Word and PowerPoint files into clean, structured text and Markdown that language models can actually use, without dropping tables or layout information. Created by the team behind Quivr, it has around 7,400 stars on GitHub and is still actively developed in 2026.

Overview

Feeding raw PDFs into a RAG pipeline usually loses tables, scrambles reading order and buries the content an LLM needs. MegaParse tackles that problem head-on: it parses PDFs, DOCX and PPTX files with high fidelity, preserves document structure, extracts tables accurately and uses vision models to OCR images embedded in documents so their text is not lost. It can be used as a Python library inside your own ingestion pipeline, run through its API, or tried instantly through the free upload demo on megaparse.com. Because the output is optimized for retrieval rather than for human reading, it slots directly into LangChain-style document loaders and other RAG stacks. The project is released under the Apache-2.0 license.

Key Features

• Parses PDF, DOCX and PPTX files into LLM-ready Markdown and structured text
• High-fidelity extraction that preserves tables, headings and reading order
• OCR for images embedded in documents, so scanned content is not lost
• Designed for RAG ingestion pipelines with minimal information loss
• Usable as a Python library, a self-hosted service, or via the web demo
• Fast, batch-friendly processing for large document collections

💳 Pricing

MegaParse is free and open source under the Apache-2.0 license. You can self-host the parser from GitHub at no cost and try it in the browser on megaparse.com before integrating it into your own stack.

Who Is It For

MegaParse is for developers building RAG applications, document-chat tools and knowledge bases who are tired of garbled PDF extraction, and for data teams that need reliable, structured text out of Office and PDF files at scale.