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RAG-Anything

A Python framework for question answering over documents that mix text, images, tables and equations.

GitHub Download v1.4.4
Stars
24.4k
Forks
2.9k
Licence
MIT
Language
Python
Last update
4 days ago
Type
Library

What it is

RAG-Anything is a Python framework that lets you ask questions about documents containing text, images, tables, and mathematical equations. It is built on the LightRAG project and aims to handle these content types in one pipeline instead of requiring separate tools. The README describes parsing documents, analyzing their content, building a knowledge graph, and then querying the result.

  • Ingest a PDF with charts and tables and query it in natural language
  • Insert pre-parsed content lists from an external source and query them without re-parsing
  • Ask questions that combine visual content from figures with surrounding text
  • Build a knowledge graph of entities and relationships extracted from mixed-content documents

Great for

  • Developers building question-answering systems over PDFs or Office documents with figures and tables
  • Researchers working with academic papers that contain equations and diagrams
  • Teams with technical or financial reports where information is spread across mixed content
  • Developers who already use or want to use LightRAG

Not ideal for

  • People who only need plain text search, since the multimodal pipeline adds complexity the README does not justify for simple text
  • Non-developers looking for a ready-made app, because the README presents it as a Python framework to integrate
  • Users needing a fully documented production deployment, since the README excerpt does not cover that
Step-by-step guide: how to use RAG-AnythingInstall, first run, examples, tips and FAQs
LightRAG vs RAG-AnythingLlamaIndex vs RAG-Anything

How to get it

  1. 1

    Install with a package manager

    You'll need: Python 3.10

    Install from PyPI

    pip install raganything
  2. 2

    Run it

    • Install the package with pip
    • Prepare a document such as a PDF containing text and images
    • Follow the README quick start to parse and index the document
    • Ask a question about the document and review the answer
  3. 3

    Get the most out of it

    • Use the context configuration module described in the docs for richer multimodal processing
    • Try the VLM-Enhanced Query mode when documents include images
    • Use direct content list insertion if you already have parsed content from another tool
    • Check the LightRAG integration noted in the README for related functionality

Downloads

Source code (.zip)main branch · straight from GitHubRelease v1.4.4Release notes and sourceOfficial website & docsarxiv.org/abs/2510.12323

Strengths

  • Covers text, images, tables, and equations in one framework rather than several separate tools
  • Builds on the established LightRAG project and is installable from PyPI as raganything
  • Offers a choice between MinerU-based parsing and direct content list insertion

Trade-offs

  • The README excerpt is promotional in places and gives limited detail on configuration and limits
  • Parsing relies on MinerU-based workflows, which adds a dependency to understand
  • Multimodal analysis may depend on vision-capable models, which the excerpt does not fully specify

How it compares

ProjectStarsTypeLevelUpdatedBest for
RAG-Anything (this)24.4kLibraryintermediate4 days agoDevelopers building question-answering systems over PDFs or Office documents with figures and tables
LightRAG40.5kLibraryintermediate8 days agoDevelopers building question-answering over their own document collections
llama_index52.8kLibraryintermediate11 days agoPython developers building retrieval-augmented generation (RAG) apps over private documents
PageIndex40kLibraryintermediate8 days agoDevelopers building question-answering over long PDFs such as annual reports or manuals
LEANN13.8kLibraryintermediate4 days agoDevelopers building a private RAG prototype on a personal machine
FlagEmbedding12.3kLibraryintermediate10 days agoDevelopers building semantic search over their own documents

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.