# RAG-Anything — All-in-One Multi-Modal RAG Framework for Text, Images, Tables, and Equations

> RAG for real-world documents — handles images, tables, equations, and charts alongside text in a single pipeline.

- Org: HKUDS
- Date: 2026-03-24
- Category: repo, tool
- Source: https://github.com/HKUDS/RAG-Anything
- Canonical page: https://ai-tldr.dev/releases/hkuds-rag-anything/

## In short

RAG-Anything extends LightRAG to handle rich mixed-content documents — images, tables, equations, and charts alongside text. Uses dual-graph construction and cross-modal hybrid retrieval. 17.6k GitHub stars, trending today.

## What it is

RAG-Anything is an open-source framework that extends LightRAG to work with multimodal documents — PDFs, reports, scientific papers — that mix text with images, tables, equations, and charts. Instead of discarding non-text content, it processes each element through specialized parsers and builds a cross-modal knowledge graph for retrieval. Version 1.2.10 added a custom parser plugin system and processing event callbacks.

## How it works

Documents are parsed by MinerU, Docling, or PaddleOCR into typed elements. Image regions go through a VLM for captioning; tables are structured into relational entries; equations are converted to LaTeX. All elements are merged into a dual-graph: one capturing cross-modal entity relationships, one for textual semantics. Queries hit both graphs and results are fused before ranking.

## Why it matters

Most production RAG systems lose a document's non-text content by converting to plain text and discarding tables and figures. RAG-Anything keeps that information in the graph, giving substantially better answers on financial reports, technical manuals, and research papers.

## Who it's for

ML engineers and backend developers building RAG over rich, mixed-content document corpora

## Links

- [Repo](https://github.com/HKUDS/RAG-Anything)
- [Paper](https://arxiv.org/abs/2510.12323)