# RAG-Anything: All-in-One RAG Framework

arXiv: https://arxiv.org/abs/2510.12323 · HTML: https://mlsanity.com/paper/2510.12323/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 17 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Placed in

- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 15 of 15)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024)
- [MinerU: An Open-Source Solution for Precise Document Content Extraction](https://mlsanity.com/paper/2409.18839/) (2024)
- [VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents](https://mlsanity.com/paper/2410.10594/) (2024)
- [MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations](https://mlsanity.com/paper/2407.01523/) (2024)
- [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://mlsanity.com/paper/2405.20139/) (2024)
- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [MM-VID: Advancing Video Understanding with GPT-4V(ision)](https://mlsanity.com/paper/2310.19773/) (2023)
- [VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos](https://mlsanity.com/paper/2502.01549/) (2025)
- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems](https://mlsanity.com/paper/2407.10701/) (2024)
- [ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.09891/) (2025)
- [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025)
- [MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs](https://mlsanity.com/paper/2507.20804/) (2025)

## Built on by (most cited first, 20 of 20)

- [MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale](https://mlsanity.com/paper/2604.04771/) (2026)
- [DocDancer: Towards Agentic Document-Grounded Information Seeking](https://mlsanity.com/paper/2601.05163/) (2026)
- [RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation](https://mlsanity.com/paper/2605.04523/) (2026)
- [DeepTutor: Towards Agentic Personalized Tutoring](https://mlsanity.com/paper/2604.26962/) (2026)
- [EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval](https://mlsanity.com/paper/2607.12764/) (2026)
- [G2-Reader: Dual Evolving Graphs for Multimodal Document QA](https://mlsanity.com/paper/2601.22055/) (2026)
- [HippoCamp: Benchmarking Contextual Agents on Personal Computers](https://mlsanity.com/paper/2604.01221/) (2026)
- [FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A](https://mlsanity.com/paper/2606.07235/) (2026)
- [MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents](https://mlsanity.com/paper/2606.10742/) (2026)
- [Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs](https://mlsanity.com/paper/2608.25986/) (2026)
- [AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction](https://mlsanity.com/paper/2603.05551/) (2026)
- [FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning](https://mlsanity.com/paper/2605.01495/) (2026)
- [Format-Constraint Coupling in Knowledge Graph Construction from Statistical Tables](https://mlsanity.com/paper/2605.21974/) (2026)
- [ChartWalker: Benchmarking the Cross-Chart RAG Task with Hierarchical Knowledge Graphs](https://mlsanity.com/paper/2606.23997/) (2026)
- [Multimodal Graph RAG for Long-range Visually Rich Document Understanding](https://mlsanity.com/paper/2606.28780/) (2026)
- [TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal Question Answering](https://mlsanity.com/paper/2607.18917/) (2026)
- [DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding](https://mlsanity.com/paper/2607.24554/) (2026)
- [TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents](https://mlsanity.com/paper/2608.12898/) (2026)
- [TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoning](https://mlsanity.com/paper/2610.09360/) (2026)
- [MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning](https://mlsanity.com/paper/2610.19341/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding Large Language Models beyond their static training limitations. However, a critical misalignment exists between current RAG capabilities and real-world information environments. Modern knowledge repositories are inherently multimodal, containing rich combinations of textual content, visual elements, structured tables, and mathematical expressions. Yet existing RAG frameworks are limited to textual content, creating fundamental gaps when processing multimodal documents. We present RAG-Anything, a unified framework that enables comprehensive knowledge retrieval across all modalities. Our approach reconceptualizes multimodal content as interconnected knowledge entities rather than isolated data types. The framework introduces dual-graph construction to capture both cross-modal relationships and textual semantics within a unified representation. We develop cross-modal hybrid retrieval that combines structural knowledge navigation with semantic matching. This enables effective reasoning over heterogeneous content where relevant evidence spans multiple modalities. RAG-Anything demonstrates superior performance on challenging multimodal benchmarks, achieving significant improvements over state-of-the-art methods. Performance gains become particularly pronounced on long documents where traditional approaches fail. Our framework establishes a new paradigm for multimodal knowledge access, eliminating the architectural fragmentation that constrains current systems. Our framework is open-sourced at: https://github.com/HKUDS/RAG-Anything.