mlsanityData to 7 Oct 2026

LightRAG: Simple and Fast Retrieval-Augmented Generation

arXiv 2410.05779 ↗First posted 8 Oct 2024cs.IR cs.AIcited by 310

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex inter-dependencies. To address these challenges, we propose LightRAG, which incorporates graph structures into text indexing and retrieval processes. This innovative framework employs a dual-level retrieval system that enhances comprehensive information retrieval from both low-level and high-level knowledge discovery. Additionally, the integration of graph structures with vector representations facilitates efficient retrieval of related entities and their relationships, significantly improving response times while maintaining contextual relevance. This capability is further enhanced by an incremental update algorithm that ensures the timely integration of new data, allowing the system to remain effective and responsive in rapidly changing data environments. Extensive experimental validation demonstrates considerable improvements in retrieval accuracy and efficiency compared to existing approaches. We have made our LightRAG open-source and available at the link: https://github.com/HKUDS/LightRAG

Key facts. Cited by 310 papers in the map (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 19 of its 21 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

19 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr

Other references · 18

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  3. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  4. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  5. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  6. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  7. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  8. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  9. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223
  10. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 200
  11. 2024LLaGA: Large Language and Graph Assistantcited by 177
  12. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
6 more
  1. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generationcited by 90
  2. 2024CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Modelscited by 50
  3. 2023Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applicationscited by 33
  4. 2023ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddingscited by 32
  5. 2023GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphscited by 28
  6. 2024R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Modelscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 117; 2026: 189.

310 papers.

Sort:

By area:

  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  2. 2025LightMem: Lightweight and Efficient Memory-Augmented Generationcited by 163
  3. 2026SimpleMem: Efficient Lifelong Memory for LLM Agentscited by 140
  4. 2025MemOS: A Memory OS for AI Systemcited by 129
  5. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  6. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  7. 2025VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videoscited by 76
  8. 2025MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Modelscited by 68
  9. 2025WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoningcited by 50
  10. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corporacited by 47
  11. 2025Retrieval-Augmented Generation with Hierarchical Knowledgecited by 46
  12. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 46
38 more
  1. 2025JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agentcited by 39
  2. 2025Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understandingcited by 30
  3. 2025SGMem: Sentence Graph Memory for Long-Term Conversational Agentscited by 29
  4. 2025GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generationcited by 24
  5. 2025GraphRAG under Firecited by 23
  6. 2025Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generationcited by 23
  7. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  8. 2025You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structurescited by 23
  9. 2025PropRAG: Guiding Retrieval with Beam Search over Proposition Pathscited by 22
  10. 2025Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoningcited by 22
  11. 2026Agentic Very Long Video Understandingcited by 19
  12. 2025RAG-Anything: All-in-One RAG Frameworkcited by 18
  13. 2026SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scalecited by 15
  14. 2025AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understandingcited by 14
  15. 2026HyperMem: Hypergraph Memory for Long-Term Conversationscited by 14
  16. 2024KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Modelscited by 12
  17. 2025Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoningcited by 11
  18. 2026VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactionscited by 11
  19. 2026Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledgecited by 11
  20. 2025RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledgecited by 10
  21. 2025Knowledge Bridger: Towards Training-free Missing Modality Completioncited by 9
  22. 2025RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integrationcited by 9
  23. 2025Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphscited by 9
  24. 2025Meta-Memory: Retrieving and Integrating Semantic-Spatial Memories for Robot Spatial Reasoningcited by 9
  25. 2026Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agentscited by 9
  26. 2025DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoningcited by 8
  27. 2025ReMindRAG: Low-Cost LLM-Guided Knowledge Graph Traversal for Efficient RAGcited by 8
  28. 2025Towards Interactive Intelligence for Digital Humanscited by 8
  29. 2026All-Mem: Agentic Lifelong Memory via Dynamic Topology Evolutioncited by 8
  30. 2026SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Executioncited by 8
  31. 2025Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQAcited by 7
  32. 2025GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoningcited by 7
  33. 2025RGMem: Renormalization Group-inspired Memory Evolution for Language Agentscited by 7
  34. 2025WebGraphEval: Multi-Turn Trajectory Evaluation for Web Agents using Graph Representationcited by 7
  35. 2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMscited by 7
  36. 2025ExpReS-VLA: Specializing Vision-Language-Action Models Through Experience Replay and Retrievalcited by 7
  37. 2026GraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learningcited by 7
  38. 2026Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guaranteescited by 7

50 most cited of 310 All 310 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2024Graph Retrieval-Augmented Generation: A Survey
  2. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
  3. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
  4. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  5. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  6. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  7. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
  8. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey