mlsanityData to 7 Oct 2026

Retrieval-Augmented Generation with Graphs (GraphRAG)

arXiv 2501.00309 ↗First posted 31 Dec 2024cs.IR cs.CL cs.LGcited by 170

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

Abstract (arXiv)

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications. As a result, we have recently witnessed increasing attention on equipping RAG with Graph, i.e., GraphRAG. However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains. Given the broad applicability, the associated design challenges, and the recent surge in GraphRAG, a systematic and up-to-date survey of its key concepts and techniques is urgently desired. Following this motivation, we present a comprehensive and up-to-date survey on GraphRAG. Our survey first proposes a holistic GraphRAG framework by defining its key components, including query processor, retriever, organizer, generator, and data source. Furthermore, recognizing that graphs in different domains exhibit distinct relational patterns and require dedicated designs, we review GraphRAG techniques uniquely tailored to each domain. Finally, we discuss research challenges and brainstorm directions to inspire cross-disciplinary opportunities. Our survey repository is publicly maintained at https://github.com/Graph-RAG/GraphRAG/.

Key facts. Cited by 170 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 253 of its 573 references that are in the map. First posted on arXiv 31 Dec 2024.

Builds on

253 of 573 references.

Closest ancestors · 13

Same direction, newest first

  1. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation28 Oct
  2. 2024Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs14 Oct
  3. 2024Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective11 Oct
  4. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation8 Oct
  5. 2024CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering29 Sep
  6. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  7. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation15 Jul
  8. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning30 May
  9. 2024Don't Forget to Connect! Improving RAG with Graph-based Reranking28 May
  10. 2024Augmenting Textual Generation via Topology Aware Retrieval27 May
  11. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  12. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering12 Feb
1 more
  1. 2023ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science21 Nov

Other references · 240

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  6. 2017Graph Attention Networkscited by 8,854
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  8. 2023Mistral 7Bcited by 3,065
  9. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  10. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  11. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  12. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 889
38 more
  1. 2023Reasoning with Language Model is Planning with World Modelcited by 846
  2. 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 810
  3. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  4. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviorscited by 580
  5. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  6. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  7. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  8. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 368
  9. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  10. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  11. 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thoughtcited by 334
  12. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  13. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 326
  14. 2024Long-context LLMs Struggle with Long In-context Learningcited by 285
  15. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  16. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 266
  17. 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 251
  18. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 221
  19. 2022LinkBERT: Pretraining Language Models with Document Linkscited by 209
  20. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 209
  21. 2018WikiHow: A Large Scale Text Summarization Datasetcited by 203
  22. 2023Knowledge Editing for Large Language Models: A Surveycited by 189
  23. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  24. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
  25. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  26. 2024LLaGA: Large Language and Graph Assistantcited by 177
  27. 2023Talk like a Graph: Encoding Graphs for Large Language Modelscited by 175
  28. 2023Conformal Language Modelingcited by 163
  29. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 160
  30. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  31. 2022A Survey on Retrieval-Augmented Text Generationcited by 157
  32. 2024The pitfalls of next-token predictioncited by 156
  33. 2019Simple BERT Models for Relation Extraction and Semantic Role Labelingcited by 154
  34. 2019Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answeringcited by 147
  35. 2018Graph Convolution over Pruned Dependency Trees Improves Relation Extractioncited by 146
  36. 2024Certifiably Robust RAG against Retrieval Corruptioncited by 144
  37. 2022Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidencecited by 142
  38. 2023TaskBench: Benchmarking Large Language Models for Task Automationcited by 138

50 most cited of 240 All 240 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 94; 2026: 76.

170 papers.

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  1. 2025Memory in the Age of AI Agentscited by 285
  2. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  3. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  4. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 51
  5. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corporacited by 47
  6. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 46
  7. 2025In-depth Analysis of Graph-based RAG in a Unified Frameworkcited by 45
  8. 2025GFM-RAG: Graph Foundation Model for Retrieval Augmented Generationcited by 40
  9. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directionscited by 30
  10. 2025Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understandingcited by 30
  11. 2025SGMem: Sentence Graph Memory for Long-Term Conversational Agentscited by 29
  12. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodescited by 25
38 more
  1. 2025GraphRAG under Firecited by 23
  2. 2025CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehensioncited by 23
  3. 2025Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoningcited by 22
  4. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  5. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarkingcited by 17
  6. 2025Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoningcited by 17
  7. 2025Empowering GraphRAG with Knowledge Filtering and Integrationcited by 15
  8. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 14
  9. 2025VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraftcited by 13
  10. 2026Position: Agentic Evolution is the Path to Evolving LLMscited by 13
  11. 2025TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphscited by 12
  12. 2026Memory in the LLM Era: Modular Architectures and Strategies in a Unified Frameworkcited by 12
  13. 2026Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledgecited by 11
  14. 2025OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosiscited by 10
  15. 2025Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Basescited by 9
  16. 2025MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAGcited by 9
  17. 2025Towards Practical GraphRAG: Efficient Knowledge Graph Construction and Hybrid Retrieval at Scalecited by 9
  18. 2025Multi-agent systems for chemical engineering: A review and perspectivecited by 9
  19. 2025What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledgecited by 9
  20. 2025Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modellingcited by 9
  21. 2025G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledgecited by 9
  22. 2026Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agentscited by 9
  23. 2025DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoningcited by 8
  24. 2025Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?cited by 8
  25. 2026Retrieval--Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trendscited by 8
  26. 2025Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompletenesscited by 7
  27. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generationcited by 7
  28. 2025Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enrichingcited by 7
  29. 2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMscited by 7
  30. 2026RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agentscited by 7
  31. 2026Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guaranteescited by 7
  32. 2025GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classificationcited by 6
  33. 2025AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model for Atmospheric Sciencecited by 6
  34. 2025Knowledge Augmented Complex Problem Solving with Large Language Models: A Surveycited by 6
  35. 2025AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Upcited by 6
  36. 2025Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinkingcited by 6
  37. 2025Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generationcited by 5
  38. 2025Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphscited by 5

50 most cited of 170 All 170 (JSON)

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  1. 2025XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
  2. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  3. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
  4. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
  5. 2025HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation
  6. 2026Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization
  7. 2026UnWeaving the knots of GraphRAG -- turns out VectorRAG is almost enough
  8. 2026Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spaces