{"id":84001,"url":"https://github.com/DEEP-PolyU/Awesome-GraphRAG","name":"Awesome-GraphRAG","description":"Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation. ","projects_count":190,"last_synced_at":"2026-07-18T22:00:23.245Z","repository":{"id":272420809,"uuid":"876000634","full_name":"DEEP-PolyU/Awesome-GraphRAG","owner":"DEEP-PolyU","description":"Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation. ","archived":false,"fork":false,"pushed_at":"2026-06-02T12:52:38.000Z","size":11456,"stargazers_count":2501,"open_issues_count":10,"forks_count":216,"subscribers_count":68,"default_branch":"main","last_synced_at":"2026-06-30T10:03:35.557Z","etag":null,"topics":["graphrag","graphrag-paper","graphrag-survey","knowledge-graph","large-language-models","rag","retrieval-augmented-generation"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2501.13958","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DEEP-PolyU.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-10-21T08:29:58.000Z","updated_at":"2026-06-30T09:59:55.000Z","dependencies_parsed_at":"2025-01-14T10:54:28.749Z","dependency_job_id":"959c2d70-2807-4ae9-8cf0-39a879f75697","html_url":"https://github.com/DEEP-PolyU/Awesome-GraphRAG","commit_stats":null,"previous_names":["deep-polyu/awesome-graphrag"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/DEEP-PolyU/Awesome-GraphRAG","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DEEP-PolyU%2FAwesome-GraphRAG","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DEEP-PolyU%2FAwesome-GraphRAG/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DEEP-PolyU%2FAwesome-GraphRAG/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DEEP-PolyU%2FAwesome-GraphRAG/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DEEP-PolyU","download_url":"https://codeload.github.com/DEEP-PolyU/Awesome-GraphRAG/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DEEP-PolyU%2FAwesome-GraphRAG/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35632495,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-18T02:00:07.223Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2025-02-01T22:01:56.079Z","updated_at":"2026-07-18T22:00:23.245Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Knowledge Integration","Knowledge Organization","Knowledge Retrieval","Uncategorized"],"sub_categories":["Fine-tuning","In-context Learning","Graphs as Knowledge Carrier","Post-retrieval","Graphs for Knowledge Indexing","GNN-based Retriever","Semantics Similarity-based Retriever","LLM-based Retriever","Hybrid Retriever","Multi-round Retriever","Hybrid GraphRAG","Logical Reasoning-based Retriever","Uncategorized"],"readme":"# Awesome-GraphRAG (GraphRAG Survey)\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://awesome.re\"\u003e\u003cimg src=\"https://awesome.re/badge.svg\"/\u003e\u003c/a\u003e\n    \u003ca href=\"http://makeapullrequest.com\"\u003e\u003cimg src=\"https://img.shields.io/badge/PRs-welcome-green.svg\"/\u003e\u003c/a\u003e\n    \u003ca href=\"https://arxiv.org/abs/2501.13958\" target=\"_blank\"\u003e\u003cimg src=\"https://img.shields.io/badge/Paper-Arxiv-red?logo=arxiv\u0026style=flat-square\" alt=\"arXiv:2506.08938\"\u003e\u003c/a\u003e\n    \u003ca href=\"http://makeapullrequest.com\"\u003e\u003cimg src=\"https://img.shields.io/github/last-commit/DEEP-PolyU/Awesome-GraphRAG?color=blue\"/\u003e\u003c/a\u003e\n    \u003ca href=\"http://makeapullrequest.com\"\u003e\u003cimg src=\"https://img.shields.io/github/stars/DEEP-PolyU/Awesome-GraphRAG\"/\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\nThis repository contains a curated list of resources on graph-based retrieval-augmented generation (GraphRAG), which are classified according to \"[**A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models**](https://arxiv.org/abs/2501.13958)\". Continuously updating, stay tuned!\n\n\n**📃 Please [cite our paper](#-citation)** if you find our survey or repository helpful!\n\n\n# 🎉 News\n- **[2026-05-17]** Our **[MemGraphRAG](https://github.com/XMUDeepLIT/MemGraphRAG)** for memory-enhanced RAG is accepted by KDD'26.\n- **[2026-04-07]** Our **[ProbeRAG](https://github.com/LinfengGao/ProbeRAG.git)** for RAG faithfulness is accepted by ACL'26.\n- **[2026-04-07]** Our **[BAPO](https://github.com/Liushiyu-0709/BAPO-Reliable-Search.git)** for reliable agentic search is accepted by ACL'26.\n- **[2026-04-07]** Our **[LegalGraphRAG](https://github.com/XMUDeepLIT/LegalGraphRAG.git)** for reliable legal reasoning is accepted by ACL'26.\n- **[2026-04-07]** Our **[LogicPoison](https://github.com/Jord8061/logicPoison.git)**, a GraphRAG attack model, is accepted by ACL'26.\n- **[2026-01-26]** Our **[LinearRAG](https://github.com/DEEP-PolyU/LinearRAG)** for efficient GraphRAG is accepted by ICLR’26.\n- **[2026-01-26]** Our **[GraphRAG Benchmark](https://github.com/GraphRAG-Bench/GraphRAG-Benchmark)** is accepted by ICLR’26.\n- **[2025-11-08]** Our **[LogicRAG](https://github.com/chensyCN/LogicRAG.git)** is accepted by AAAI'26.\n- **[2025-10-27]** We release **[LinearRAG](https://github.com/DEEP-PolyU/LinearRAG)**, a relation-free graph construction method for efficient GraphRAG.\n- **[2025-06-06]** We release the **[GraphRAG Benchmark](https://github.com/GraphRAG-Bench/GraphRAG-Benchmark.git)** for evaluating GraphRAG models.\n- **[2025-05-14]** We release the [GraphRAG Benchmark dataset](https://huggingface.co/datasets/GraphRAG-Bench/GraphRAG-Bench).\n- **[2025-01-21]** We release the [GraphRAG survey](https://github.com/DEEP-PolyU/Awesome-GraphRAG).\n\n---\n\n\u003cdiv\u003e\n\u003ch3 align=\"left\"\u003e\n       \u003cp align=\"center\"\u003e\u003cimg width=\"100%\" src=\"figs/main_fig.png\" /\u003e\u003c/p\u003e\n    \u003cp align=\"center\"\u003e\u003cem\u003eOverview of traditional RAG and two typical GraphRAG workflows. \u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\n- **Non-graph RAG** organizes the\ncorpus into chunks, ranks them by similarity, and retrieves the most relevant text for generating responses.\n- **Knowledge-based\nGraphRAG** extracts detailed knowledge graphs from the corpus using entity recognition and relation extraction, offering\nfine-grained, domain-specific information.\n- **Index-based GraphRAG** summarizes the corpus into high-level topic nodes, which\nare linked to form an index graph, while the fact linking maps topics to text.\n\n---\n\n# RAG vs. GraphRAG\nGraphRAG is a new paradigm of RAG that revolutionizes domain-specific LLM applications, by addressing traditional RAG limitations through three key innovations: **(i) graph-structured knowledge representation** that explicitly captures\nentity relationships and domain hierarchies, **(ii) graph-aware retrieval mechanisms** that enable multi-hop reasoning and context-preserving knowledge acquisition, and **(iii) structure-guided\nknowledge search algorithms** that ensure efficient retrieval across large-scale corpora.\n    \n\n\u003ch3 align=\"center\"\u003e\n   \u003cp align=\"center\"\u003e\u003cimg width=\"100%\" src=\"figs/rag_vs_graphrag.png\" /\u003e\u003c/p\u003e\n    \u003cp align=\"center\"\u003e\u003cem\u003eComparison between traditional RAG and GraphRAG.\u003c/em\u003e\u003c/p\u003e\n\n\n\n# 📫 Contact Us\nWe welcome researchers to share related work to enrich this list or provide insightful comments on our survey. Feel free to reach out to the corresponding co-first authors: [Qinggang Zhang](https://qing145.github.io/), [Shengyuan Chen](https://chensycn.github.io/).\n\n\n## Table of Content\n- [🍀 Citation](#-citation)\n- [📫 Contact Us](#-contact-us)\n- [📈 Trend of GraphRAG Research](#-trend-of-graphrag-research)\n- [📜 Research Papers](#-research-papers)\n    - [Knowledge Organization](#knowledge-organization)\n        - [Graph for Knowledge Indexing](#graphs-for-knowledge-indexing)\n        - [Graph as Knowledge Carrier](#graphs-as-knowledge-carrier)\n            - [Knowledge Graph Construction from Corpus](#knowledge-graph-construction-from-corpus)\n            - [GraphRAG with Existing KGs](#graphrag-with-existing-kgs)\n        - [Hybrid GraphRAG](#hybrid-graphrag)\n    - [Knowledge Retrieval](#knowledge-retrieval)\n        - [Semantics Similarity-based Retriever](#semantics-similarity-based-retriever)\n        - [Logical Reasoning-based Retriever](#logical-reasoning-based-retriever)\n        - [LLM-based Retriever](#llm-based-retriever)\n        - [GNN-based Retriever](#gnn-based-retriever)\n        - [Multi-round Retriever](#multi-round-retriever)\n        - [Post-retrieval](#post-retrieval)\n        - [Hybrid Retriever](#hybrid-retriever)\n    - [Knowledge Integration](#knowledge-integration)\n        - [Fine-tuning](#fine-tuning)\n            - [Fine-tuning with Node-level Knowledge](#fine-tuning-with-node-level-knowledge)\n            - [Fine-tuning with Path-level Knowledge](#fine-tuning-with-path-level-knowledge)\n            - [Fine-tuning with Subgraph-level Knowledge](#fine-tuning-with-subgraph-level-knowledge)\n        - [In-context Learning](#in-context-learning)\n            - [Graph-enhanced Chain-of-Thought](#graph-enhanced-chain-of-thought)\n            - [Collaborative Knowledge Graph Refinement](#collaborative-knowledge-graph-refinement)\n- [📚 Related Survey Papers](#-related-survey-papers)\n- [🏆 Benchmarks](#-benchmarks)\n- [💻 Open-source Projects](#-open-source-projects)\n\n\n# 📈 Trend of GraphRAG Research\n\n\u003ch3 align=\"center\"\u003e\n   \u003cp align=\"center\"\u003e\u003cimg width=\"100%\" src=\"figs/trend.png\" /\u003e\u003c/p\u003e\n    \u003cp align=\"center\"\u003e\u003cem\u003eThe development trends in the field of GraphRAG with representative works.\u003c/em\u003e\u003c/p\u003e\n\n# 📜 Research Papers\n## Knowledge Organization\n\n### Graphs for Knowledge Indexing\n- (ICLR 2026) **LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora**  [[Paper]](https://arxiv.org/abs/2510.10114)\n- (arXiv 2025) **Improving Multi-step RAG with Hypergraph-based Memoryfor Long-Context Complex Relational Modeling** [[Paper]](https://arxiv.org/abs/2512.23959)\n- (EMNLP 2025) **Don’t Forget the Base Retriever! A Low-Resource Graph-based Retriever for Multi-hop Question Answering** [[Paper]](https://aclanthology.org/2025.emnlp-industry.174/)\n- (arXiv 2025) **Query-Centric Graph Retrieval Augmented Generation** [[Paper]](https://arxiv.org/abs/2509.22009)\n- (arXiv 2025) **Multi-Agent GraphRAG: A Text-to-Cypher Framework for Labeled Property Graphs** [[Paper]](https://arxiv.org/abs/2510.09156)\n- (arXiv 2025) **Grounded by Experience: Generative Healthcare Prediction Augmented with Hierarchical Agentic Retrieval** [[Paper]](https://arxiv.org/abs/2511.13293)\n- (ICML 2025) **HippoRAG2: From RAG to Memory: Non-Parametric Continual Learning for Large Language Models** [[Paper]](https://arxiv.org/abs/2502.14802)\n- (arXiv 2025) **PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLM** [[Paper]](https://arxiv.org/abs/2511.17467)\n- (arXiv 2025) **E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness** [[Paper]](https://arxiv.org/abs/2505.24226)\n- (arXiv 2025) **DIGIMON: A unified and modular graph-based RAG framework** [[Paper]](https://github.com/JayLZhou/GraphRAG.git)\n- (arXiv 2025) **ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation** [[Paper]](https://arxiv.org/abs/2502.09891)\n- (arXiv 2025) **KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG** [[Paper]](https://arxiv.org/abs/2502.09304)\n- (arXiv 2025) **PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation** [[Paper]](https://arxiv.org/abs/2501.11551)\n- (EMNLP 2025 Findings) **Retrieval-Augmented Generation with Hierarchical Knowledge** [[Paper]](https://arxiv.org/abs/2503.10150)\n- (arXiv 2024) **Graph Neural Network Enhanced Retrieval for Question Answering of LLMs** [[Paper]](https://arXiv.org/abs/2406.06572)\n- (arXiv 2024) **KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation** [[Paper]](https://arxiv.org/abs/2409.13731)\n- (arXiv 2024) **OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models** [[Paper]](https://arxiv.org/abs/2412.15235)\n- (arXiv 2024) **GRAG: Graph Retrieval-Augmented Generation** [[Paper]](https://arxiv.org/abs/2405.16506)\n- (arXiv 2024) **Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning** [[Paper]](https://arXiv.org/abs/2405.16933)\n- (ICLR 2024) **RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval** [[Paper]](https://arxiv.org/abs/2401.18059)\n- (AAAI 2024) **Knowledge graph prompting for multi-document question answering** [[Paper]](https://dl.acm.org/doi/10.1609/aaai.v38i17.29889)\n- (arXiv 2024) **GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model** [[Paper]](https://arXiv.org/abs/2406.07003)\n- (NeurIPS 2023) **Avis: Autonomous visual information seeking with large language model agent** [[Paper]](https://openreview.net/forum?id=7EMphtUgCI\u0026noteId=yGw4rbGozi)\n- (CoRL 2023) **Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning** [[Paper]](https://proceedings.mlr.press/v229/rana23a/rana23a.pdf)\n- (arXiv 2020) **Answering complex open-domain questions with multi-hop dense retrieval** [[Paper]](https://arXiv.org/abs/2009.12756)\n- (arXiv 2019) **Knowledge guided text retrieval and reading for open domain question answering** [[Paper]](https://arXiv.org/abs/1911.03868)\n\n### Graphs as Knowledge Carrier\n#### Knowledge Graph Construction from Corpus\n- (AAAI 2026) **You Don’t Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures** [[Paper]](https://arxiv.org/abs/2508.06105)\n- (arXiv 2025) **AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction** [[Paper]](https://arxiv.org/abs/2510.15339)\n- (arXiv 2025) **AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs** [[Paper]](https://arxiv.org/abs/2511.05549)\n- (EMNLP 2025) **MaGiX: A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual RAG** [[Paper]](https://aclanthology.org/anthology-files/anthology-files/pdf/findings/2025.findings-emnlp.279.pdf)\n- (CIKM 2025) **Context-Aware Fine-Grained Graph RAG for Query-Focused Summarization** [[Paper]](https://dl.acm.org/doi/10.1145/3746252.3760935)\n- (CIKM 2025) **DocPolicyKG: A Lightweight LLM-Based Framework for Knowledge Graph Construction from Chinese Policy Documents** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3746252.3760904)\n- (arXiv 2025) **SUBQRAG: SUB-QUESTION DRIVEN DYNAMIC GRAPH RAG** [[Paper]](https://arxiv.org/abs/2510.07718)\n- (arXiv 2025) **Ontology Learning and Knowledge Graph Construction: A Comparison of Approaches and Their Impact on RAG Performance** [[Paper]](https://arxiv.org/abs/2511.05991)\n- (NeurIPS 2025) **GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation** [Paper](https://arxiv.org/abs/2502.01113)\n- (arXiv 2025) **G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge** [[Paper]](https://arxiv.org/abs/2509.24276)\n- (CVPR 2025) **Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation** [[Paper]](https://arxiv.org/abs/2408.04187)\n- (arXiv 2025) **Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning** [[Paper]](https://www.arxiv.org/abs/2508.19855)\n- (arXiv 2025) **Retrieval-Augmented Generation with Hierarchical Knowledge** [[Paper]](https://arxiv.org/abs/2503.10150)\n- (arXiv 2025) **MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot** [[Paper]](https://arxiv.org/abs/2502.04413)\n- (arXiv 2025) **PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths** [[Paper]](https://arxiv.org/abs/2502.14902)\n- (EDBT 2025) **DBCopilot: Natural Language Querying over Massive Databases via Schema Routing** [[Paper]](https://openproceedings.org/2025/conf/edbt/paper-209.pdf)\n- (arXiv 2024) **From local to global: A graph rag approach to query-focused summarization** [[Paper]](https://arXiv.org/abs/2404.16130)\n- (EMNLP 2024) **Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text** [[Paper]](https://aclanthology.org/2024.emnlp-main.528.pdf)\n- (EMNLP 2024 Findings) **GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models** [[Paper]](https://aclanthology.org/2024.findings-emnlp.746/)\n- (SIGIR 2024) **Retrieval-augmented generation with knowledge graphs for customer service question answering** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3626772.3661370)\n- (arXiv 2024) **DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation** [[Paper]](https://arxiv.org/abs/2412.18644)\n- (arXiv 2024) **FastRAG: Retrieval Augmented Generation for Semi-structured Data** [[Paper]](https://arxiv.org/abs/2411.13773)\n- (TechRxiv 2024) **LuminiRAG: Vision-Enhanced Graph RAG for Complex Multi-Modal Document Understanding** [[Paper]](https://www.techrxiv.org/users/867713/articles/1248304-luminirag-vision-enhanced-graph-rag-for-complex-multi-modal-document-understanding)\n- (BigData 2023) **AutoKG: Efficient automated knowledge graph generation for language models** [[Paper]](https://ieeexplore.ieee.org/abstract/document/10386454)\n- (ACL 2019) **Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs** [[Paper]](https://aclanthology.org/D19-1428.pdf)\n- (SIGIR 2019) **Answering complex questions by joining multi-document evidence with quasi knowledge graphs** [[Paper]](https://dl.acm.org/doi/10.1145/3331184.3331252)\n\n#### GraphRAG with Existing KGs\n- (arXiv 2025) **GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation** [[Paper]](https://arxiv.org/abs/2509.22009)\n- (arXiv 2025) **Detecting Hallucinations in Graph Retrieval-Augmented Generation via Attention Patterns and Semantic Alignment** [[Paper]](https://arxiv.org/abs/2512.09148)\n- （arXiv 2025） **Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs** [[Paper]](https://arxiv.org/abs/2506.19967)\n- (AAAI 2025) **LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph** [[Paper]](https://arxiv.org/abs/2504.03137)\n- (ICLR 2025) **Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation** [[Paper]](https://openreview.net/forum?id=JvkuZZ04O7)\n- (arXiv 2025) **Empowering GraphRAG with Knowledge Filtering and Integration** [[Paper]](https://arxiv.org/abs/2503.13804)\n- (arXiv 2024)**StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization** [[Paper]](https://arXiv.org/abs/2410.08815)\n- (ICLR 2024) **Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning** [[Paper]](https://openreview.net/forum?id=ZGNWW7xZ6Q)\n- (AAAI 2024) **Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting** [[Paper]](https://dl.acm.org/doi/10.1609/aaai.v38i16.29770)\n- (ICLR 2024) **Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph** [[Paper]](https://openreview.net/forum?id=nnVO1PvbTv)\n- (Bioinformatics 2024) **Biomedical knowledge graph-enhanced prompt generation for large language models** [[Paper]](https://academic.oup.com/bioinformatics/article/40/9/btae560/7759620)\n- (NeurIPS 2024) **KnowGPT: Knowledge Graph based PrompTing for Large Language Models** [[Paper]](https://openreview.net/forum?id=PacBluO5m7\u0026referrer=%5Bthe%20profile%20of%20Daochen%20Zha%5D(%2Fprofile%3Fid%3D~Daochen_Zha1))\n- (ACL 2024 Findings) **Knowledge Graph-Enhanced Large Language Models via Path Selection** [[Paper]](https://aclanthology.org/2024.findings-acl.376/)\n- (IEEE VIS 2024) **KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integration** [[Paper]](https://arxiv.org/abs/2407.13598)\n- (CoLM 2024) **ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction** [[Paper]](https://openreview.net/forum?id=2nTzomzjjb#discussion)\n- (arXiv 2024) **LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration** [[Paper]](https://arxiv.org/abs/2411.05844)\n- (arXiv 2024) **Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation** [[Paper]](https://arXiv.org/abs/2407.10805)\n\n### Hybrid GraphRAG\n- (NAACL 2025) **Knowledge Graph-Guided Retrieval Augmented Generation** [[Paper]](https://arxiv.org/abs/2502.06864)\n- (ACL 2024 Findings) **HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases**[[Paper]](https://arxiv.org/abs/2412.16311)\n- (arXiv 2024) **Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs** [[Paper]](https://arXiv.org/abs/2410.11001)\n- (arXiv 2024) **Medical graph rag: Towards safe medical large language model via graph retrieval-augmented generation** [[Paper]](https://arXiv.org/abs/2408.04187)\n- (arXiv 2024) **Codexgraph: Bridging large language models and code repositories via code graph databases** [[Paper]](https://arXiv.org/abs/2408.03910)\n\n## Knowledge Retrieval\n\n### Semantics Similarity-based Retriever\n- (AAAI 2024) **StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation** [[Paper]](https://ojs.aaai.org/index.php/AAAI-SS/article/view/31798/33965)\n- (arXiv 2024) **G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering** [[Paper]](https://arXiv.org/abs/2402.07630)\n- (arXiv 2024) **CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care** [[Paper]](https://arXiv.org/abs/2501.00223)\n- (arXiv 2024) **Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning** [[Paper]](https://arXiv.org/abs/2405.16933)\n- (arXiv 2024) **GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model** [[Paper]](https://arXiv.org/abs/2406.07003)\n- (arXiv 2024) **Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation** [[Paper]](https://arXiv.org/abs/2408.04187)\n- (arXiv 2024) **How to Make LLMs Strong Node Classifiers?** [[Paper]](https://arxiv.org/abs/2410.02296)\n\n### Logical Reasoning-based Retriever\n- (AAAI 2026) **You Don’t Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures** [[Paper]](https://arxiv.org/abs/2508.06105)\n- (NeurIPS 2024) **KnowGPT: Knowledge Graph based PrompTing for Large Language Models** [[Paper]](https://openreview.net/forum?id=PacBluO5m7\u0026referrer=%5Bthe%20profile%20of%20Daochen%20Zha%5D(%2Fprofile%3Fid%3D~Daochen_Zha1))\n- (ACL 2024 Findings) **Knowledge Graph-Enhanced Large Language Models via Path Selection** [[Paper]](https://aclanthology.org/2024.findings-acl.376/)\n- (ICLR 2024) **Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph** [[Paper]](https://openreview.net/forum?id=nnVO1PvbTv)\n- (CIKM 2024) **RD-P: A Trustworthy Retrieval-Augmented Prompter with Knowledge Graphs for LLMs** [[Paper]](https://dl.acm.org/doi/10.1145/3627673.3679659)\n- (arXiv 2024) **RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering** [[Paper]](https://arXiv.org/abs/2410.22353)\n- (LHB 2024) **Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration** [[Paper]](https://www.tandfonline.com/doi/full/10.1080/27678490.2024.2397337)\n- (arXiv 2024) **RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs** [[Paper]](https://arXiv.org/abs/2410.13987)\n\n### LLM-based Retriever\n- (AAAI 2024) **Knowledge graph prompting for multi-document question answering** [[Paper]](https://www.overleaf.com/project/667419080bc7191bc75f5880)\n- (EMNLP 2024) **Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text** [[Paper]](https://aclanthology.org/2024.emnlp-main.528.pdf)\n- (ACML 2024) **Enhancing Textbook Question Answering with Knowledge Graph-Augmented Large Language Models** [[Paper]](https://openreview.net/forum?id=ATiIqCCqR2)\n- (ICLR 2024) **Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph** [[Paper]](https://arXiv.org/abs/2307.07697)\n- (arXiv 2024) **LightRAG: Simple and Fast Retrieval-Augmented Generation** [[Paper]](https://arXiv.org/abs/2410.05779)\n- (arXiv 2024) **MEG: Medical Knowledge-Augmented Large Language Models for Question Answering** [[Paper]](https://arXiv.org/abs/2411.03883)\n- (arXiv 2024) **From local to global: A graph rag approach to query-focused summarization** [[Paper]](https://arXiv.org/abs/2404.16130)\n\n### GNN-based Retriever\n- (arXiv 2025) **CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs** [[Paper]](https://arxiv.org/abs/2501.15067)\n- (arXiv 2024) **Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation** [[Paper]](https://arXiv.org/abs/2411.03572)\n- (arXiv 2024) **Language Models are Graph Learners** [[Paper]](https://arxiv.org/abs/2410.02296)\n- (arXiv 2024) **Graph Neural Network Enhanced Retrieval for Question Answering of LLMs** [[Paper]](https://arXiv.org/abs/2406.06572)\n- (arXiv 2024) **Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation** [[Paper]](https://arXiv.org/abs/2305.18846)\n\n\n### Multi-round Retriever\n- (arXiv 2024) **Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs** [[Paper]](https://arXiv.org/abs/2404.07103)\n- (arXiv 2024) **Generative Subgraph Retrieval for Knowledge Graph-Grounded Dialog Generation** [[Paper]](https://arXiv.org/abs/2410.09350)\n- (arXiv 2024) **Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs** [[Paper]](https://arXiv.org/abs/2410.11001)\n### Post-retrieval \n- (ACL 2024) **Boosting Language Models Reasoning with Chain-of-Knowledge Prompting** [[Paper]](https://arXiv.org/abs/2306.06427)\n- (ACL 2024 Findings) **Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments** [[Paper]](https://arxiv.org/abs/2403.08593)\n- (arXiv 2024) **Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models** [[Paper]](https://arxiv.org/abs/2410.13080)\n- (arXiv 2024) **Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting** [[Paper]](https://arXiv.org/abs/2311.13314)\n\n### Hybrid Retriever\n- (arXiv 2024) **Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation** [[Paper]](https://arXiv.org/abs/2407.10805)\n- (arXiv 2024) **StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization** [[Paper]](https://arXiv.org/abs/2410.08815)\n\n## Knowledge Integration\n### Fine-tuning\n#### Fine-tuning with Node-level Knowledge\n- (arXiv 2025) **Large Language Models based Graph Convolution for Text-Attributed Networks?** [[Paper]](https://openreview.net/forum?id=x5FfUvsLIE)\n- (SIGIR 2024) **Graphgpt: Graph instruction tuning for large language models** [[Paper]](https://dl.acm.org/doi/10.1145/3626772.3657775)\n#### Fine-tuning with Path-level Knowledge\n- (AAAI 2024) **Exploring large language model for graph data understanding in online job recommendations** [[Paper]](https://dl.acm.org/doi/10.1609/aaai.v38i8.28769)\n- (arXiv 2024) **MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining** [[Paper]](https://arXiv.org/pdf/2403.04780)\n- (WWW 2023) **Structure pretraining and prompt tuning for knowledge graph transfer** [[Paper]](https://dl.acm.org/doi/10.1145/3543507.3583301)\n- (ICLR 2023) **Reasoning on graphs: Faithful and interpretable large language model reasonin**g [[Paper]](https://openreview.net/forum?id=ZGNWW7xZ6Q)\n\n#### Fine-tuning with Subgraph-level Knowledge\n- (ICML 2024) **Llaga: Large language and graph assistant** [[Paper]](https://openreview.net/pdf?id=B48Pzc4oKi)\n- (KDD 2024) **Graphwiz: An instruction-following language model for graph problems** [[Paper]](https://graph-wiz.github.io/)\n- (AAAI 2024) **Graph neural prompting with large language models** [[Paper]](https://dl.acm.org/doi/10.1609/aaai.v38i17.29875)\n- (ACL 2024 Findings) **Rho:Reducing hallucination in open-domain dialogues with knowledge\ngrounding** [[Paper]](https://aclanthology.org/2023.findings-acl.275/)\n- (EACL 2024 Findings) **Language is All a Graph Needs** [[Paper]](https://aclanthology.org/2024.findings-eacl.132.pdf)\n\n### In-context Learning\n#### Graph-enhanced Chain-of-Thought\n- (KBS 2025) **Different paths to the same destination: Diversifying LLMs generation for multi-hop open-domain question answering** [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0950705124014230)\n- (ICLR 2024) **Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning** [[Paper]](https://openreview.net/forum?id=ZGNWW7xZ6Q)\n- (ICLR 2024) **Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph** [[Paper]](https://openreview.net/forum?id=nnVO1PvbTv)\n- (arXiv 2024) **Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation** [[Paper]](https://arXiv.org/abs/2407.10805)\n- (arXiv 2024) **Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs** [[Paper]](https://arXiv.org/abs/2404.07103)\n- (ICLR 2024) **Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources** [[Paper]](https://openreview.net/forum?id=cPgh4gWZlz)\n- (ACL 2024 Findings) **Visual In-Context Learning for Large Vision-Language Models** [[Paper]](https://www.semanticscholar.org/Paper/Visual-In-Context-Learning-for-Large-Models-Zhou-Li/b00d1028291ae64e9d7485a34ec5f1b7b5a37909)\n- (NeurIPS 2023) **What makes good examples for visual in-context learning?** [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/398ae57ed4fda79d0781c65c926d667b-Abstract-Conference.html)\n- (ACL 2023) **Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models** [[Paper]](https://aclanthology.org/2023.acl-long.147.pdf)\n- (AAAI 2024) **When Do Program-of-Thought Works for Reasoning?** [[Paper]](https://dl.acm.org/doi/10.1609/aaai.v38i16.29721)\n- (ICLR 2022) **An Explanation of In-context Learning as Implicit Bayesian Inference** [[Paper]](https://openreview.net/forum?id=RdJVFCHjUMI)\n- (EMNLP 2023) **KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases** [[Paper]](https://arXiv.org/abs/2308.11761)\n\n#### Collaborative Knowledge Graph Refinement\n- (AAAI 2024) **Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting** [[Paper]](https://arXiv.org/abs/2311.13314)\n- (ACL 2024 Findings) **Knowledge Graph-Enhanced Large Language Models via Path Selection** [[Paper]](https://aclanthology.org/2024.findings-acl.376/)\n- (NeurIPS 2024) **Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs** [[Paper]](https://arxiv.org/abs/2410.23875)\n- (arXiv 2024) **Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph** [[Paper]](https://arXiv.org/abs/2406.01145)\n- (ACL 2024) **CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph** [[Paper]](https://aclanthology.org/2024.acl-demos.35/)\n\n\n\n# 📚 Related Survey Papers\n- (arXiv 2025) **Retrieval-Augmented Generation with Graphs (GraphRAG)** [[Paper]](https://arxiv.org/abs/2501.00309)\n- (arXiv 2024) **Graph Retrieval-Augmented Generation: A Survey** [[Paper]](https://arXiv.org/pdf/2408.08921)\n- (AIxSET 2024) **Graph Retrieval-Augmented Generation for Large Language Models: A Survey** [[Paper]](https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4895062)\n\nTo explore the applications of LLMs on graph tasks, we recommend the following repositories:\n- [Awesome-LLMs-in-Graph-tasks](https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks) by [Yuhan Li](https://yhleeee.github.io/) from HKUST(GZ).\n- [Awesome-Graph-LLM](https://github.com/XiaoxinHe/Awesome-Graph-LLM) by [Xiaoxin He](https://xiaoxinhe.github.io/) from NUS.\n- [Awesome-Graph-Prompt](https://github.com/WxxShirley/Awesome-Graph-Prompt), created by [Xixi Wu](https://wxxshirley.github.io/) from CUHK.\n\n\n# 🏆 Benchmarks\n| Dataset | Task | Paper | Repo |\n| --- | --- | --- | --- |\n| GraphRAG-Bench | GraphRAG evaluation | [[arXiv 2025]](https://arxiv.org/abs/2506.05690) | [[Github]](https://github.com/GraphRAG-Bench/GraphRAG-Benchmark) |\n| DIGIMON | Large-scale graphRAG | [[arXiv 2025]](https://www.arxiv.org/pdf/2503.04338) | [[Github]](https://github.com/JayLZhou/GraphRAG) |\n| PolyG | GraphRAG evaluation | [[arXiv 2025]](https://arxiv.org/pdf/2504.02112) | [[Github]](https://github.com/Liu-rj/PolyG) |\n| SimpleQuestion | Simple Question Answering | [[arXiv 2015]](https://arXiv.org/abs/1506.02075) | [[Github]](https://github.com/Jerryzhao-z/simple-question-answering-with-memory-networks) |\n| WebQ | Simple Question Answering | [[EMNLP 2013]](https://nlp.stanford.edu/pubs/semparseEMNLP13.pdf) | [[CodaLab]](https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a) |\n|Multihop-RAG | Multi-hop Reasoning | [[COLING 2024]](https://arxiv.org/pdf/2401.15391) |    [[Github]](https://github.com/yixuantt/MultiHop-RAG/) |\n| CWQ | Multi-hop Reasoning | [[NAACL 2018]](https://aclanthology.org/N18-1059/) | [[TAU-NLP]](https://www.tau-nlp.org/compwebq) |\n| MetaQA | Multi-hop Reasoning | [[AAAI 2018]](https://arXiv.org/abs/1709.04071) | [[Github]](https://github.com/yuyuz/MetaQA) |\n| MetaQA-3 | Multi-hop Reasoning | [[AAAI 2018]](https://arXiv.org/abs/1709.04071) | [[Github]](https://github.com/yuyuz/MetaQA) |\n| CURD |  Large-scale Complex QA | [[arXiv 2024]](https://arXiv.org/abs/2401.17043) | [[Github]](https://github.com/IAAR-Shanghai/CRUD_RAG) |\n| KQAPro | Large-scale Complex QA | [[ACL 2022]](https://aclanthology.org/2022.acl-long.422/) | [[Github]](https://github.com/shijx12/KQAPro_Baselines) |\n| LC-QuAD v2 | Large-scale Complex QA | [[ISWC 2019]](https://link.springer.com/chapter/10.1007/978-3-030-30796-7_5) | [[figshare]](https://figshare.com/projects/LCQuAD_2_0/62270) |\n| LC-QuAD | Large-scale Complex QA | [[ISWC 2017]](https://dl.acm.org/doi/10.1007/978-3-319-68204-4_22) | [[Github]](https://github.com/AskNowQA/LC-QuAD) |\n| UltraDomain | Domain-specific QA | [[arXiv 2024]](https://arXiv.org/abs/2409.05591) | [[Github]](https://github.com/qhjqhj00/MemoRAG#dataset) |\n| TutorQA | Domain-specific QA | [[arXiv 2024]](https://arXiv.org/abs/2407.10794) | [[Github]](https://github.com/IreneZihuiLi/CGPrompt) |\n| FACTKG  | Domain-specific QA | [[ACL 2023]](https://aclanthology.org/2023.acl-long.895.pdf) | [[Github]](https://github.com/jiho283/FactKG) |\n| Mintaka | Domain-specific QA | [[ACL 2022]](https://aclanthology.org/2022.coling-1.138/) | [[Github]](https://github.com/amazon-science/mintaka) |\n| GrailQA | Domain-specific QA | [[WWW 2021]](https://dl.acm.org/doi/10.1145/3442381.3449992) | [[Github]](https://github.com/dki-lab/GrailQA) |\n| WebQSP | Domain-specific QA | [[ACL 2016]](https://aclanthology.org/P16-2033.pdf) | [[Microsoft]](http://aka.ms/WebQSP) |\n\n# 💻 Open-source Project\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/Hawksight-AI/semantica) Semantica: an open-source, production-ready semantic layer and GraphRAG framework that sits between raw corpora and LLMs.\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/bibinprathap/VeritasGraph) Graph RAG pipeline that runs locally with ollama and has full source attribution \n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://graphrag-bench.github.io/) GraphRAG-Bench: A Comprehensive Benchmark and Analysis for Graph Retrieval-Augmented Generation. \n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/chensyCN/Agentic-RAG) Agentic-RAG: A clean and extensible agentic RAG system. \n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/apecloud/ApeRAG) ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/getzep/graphiti) Graphiti: Build Real-Time Knowledge Graphs for AI Agents.\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/JayLZhou/GraphRAG) DIGIMON: A unified and modular graph-based RAG framework\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/microsoft/graphrag.git) Microsoft-GraphRAG: A modular graph-based Retrieval-Augmented Generation (RAG) system\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/gusye1234/nano-graphrag) Nano-GraphRAG: A simple, easy-to-hack GraphRAG implementation\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/circlemind-ai/fast-graphrag) Fast GraphRAG: RAG that intelligently adapts to your use case, data, and queries\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/HKUDS/LightRAG) LightRAG: Simple and Fast Retrieval-Augmented Generation\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/tpoisonooo/HuixiangDou2) HuixiangDou2: A Robustly Optimized GraphRAG Approach\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/FalkorDB/GraphRAG-SDK) GraphRAG-SDK: a specialized toolkit for building GraphRAG systems.\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/vitali87/code-graph-rag) Code-Graph-RAG: A graph-based RAG system that analyzes multi-language codebases using Tree-sitter, builds knowledge graphs, and enables natural language querying and editing via MCP server.\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/onestardao/WFGY) WFGY Problem Map: a specialized toolkit that defines 16 recurring failure modes that show up in RAG and LLM pipelines.\n- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/topoteretes/cognee) Cognee: Open-source memory engine that turns data into knowledge graphs via an ECL pipeline, combining graph and vector retrieval for AI agents.\n\n# 🍀 Citation\nIf you find this survey helpful, please cite our paper:\n```\n@article{zhang2025survey,\n  title={A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models},\n  author={Zhang, Qinggang and Chen, Shengyuan and Bei, Yuanchen and Yuan, Zheng and Zhou, Huachi and Hong, Zijin and Dong, Junnan and Chen, Hao and Chang, Yi and Huang, Xiao},\n  journal={arXiv preprint arXiv:2501.13958},\n  year={2025}\n}\n```\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/deep-polyu%2Fawesome-graphrag/projects"}