{"ID":2922021,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T08:10:00.336737273Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.00610","arxiv_id":"2606.00610","title":"MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation","abstract":"Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although effective for simple queries, traditional RAG struggles with large-scale, unstructured corpora where information is highly fragmented. Graph-based RAG (GraphRAG) incorporates knowledge graphs to capture structural relationships, enabling more comprehensive retrieval for complex reasoning. However, existing GraphRAG methods rely on isolated, fragment-level extraction for graph construction, lacking a global perspective on the whole corpus. As a result, these methods frequently lead to thematically inconsistent, logically conflicting, and structurally fragmented graphs that degrade retrieval performance. In this paper, we propose MemGraphRAG, a novel framework that introduces a memory-based multi-agent system to ensure high-quality graph construction. Specifically, MemGraphRAG employs a collaborative society of agents supported by shared memory, which provides a unified global context throughout the extraction process. This mechanism allows agents to dynamically resolve logical conflicts and maintain structural connectivity throughout the corpus. Furthermore, we propose a memory-aware hierarchical retrieval algorithm tailored for the constructed graph. Extensive experiments on multiple benchmarks demonstrate that MemGraphRAG outperforms the state-of-the-art baseline models with comparable efficiency. Our code is available at https://github.com/XMUDeepLIT/MemGraphRAG.","short_abstract":"Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although effective for simple queries, traditional RAG struggles with large-scale, unstructured corpora where information is highly fragmented. Graph-based...","url_abs":"https://arxiv.org/abs/2606.00610","url_pdf":"https://arxiv.org/pdf/2606.00610v1","authors":"[\"Chuanjie Wu\",\"Zhishang Xiang\",\"Yunbo Tang\",\"Zerui Chen\",\"Qinggang Zhang\",\"Jinsong Su\"]","published":"2026-05-30T08:18:53Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.MA\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":612629,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T02:42:49.606572591Z","DeletedAt":null,"paper_id":2922021,"paper_url":"https://arxiv.org/abs/2606.00610","paper_title":"MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation","repo_url":"https://github.com/XMUDeepLIT/MemGraphRAG","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}