MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2606.00610v1
• Date: Sat, 30 May 2026 08:18:53 GMT
• Title: MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
• Abstract summary: Graph-based RAG (GraphRAG) incorporates knowledge graphs to capture structural relationships.<n>Existing GraphRAG methods rely on isolated, fragment-level extraction for graph construction.<n>We propose MemGraphRAG, a novel framework that introduces a memory-based multi-agent system to ensure high-quality graph construction.
• Score: 30.464508917946855
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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