MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
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, logicall...
| Authors | Chuanjie Wu, Zhishang Xiang, Yunbo Tang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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