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

ArXi:2606.00610v1 Announce Type: cross 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 comprehensive retrieval for complex reasoning.