[Paper Reading]: MemGraphRAG: Memory-based Multi-Agent System for Graph RAG

Why we picked it

This week, we will walk through and discuss the paper: MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation [https://arxiv.org/pdf/2606.00610] **Abstract of the Paper:** 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