Abstract
Recent advances in large language model (LLM) agents have shown promise for autonomous decision-making in recommender systems. However, existing approaches suffer from two fundamental limitations: flat agent memories that conflate different information modalities and prohibitive computational costs that prevent scaling beyond a few hundred users. We propose Hybrid-GraphRAG, a recommender system that integrates hierarchical agent memory structures, graph-based retrieval-augmented generation (Graph RAG), and knowledge distillation for scalable deployment. Our approach extends agent-based collaborative filtering by structuring agent memories into intrinsic, collaborative, and interaction tiers that disentangle different information types; performing multi-hop retrieval over a dynamically constructed heterogeneous interaction graph to enable relational reasoning; and distilling LLM-generated memory dynamics into efficient graph neural encoders with adaptive gating between full and efficient inference paths. Experiments on Amazon review datasets (CDs and Vinyl, Office Products) demonstrate that Hybrid-GraphRAG achieves recommendation quality comparable to full LLM-based agents while reducing computational cost by 85% and improving NDCG@10 by 12.7% over flat-memory agent baselines. Our results establish a principled bridge between semantic agent reasoning and scalable graph-based recommendation.