SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory

Long-term memory is becoming a central bottleneck for language agents. Existing RAG and GraphRAG systems largely treat memory graphs as static retrieval middleware, which limits their ability to recover complete evidence chains from partial cues, exploit reusable graph-structural roles, and improve the memory itself through downstream feedback. We introduce SAGE, a Self-evolving Agentic Graph-memory Engine that models graph memory as a dynamic long-term memory substrate. SAGE couples two roles: a memory writer that incrementally constructs structured graph memory from interaction histories, and a Graph Foundation Model-based memory reader to perform retrieval and provide feedback to the memory writer. We provide rigorous theoretical analyses supporting the effectiveness of carefully designed architectural components and the framework. Across multi-hop QA, open-domain retrieval, domain-specific review QA, and long-term agent-memory benchmarks, SAGE improves evidence recovery, answer grounding, and retrieval efficiency: after two self-evolution rounds, it achieves the best average rank on multi-hop QA; in zero-shot open-domain transfer, it reaches EM/F1 of 46.20/54.21 and 53.00/66.06 on NQ and PopQA. Most importantly, when used as a replacement memory-access backend for existing memory systems, SAGE improves LongMemEval overall accuracy by 7.4 to 9.6 absolute points over the native MemOS, Memobase, and Zep backends after one evolution round, while paired HaluMem experiments improve most operation-level memory metrics and reduce QA hallucination. These results suggest that self-evolving, structure-aware graph memory is a promising way for robust long-horizon language agents. Our code is available https://anonymous.4open.science/r/Unified-Representation-A9D9/here.