Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), where memory can enable knowledge accumulation, iterative reasoning and self-evolution. Among diverse paradigms, graph stands out as a powerful structure for agent memory due to the intrinsic capabilities to model relational dependencies, organize hierarchical information, and support efficient retrieval. This survey presents a comprehensive review of agent memory from the graph-based perspective. First, we introduce a taxonomy of agent memory, including short-term vs. long-term memory, knowledge vs. experience memory, non-structural vs. structural memory, with an implementation view of graph-based memory. Second, according to the life cycle of agent memory, we systematically analyze the key techniques in graph-based agent memory, covering memory extraction for transforming the data into the contents, storage for organizing the data efficiently, retrieval for retrieving the relevant contents from memory to support reasoning, and evolution for updating the contents in the memory. Third, we summarize the open-sourced libraries and benchmarks that support the development and evaluation of self-evolving agent memory. We also explore diverse application scenarios. Finally, we identify critical challenges and future research directions. This survey aims to offer actionable insights to advance the development of more efficient and reliable graph-based agent memory systems. All the related resources, including research papers, open-source data, and projects, are collected for the community in https://github.com/DEEP-PolyU/Awesome-GraphMemory.

翻译：记忆已成为基于大型语言模型（LLM）的智能体处理长周期复杂任务（如多轮对话、游戏博弈、科学发现）的核心模块，其能够支持知识积累、迭代推理与自我演化。在多种范式中，图因其固有的关系依赖建模能力、层次化信息组织能力以及高效检索支持能力，成为构建智能体记忆的强大结构。本综述从图的角度对智能体记忆进行了全面梳理。首先，我们提出了智能体记忆的分类体系，包括短期记忆与长期记忆、知识记忆与经验记忆、非结构化记忆与结构化记忆，并从实现视角阐述了基于图的记忆架构。其次，依据智能体记忆的生命周期，我们系统分析了基于图的智能体记忆关键技术，涵盖将数据转化为记忆内容的记忆提取、高效组织数据的记忆存储、从记忆中检索相关内容以支持推理的记忆检索，以及更新记忆内容的记忆演化。第三，我们总结了支持自演化智能体记忆开发与评估的开源工具库及基准数据集，并探讨了多样化的应用场景。最后，我们指出了当前面临的关键挑战与未来研究方向。本综述旨在为推动更高效、可靠的基于图智能体记忆系统的发展提供可操作的见解。所有相关资源，包括研究论文、开源数据与项目，已整理于 https://github.com/DEEP-PolyU/Awesome-GraphMemory 供学术界参考。