Graph-based Agent Memory: Taxonomy, Techniques, and Applications
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910012938387456 |
|---|---|
| author | Yang, Chang Zhou, Chuang Xiao, Yilin Dong, Su Zhuang, Luyao Zhang, Yujing Wang, Zhu Hong, Zijin Yuan, Zheng Xiang, Zhishang Chen, Shengyuan Zhou, Huachi Zhang, Qinggang Liu, Ninghao Su, Jinsong Wang, Xinrun Chang, Yi Huang, Xiao |
| author_facet | Yang, Chang Zhou, Chuang Xiao, Yilin Dong, Su Zhuang, Luyao Zhang, Yujing Wang, Zhu Hong, Zijin Yuan, Zheng Xiang, Zhishang Chen, Shengyuan Zhou, Huachi Zhang, Qinggang Liu, Ninghao Su, Jinsong Wang, Xinrun Chang, Yi Huang, Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05665 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Graph-based Agent Memory: Taxonomy, Techniques, and Applications Yang, Chang Zhou, Chuang Xiao, Yilin Dong, Su Zhuang, Luyao Zhang, Yujing Wang, Zhu Hong, Zijin Yuan, Zheng Xiang, Zhishang Chen, Shengyuan Zhou, Huachi Zhang, Qinggang Liu, Ninghao Su, Jinsong Wang, Xinrun Chang, Yi Huang, Xiao Artificial Intelligence 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. |
| title | Graph-based Agent Memory: Taxonomy, Techniques, and Applications |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.05665 |