Graph-based Agent Memory: Taxonomy, Techniques, and Applications
🤖 AI Summary
This work addresses the challenge that large language model agents often struggle to accumulate knowledge and evolve autonomously in long-term, complex tasks due to the absence of efficient, structured memory mechanisms. From a graph-structured perspective, the paper presents the first systematic taxonomy for agent memory, encompassing dimensions such as short-term versus long-term, knowledge versus experience, and unstructured versus structured representations. It further delineates key techniques across the full lifecycle of memory—including encoding, storage, retrieval, and dynamic evolution. By integrating open-source tools, evaluation benchmarks, and real-world applications, the authors release a graph-based memory resource repository, offering both theoretical foundations and practical guidance for developing efficient, self-evolving agent memory systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
graph-based memory
agent memory
large language models
memory taxonomy
relational dependencies
Innovation
Methods, ideas, or system contributions that make the work stand out.
graph-based memory
agent memory taxonomy
memory lifecycle
self-evolving agents
relational reasoning
🔎 Similar Papers
No similar papers found.
