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A survey of Graph-based Agent Memory | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based agent memory.
| Date | Stars |
|---|---|
| 2026-07-31 | 327 |
| 2026-08-06 | 329 |
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# Awesome Graph-based Agent Memory
<div align="center">
<a href="https://arxiv.org/abs/2602.05665" target="_blank"><img src="https://img.shields.io/badge/Paper-Arxiv-red?logo=arxiv&style=flat-square" alt="arXiv:2602.05665"></a>
<a href="https://drive.google.com/file/d/1svybBAKkuX6AYbikXzc9L_8wgrABxGJU/view?usp=sharing"><img src="https://img.shields.io/badge/Slides-📊-blue"/></a>
<a href="http://makeapullrequest.com"><img src="https://img.shields.io/github/stars/DEEP-PolyU/Awesome-GraphMemory"/></a>
<a href="http://makeapullrequest.com"><img src="https://img.shields.io/github/forks/DEEP-PolyU/Awesome-GraphMemory"/></a>
</div>
This repository provides a comprehensive collection of research papers, benchmarks, and open-source projects on **Graph-based Agent Memory**. It includes contents from our survey paper 📖<em>"[**Graph-based Agent Memory: Taxonomy, Techniques, and Applications**](https://arxiv.org/abs/2602.05665)"</em> and will be continuously updated.
🤗 **You are very welcome to contribute to this repository** by launching an issue or a pull request. If you find any missing resources or come across interesting new research works, please don’t hesitate to open an issue or submit a PR!
📫 **Contact us via emails:** `[email protected]`, `[email protected]`
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<div>
<h3 align="center">
<p align="center"><img width="100%" src="figures/illustration_memory_comparison.png" /></p>
<p align="center"><em>Comparison between Traditional Agent Memory and Graph-based Agent Memory.</em></p>
</div>
## 📜 Catalog
> **[Awesome Graph-based Agent Memory](#awesome-graph-based-agent-memory)**
>
> - **[🔥 News](#-news)**
> - **[📖 Overview](#-overview)**
> - **[📚 Related Survey](#-survey)**
> - **[🪴 Taxonomy](#-taxonomy)**
> - [Memory Extraction](#memory-extraction)
> - [Memory Storage](#memory-storage)
> - [Memory Retrieval](#memory-retrieval)
> - [Memory Evolution](#memory-evolution)
> - **[🏆 Benchmark](#-benchmark)**
> - **[📦 Projects](#-projects)**
> - **[📃 Citation](#-citation)**
---
## 🔥 News
* **[2026-02-03]** 🔥🔥 Our survey on Graph-based Agent Memory is released.
## 📚 Related Survey Papers
- (TOIS'25) **A Survey on the Memory Mechanism of Large Language Model-based Agents** [[Paper]](https://dl.acm.org/doi/full/10.1145/3748302)
- (TMLR'25) **The AI Hippocampus: How Far are We From Human Memory?** [[Paper]](https://openreview.net/forum?id=Sk7pwmLuAY)
- (arXiv'25) **Memory in the Age of AI Agents** [[Paper]](https://arxiv.org/abs/2512.13564)
- (arXiv'25) **Memory in LLM-based Multi-agent Systems: Mechanisms, Challenges, and Collective Intelligence** [[Paper]](https://www.techrxiv.org/doi/full/10.36227/techrxiv.176539617.79044553)
- (arXiv'25) **AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents** [[Paper]](https://arxiv.org/abs/2512.23343)
- (arXiv'25) **From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs** [[Paper]](https://arxiv.org/abs/2504.15965)
## 🪴 Taxonomy
### Memory Extraction
<p align="center"><img width="100%" src="figures/extraction.png" /></p>
#### Textual Data
- (arXiv‘25) **Can an LLM Induce a Graph? Investigating Memory Drift and Context Length** [[Paper]](https://arxiv.org/pdf/2510.03611)
- (arXiv‘24) **On the structural memory of llm agents** [[Paper]](https://arxiv.org/pdf/2412.15266?)
- (arXiv‘25) **Personaagent with graphrag: Community-aware knowledge graphs for personalized llm** [[Paper]](https://arxiv.org/abs/2511.17467)
- (arXiv‘25) **Scaling graph chain-of-thought reasoning: A multi-agent framework with efficient llm serving** [[Paper]](https://arxiv.org/pdf/2511.01633)
- (ACL‘25) **HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model** [[Paper]](https://aclanthology.org/2025.acl-long.1575.pdf)
#### Sequential Data
- (NeurIPS‘23) **Reflexion: Language agents with verbal reinforcement learning** [[Paper]](httExcerpt of 31,210 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f7cc9590824965e4, llm:description: 'A survey of Graph-based Agent Memory | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based agent memory'; topics: agent, agent-memory, agent-memory-survey, graph-based-agent-memory, memory-survey
matched fp:f7cc9590824965e4, llm:description: 'A survey of Graph-based Agent Memory | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based agent memory'; topics: agent, agent-memory, agent-memory-survey, graph-based-agent-memory, memory-survey