by nuster1128 · AI Tool · ★ 476
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A Survey on the Memory Mechanism of Large Language Model based Agents Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. The key component to support agent-environment interactions is the memory of the agents. While previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies. To bridge this gap, in this paper, we propose a comprehensive survey on the memory mechanism of LLM-based agents. In specific, we first discuss ''what is'' and ''why do we need'' the memory in LLM-based agents. Then, we systematically review previous studies on how to design and evaluate the memory module.
| Stars | 476 |
| Forks | 9 |
| Category | AI Tool |
| Quality Score | 43.9199584322959/100 |
| Last Updated | 2025-07-28 |
| Created | 2024-04-13 |
| Est. Tokens | ~782k |
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LLM_Agent_Memory_Survey is an open-source ai tool by nuster1128 with 476 GitHub stars.
You can find installation instructions and usage details in the LLM_Agent_Memory_Survey GitHub repository at github.com/nuster1128/LLM_Agent_Memory_Survey. The project has 476 stars and 9 forks, indicating an active community.
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