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. In addition, we also present many agent applications, where the memory module plays an important role. At last, we analyze the limitations of existing work and show important future directions. To keep up with the latest advances in this field, we create a repository at \url{https://github.com/nuster1128/LLM_Agent_Memory_Survey}.

中文翻译：

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基于大语言模型的Agent记忆机制综述

基于大语言模型（LLM）的代理最近引起了研究界和工业界的广泛关注。与原始LLM相比，基于LLM的智能体具有自我进化能力，这是解决需要长期且复杂的智能体与环境交互的现实问题的基础。支持智能体与环境交互的关键组件是智能体的记忆。虽然之前的研究提出了许多有前途的记忆机制，但它们分散在不同的论文中，并且缺乏系统的综述来从整体角度总结和比较这些工作，未能抽象出共同且有效的设计模式来启发未来的研究。为了弥补这一差距，在本文中，我们提出了对基于 LLM 的智能体的记忆机制的全面调查。具体来说，我们首先讨论基于 LLM 的代理中“什么是”以及“为什么我们需要”内存。然后，我们系统地回顾了先前关于如何设计和评估内存模块的研究。此外，我们还展示了许多代理应用程序，其中内存模块发挥着重要作用。最后，我们分析了现有工作的局限性并指出了未来的重要方向。为了跟上该领域的最新进展，我们在 \url{https://github.com/nuster1128/LLM_Agent_Memory_Survey} 创建了一个存储库。