打造统一模块化内存库，让大模型智能体记忆开发更简单
MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
- 构建统一框架，整合多种先进记忆模型
- 支持便捷扩展与即插即用的内存组件
- 适合研究者快速实验和开发者部署记忆功能
近期，基于大语言模型（LLM-based）的智能体已在多个领域广泛应用。作为关键组成部分，其记忆能力受到学术界和工业界的广泛关注。尽管近年来提出了众多先进的记忆模型，但缺乏在通用框架下的统一实现。为解决此问题，我们开发了一个名为MemEngine的统一且模块化的库，用于构建基于大语言模型智能体的高级记忆模型。基于该框架，我们实现了来自近期研究工作的多种记忆模型。此外，该库支持便捷、可扩展的记忆开发，并提供用户友好的即插即用式内存使用方式。为回馈社区，项目已公开发布于 https://github.com/nuster1128/MemEngine。
原文摘要 · Abstract (English)
Recently, large language model based (LLM-based) agents have been widely applied across various fields. As a critical part, their memory capabilities have captured significant interest from both industrial and academic communities. Despite the proposal of many advanced memory models in recent research, however, there remains a lack of unified implementations under a general framework. To address this issue, we develop a unified and modular library for developing advanced memory models of LLM-based agents, called MemEngine. Based on our framework, we implement abundant memory models from recent research works. Additionally, our library facilitates convenient and extensible memory development, and offers user-friendly and pluggable memory usage. For benefiting our community, we have made our project publicly available at https://github.com/nuster1128/MemEngine.
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