MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
This provides a practical tool for researchers and developers working on LLM-based agents, but it is incremental as it unifies and packages existing methods rather than introducing new ones.
The authors tackled the lack of unified implementations for advanced memory models in LLM-based agents by developing MemEngine, a modular library that implements various existing models and facilitates extensible development, with the project made publicly available.
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.