MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
Year: 2025
Type: conference-paper
Abstract: 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.
Institutions: Renmin University of China, Huawei Technologies (China)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410637010