MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910927700361216 |
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| author | Zhang, Zeyu Dai, Quanyu Chen, Xu Li, Rui Li, Zhongyang Dong, Zhenhua |
| author_facet | Zhang, Zeyu Dai, Quanyu Chen, Xu Li, Rui Li, Zhongyang Dong, Zhenhua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents Zhang, Zeyu Dai, Quanyu Chen, Xu Li, Rui Li, Zhongyang Dong, Zhenhua Artificial Intelligence 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. |
| title | MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.02099 |