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MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
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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.
Forward citations
Cited by 2 Pith papers
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MemTools: A Unified Research Framework for Interoperable Agent Memory
MemTools decouples agent-memory lifecycle stages via declarative data contracts, separates evaluation protocols from benchmark datasets, and unifies symbolic, neural, and multimodal memory in one runtime, enabling hyb...
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Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework
A learnable memory cycle with adaptive retrieval, merging, and storage, trained online, improves LLM agent accuracy on HotpotQA and MemDaily for most backbones.
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