Zep: Temporal Knowledge Graphs for AI Agent Memory

https://arxiv.org/abs/2501.13956 The research introduces Zep, a novel memory service for AI agents, designed to overcome the limitations of current retrieval-augmented generation (RAG) frameworks, which struggle with dynamic and continuously evolving data. Zep utilizes Graphiti, a temporally-aware knowledge graph engine, to synthesize both unstructured conversational data and structured business information while preserving historical relationships. The paper highlights Zep's superior performance over MemGPT in the Deep Memory Retrieval (DMR) benchmark and demonstrates significant improvements in accuracy and reduced latency on the more complex LongMemEval benchmark, which better reflects real-world enterprise scenarios. Zep's architecture, inspired by human memory models, involves three hierarchical subgraphs—episode, semantic entity, and community—enabling sophisticated and nuanced memory structures. The authors also discuss Zep's advanced memory retrieval system, which employs various search and reranking functions to provide relevant context for large language model (LLM) agents.