Zep vs Graph RAG

While traditional GraphRAG excels at static document summarization, Zep is designed for dynamic and frequently updated datasets with continuous data updates, temporal fact tracking, and sub-200ms query latency. This makes Zep particularly suitable for providing an agent with up-to-date knowledge about an object/system or user.

GraphRAG builds a static knowledge structure through batch processing and answers queries by summarizing entity clusters with an LLM. That design fits document corpora that rarely change. Zep instead constructs a Context Graph that updates incrementally as new data arrives, tracks when each fact becomes valid or invalid using bi-temporal modeling, and retrieves with combined semantic, keyword, and graph search rather than sequential LLM summarization. The result is up-to-date context returned with sub-200ms retrieval latency, even as the underlying data continues to change.

The table below summarizes how the two approaches differ across data handling, retrieval, temporal modeling, and scalability.