Zep vs Graph RAG
How Zep compares to traditional GraphRAG approaches
GraphRAG is built for static document summarization. Zep supports frequently updated data, temporal facts, and hybrid retrieval.
GraphRAG builds a knowledge structure through batch processing. It answers queries by summarizing entity clusters with a large language model (LLM).
Zep updates a Context Graph as new data arrives. It tracks when each fact becomes valid or invalid with bitemporal modeling.
Zep combines semantic, keyword, and graph search. Measure retrieval latency with representative graphs, queries, filters, result limits, and deployment conditions.
The table below summarizes how the two approaches differ across data handling, retrieval, temporal modeling, and scalability.