Zep vs Graph RAG | Zep Documentation

GraphRAG is built for static document summarization. Zep supports frequently updated data, temporal facts, and retrieval in less than 200 ms.

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. Retrieval has a latency of less than 200 ms at the 95th percentile.

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

Aspect	GraphRAG	Zep
Primary use	Static document summarization	Context from data that changes over time
Data handling	Batch-oriented processing	Incremental updates
Knowledge structure	Entity clusters and community summaries	Episodes, entities, facts, and derived summaries
Retrieval method	LLM summaries of entity communities	Semantic, keyword, and graph search
Temporal handling	Source timestamps	Bitemporal fact validity
Contradiction handling	New summaries can replace prior summaries	New facts can invalidate prior facts
Query latency	Includes LLM summarization time	Less than 200 ms at the 95th percentile