Agent memory is everything an AI agent knows across time about the users, the business, and the world it operates in — so it can reason, personalize, and act without starting over every turn. These guides cover how agent memory works, why chat history and RAG fall short, and how to build, test, and scale it. Zep is the Context Lake for AI agents: the platform that manages, governs, and serves agent memory at enterprise scale.
If you're new to the topic, read What is agent memory? first — it defines the category and explains why chat history and RAG don't scale to it. From there, Agent memory vs RAG draws the distinction most teams get wrong, and What is a temporal knowledge graph? explains the structure underneath. When you're ready to build, How to give an AI agent long-term memory walks through the approaches, and the persistent-memory tutorial is the hands-on version.
The infrastructure these guides point to is the Context Lake — Zep manages agent memory (ingest, construct, invalidate, evolve), governs it (ABAC, retention, audit), and serves it as assembled context with sub-200ms p95 retrieval. The graph itself is built with Graphiti, Zep's open-source temporal context graph library, which runs on top of the Context Graph Engine at scale. For how Zep's accuracy is measured, see the LoCoMo and LongMemEval results.
Agent memory is everything an AI agent knows across time about the users, the business, and the world it operates in — so it can reason, personalize, and act without starting from scratch every turn. It's the category; chat buffers, vector stores, and Context Lakes are different ways to implement it. See What is agent memory?
RAG retrieves static documents by similarity at query time. Agent memory tracks evolving, provenance-stamped facts about users and the business over time, with a sense of what's true now versus what was true then. They're complementary — most production agents use RAG for documents and agent memory for state. See Agent memory vs RAG.
Add a memory layer that builds a temporal context graph from the agent's inputs and serves the relevant context back per turn — rather than stuffing chat history into the context window. With Zep this is a few lines of code and works with any agent framework. See How to give an AI agent long-term memory.
Measure context completeness first — did the system retrieve the facts needed to answer — then answer correctness, retrieval latency, and token use, across multiple sessions and over time. Industry benchmarks include LoCoMo and LongMemEval. See How to test agent memory.
A Context Lake — a governed system of context graphs that manages, governs, and serves agent memory across millions of users with sub-200ms retrieval, attribute-based access control, retention, and audit. Zep is the Context Lake for AI agents.
Part of the Zep AI agent memory guides. Built on Graphiti and the Context Graph Engine. See the research and benchmarks.
