Zep AI

Combine conversations, business data, and user activity into context that agents can retrieve for later tasks.

Context engineering is the practice of designing and managing the context AI agents see at every call. Unified context is what agents need. The Context Lake is how Zep delivers it.

Unify enterprise data into shared context, with access controls for every user and agent.

Writings on Zep, LLMs, and other AI ecosystem tools.

The page presents a framework for deciding whether to build, buy, use open source, or adopt a framework for agent memory.

The page lists open roles in engineering and applied research for building the Context Lake for AI agents.

Choose a credit-based plan for unified context. Enterprise plans include governance and deployment options.

AWS AgentCore Memory is bound to the AWS ecosystem; Zep is a neutral, multi-LLM, multi-cloud Context Lake. The lock-in-vs-neutrality decision, compared.

A Cognee alternative: Zep serves agent memory as a managed Context Lake — bi-temporal context graphs, entity-level governance, and sub-200ms retrieval at scale.

A Supermemory alternative: Supermemory handles conversational memory; Zep fuses every touchpoint — chat, CRM, support, billing, docs, events — into governed context graphs.

Zep is the unified context layer for production agents. Higher accuracy, faster retrieval, fewer tokens than mem0 — plus the customization, governance, and scale that agents need.

Zep is building the unified context layer for enterprise data — infrastructure that connects, governs, and serves the context agents need to complete tasks correctly.

Zep unifies business data, documents, and conversations so agents have the context they need to complete tasks correctly.

Zep’s privacy policy describes how it collects, uses, and protects personal information across its agent memory platform.

The terms and conditions govern use of Zep’s agent memory and Context Lake services.