Agent Runtime
LangChain·LlamaIndex·CrewAI·Google ADK·custom
Any agent framework — or none. The Context Lake is invoked through a single SDK.
A Context Lake is the infrastructure that implements agent memory at enterprise scale — a governed system of context graphs. The data-lake parallel, explained.
Any agent framework — or none. The Context Lake is invoked through a single SDK.
Raw signal arrives from any source the agent touches.
Relevant context is assembled on demand into token-efficient blocks.
Signal becomes a temporal context graph as new facts arrive and stale ones are invalidated.
Selects what's relevant and what adds the most information within the token budget.
Native to the substrate, not a layer bolted on. Every read and write is policy-gated for access and provenance; retention runs across the data lifecycle.
Temporal context graph with provenance — sub-200ms retrieval at scale.
A Context Lake is the infrastructure that implements agent memory at enterprise scale — a governed system of context graphs that manages, governs, and serves everything AI agents need to know. It is to agent memory what the data lake is to analytics: a single, governed substrate that holds many sources and serves many consumers. Zep is the Context Lake for AI agents.
Giving one agent memory is straightforward. Running memory for an enterprise — millions of users, hundreds of agents, dozens of data sources, all under access control, retention, and audit requirements — is an infrastructure problem. Bolting memory onto each agent produces silos, inconsistent governance, and no way to audit what an agent knew or why it acted. The Context Lake solves this the way the data lake solved analytics sprawl: one governed system, many graphs, served as a platform.
The analogy is deliberate and precise — and complementary, not competing:
Tables, transactions, logs, metrics.
Conversations, documents, events, decisions.
Optimized for aggregation and retrospective analysis.
Temporal context graphs with entity-aware retrieval.
Batch jobs, scheduled pipelines, dashboard refresh.
Real-time context at agent inference speed.
BI tools, data scientists, reporting pipelines.
LLM-powered applications that need memory and context.
Table-level permissions, role-based access.
Attribute-based policies, retention rules, full audit trail.
| Data lake | Context Lake | |
|---|---|---|
| Holds | Raw + processed business data | Context graphs (what agents know) |
| Consumers | BI tools, analysts, ML pipelines | AI agents |
| Access pattern | Batch + query | Millisecond retrieval at run time |
| Shared trait | Governed at the substrate | Governed at the substrate |
A Context Lake doesn't replace your data lake. They hold different data for different consumers with different access patterns — but the same governance rigor. The data lake feeds analytics; the Context Lake feeds agents.
Engineering and AI leaders building agents at production scale — especially in regulated or data-sensitive environments (financial services, healthcare, enterprise support) where governance, provenance, and deployment control aren't optional. S&P Global Market Intelligence (451 Research) initiated coverage on Zep as a likely de facto partner in the enterprise agent stack, citing exactly this enterprise-plus-temporal-graph differentiation.
Related: What is agent memory? · Context Graph Engine · Graphiti · Enterprise & deployment
No, and it doesn't replace one. A data lake serves analytics; a Context Lake serves agents. Different data, different consumers, different access patterns — same governance rigor. They're complementary.
No. It's a governed system that manages many temporal context graphs and serves them at scale with millisecond retrieval, access control, retention, and audit. A single vector store or graph database is a component, not the governed substrate.
Agent memory is the category — everything an agent knows over time. A Context Lake is the infrastructure that implements agent memory at enterprise scale.
The Context Graph Engine (the proprietary runtime) with Graphiti (open source) constructing the graphs on top of it.