## Memory layer for AI agents

URL: https://docs.atlan.com/agents/concepts/memory-layer

> Understand how a memory layer maps to Atlan's architecture, and how context repositories and the Atlan MCP server compare to vector databases and RAG.

# Memory layer for AI agents

## Map memory layer to Atlan's architecture

"Memory layer" is the market term used by agent frameworks.

Atlan delivers the same outcome as the context layer for AI: a governed tier that gives agents persistent, trustworthy memory of your data through metadata and MCP.

Atlan doesn't ship a standalone product named "memory layer." The same outcome comes from two components working together:

- **Context repository:** a bounded, versioned package of governed context for a domain. It holds the semantic model, business logic, verified question-answer pairs, and the artifacts an agent uses to answer questions about that domain. Context repositories are built in Context Studio.
- **Atlan MCP server:** the interface that lets AI agents search, inspect, and update Atlan metadata from their runtime using governed, real-time context.

The practical distinction matters. Atlan is a fit for governed, shared enterprise context across agents and runtimes. It's not a drop-in replacement for a lightweight per-agent personalization store such as a standalone vector database.

:::note
Context Studio is in Private Preview. Availability depends on your tenant.
:::

## Memory layer vs vector database vs RAG

A memory layer isn't the same as RAG or a raw vector database.

- **RAG** retrieves passages from a corpus at query time.
- A **vector database** stores embeddings for similarity search.
- A **memory layer** persists and re-injects context across runs.

In Atlan's model, the governed context layer is the enterprise substrate that agents read from, with lineage, policy, ownership, and semantic meaning attached.

| Approach | Persistence | Governance | Best fit |
| --- | --- | --- | --- |
| Vector database | Per-agent or per-app | Application-level | Single-agent personalization |
| RAG pipeline | Query-time retrieval | Depends on corpus controls | Retrieval from a document corpus |
| Atlan context repository + MCP | Versioned, shared context across runs | Enterprise governance, lineage, policy, and RBAC | Multi-agent, multi-runtime enterprise deployments |

## See also

- [Set up memory layer for AI agents](https://docs.atlan.com/llms/platform/agents/set-up-a-memory-layer-for-ai-agents/llms.txt)
- [What is Context Engineering Studio?](https://docs.atlan.com/llms/governance/context-engineering-studio/what-is-context-engineering-studio/llms.txt)

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