Walrus Memory

What is Walrus Memory?

Walrus Memory is a portable memory layer for AI agents. It is designed to store and retrieve persistent context across apps, sessions, and runtimes so agents can continue working with the same state instead of starting over each time.

The product is positioned for developers building agents and applications that need shared, verifiable memory. The source shows usage from both Python and TypeScript, and it can also be added to AI clients such as Claude Code, Cursor, Codex, and Gemini CLI through a setup command.

Key Features

• Persistent agent memory: Stores memories that can be recalled later, so an agent can continue from prior context instead of losing state at the end of a session.
• Portable across apps and runtimes: The same memory layer is described as usable across different apps and environments, which helps when workflows move between tools or deployments.
• Recall by query: Agents can search stored memories with a natural-language query and receive matching results with distances, which supports retrieval of relevant context.
• Support for Python and TypeScript: The page includes example code for both memwal in Python and @mysten-incubation/memwal in TypeScript, indicating integration paths for application builders.
• Works with AI clients and coding tools: The setup flow mentions Claude Code, Cursor, Codex, Gemini CLI, and other clients, suggesting it can be connected beyond custom apps.
• Shared memory for multi-agent workflows: The product is presented as a way to create workflows where multiple agents can access the same memory and coordinate around shared state.
• Verifiable and controlled access: The page emphasizes that memory is persistent and under user control, and the meta description mentions programmable access control and reliable coordination.

How to Use Walrus Memory

A typical setup starts by running the provided curl command to retrieve setup instructions for the AI client or environment you want to use. Developers then connect an agent or application using the appropriate SDK or client instructions, configure keys, account information, and a namespace, and verify the service with a health check.

From there, the workflow is to write memories with remember or remember_and_wait, then call recall with a query when the agent needs context. The examples show that the product can be used both as an external memory layer for an AI client and as a library embedded directly in an application.

Use Cases

• Cross-session agent continuity: Useful when an assistant or agent needs to remember facts about a user or task after the original session has ended.
• Multi-agent coordination: Helpful when separate agents contribute to the same workflow and need access to shared state rather than isolated session memory.
• Application-embedded memory: Suitable for developers building AI apps in Python or TypeScript that need persistent recall inside the product itself.
• Memory for coding assistants: Can be connected to tools such as Claude Code, Cursor, Codex, or Gemini CLI when a coding workflow needs persistent context.
• Audit-friendly agent workflows: The source notes verifiability and audit trails, which makes the product relevant for workflows where it is important to trace what the agent acted on.

FAQ

Does Walrus Memory store context across sessions? Yes. The page describes it as a portable memory layer that keeps context persistent across apps and sessions.

Can it be used in more than one programming language? Yes. The examples shown are for Python and TypeScript.

Can it be connected to existing AI clients? Yes. The source specifically mentions Claude Code, Cursor, Codex, Gemini CLI, and similar clients.

Does it support shared memory for multiple agents? Yes. The page explicitly mentions multi-agent workflows with shared memory.

Is the product described as controlled and verifiable? Yes. The source says it is persistent, verifiable, and under the user’s control, and it also references programmable access control.

Alternatives

• Session-based memory inside a single AI app: This is the simplest alternative, but it usually resets when the session ends and does not provide portable context across tools.
• Custom database-backed memory layer: Teams can build their own persistence and retrieval system, but that usually requires handling schema design, recall logic, and access control themselves.
• Vector database plus retrieval pipeline: This can support semantic recall, but it is usually a broader infrastructure setup rather than a purpose-built agent memory product.
• Agent frameworks with built-in memory modules: Some agent frameworks include memory features, but they are often tied to a specific runtime or workflow rather than being presented as a portable memory layer.