Walrus Memory: How to Give AI Agents Persistent, Verifiable Memory?

Walrus Memory is a portable, verifiable long-term memory layer for AI agents, now in beta. It lets agents persist conversations, checkpoints, and reasoning across sessions instead of rebuilding state every time. As AI agents move from demos to always-on production systems, memory – not models or compute – has become one of the bottlenecks.

Frameworks like NemoClaw and OpenClaw show where this is heading: always-on agents operating across environments, making decisions and executing tasks on behalf of users. But as these systems scale, a critical gap appears – agents don't have persistent, shareable memory.

Why is memory the bottleneck for AI agents?

AI agents need to remember conversations, learned skills, reasoning traces, and workflow outputs to be useful. Today, developers stitch this together using tools like Redis, S3, and vector databases. Each solves the problem partially, but none are designed for agent memory as a system primitive.

As systems scale across teams and organizations, memory becomes fragmented, hard to verify, and difficult to reproduce. When something goes wrong, like an agent ignoring key context you know it has access to, it’s unclear whether the issue came from the model, the data, the tool, or the agent’s prior state. Agents lose context, workflows fail, and decisions become harder to explain.

As agents move into real-world use cases, storage evolves into more than just infrastructure. It has to be a part of system reliability. AI systems depend on states that can be preserved, workflows that can be reproduced, and data that can be trusted at the moment it is used. Without this, even advanced agents remain fragile.

Stuffing history into the prompt doesn't scale either. In a Stanford and Berkeley study, models retrieved information reliably at the start and end of their context but degraded sharply when the relevant information sat in the middle – and the effect held even for models built for long context. Memory that lives only in the context window gets less reliable exactly as a workflow runs longer.

What is Walrus Memory?

Enterprise-grade agents need memory that is durable, portable, shareable, and verifiable. What’s missing today isn’t better models or faster compute. It’s a memory layer that systems can rely on. Walrus Memory creates a verifiable, long-term memory layer for AI agents on Walrus.

It’s a developer SDK plus backend relayer based architecture for building agents with persistent memory. It gives developers a clean abstraction over storage, so they don’t have to build memory systems from scratch or force-fit tools built for other purposes.

Walrus Memory gives you:

• Structured Memory Spaces
  Organize agent memory into durable, purpose-built containers instead of unstructured logs.
• Flexible Ownership Models
  Define who owns, controls, and retains memory across users, agents, and applications.
• Programmable Access Control
  Enforce fine-grained permissions over how memory is read, written, and shared.
• Typed Memory Systems
  Natively support distinct memory types like conversations, checkpoints, and reasoning traces. 

Under the hood, Walrus Memory connects:

Agent → Walrus Memory SDK → backend relayer → Walrus (data) + Sui (ownership & access)

Think of Walrus Memory as the memory layer between your agent and persistent storage. This gives developers flexibility: plug into an existing relayer or self-host for full control.

How does Walrus Memory change how agents remember?

Walrus Memory changes how agent systems behave. Instead of rebuilding state every time, agents can carry forward:

• Conversation memory
• Workflow checkpoints
• Reasoning traces
• Long-term knowledge

Workflows can restart without losing progress. Multi-agent systems can coordinate around shared memory. Decisions can be traced back to the data and reasoning that produced them.

Memory becomes something systems can rely on, not something they have to reconstruct.

What can you build with Walrus Memory?

Walrus Memory enables a new class of agent systems:

Long-running agents that maintain context across sessions

A code review agent that continuously monitors a repo, remembers past bugs it flagged, tracks which ones were fixed, and adapts its suggestions based on the team’s coding patterns over time.

Workflow agents with restartable state

A data pipeline agent that ingests, cleans, and transforms data across multiple steps, storing checkpoints so it can resume exactly where it left off after failures instead of restarting the entire job.

Research agents that accumulate knowledge over time

A market research agent that reads reports daily, builds a structured knowledge base of companies and trends, and refines its hypotheses instead of reprocessing the same information from scratch.

Multi-agent systems that share and coordinate memory

A product development system where one agent gathers user feedback, another analyzes patterns, and a third proposes features, all coordinating through shared memory so insights compound instead of getting lost.

How do you get started with Walrus Memory?

Building an AI agent that remembers across sessions starts with one change: stop rebuilding state on every run. If you’re building agents, your memory layer defines your reliability. Walrus Memory is how you build agents that don’t forget. You don’t have to go big right away – try replacing one part of your agent’s memory (like conversation history) and build from there.

Install the SDK →
Refer to the quickstart guide to configure your agent →
Explore the GitHub repo, including example apps →

About Walrus Memory

Walrus Memory is a portable memory layer for AI agents. It enables agents to operate reliably across apps and sessions without losing context — portable, verifiable, and fully under builders' control. Walrus Memory lets agents handle complex, long-running workflows and coordinate using data they can trust.