AI agents are becoming always-on systems, but they still lack the memory needed to make that persistence meaningful.
Today’s tools like Redis, S3, and vector databases can store information, but they don’t give AI agents reliable memory. Information is scattered across systems, hard to verify, and difficult to keep consistent over time.
MemWal introduces a long-term, verifiable memory layer on Walrus, allowing agents to remember, share, and reuse information reliably. It is available in beta.
AI agents are evolving from prompt-driven interactions into systems that run continuously, coordinate workflows, and act over time. Frameworks like OpenClaw and NVIDIA’s NemoClaw are two examples: always-on agents operating across environments, making decisions, and executing tasks on behalf of users.
But as these systems scale, a critical gap is becoming clear: agents don’t have reliable and easily shareable memory.
Memory is the Bottleneck
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.
Why is this a problem?
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.
Introducing MemWal
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.MemWal 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 is available in beta, and 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.
MemWal 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, MemWal connects:
Agent → MemWal SDK → backend relayer → Walrus (data) + Sui (ownership & access)
Think of MemWal 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.
From Rebuilding State to Reusing it
MemWal 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 you can Build
MemWal 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.
Get Started with MemWal Beta
If you’re building agents, your memory layer defines your reliability. MemWal 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.
