Agentic Memory: Walrus Takes On AI's Next Big Bottleneck

In brief

• Walrus has launched MemWal, an SDK for AI agents.
• MemWal brings verifiability, availability, portability and sharability to agentic memory.
• Improved agentic memory opens up an array of new applications, such as customer support agents that retain contextual cues about users.

As AI agents become increasingly ubiquitous, agentic memory is becoming one of the most important issues in the artificial intelligence space.

Enterprises and individuals are coming to rely on agents for ever more complex, high-stakes tasks—but the memory layer that most agents run on today has limitations that impact on the quality of their work.

That’s something Walrus, coupled with a recently launched SDK called MemWal, is looking to solve—bringing verifiability, availability, portability and sharability to agentic memory, Mysten Labs Group Product Manager Abinhav Garg told Decrypt.

“With Walrus plus MemWal, memory lives on an open, verifiable data layer, so that means it's not tied to any one model or vendor,” Garg explained. That means users can switch between model providers such as OpenAI and Anthropic, while data is stored with verifiable guarantees, so it’s tamper-proof—something that’s “especially important as agents start operating in more critical workflows where correctness and auditability matter,” he said.

Data stored on Walrus inherits its built-in guarantees around verifiability, portability and availability, enabling “easier sharing of memory between agents across teams and organizations,” he added, making it a “must for agent collaboration.”

MemWal also integrates with popular agent orchestration frameworks OpenClaw and NemoClaw, through a plugin released this week. “We wanted to make the verifiable long term memory easy to adapt in real systems,” Garg said, adding that it enables a “seamless” workflow for builders.

“Without this, developers would have to understand the integration of a decentralized storage layer like Walrus, which could add friction and complexity,” he explained. “With the integration, they can just equip their agents with durable, verifiable memory directly with the tools that they're already using.”

MemWal and privacy

Privacy is becoming “a much bigger issue for AI systems in general,” Garg said, noting that agents are increasingly being called upon to handle sensitive and proprietary data. “Whether that's enterprise workflows, financial information or personal context, the expectations around confidentiality increase significantly,” he added.