Walrus has launched Walrus Memory, a portable memory layer designed specifically for AI agents. The platform enables agents to carry context across applications and sessions whilst maintaining verifiable data integrity. Walrus Memory supports major AI platforms including Claude, ChatGPT and Gemini, along with OpenClaw and NemoClaw agentic frameworks. Memories are encrypted by default with programmable access permissions, allowing coordinated multi-agent workflows through shared memory spaces. The platform launches with native integrations including SDKs for Python and TypeScript, and MCP support. Launch partners include Allium, Conso Labs, Inflectiv, OpenGradient, Talus Labs and Tatum. Walrus Memory is available now, with developers able to start for free at walrus.xyz/memory. The platform was created by Walrus, the verifiable data platform built by former Meta engineers.

MemWal now supports NemoClaw and OpenClaw for long-term agent memory. Walrus foundation. As AI moves from single prompts to persistent agents, one infrastructure gap is becoming obvious: agents need long-term persistent and shareable memory. For enterprises, the infra gap results in fragile workflows, duplicated effort, and no way to share knowledge across teams or systems. Today Walrus is bridging that gap by introducing a NemoClaw/OpenClaw plugin for MemWal, a long-term, verifiable memory layer on Walrus. This agent orchestration frameworks for building autonomous AI workflows allows such agents to easily store durable memory on Walrus and retrieve it across runs, environments, and teams without building custom storage infrastructure. "Memory is quickly becoming the limiting factor for what AI agents can actually do in the real world," explains Abhinav Garg, product manager at Mysten Labs. "Agents effectively reset every session, so an agent that forgets what it did yesterday can't build context, can't improve over time, and ultimately cannot be trusted for anything beyond short, stateless tasks." Walrus, a verifiable data platform, complements NemoClaw and OpenClaw by acting as a persistence layer for agent memory, workflow state, and AI artifacts. This is relevant as the ecosystem around agent frameworks evolves and enterprises look for infrastructure that is open, portable, and not tied to a single vendor. How it works. The MemWal plugin connects NemoClaw and OpenClaw agents to MemWal's memory model so developers can add persistent memory with minimal integration work. The plugin exposes MemWal memory operations as tools the agent can call during execution, allowing memory reads and writes to become part of the normal agent workflow. At a high level, the integration pattern is simple: NemoClaw/OpenClaw | agent execution MemWal plugin | memory interface MemWal service | memory management Walrus | durable storage Why long-term memory matters for NemoClaw and OpenClaw agents. Enterprise agents need more than short-term context windows or temporary databases. They need memory that persists across deployments, teams, and time. For example, agents supporting research, compliance, finance, or operations often need to maintain knowledge that evolves over months or years. Walrus via MemWal enables this by acting as durable infrastructure where NemoClaw and OpenClaw agents periodically store: * memory snapshots and knowledge states * workflow checkpoints and execution traces * reasoning summaries and tool outputs * environment state needed for reproducibility With the MemWal plugin, NemoClaw and OpenClaw agents can automatically persist these artifacts as structured memories rather than treating them as logs or temporary outputs. This enables agents to be restarted, audited, or migrated without losing institutional knowledge. Instead of agents being ephemeral processes, they become persistent systems with explainable histories. This becomes particularly valuable for enterprises that need to answer questions like: * Why did the agent take this action? * What data did it rely on? * Can Walrus reproduce this workflow? How NemoClaw and OpenClaw agents integrate with MemWal. MemWal allows NemoClaw and OpenClaw agents to use long-term memory without needing to directly manage storage workflows. Instead of interacting with Walrus directly, agents can use MemWal's memory abstractions to write checkpoints, retrieve context, and maintain long-term state. While NemoClaw and OpenClaw agents can technically write directly to storage systems, MemWal simplifies this by providing a structured memory model, reusable memory abstractions, ownership & access controls, and portable memory across environments. This allows NemoClaw and OpenClaw developers to treat memory as a system capability rather than infrastructure they must build themselves. The MemWal NemoClaw/OpenClaw plugin allows agents to: * Store structured agent memories through MemWal memory spaces * Retrieve relevant memories during future agent runs * Persist workflow checkpoints automatically * Share memory across agents using common memory spaces * Separate short-term reasoning from durable knowledge As a result, developers can add long-term memory to existing agents without redesigning their architecture. This allows memory to become part of the agent's reasoning loop rather than an external system. Developers can add MemWal memory to NemoClaw or OpenClaw agents by: * Installing the MemWal plugin * Configuring a memory space * Calling memory write/read tools from the agent loop Why this matters now. As agents move from demos to production systems, the ability to maintain durable memory will become as important as reasoning quality. The MemWal plugin shows how memory can become a standard layer in agent architecture rather than an afterthought. The composable layer of NemoClaw or OpenClaw, MemWal, and Walrus illustrates a broader architectural direction emerging in AI: Compute should be flexible. Memory should be persistent and shareable. Data should be verifiable, private, and portable. "Our view is that memory and data shouldn't be tied to any single model or platform," said Abhinav. "Walrus becomes that verifiable data layer, and MemWal becomes the memory layer on top of it. If that works, agents can become more portable, more collaborative, and much more reliable over time." For enterprises adopting agentic systems and for open-source developers evaluating alternatives to increasingly centralized AI stacks, this combination represents an interesting path forward. Use the quick start guide to add MemWal memory to your agents Reach out to Walrus on Discord via the developers channel if you're building with these tools and are interested in collaborating

Tatum integrates Walrus to accelerate blockchain data access for developers and AI agents. Walrus Foundation. Tatum, a leading blockchain infrastructure provider trusted by Chainalysis, TRM Labs, and xPortal, is making 11 TB of historical blockchain datasets available through Walrus. The integration unlocks significant efficiency improvements for developers, businesses and AI agents building on blockchain data. From bottlenecks to instant access. To date, accessing large-scale blockchain datasets, including transaction histories, logs, and indexed block data, meant relying on slow, costly, and sequential RPC-based data reconstruction. With Walrus, Tatum is replacing that with instantly accessible datasets optimized for analytics, machine learning, and large-scale consumption. "At Tatum, our mission has always been to make blockchain development as simple and accessible as possible," said Dion Cornett, CEO of Tatum. "By partnering with Walrus, we're taking a major step forward - not just by simplifying how developers access blockchain data, but by changing how it's delivered while maintaining decentralization. Our partnership clears the path for developers to access decentralized data with the same speed and simplicity as a centralized solution." Structured data, ready for AI. Walrus enables Tatum to package and distribute blockchain data as structured, programmable files directly onchain, without relying on centralized storage or custom delivery pipelines. By eliminating the need for custom pipelines, Tatum's data becomes ready for agentic AI workflows, empowering developers to train models on onchain activity with unprecedented efficiency. "We built Walrus to make data verifiable and globally available. Our work with Tatum is another strong example of what that makes possible," said Rebecca Simmonds, Managing Executive of the Walrus Foundation. "As autonomous agents increasingly interact with blockchain data, being able to verify where that data came from, and have it be highly performant, is mission-critical. We're giving builders and agents what they need to work with onchain data at scale." Phase one and what comes next. The first phase of the integration includes complete historical datasets for Ethereum, Bitcoin, and Binance Smart Chain (BSC), with more to follow from Tatum's 100+ supported networks. The integration opens the door to future onchain data products, including transaction histories, pricing data, and portfolio datasets offered as directly consumable assets, along with new monetization models built around packaging blockchain data.