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Walrus Memory
A portable, verifiable memory layer for AI agents.
What it does
Stores agent memories with semantic vector embeddings so data is searchable by meaning rather than keywords. Enforces programmable ownership and access rules via blockchain smart contracts to manage who reads and writes data. Extracts structured facts automatically from text conversations for precise, long-term context recall. Provides drop-in AI middleware for frameworks like the Vercel AI SDK to maintain continuous conversation context.
Why it works
Decentralized blob storage on Walrus removes single points of failure and prevents vendor lock-in for data. Onchain ownership via Sui smart contracts grants users verifiable control over access permissions. Portable design allows memory to travel seamlessly across different apps, agents, and runtimes. Semantic search capabilities surface exact contextual memories instantly using natural language queries.
Category: AI & Automation
Team Size: 11-25
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As of 2026
AI & Automation
Revenue: 0-$100k ARR
A portable, verifiable memory layer for AI agents.
Overview
Walrus Memory is a decentralized, portable memory layer designed to help AI agents retain context and coordinate reliably across apps and sessions. Built on decentralized storage protocols and smart contracts, it gives users explicit programmable ownership over their memory assets. The platform prevents agents from losing context between tasks, solving a key bottleneck in multi-step AI workflows. Developers can easily embed it into applications via comprehensive TypeScript and Python SDKs.
HQ: Remote
Funding status: Funded
Revenue source: Open Source Infrastructure
Customer type: B2B
Pricing: Free (Open Source)
Tech stack: TypeScript, Python, Smart Contracts, Walrus Storage, Vector Embeddings
Platform: Web
Integrations: Vercel AI SDK, TypeScript SDK, Python SDK, Sui Smart Contracts, Walrus Storage
Founder story
Walrus Memory was developed by the Walrus Foundation team, including community contributors like Abner Pinto, to solve the core limitation of AI agents losing state between sessions. Launched in 2026, the project was built to give developers a production-grade, verifiable memory architecture that decouples agent memory from single proprietary platforms.
What it does
- Stores agent memories with semantic vector embeddings so data is searchable by meaning rather than keywords
- Enforces programmable ownership and access rules via blockchain smart contracts to manage who reads and writes data
- Extracts structured facts automatically from text conversations for precise, long-term context recall
- Provides drop-in AI middleware for frameworks like the Vercel AI SDK to maintain continuous conversation context.
Who it's for
- AI Developers
- Software Engineers
- Multi-Agent System Builders
- Web3 Developers
Why it works
- Decentralized blob storage on Walrus removes single points of failure and prevents vendor lock-in for data
- Onchain ownership via Sui smart contracts grants users verifiable control over access permissions
- Portable design allows memory to travel seamlessly across different apps, agents, and runtimes
- Semantic search capabilities surface exact contextual memories instantly using natural language queries.
Growth strategies
- Open- source repository distribution and developer documentation portals
- Product Hunt community launches and AI infrastructure showcases
- Ecosystem integration with developer frameworks like Vercel AI SDK
- Strategic alignment with decentralized storage and Sui developer networks
Alternatives
Comparison overview
- Walrus Memory utilizes decentralized storage and smart-contract-enforced ownership to keep agent memory portable across independent applications, whereas traditional memory tools like Mem0 or Zep typically store data within centralized databases or vendor-locked infrastructure.