Lighthouse 为 AI agent 做持久记忆 MCP

Dracule Mihawk @Mihawk_Research原推发布于 2026.10.22

在 X 上打开原推

I just came across a new project in the @Virtuals_io ecosystem that could bring a crucial piece to Agentic Economics: persistent, shared, verifiable memory for AI agents. That project is @LighthouseWeb3, building decentralized storage and persistent memory for AI agents. A few numbers from Lighthouse’s storage infrastructure caught my attention: – ~34,000–34,600 builders/users. – ~50 TiB of storage consumed. – Nearly 10 million file objects. – Over 10 million API requests. – 500+ projects previously reported to be using it in production. The project also counts @WalrusProtocol, @fenbushi, and @balajis among its investors and supporters. With so many AI tools losing context between sessions or when switching models, @LighthouseWeb3 is tackling a frustrating problem. Here’s what stands out: – Expanding from decentralized storage with a “pay once, store forever” model into a permanent state layer and persistent brain for AI agents. – Multiple storage backends: IPFS for hot retrieval, Filecoin for durability and proofs, and Walrus. – A Memory product with remember / flush / recall primitives, local semantic search, tags/namespaces, and selective sharing controls. – AI Backups: Backup-as-a-Service designed to protect against agents deleting data. – An MCP server + SDK for easier integration, supporting clients and frameworks including Claude, ChatGPT, Cursor, LangChain, and CrewAI. – Client-side encryption and strong access controls. These tools could play a meaningful role in making the agent economies around @Virtuals_io and @RobinhoodCrypto work more efficiently: – Agents retain context briefs, decisions, research notes, constraints, and preferences across sessions and between different agents. – Longer workflows become easier to maintain across research, trading, content, coordination, and more. – A natural fit for Agent Commerce Protocol and Virtuals tooling: context can travel with a task instead of disappearing after every session. Lighthouse approach to persistent agent memory has earned my attention. Agents handling ongoing work need a reliable way to retain and share what they learn. robinhood:0xfc5a5c521c80d8e1d6b5aee6454b8c9483c91bff has also completed its TGE and currently sits at around a $3M market cap. With the focus now on the Memory product and deeper integration into the agent ecosystem, robinhood:0xfc5a5c521c80d8e1d6b5aee6454b8c9483c91bff could have plenty of room to run if Agentic Economics takes off. DYOR and NFA.