From 600 Notes to 3,500: Semantic Search for AI Agent Memory

Updated March 22, 2026: added section on workspaces, document pinning, and weighted multi-workspace search. Two weeks ago I fixed an authentication bug. Today I can't find the note about it. I search for "login problems". Nothing. I search for "auth". Nothing, because the note says "fixed session token validation in middleware". Grep is useless when you don't remember the exact words you used. But that's a small problem. The real one is bigger. I run a dozen AI agents. Each one starts fresh every session. No memory of yesterday's decisions. No context from last week's architecture change. Every morning, my first 30 minutes go to re-pasting context that existed yesterday but vanished overnight. When AI agents became central to my workflow, memory stopped being optional. I needed infrastructure that remembers. This is why I built Mesh. How I got here In December I built mem-cli — a CLI tool backed by PostgreSQL. It worked but was rough: 600 documents, basic tagging,