If you've been building with AI agents for any length of time, you've hit the wall. You know the one — your agent finishes a brilliant multi-step task, you close the session, and next time it starts from absolute zero. No memory of what it learned. No context about your project. Nothing. I've been wrestling with this problem across a couple of projects, and a tool called Stash just showed up on GitHub trending that takes an interesting approach to solving it. The Problem Stash Is Trying to Solve Most AI agent frameworks treat memory as an afterthought. You get a context window, maybe some vector search bolted on, and that's about it. But real persistent memory — the kind where an agent remembers that your production database uses a specific naming convention, or that you prefer tabs over spaces (fight me) — that's been surprisingly hard to do well. Stash positions itself as a "persistent memory layer" for AI agents. The pitch: episodes, facts, and working context stored in Postgres.…