As someone who runs multiple AI agents for day-to-day tasks, I kept hitting the same wall: every new conversation was a total memory wipe. I'd ask my agent about a project we discussed yesterday, and it had no idea what I was talking about. Frustrating.
Sure, you can try to feed context into each prompt manually, or build custom plugins for each agent framework. But that's brittle and doesn't scale. I needed something that worked across any agent — Hermes, Claude Code, even my own custom scripts — without invasive surgery.
That's why I built Memory Sidecar.
The Core Idea
A sidecar process that runs alongside your agent. It watches your conversations, extracts what's important, and builds a long-term memory structure. When your agent needs context, the sidecar injects relevant information into the system prompt. All without patching the agent or rewriting its internals.
