If you've ever worked with AI agents in production, you know the frustration: every new session starts from scratch. The agent has no memory of previous conversations, no context about ongoing projects, and you have to repeat yourself constantly. It's like Groundhog Day for your AI.
I ran into this with a code assistant I was using for a multi-week refactoring project. It was great for one-off questions, but it couldn't remember what we discussed yesterday. I'd ask it about the architecture decisions we made last week, and it would stare at me blankly. I needed something that could carry context across sessions without forcing me to patch the agent's internals.
I looked at the usual suspects: vector databases for RAG, ad-hoc session dumping, even fine-tuning. Each had a cost. RAG setups are powerful but often require custom tooling and tight integration. Session logs without structure are just noise. Fine-tuning is expensive and slow to iterate on. What I wanted was a self-contained system that worked with any agent, required no code changes to the agent, and actually understood what to keep and what to forget.
That's when I found Memory Sidecar. It's an open-source project designed to run alongside any AI agent—Hermes, Claude Code, Cursor, Codex, or your own custom setup—as a separate process. It watches your agent's output, archives important conversations, builds a long-term knowledge base, and injects relevant context back before each new session. No patches, no invasive changes.
