We've all been there. You're working with an AI coding assistant, having a great conversation where it learns your project's architecture, your preferred patterns, the quirks of that legacy module. Then you close the session, open a new one, and it's like you've never met. Blank slate. Every. Single. Time.
Sure, you can paste context into the prompt each time, but that's manual, error-prone, and blows up your token budget. Some folks fine-tune, but that's expensive and slow to update. Others rely on RAG pipelines, but those are complex to set up and often fail to surface the most relevant context at the moment you need it.
What I wanted was simple: a memory system that sits next to my agent, learns from every conversation, and automatically feeds back what matters — without me having to patch the agent's code or redesign my workflow.
That's why I've been using and contributing to Memory Sidecar (v3.1.0), an open-source project that does exactly this.
How It Works, Briefly
