How I Built a Persistent Memory Layer for AI Coding Tools

If you use AI coding assistants daily, you have felt this pain. You open a new session with Claude Code, Cursor, or Copilot, and you spend the first twenty minutes re-explaining your project structure, your preferences, the bug you fixed yesterday, the architectural decisions you made last week. The AI has no idea. Every session starts from absolute zero. I started measuring this. In my own workflow, I was burning 20-25 minutes per session on context restoration alone. That is not the worst part. MCP servers — the tools that extend these AI assistants — consume tokens just by loading. I have watched 67,000 tokens disappear before I even typed my first prompt. That is roughly half the context window on most models, gone before any actual work begins. Context fills up. The conversation dies. You start a new one. The cycle repeats. Now multiply this across a team. Five developers, each running four AI sessions per day, each losing twenty minutes to context re-establishment. That is nearly