I got tired of re-explaining my project to every AI tool, so I built a local memory layer for them

I kept running into the same problem: ChatGPT would help me think through an architecture, Claude Code would help me implement it, Cursor or Windsurf would touch the repo later, and every handoff would lose context. Not just “what files exist,” but the stuff that actually matters: why we chose one approach, what we already rejected, what the project conventions are, what setup detail will bite later, and what the agent learned last time. So I built mem-port: a local MCP server that gives AI copilots shared long-term memory. The short version is: a pendrive for your AI context. It runs locally, uses embedded SurrealDB for graph + vector memory, and doesn’t require Postgres, Qdrant, Neo4j, or a hosted service. Tools can save and search the same memory instead of each one starting from zero. It’s free and open source. Curious if anyone else is dealing with this context drift between AI tools, and how you’re solving it. See more here: (Started getting github stars as well!) https://github.com/rsl-innovation/mem-port#mem-port