Existing solutions (Nowledge Mem, Mem0) want you to install apps, configure MCP servers, run local LLMs, and set up plugins per tool. For what? To remember that I prefer tabs over spaces?
Mem-Forever is a GitHub template repo. You click "Use this template", set it to private, and open it with any AI tool. The repo contains instruction files that every major tool auto-reads:
Claude Code reads CLAUDE.md, Cursor reads .cursorrules, Codex reads AGENTS.md, Copilot reads .github/copilot-instructions.md, Gemini CLI reads GEMINI.md.
On first use, the AI asks you a few questions and builds your profile. After that, it saves decisions, lessons, and preferences -- committed and pushed after every update, not batched to session end.
Switch tools? Give the new one your repo URL and a PAT. One sentence, full context.
No server. No app. No account. No vendor lock-in. Your data lives in your private GitHub repo.
Free, MIT licensed.
smadam9•4m ago
I've seen many users that need a wider breadth of memory across more topics, where structure and organization of that memory plays a big part in the LLM's performance.
My response to that was a local system that I ended up turning into Sig <https://sig-ai.app/>
It has some overlap to how you've approached it, but differs in other obvious ways.
Having said all that, I'm just highlight another use case for memory. I think your approach is a very valid approach for a lot of people. I appreciate the simplicity and lack of lock-in.