Sharing something I’ve been running with Cursor for a few months that solved
a real annoyance for me — the morning context-reset problem.
The setup: a small MCP server (~600 lines, Python) that gives Cursor a
persistent “brain” it can write to and read from across sessions. Plus a
tiered file-memory convention so routing rules and preferences auto-load
on cold start, while detailed reference material stays on-demand.
Why I bothered: every morning I was losing 20-40 minutes re-explaining
the same project context to Cursor. With this, the agent walks in already
knowing what we worked on yesterday, what we tried, what worked. It’s not
magic — it’s just a project log the agent writes to and reads from
deliberately.
What you get:
- MCP server (compose file, runs locally or in any container host)
- A tiered
/memories/convention you wire into your agent customization - Cold-start protocol: ~3 KB brief that pulls everything else on demand
- Brand stamp + audit trail discipline (this part is convention, not
enforced — gap I’m honest about)
Six pillars total documented in the whitepaper. MIT licensed.
Site (whitepaper, architecture, hardware reality, comparison): https://www.recall.works/
Repo: GitHub - RecallWorks/Recall: Open-source memory for AI agents. MCP-native. Self-hosted. One Docker image. · GitHub
Curious if anyone here has solved the cross-session memory problem
differently — I haven’t seen many open patterns for it that aren’t
locked into a SaaS.