Most 'AI memory' demos hardcode the store. That breaks the moment you change models or add a second agent. The fix is to treat memory as a capability your tools expose, not a database you wire by hand.
Enter MCP. A local memory layer can expose recall / record / consolidation as MCP resources and tools. Any MCP-aware client — Claude, Codex, your own agent — can then read and write memory through one standard interface, with the actual data staying on your disk.
What that buys you:
- Swap models without rewriting memory code.
- One agent's record is visible to the next.
- The store stays local; only the interface is shared.
I am building HyperMarrow, whose four building blocks are exposed this way. The docs are in my profile (?from=devto).
If you have wired MCP before, what was the hardest part to get right?
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