You use more than one AI. Claude for writing, Cursor for code, ChatGPT on your phone, maybe an agent in a script. Each one starts from zero and forgets the moment you close it — and none of them share what they learn. A shared memory layer fixes both problems at once: one persistent store every model reads from and writes to.
The problem: every model is an island
Native memory features (ChatGPT Memory, Claude Projects) are per-vendor silos — locked to one app, invisible to the others. Tell ChatGPT your stack and Claude still has no idea. Switch tools and your context is gone. Memory that only works inside one product isn't memory; it's lock-in.
What a shared memory layer is
- A store that outlives the session — facts, decisions, and preferences saved as retrievable records.
- Retrieval by meaning, not keywords — "what did we decide about auth?" finds the right memory.
- One address every tool can reach — the same memory from your IDE agent, your chatbot, and your scripts.
How it works with Kit for AI
Kit for AI is a cloud memory layer with two tools at its core — remember to store a fact, recall to retrieve by meaning. MCP-native agents call them directly; everything else uses the REST API or the SDK. Either way it's one account, one memory, shared across every model you connect.
Set it up for your models
Wire up the tools you already use: Claude, Cursor, Windsurf, Cline, ChatGPT, Gemini, GitHub Copilot. Each connects to the same memory — MCP-native clients in one config block, the rest over REST.
Stop re-explaining yourself to every AI. Start free and give all your models one shared memory.