15 September 2026 · 5 min read
You work with an AI for weeks. It gets to know you well: your decisions, your constraints, your way of doing things. Then you want to try another AI, and it all has to be redone. That is the problem a portable memory solves.
What portable means
A memory is portable when it does not live inside a model, but alongside it, so any AI can read it. You plug in Claude today, ChatGPT or Gemini tomorrow, and the thread stays the same. When a new model comes out, you connect it, and your years of context are already waiting.
The standard that makes it possible
Portability rests on MCP, an open standard the major AIs already share. Nothing to install, nothing to code: you connect the AI you use, in a few clicks, to read and to write. Claude, ChatGPT, Gemini, Mistral, Perplexity and the others speak the same language with your memory.
What a good memory keeps
Keeping facts is not enough. What matters is the why: the decision made, the constraint set, the idea set aside and the reason that went with it. A portable memory captures this as you work, without you filing anything, and brings it back at the right moment the next day or six months later.
Who owns it
This is the real question. If your memory lives inside a model, it belongs to whoever runs the model. If it lives with you, it is yours. With Nawame it is private by default, encrypted, and the key is yours: you can even host it on your own free key server, so that no one else, us included, can read it. Your data stays hosted in Europe, and you export everything in Markdown or JSON whenever you want.
When it changes everything
The day you switch tools without re-explaining anything. The day a new AI starts already up to speed. The day a team shares the same context and nobody starts from scratch. A portable memory is not one more feature: it is the end of starting over.