How does AI memory work across different LLMs?

Most LLM memory — ChatGPT, Claude, Gemini — is locked to that one provider and does not move with you. To keep memory across different models you need an external layer (via MCP, an API, or an app) that stores your memory independently and injects it into whichever model you are using.

Native memory is siloed

ChatGPT’s memory stays inside OpenAI and caps at roughly 1,200–1,400 words total. Claude’s memory lives on Anthropic’s servers. Google’s Gemini personalization is not available in the EEA, Switzerland or the UK. None of them lets you take a structured memory and use it inside a different provider’s model.

MCP is the portability standard

The Model Context Protocol (MCP) is an open standard that lets AI clients connect to external tools and data sources. An MCP memory server is provider-independent: the same memory can be read by Claude Desktop, Cursor, VSCode and other MCP-compatible clients. That is how memory becomes portable instead of trapped.

How Alma does it

Alma stores your memory independently of any single model and exposes it three ways: inside its own chat (Claude Haiku, Sonnet, Opus), over its MCP server to other AI clients, and via a REST API and SDK on every paid plan. Switch models without losing context, and keep one memory underneath all of them.

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