Cross-model AI memory

Cross-model AI memory is shared context that survives across different assistants and UIs. Teams rarely standardize on one model vendor; they standardize on outcomes (shipping software, supporting customers, drafting policy). Memory should follow the work, not the brand on the chat box.

The anti-pattern: screenshots and long system prompts

Without a memory layer, teams paste the same ten paragraphs into every tool's system prompt or maintain parallel Notion docs. That breaks the moment someone updates a rule in one place but not the other. Persistent, centralized memory reduces that drift: one write, many readers.

Private deploy: one memory pool for a pilot team

In a typical private deployment, every engineer points their MCP client (for example Cursor) at the same internal endpoint and uses the same agent key. They then share one logical memory pool for project rules and corrections, without each person maintaining a separate sandbox. If you later need strict per-user isolation, you can issue per-user API keys instead; that is a product choice, not a protocol limitation.

Tags, origins, and governance

Useful cross-model setups rely on conventions: tags for team vs. personal, origins that record which tool wrote a memory, and occasional human review of what got captured during a sprint. Version history helps when a rule changes; you can see what the assistant believed last week versus today.