What is cross-AI memory?

Cross-AI memory is a persistent memory layer shared between several AI tools. Instead of being restricted to the history within one tool, supported clients such as ChatGPT, Claude, Cursor, and Codex can read from and write to the same store. Saved context, decisions, and preferences are then available across the tools you connect.

Why the problem exists

Many AI tools keep chat history, instructions, or native memory within a product. That context isn't automatically shared with your other tools. You can explain your stack to Claude, switch to Cursor, and need to explain it again; a decision saved in ChatGPT may be missing from your next Codex session.

Using several AI tools can mean re-explaining the same project, preferences, and decisions. Re-pasting rules and restating context interrupts the work itself. Each tool may learn about you within its own product, while the rest of your AI workflow still lacks that information.

Cross-AI memory solves this by decoupling memory from any one tool and making it a shared infrastructure layer, like how a database is shared across services in your app. The details you save are available to the supported tools you connect to that layer.

How MCP makes it possible

The technical enabler is the Model Context Protocol (MCP), an open standard allowing AI tools to connect to external servers as first-class context providers. Before MCP, injecting persistent memory into an AI tool needed a browser extension or a proprietary integration built separately for each tool. MCP standardizes the interface, allowing one server to serve many tools.

With MCP, a memory server exposes tools the AI can call, such as get_context to load relevant memories and save_turn to persist an exchange. Connect and authenticate each client, then add the Vilix AI custom instructions or wire those calls into the agent loop. Registering a server makes the tools available; the client still needs to invoke them.

How Vilix AI implements cross-AI memory

Vilix AI is an MCP-native persistent memory layer. Connect each supported client by adding api.vilix.ai/mcp as a custom MCP connector in each AI tool you use. After authentication, add Vilix AI's custom instructions and verify this sequence in a real conversation:

• Before each reply: the AI calls get_context, which loads your recent messages, saved memories, related past conversations, your user_rules, and live project and task state into its context window.

• After composing the reply, before sending it: the AI calls save_turn with the exact exchange. It then sends that saved reply and reuses the returned chat_id within the same conversation.

The memory lives server-side in your Vilix AI account, tied to you rather than to any one tool. That is what makes it genuinely cross-AI: the same store backs ChatGPT, Claude, Claude Code, Cursor, Codex, Manus, Grok, GitHub Copilot, Windsurf, Lovable, and other clients that support Vilix AI's remote MCP connection.

Three surfaces, one memory

Vilix AI exposes cross-AI memory through three distinct surfaces:

• Cross-tool memory with semantic and keyword search. Available past conversations, decisions, and facts can be retrieved when relevant. Search and history access depend on your plan.

• Projects and Tasks, a lightweight project manager whose state auto-injects into context. Every connected AI tool sees what is open, what is done, and what the current goal is.

• Rules: user_rules (short personal style directives like “short answers” or “always use TypeScript”) and project_rules (per-project stack, conventions, tone). These rules are available when connected tools request context; the model still needs to follow them.

Why it matters more than tool-specific memory

ChatGPT has memory. Claude has Projects. Cursor has its rules file. Each is a local solution for a global problem. They store context inside one tool and it goes nowhere else. When you switch tools, some relevant context may be missing.

Cross-AI memory keeps saved information in a store independent of any one assistant. Connected tools can retrieve relevant information from that store, reducing the context you have to transfer by hand. A context request returns a useful selection, rather than loading the entire archive or guaranteeing that the model follows every detail.

Every model launch makes the gap concrete. Does Claude Fable 5 have memory across chats? breaks down exactly where the model's native and app-level memory stops at the app boundary, and how the cross-AI layer extends it.

Go deeper

The posts below cover every dimension of cross-AI memory in detail. Each is a standalone read, but they are designed to reinforce each other.

• A unified memory layer for AI development workflows covers how cross-AI memory fits into a multi-tool dev stack.

• A centralized memory layer for AI explains the architecture behind a single shared store.

• Same AI memory across devices shows how server-side memory means context follows you across machines.

• Avoid copy-pasting context between AI tools walks through the manual workarounds cross-AI memory replaces.

• Share project rules across AI assistants covers how project_rules and user_rules propagate to every tool.

• Stop re-explaining projects to AI is the practical quickstart for getting persistent project context working across your stack.

Getting started

Vilix AI is free to try with a 7-day full-Pro trial. Connect it to one AI tool in under two minutes by adding api.vilix.ai/mcp as a custom MCP endpoint. See the MCP setup guide for step-by-step instructions for each supported tool.

Try Vilix AI free and build the memory layer your whole AI stack shares.

Frequently asked questions

What is cross-AI memory?

Cross-AI memory is a persistent memory layer shared between several AI tools. Instead of each tool keeping isolated history, one store holds your context, decisions, and preferences, and configured clients can read from and write to it through MCP.

How does Vilix AI implement cross-AI memory?

Vilix AI connects to supported AI tools as a custom MCP server at api.vilix.ai/mcp. With the custom instructions installed, the AI calls get_context, composes its reply, calls save_turn with the exact exchange, and then sends the saved reply. Memory lives server-side in your Vilix AI account.

Which AI tools support cross-AI memory via Vilix AI?

Supported clients include ChatGPT, Claude, Claude Code, Cursor, Codex, Grok, Manus, GitHub Copilot, Windsurf, Lovable, OpenClaw, Hermes, and more. You connect Vilix AI once per tool and the same memory is available in all of them.

Is cross-AI memory different from ChatGPT memory or Claude Projects?

Yes. Tool-specific memory stays within its own product unless you transfer it. Cross-AI memory makes saved information available to other connected clients, reducing the context you need to repeat when you switch tools.

Do I need a browser extension to use Vilix AI?

No. Vilix AI is MCP-native and requires no browser extension. You connect it by adding api.vilix.ai/mcp as a custom MCP endpoint in each supported AI tool. Setup takes under two minutes.