AI tools do not automatically share all of their conversation history or saved memory with each other. To carry context from one product to another, you need an explicit transfer or an authorized integration. A shared memory layer can make saved context available across supported clients; a maintained brief can also work.
This answer surprises people, because the tools feel familiar enough that continuity should be free. It isn't. Understanding why informs what to do instead.
Why can't ChatGPT see my Claude conversations?
Your Claude conversation is stored in that product's account history; ChatGPT does not receive it merely because you use both products. ChatGPT does not automatically have access to what you said in a Claude conversation; you need to transfer the relevant context or use an integration you've authorized. The same applies in the other direction, and for Gemini and Cursor as well.
In practice, the models themselves are stateless. As explained in Why AI forgets conversations, a language model has no memory between requests. Continuity comes from the application supplying history, instructions, or saved context with a request. Another product needs that information to be transferred or made available through an integration before it can use it.
Don't built-in memory features solve this?
They solve part of it, but only within one tool. ChatGPT's memory, Claude's project knowledge, and similar features genuinely help when staying inside that one product. They have three structural limits when you don't:
- They are provider-bound. Native memory is not automatically shared with every other provider; explicit transfer or an integration is needed.
- Their scope varies. Native features can use saved facts, conversation history, files, or project knowledge, depending on the product and settings.
- Visibility varies. Check which memories you can view, edit, export, and delete, and whether a summary includes everything that may be used.
Built-in memory can provide useful continuity within a product. Sharing that information across products is a separate requirement.
What does cross-tool AI memory actually require?
For one tool to use context from another, the relevant information must be transferred or made accessible to it. A shared memory store can provide three useful capabilities:
- Provider-agnostic capture. Configured clients save exchanges, decisions, or facts to a shared store.
- Independent storage. Saved records can be held separately from a particular client's conversation history.
- Retrieval into compatible clients. A connected client requests relevant available context for the current task.
The model itself still has no inherent memory. A configured client can request relevant available context and include it in the prompt. From your seat it looks like the AI remembered, even though, mechanically, it never did.
How is a memory layer connected to all these tools?
The practical mechanism is the Model Context Protocol, an open standard for connecting AI tools to external context sources. A growing set of clients, Claude, Cursor, and others, speak MCP, which means a single MCP-based memory server can serve supported clients through a common protocol. Each client still needs its own connection and authentication. The background is in What is MCP (Model Context Protocol).
This is the approach Vilix AI takes. It is a persistent memory layer across supported clients such as ChatGPT, Claude, Cursor, and Codex. Connect and authenticate each client, then add the Vilix AI instructions or agent-loop calls. Verify get_context with the latest message, compose the reply, call save_turn with the exact exchange, and return the saved reply. Reuse chat_id within a conversation. The Gemini app is not currently a supported custom-MCP client. Vilix AI stores the exchanges clients send and returns relevant available context. History and search features depend on the plan, and you can inspect, export, or delete saved memories through your account controls. Vilix AI is not the only way to build this, but is a working example of the layer-above-providers design.
A concrete walkthrough
Say you research a vendor comparison in ChatGPT on Monday, make the architecture call with Claude on Tuesday, and start implementing in Cursor on Wednesday. Unless that research and decision were shared, Wednesday's editor may lack them. You may need to supply the relevant details yourself.
With all three clients configured to save and retrieve context, Monday's successfully saved findings can inform Tuesday's discussion. Cursor can then retrieve relevant saved decisions for Wednesday's implementation. Check that the returned context includes the details the task needs.
Frequently asked questions
Does Claude remember ChatGPT conversations?
Not automatically. Claude and ChatGPT keep their own conversation history. Relevant information can be transferred through a brief, export, or authorized integration; a shared memory layer is one option.
Can Cursor access my ChatGPT or Claude memory?
Not natively. Cursor keeps its own workspace context and cannot reach into other vendors' memory. An MCP-connected memory server can supply shared context to Cursor and the others alike.
Will providers eventually make memory portable themselves?
Providers may add more import, export, or integration options over time. A shared store is an approach available today, discussed in Why cross-AI memory matters.
Is cross-tool memory just pasting a brief into each tool?
A maintained brief is a useful way to transfer context. A configured memory layer can automate saving and retrieval, reducing manual copying while still requiring current information and appropriate context requests.
Do I have to give a memory layer all my chat data?
You should expect to control what is captured and to be able to inspect, export, and delete it. Memory you cannot audit is memory you should not trust.
The short version
AI cannot remember across tools on its own, without a way to share the relevant context. The realistic answer is to own the memory layer yourself or use one built for the job. If a shared memory across your AI tools is what you want, you can try Vilix AI free and see whether the cross-tool continuity holds up in your own workflow. The Free plan has no time limit, and Pro includes a 7-day trial.
Originally published on Vilix AI.
Top comments (0)