The best cross-AI memory tool: stop re-explaining yourself across ChatGPT, Claude, and Cursor

If you use only one AI assistant, you may rarely think about moving context between tools. If you use ChatGPT for writing, Claude for code, Cursor in the editor, Codex in the terminal, or Gemini in Workspace, you've probably had to repeat a brief. Those products can keep their own histories, but switching between them does not automatically carry your project decisions along. The more tools you use, the more time you can spend explaining the same work.

Tools that help with this are often called cross-AI memory. This post explains how they work alongside ChatGPT memory and Claude Projects, and what to check before using one across your supported AI clients.

What is a cross-AI memory tool?

A cross-AI memory tool stores context outside an individual AI assistant and lets supported clients retrieve it. What it can save and return depends on its integrations, permissions, and the requests those clients make. Look for three capabilities:

• It captures from supported sources. Look at the import/integration path for each AI tool you use.
• Storage is provider-agnostic. Your facts, decisions, and preferences can remain outside an individual AI provider when you switch tools. Check export options and keep access to the memory account that holds them.
• It retrieves into supported destinations. A connected client can request relevant saved context before replying.

You may see phrases such as "one memory across every AI tool" or "universal AI memory layer." In practice, compatibility has limits. MCP, the Model Context Protocol, is one way a memory service can expose tools to compatible AI clients. Each client still needs a supported connection, authentication, and instructions or an agent loop that makes the required calls.

How is it different from ChatGPT memory or Claude Projects?

ChatGPT memory and Claude Projects both reduce repetition, but they're mismatched for the cross-tool use case. We covered the side-by-side in detail in ChatGPT memory vs Claude Projects: what's the difference?. The short version:

• ChatGPT memory can use useful context from chats, files, and connected apps when enabled. You can review and edit a memory summary and manage memory in Settings. These features personalize ChatGPT; they do not automatically bring your Claude or Cursor history into it.
• Claude Projects organizes chats, uploaded knowledge, and instructions in a project workspace. That knowledge helps Claude within the project. Leaving the project does not delete it or automatically make it available in another AI product.
• Neither automatically shares memory across providers. Built-in ChatGPT memory and Claude project knowledge primarily support their own products. Moving selected context into other tools requires a separate integration or transfer.

You can keep using those features for continuity and project knowledge within each assistant, then add a cross-AI memory service for supported transfers between tools.

How does Vilix AI work?

Vilix AI is a cross-AI memory layer. After connecting each client and adding the Vilix AI instructions, the workflow is:

• Before each AI reply, the configured client calls get_context with your latest message. It uses the returned context, which may include relevant saved conversations, project information, and preferences, to compose its answer. Check that the request succeeds; the result is a selected set of context, not the entire history.
• After composing the reply, before sending it, the client calls save_turn with the exact user message and composed reply. Once the save succeeds, it returns that same reply. Reuse chat_id within the conversation.
• Across every connected tool, clients authenticated to your same Vilix AI account can request relevant context that other clients have successfully saved. For example, Cursor can request a decision saved from ChatGPT. A connection does not guarantee that a particular exchange will be retrieved, and it does not grant other users access to your memory.

Connect each supported client to the same Vilix AI account. OAuth authenticates for clients that support it; headless clients can use the supported API-key/Bearer setup. Configuration varies by client. Add the Vilix AI custom instructions or agent-loop calls, and verify get_context and save_turn in a real conversation.

Alongside conversation memory, Vilix AI offers two other kinds of context that configured clients can request:

• Projects & Tasks. Create a project and add tasks with state (todo / doing / blocked / done) and priority. A configured client can request relevant project context and use the available project tools to read or update records you authorize. Keep task status current and verify updates. See the features page.
• user_rules. Personal style directives in plain English, "Short answers," "No tables," "Show code first." Configure once so connected tools can retrieve them. Review the output, because a model may miss a rule.

What does it remember, facts about me, or actual work?

Check whether the service stores only profile details or can also preserve the work and decisions you want to revisit.

A one-time profile import or questionnaire can be useful, but it can become outdated as your projects change. Look for a way to add new decisions, correct old information, and keep project records current.

Vilix AI stores the exchanges a client successfully submits through save_turn, including the decisions and preferences discussed in them. Project and task records can be maintained through the available tools. Retrieval selects context relevant to a request within the limits of your plan. It can miss a useful detail, so check important answers against the saved records.

Try asking, "What was I working on three days ago, and what is still open?" Compare the answer with the exchanges you saved and the current task records. This tests both what was captured and what the client retrieved.

Is my data private?

The drafts, decisions, and unfinished ideas you save can be sensitive. Vilix AI's privacy policy describes these controls:

• Your memory is scoped to your account. Another Vilix AI user cannot retrieve it through their own account.
• Vilix AI uses secure transport such as HTTPS and account-scoped access controls to protect data.
• Vilix AI says it does not sell your conversations or use them to train or fine-tune foundation models. Context you send to a connected AI tool is also subject to that provider's terms and privacy policy.
• You can export your stored data from the dashboard in a portable format.
• You can delete records from the active service and request account deletion. Backup removal follows the retention schedule in Vilix AI's privacy policy.

For a deeper read on where memory should live and how to think about the trade-offs, see Local vs cloud AI memory.

How do I set it up?

The fast version, in order:

• Start the 7-day free trial, no credit card required.
• Copy your MCP URL from the dashboard.
• Paste it into your AI tool's connector settings (see the setup docs for the exact path in each tool).
• Authenticate, add the Vilix AI custom instructions, and verify get_context and save_turn in a real conversation.

Some clients require a desktop or web interface to create a custom connector. The same Vilix AI account holds your saved context, but whether a particular mobile app can call the connector depends on that client's current support. Check the setup path and verify the client you plan to use.

Connector availability depends on the client, your account, and workspace policy. In ChatGPT, the current OpenAI instructions place Developer Mode under Settings → Security and login, then add the MCP connection through ChatGPT Plugins. Claude provides custom-connector setup through Customize → Connectors; Team and Enterprise organizations may require an owner to add the connector first. Check the current setup instructions for your client and verify both retrieval and saving.

Which AI tools does it work with?

Today, Vilix AI connects to:

• Claude (supported desktop or web accounts, subject to plan and workspace settings)
• ChatGPT (eligible accounts or workspaces with Developer Mode and a configured MCP connection)
• Cursor
• Codex
• GitHub Copilot
• Windsurf
• Other clients with compatible custom MCP, transport, and authentication support; check their setup requirements

The Gemini app is not currently a supported Vilix AI custom-MCP client. This is separate from using Gemini models through a developer client with compatible tool support. To reuse work from the Gemini app, copy the relevant findings into a supported connected client, save them to Vilix AI, and verify the saved content before asking another tool to retrieve it.

What good cross-AI memory feels like

The real test is how well your tools can pick up the work you've already saved. Here is an example to try after configuring and testing each client:

Monday morning, you plan a billing flow in a supported ChatGPT client and verify that the exchange is saved to Vilix AI. On Tuesday, ask Claude to retrieve those decisions and draft a spec, then save the completed exchange. On Wednesday, ask Cursor to retrieve the spec before implementing it. Once you've verified what shipped, save those release details. On Thursday, ChatGPT can request them to help draft a launch email. At each handoff, check that the retrieved details match the work.

The goal is to spend less time repeating project decisions across supported tools. Reliable use still involves maintaining important records and checking the context each client receives.

What to look for in a cross-AI memory tool

• Captures full conversations, not just distilled facts. Check what the service actually stores and whether a saved exchange retains the details you need to revisit.
• MCP-native. A standard protocol can connect a memory service to multiple compatible clients. Check each client's transport, authentication, and tool-call support.
• Clear setup per tool. Look for documented connection, authentication, and memory instructions. OAuth, API keys, or configuration files may be appropriate depending on the client.
• Source-aware retrieval. Tool and project metadata should help you check where a record came from and whether it belongs to the work at hand.
• Export and deletion controls. Check how to export records, request deletion, and understand backup retention before relying on a memory service.
• No training on your private memory. Check the memory service's policy and the policies of AI providers that receive retrieved context.
• Projects, tasks, and user_rules, not just chat memory. The work AIs need to see is bigger than a transcript.

Try it

If the cross-tool problem describes your day, you can connect your supported tools and test a shared-memory workflow. Start the 7-day Vilix AI free trial, Pro features and limits included, no credit card. If you don't upgrade, you keep a limited Free plan, and your data stays exportable.

Related reading: Why AI forgets conversations is the prequel to this post, why memory has to live outside any single model. Why cross-AI memory matters covers the compounding cost of context resets.

Originally published on Vilix AI.