Share Context Between ChatGPT, Claude, and Cursor: 4 Approaches, Honestly Compared

You do not use one AI tool anymore. Nobody does. You draft the plan with ChatGPT, you build it with Cursor, you debug the hard part with Claude. Each of them keeps its own diary, and none of them talk to each other. The moment you switch tools, your context starts over: decisions vanish, preferences reset, and the plan you made this morning is a stranger to the assistant you are talking to tonight. That is the tax on multi-tool work, and you pay it in re-typing.

The question "how do I share context between ChatGPT, Claude, and other AI tools" has four honest answers. They differ in what you pay: minutes, effort, or trust.

Approach 1: Carry the conversation yourself

The manual answer has grown up a lot. ctxport copies a thread into a clean Markdown bundle with one click, in full, user-only, code-only, or compact formats, even from the sidebar without opening the conversation. It is open source MIT and runs entirely in the browser, with nothing uploaded anywhere. ThreadPort moves the live conversation itself between ChatGPT, Claude, Gemini, Perplexity, and Mistral: the full transcript arrives wrapped in a handoff prompt, attachments get noted so the next AI knows what it cannot see, and code blocks travel as real fenced code. Ten transfers a month are free.

The real use cases are concrete: pulling a long ChatGPT thread into Claude for a second opinion, or shifting a chat off Claude onto ChatGPT when you hit a usage limit. The vendors are adding their own moves too, like Claude's memory import that pulls your saved ChatGPT memories in through a guided flow.

All of these are handoffs, not sync. Every context move is a conscious act, and whatever you do not carry stays behind.

Approach 2: Snapshot the codebase

For developers, most of the missing context is the repository itself. The context CLI by Mees Akveld turns a project into a structured text representation: install it with brew install meesakveld/tap/context, run context --clipboard inside the project, and paste the result into Claude, ChatGPT, or Gemini. A single cross-platform binary with no runtime required, output in text, Markdown, JSON, or ZIP, a YAML config, tree-only mode, built-in exclusions for dependencies and build output, and safe handling of environment files so credentials stay out of the paste. The project is at github.com/meesakveld/context.

This answers "here is my project" perfectly. It cannot answer what you decided about the project, what you tried and abandoned, or what the other tool already knows. Repository context is not working memory.

Approach 3: Self-host shared memory over MCP

This is the first approach where you stop carrying anything. second-brain-cloudflare runs the entire memory layer on a Cloudflare Worker: D1 for storage, Vectorize for semantic search, and five MCP tools (remember, append, recall, list_recent, forget) exposed to any compatible client. One-click deploy on Cloudflare's free tier, an AUTH_TOKEN you set during deployment guarding it, and a small dashboard to browse what is stored. Connect each MCP-capable client to the worker and they all read one store: a note saved in Claude Desktop shows up when Cursor asks for it.

The tradeoff is the honest one: free hosting, paid in your time. You own the deploy, the updates, the schema, and the occasional 2 a.m. breakage. If you are the kind of person who reads the migration notes before upgrading, this is your approach. One caution before you commit: the protocol is standard, but client support is not uniform, so verify that every tool you use actually accepts the MCP server you deploy.

Approach 4: Let a hosted service carry it

The same architecture, run by someone else. Vilix AI is a cloud-hosted memory layer over MCP: one account, and every connected client reads and writes the same memory. Plan in Claude, build in Codex, your context comes with you. Agents pull what is relevant with get_context and save new exchanges with save_turn, so the store holds full conversation history rather than a summary of facts.

Cloud-hosted means you manage nothing: no worker to deploy, no database to back up, no tokens to rotate. Data is isolated per user, everything can be pulled out in a portable format anytime, and individual memories or the entire account can be deleted instantly. The free plan is free forever, and the 7-day Pro trial asks for no credit card.

The tradeoff is the mirror of approach 3: a managed service, not open source, and the memory lives on hosted servers. If that sentence bothers you, approach 3 is waiting, and no sales paragraph was going to change your mind.

A 30-second decision

Frequency is the whole decision:

• You switch tools once a month: carry the conversation by hand.
• You switch tools inside one codebase every day: snapshot the repo with the CLI.
• You switch tools daily and want full control: self-host the memory layer.
• You switch tools daily and want zero operations: use a hosted layer like Vilix AI.

FAQ

Can one setup really serve all three at once? With the MCP approaches (3 and 4), yes: register the same server in each client and they share the store, with the caveat to confirm client support first. The manual tools move one thread at a time.

Does connecting a memory layer import my old chats? No. Connecting does not import history. Memory starts accumulating from the moment you connect; the past stays where it happened.

What happens when two tools save conflicting decisions? With shared memory this has a clean answer: last write wins. Correct it once, for example by saying the old decision no longer stands, and that becomes the truth every tool sees going forward. You fix it in one place instead of re-briefing every client.

What about ChatGPT's built-in memory and Claude's projects? Both are provider-locked. ChatGPT memory does not follow you to Claude, and Claude projects do not follow you to Cursor. That boundary is exactly the gap approaches 3 and 4 exist to close.

Is my memory used to train models? With Vilix AI, data is isolated per user: no sale of user data, and no training third-party models on private memory.

Context should outlive the chat

The point was never which tool is best. It is that context should survive the session it was created in. The four approaches above are four different ways to make that true, at four different prices in time, effort, and trust. If the daily handoff is the pain, stop routing it through your clipboard: a shared memory layer is the only answer that works while you are not looking. The free plan is a low-stakes way to test the claim: connect two clients, save a decision in one, recall it from the other, and let the result decide.