# Share Context Across Claude, ChatGPT and Cursor

> Native memory does not travel between AI clients. Here is how a shared context layer over MCP gives Claude, ChatGPT and Cursor the same facts about your work.

Canonical page: https://www.greatarrow.ai/guides/share-context-across-claude-chatgpt-cursor · updated 2026-09-15

If you use more than one AI client, the fix is an external context layer that every client connects to — over Model Context Protocol for Claude and Cursor, and as a custom connector for ChatGPT. Each vendor's built-in memory stays inside that vendor's account and cannot be read by the others, so the shared store has to sit underneath all three rather than inside any one of them.

## Why native memory does not travel

Claude, ChatGPT and Cursor each have some notion of memory, and none of them can see the others'. Claude's memory is attached to your Anthropic account. ChatGPT's saved memories are attached to your OpenAI account. Cursor keeps project rules and indexed code local to the editor. These are three separate stores, owned by three separate vendors, with no interchange format and no API that lets one read another.

That is not an oversight anyone is about to fix. Memory is a retention feature for each vendor — it is part of why you stay — so there is no commercial reason for Anthropic to expose your Claude memory to OpenAI. Waiting for interoperability between the vendors is not a plan.

The consequence is the tax you already pay. You brief Claude on a client's constraints in the morning. In the afternoon you are in ChatGPT and it knows none of it. You move to Cursor to implement, and it knows neither conversation. Each switch costs a re-brief, and the three pictures drift apart as soon as one of them learns something the others do not.

## What a shared context layer actually is

A shared context layer is a single store of durable facts that every AI client reads from and writes to as an external tool. The clients stay exactly what they are; what changes is where the facts live.

The architecture has four parts:

1. **Sources and decisions.** Your connected tools (email, chat, documents, code) plus the conclusions you reach in conversation.
2. **A workspace.** One store that holds those facts, scoped to you or your team.
3. **Permission-aware retrieval.** When a client asks a question, the layer searches the workspace and returns the relevant facts with a pointer back to where each one came from.
4. **The clients.** Claude, ChatGPT and Cursor, each connected to the same workspace.

The important property is direction: facts flow _into_ the workspace from any client and _out_ to every client. A decision recorded during a Claude conversation is retrievable from ChatGPT twenty minutes later, because both are querying the same store rather than their own.

Model Context Protocol is what makes this practical rather than bespoke. MCP is [an open standard](https://modelcontextprotocol.io/) for letting an AI client call an external tool over a consistent interface. Write one server, and every MCP-compatible client can talk to it. [What Is MCP? A Guide for Teams](/guides/what-is-mcp-for-teams) covers the protocol at evaluator level.

## Shared context is not synced chat history

This distinction is worth getting right before you set anything up, because the two are often confused and only one of them is a good idea.

**Synced chat history** means copying every message from every client into a common pile. It is expensive, it is mostly redundant, and it makes retrieval worse rather than better — the signal you want is buried under thousands of turns of "sounds good" and "can you try again". It also drags the maximum amount of content into a third-party store for the minimum amount of benefit.

**Shared context** means the durable conclusions: the decision and the reason for it, the client's actual constraint, the current state of the project, the thing that must never be done again. That is a much smaller set, it is what you would have re-explained anyway, and it is what stays true after the conversation ends.

Practically: let each client keep its own scrollback, and push the conclusions to the shared layer.

## How GreatArrow.ai implements this

GreatArrow.ai runs the shared workspace as a remote MCP server at `https://www.greatarrow.ai/api/mcp`. Every client connects to that one endpoint. Web clients authenticate with OAuth; file-based clients (desktop apps, CLIs, editors) use a personal API token that is stored as a one-way hash and shown to you exactly once.

Stored content and the credentials for connected tools are encrypted at rest with AES-256-GCM, and every workspace-scoped request resolves its tenancy server-side before it reaches storage, so another customer's AI cannot read your data. The full statement is on the [security page](/security).

Retrieval returns the source alongside the fact, so an answer can point at the record it came from rather than asserting something you have to take on faith. [Give ChatGPT and Claude Company Data With Citations](/guides/ai-access-company-data-with-citations) covers that half in detail.

## Setting it up for all three clients

Create an account at [/sign-up](/sign-up), then open [/install](/install) while signed in — that page mints your credentials, which is why it is behind sign-in.

**Claude.** GreatArrow.ai is published in Claude's connector directory, so you click Connect on the listing and Claude runs the OAuth sign-in itself. That one connector covers Claude on web, desktop and mobile. On plans without directory access, use Settings → Connectors → Add custom connector with the MCP URL above. For Claude Desktop specifically, [Persistent Memory for Claude Desktop](/guides/persistent-memory-for-claude-desktop) covers the one-click installer.

**ChatGPT.** ChatGPT connects as a custom connector authenticated with OAuth. In ChatGPT, open Settings → Connectors and add the MCP URL; managed Business, Enterprise and Edu workspaces may need an OpenAI admin to allow connectors first. [Connect ChatGPT to Your Business Data](/guides/connect-chatgpt-to-your-business-data) walks the whole flow.

**Cursor.** Cursor installs from [/install](/install) via a deeplink — no terminal required. If you point the connection at a repository URL, Cursor gets a workspace scoped to that repo, so the context it reads is the context for that codebase. [Give Cursor Persistent, Project-Aware Memory](/guides/give-cursor-persistent-memory) covers repository scoping.

There is no separate sync step. Connecting a second client just opens a second window onto the workspace the first one is already writing to.

## A concrete cross-client example

Say a client tells you on a call that they will not accept a monthly billing cycle — it has to be annual, invoiced.

In Claude, you record it: _"Remember that Northwind will not accept monthly billing. Annual, invoiced only. They said so on the 3rd."_ Claude writes it to the workspace.

Two days later you are drafting the proposal in ChatGPT and ask about billing terms. ChatGPT searches the workspace before answering, finds the fact, and uses annual invoiced terms — and, because retrieval carries the source, it can tell you where the constraint came from rather than presenting it as its own inference.

You never re-typed it, and the two clients did not disagree.

## Who this is for, and who it is not for

**It is for you if** you routinely use two or more AI clients, work on things that persist for weeks, or hand context between a chat assistant and a coding tool.

**It is not for you if** you use exactly one AI client and intend to keep it that way — native memory is simpler and free. It is also not the right shape if what you actually want is full-text search over a document corpus with no conversational capture; a document search tool is a better fit for that.

## Where to go next

[Shared Memory Across Claude, ChatGPT, Gemini and Cursor](/guides/shared-memory-claude-chatgpt-gemini-cursor) covers which clients support what, and what the current limits are. If the problem you actually feel is the re-briefing itself, [Stop Re-Explaining Your Company to Every AI Tool](/guides/stop-re-explaining-your-company-to-ai) starts with a manual template you can use today, with no account required.

You can also see how this compares to built-in assistant memory, memory libraries and enterprise search on the [comparison page](/compare), check what the layer can do on [features](/features), or read the plans on [pricing](/pricing).

## Questions

### Can Claude and ChatGPT actually read the same memory?

Yes, but not through either vendor's own memory feature. Claude's memory lives in your Anthropic account and ChatGPT's lives in your OpenAI account, and neither exposes an API the other can read. The working approach is a third store that both clients connect to as an external tool — over Model Context Protocol for Claude and Cursor, and as a custom connector for ChatGPT. Each client then reads and writes the same workspace.

### Do I have to copy my chat history into the shared layer?

No, and you generally should not. Shared context means the durable facts — decisions, client details, constraints, project state — not every message you have ever sent. Transcript history is long, mostly redundant, and expensive to embed. Store the conclusions and let each client keep its own scrollback.

### What happens if I stop using one of the clients?

Nothing is lost. Because the facts live in the shared workspace rather than inside a vendor's account, removing a client only removes that client's access. The memory it wrote stays readable from every client still connected, which is the main practical difference between a shared layer and per-vendor memory.

### Does this work with Cursor specifically, or only chat apps?

Cursor is an MCP client, so it connects the same way Claude Desktop does. The useful wrinkle for coding tools is repository scoping: a workspace can be pinned to a repository URL, so the context a coding agent reads is the context for that codebase rather than everything you have ever stored.

### Is a shared context layer the same thing as RAG over my documents?

They overlap but are not the same. Retrieval over documents answers questions from files you have already written down. A shared context layer also captures facts that were never written into a document — a decision made in conversation, a constraint a client mentioned once — and makes those retrievable from any connected client.