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:
- Sources and decisions. Your connected tools (email, chat, documents, code) plus the conclusions you reach in conversation.
- A workspace. One store that holds those facts, scoped to you or your team.
- 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.
- 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 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 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 is isolated at the database layer, so another customer's AI cannot read your data. The full statement is on the security page.
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 covers that half in detail.
Setting it up for all three clients
Create an account at /sign-up, then open /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 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 walks the whole flow.
Cursor. Cursor installs from /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 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 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 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, check what the layer can do on features, or read the plans on pricing.