The problem: your AI has amnesia between tabs
You explain your project to Claude in the morning. By the afternoon you're in ChatGPT, and it has no idea what you told Claude three hours ago. Switch to Cursor to write the code, and it doesn't know about either conversation. Every AI client you use — chat app, coding assistant, browser extension — keeps its own private memory, if it keeps one at all. The result is a familiar tax: you re-paste the same background, re-explain the same decisions, and re-upload the same documents into every tool you touch.
This isn't a limitation of any single AI model. Claude, ChatGPT, Gemini, and the coding agents built on top of them are all capable of holding useful context — the problem is that each client's memory (when it has one) is siloed to that client, tied to that vendor's account, and invisible to every other tool you use. A decision your team made in a Claude conversation doesn't exist for ChatGPT. A document you gave to ChatGPT doesn't exist for the AI inside your code editor. You end up maintaining the same knowledge in N different places, and it drifts every time.
The fix isn't a better memory feature inside any one client — it's a memory layer that sits underneath all of them, so the client you happen to be using today is just a window onto the same underlying knowledge.
How it works
Great Arrow Digital runs a memory server that every AI client connects to over Model Context Protocol (MCP), an open standard for letting an AI client call external tools and data sources through a consistent interface. Instead of each client keeping its own private notes, they all read from and write to the same workspace: memories, documents, chat history, and connected-integration data (Gmail, Slack, GitHub, and more) are stored once, encrypted at rest, and made searchable to whichever client is asking.
Day to day, this looks like: you tell Claude about a client requirement, and that fact is embedded and stored in your workspace. Later, when you ask ChatGPT a related question, it searches the same memory before answering and finds what Claude stored — no copy-paste required. The MCP endpoint (https://www.greatarrowdigital.com/api/mcp) is the single door every client walks through; web clients authenticate with OAuth 2.1 and dynamic client registration, and file-based clients (CLIs, desktop apps, IDEs) use a personal API token. Either way, the memory on the other side is identical.
Setting it up
- Create an account. Sign up at /sign-up — no credit card required for the 14-day trial.
- Open the install page. Go to /install once you're signed in. This page mints your personal install credentials (an OAuth connection for web clients, or an API token for file-based ones) and is intentionally gated behind sign-in so those credentials are never exposed publicly.
- Pick your client. The install page groups clients by how much setup they take:
- One-click — Claude Desktop, Cursor, and GitHub Copilot install via a deeplink or a universal installer script; no terminal required.
- One-liner — Claude Code, Codex, and Gemini CLI connect with a single copy-pasted terminal command that wires up the MCP endpoint, your token, and a client identifier in one shot.
- Guided (OAuth) — Claude.ai, ChatGPT, Gemini web, and similar browser-based clients walk you through adding a custom connector in that client's own settings (typically Settings → Connectors → Add custom connector), then complete an OAuth consent screen — no token to copy or paste.
- Repeat for every client you use. Because every client points at the same MCP endpoint and the same workspace, there's no extra step to "sync" them — connecting a second client just gives it a second window onto the memory the first one already built.
- Confirm the connection. The install page can detect a successful connection automatically for most clients; for the few where automatic detection isn't reliable, a manual confirmation step is available.
From here, anything one client learns is available to the next one you open — with no export, import, or copy-paste in between.
What this changes day to day
The practical effect is that context stops resetting when you switch tools. If you use Claude for research and drafting but Cursor for implementation, the requirements and decisions captured in Claude are already there when you open Cursor. If your team uses ChatGPT for one workflow and Claude for another, both are drawing on the same underlying facts about your project, your customers, and your integrations — rather than two separate, slowly diverging pictures.
Connected integrations extend the same idea beyond chat: syncing Gmail, Slack, GitHub, or Notion means the content from those tools becomes part of the shared memory too, so any connected AI client can search across your conversations, code, and documents — not just what you've typed into it directly.
Where to go next
If you're deciding between connecting Claude and ChatGPT specifically, Share Memory Between Claude and ChatGPT walks through both setups side by side and what each client sees once connected. If you want the protocol-level explanation before rolling this out to a team, What Is MCP? A Guide for Teams covers what MCP does, remote vs. local servers, and what to evaluate in a memory server before adopting one.