Switching AI tools is easy. Moving the useful context is not.
You may plan in ChatGPT, investigate in Claude, implement in Cursor, and continue through Codex. Each tool sees the prompt, files, and connections available in its own session. The reasoning that produced yesterday’s decision does not automatically follow you.
There are four practical ways to move context: copy and paste, shared project files, export and import, and an external memory layer.
First decide what needs to move
Do not transfer an entire transcript unless the transcript itself is the artifact.
Most continuations need a compact handoff:
Goal:
Current state:
Decisions and rationale:
Constraints:
What was tried:
Open questions:
Next action:
Relevant files or links:
This format works regardless of the transport. It separates durable project state from conversational noise.
Never include secrets merely because the next tool will need access to the same system. Configure credentials through that tool’s approved secret or connection mechanism.
Method 1: Copy and paste a handoff
The simplest method is often the right one for a one-time move.
Ask the current assistant:
Create a handoff for another AI tool. Include the goal, current state, final decisions and rationale, failed approaches, unresolved questions, and exact next step. Do not include secrets.
Review it, correct mistakes, and paste it into the new client.
Advantages
- Works everywhere
- Requires no integration
- User reviews exactly what crosses the boundary
- Good for a single transition
Limitations
- Manual and repetitive
- Becomes stale immediately
- Easy to omit important history
- Produces separate copies with no ongoing synchronization
Copy-paste is a transport, not persistent memory.
Method 2: Use a shared project file
For coding work, store the handoff in a repository file such as:
docs/ai-handoff.md
Or maintain durable decisions in ADRs, runbooks, and project documentation that every coding tool can read.
Advantages
- Version-controlled and reviewable
- Available to tools that can access the repository
- Keeps authoritative project context close to the code
- Supports team collaboration
Limitations
- Requires maintenance
- Does not naturally cover context across unrelated repositories
- Personal preferences do not belong in every project
- Chat-oriented tools may not have access to the files
Project files are excellent for current shared truth. They are less effective as a searchable history of every relevant episode across tools.
Method 3: Export and import native memory
Some AI products let users export remembered information and import it into another service.
This is helpful when migrating. It provides a snapshot without forcing the user to reconstruct preferences manually.
But import/export does not create live synchronization. After the transfer:
- New context in the original product stays there
- New context in the destination product stays there
- Conflicting updates can develop
- The process must be repeated to refresh the snapshot
Use this method when you are changing primary tools, not when you plan to move between them every day.
Method 4: Connect tools to a shared memory layer
A shared memory layer stores selected context outside any single AI client. Compatible clients connect to it and use the same account.
The workflow becomes:
- Recall relevant context when a session starts
- Work normally in the chosen tool
- Store the durable decision or outcome
- Retrieve it later from another connected client
This is the model behind How to Give ChatGPT and Claude the Same Memory and What Is a Memory Layer for AI Agents?.
Advantages
- Ongoing rather than one-time transfer
- Searchable across sessions and projects
- Avoids duplicating independent memory copies
- Can preserve decisions, episodes, and relationships
Limitations
- Each client needs a supported connection
- Recall may need explicit tool use or automation
- The memory provider becomes an important security boundary
- Users still need to decide what should be stored
What works with Nexus-Catalyst today
Nexus-Catalyst currently publishes connection documentation for:
- Cursor
- Claude Code
- Claude Desktop
These clients can connect to the same authenticated remote MCP server and use the NC memory tools.
ChatGPT connects as a custom connector under Developer Mode, and Perplexity through its Add Custom Connector dialog — both are documented step by step (ChatGPT, Perplexity). Codex is CLI-driven and uses an API key (Codex). Any other client should be evaluated on its current support for remote MCP connections; do not publish a workflow that assumes a client can connect until that path is documented.
A client with no MCP support at all can still participate through an explicit reviewed handoff file or copy-paste summary. That is less automatic, but it keeps the workflow honest.
Connecting Cursor
Cursor can use the remote MCP endpoint globally or at project scope. The server URL is:
https://mcp.nexus-catalyst.com
After browser-based OAuth authentication, test storage and recall from a new session. Follow the Cursor setup guide for the current steps.
The full walkthrough is also available in How to Give Cursor Persistent Memory Across Projects.
Connecting Claude Code
Register the remote server:
claude mcp add --transport http nexus-catalyst https://mcp.nexus-catalyst.com
Then run /mcp inside Claude Code and complete authentication in the browser. The Claude Code setup guide contains the current verification and troubleshooting steps.
See How to Add Persistent Memory to Claude Code with MCP for the tutorial version.
Test context transfer with one decision
Use a specific, non-sensitive test:
- In the first connected client, store: “The Atlas project uses PostgreSQL, and migrations require a documented rollback.”
- Open a fresh session in the second connected client.
- Ask: “What database and migration convention did we choose for Atlas? Recall relevant context before answering.”
- Confirm that the answer identifies the stored decision.
- Correct or delete the test memory if it should not remain.
This verifies actual cross-client retrieval. Seeing the MCP server listed is necessary but not sufficient.
Use the right method for the situation
| Situation | Best starting method |
|---|---|
| One-time move | Reviewed copy-paste handoff |
| Shared truth inside a repository | Project documentation or ADR |
| Leaving one AI service | Export and import |
| Daily work across compatible clients | Shared memory layer |
| Client has no supported connection | Handoff file until support exists |
These methods can coexist. Keep authoritative facts in authoritative systems, use handoffs for deliberate transitions, and use memory for searchable continuity.
Context should follow the work, not the tab
The objective is not to merge every AI product. It is to stop rebuilding the same project understanding each time you choose a different interface.
Start with a clean handoff format. Then automate the transfer where compatible clients and your security requirements allow it.
Give connected clients a common history
Start your 14-day Nexus-Catalyst trial to test persistent context across supported MCP clients.