Setup Guides/ChatGPT
ChatGPT

Memstate + ChatGPT

Give ChatGPT Desktop persistent, structured memory via MCP.

ChatGPT's built-in memory is limited to personal facts and preferences. Memstate gives ChatGPT Desktop a full structured memory layer via MCP — with versioned keypaths, semantic search, and project-scoped context that persists across every conversation.

1

Get Your API Key

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2

Add MCP Configuration

Add this to your ChatGPT MCP config file. Replace YOUR_API_KEY_HERE with your key.

MCP Configuration (JSON)
{
  "mcpServers": {
    "memstate": {
      "command": "npx",
      "args": ["-y", "@memstate/mcp"],
      "env": {
        "MEMSTATE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}
📁

Save to:

~/Library/Application Support/ChatGPT/claude_desktop_config.json

ChatGPT Desktop MCP support

MCP is supported in ChatGPT Desktop (macOS and Windows). The MCP config file path is:

  • macOS: ~/Library/Application Support/ChatGPT/claude_desktop_config.json
  • Windows: %APPDATA%\ChatGPT\claude_desktop_config.json

After saving the config, restart ChatGPT Desktop. The Memstate tools will appear in the tool picker.

ChatGPT.com (browser) does not support MCP

MCP is only available in the ChatGPT Desktop app. The web version at chatgpt.com does not support MCP servers.

3

Add Agent Rule

This rule tells ChatGPT to load your project context before tasks and save summaries after — automatically, every session.

Custom Instructions
# Memstate MCP - Memory Storage

This project uses Memstate MCP for persistent, versioned memory across sessions.

## REQUIRED: Start and end every task with memory

**BEFORE starting any task**, load relevant context so you don't redo past work or revert intentional decisions. **Prefer targeted retrieval** — do not dump the full memory tree by default.

**Default — search by what you're working on:**
```
memstate_search(query="<task topic>", project_id="<your_project>")
```

**When you know the exact keypath — fetch that subtree only:**
```
memstate_get(project_id="<your_project>", keypath="<subtree>")
```

**Full tree (discovery only):** `memstate_get(project_id="<your_project>")` with **no keypath** returns **every memory** in the project. Use this only when you need to explore the full tree to discover keypaths and values — not as the default before every task.

**AFTER completing any task**, save what you did so the next session has context:
```
memstate_remember(project_id="<your_project>", content="## Task Summary\n- What was done\n- Key decisions made\n- Files modified", source="agent")
```

Reading before tasks and saving after are both important. Skipping them means the next session starts blind.

## Tool reference

| Tool | When to use |
|------|-------------|
| memstate_search | **Default before tasks.** Find relevant memories by meaning. Returns ranked results without flooding context. |
| memstate_get | Fetch a **specific subtree** when you know the keypath. With no keypath, returns the **full project tree** — discovery/exploration only. |
| memstate_remember | **End of every task.** Save markdown summaries, notes, decisions. |
| memstate_set | Store a single key=value fact (e.g. config.port). Not for summaries. |
| memstate_history | View version history of a keypath. |
| memstate_delete | Soft-delete a keypath (history preserved). |

## Project naming
Use a short snake_case name matching your repo or topic (e.g. my_app, api_service). All related memories should share the same project_id.

Paste into Custom Instructions or AGENTS.md at your project root.

Test It — Onboard Your Project

Restart ChatGPT, open a project, and paste this prompt. It will create your first memories and confirm everything is working.

Replace <your_project> with a short name for your repo (e.g. my_app).

Paste into your AI agent
I'm onboarding Memstate AI memory for this project. Please:
1. Analyze this codebase and write a concise high-level architecture overview in markdown — covering the main components, tech stack, key directories, and how data flows through the system.
2. Save it to Memstate using: memstate_remember(project_id="<your_project>", content="<the markdown>", source="agent")
3. Then call memstate_search(query="architecture overview", project_id="<your_project>") and show me the results so I can confirm it worked.

What to expect

Your agent will analyze the codebase, write an architecture overview, save it to Memstate, then confirm with search results. You'll see structured memories like project.my_app.architecture — this context is now available in every future session automatically.