Memstate + ChatGPT

{
  "mcpServers": {
    "memstate": {
      "command": "npx",
      "args": ["-y", "@memstate/mcp"],
      "env": {
        "MEMSTATE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

# 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 existing context so you don't redo past work or revert intentional decisions:
```
memstate_get(project_id="<your_project>")
```

**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")
```

These two calls are mandatory. Skipping them means the next session starts blind.

## Tool reference

| Tool | When to use |
|------|-------------|
| memstate_get | **Start of every task.** Browse project tree or fetch content at a keypath. |
| memstate_remember | **End of every task.** Save markdown summaries, notes, decisions. |
| memstate_search | Find memories by meaning when you don't know the exact keypath. |
| 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.

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_get(project_id="<your_project>") and show me the memory tree so I can confirm it worked.