# membase

The membase MCP server gives agents one long-term memory: search, profile, adding facts and documents. Handy when you switch between models and tools.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed · ★ 80
- Author: unibaseio
- License: not stated · policy=link-only · description only; the file stays with the author
- Source: https://github.com/unibaseio/membase
- Updated: 2026-10-20
- HTML: https://affpapa.org/en/nevibecode/mcp/membase

## What it does

membase keeps one memory that all your AIs read from. You add facts, documents, pages and chat exports, then search, ask questions and drop what should go. Containers keep projects apart.

The MCP server runs two ways. Local starts with `membase --local mcp` and needs `unibaseio-membase[local]` on Python 3.12 or 3.13 plus an `OPENAI_API_KEY`. Hosted connects to `https://api.app.membase.com/mcp-http`; without the `Authorization` header the connection is read-only. The `agent`, `import` and `serve` commands need the local package.

Hosted mode needs `MEMBASE_API_KEY`. The Python package is `unibaseio-membase`, while `membase` on PyPI is a different project, so don't mix them up at install time. The license is proprietary.

## Requirements
- Needs: Python and uv (commands run via uvx)
- Remote server: `https://api.app.membase.com/mcp-http`

## Install

### Claude Code (native platform)

**Platform CLI: remote server**
```bash
claude mcp add --transport http membase https://api.app.membase.com/mcp-http
```

### Codex

**Platform CLI: remote server**
```bash
codex mcp add membase --url https://api.app.membase.com/mcp-http
```

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "membase": {
            "url": "https://api.app.membase.com/mcp-http"
        }
    }
}
```

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "membase": {
            "type": "http",
            "url": "https://api.app.membase.com/mcp-http"
        }
    }
}
```

### Gemini CLI

**Platform CLI: remote server**
```bash
gemini mcp add -t http membase https://api.app.membase.com/mcp-http
```

## When to use it
- stop re-explaining project context when you switch models
- find which database a service uses
- pull a Claude or ChatGPT chat export into one memory
- keep memory separated per project with containers
- ask a question over accumulated documents and get an answer

## Example prompts
- Connect the membase MCP server and look up which database we picked for the ledger
- Add a fact to the Engineering container: we picked Postgres for the ledger
- Import the chat export in `claude-export.json` into memory

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NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md