io.github.n24q02m/mnemo-mcp

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# Mnemo MCP Server

mcp-name: io.github.n24q02m/mnemo-mcp

**Persistent AI memory with hybrid search and embedded sync. Open, free, unlimited.**

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<a href="https://glama.ai/mcp/servers/n24q02m/mnemo-mcp">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/n24q02m/mnemo-mcp/badge" alt="Mnemo MCP server" />
</a>

## Features

- **Hybrid search** -- FTS5 full-text + sqlite-vec semantic + reranking for precision
- **Knowledge graph** -- Automatic entity extraction and relation tracking across memories
- **Importance scoring** -- LLM-scored 0.0-1.0 per memory for smarter retrieval
- **Auto-archive** -- Configurable age + importance threshold to keep memory clean
- **STM-to-LTM consolidation** -- LLM summarization of related memories in a category
- **Duplicate detection** -- Warns before adding semantically similar memories
- **Zero config** -- Built-in local Qwen3 embedding + reranking, no API keys needed. Optional cloud providers (Jina AI, Gemini, OpenAI, Cohere)
- **Multi-machine sync** -- JSONL-based merge sync via embedded rclone (Google Drive, S3, Dropbox)
- **Proactive memory** -- Tool descriptions guide AI to save preferences, decisions, facts

## Quick Start

### Claude Code Plugin (Recommended)

Via marketplace (includes skills: /session-handoff, /knowledge-audit):

```bash
/plugin marketplace add n24q02m/claude-plugins
/plugin install mnemo-mcp@claude-plugins
```

Or install this plugin only:

```bash
/plugin marketplace add n24q02m/mnemo-mcp
/plugin install mnemo-mcp
```

Configure env vars in `~/.claude/settings.local.json` or shell profile. See [Environment Variables](#environment-variables).

### MCP Server

#### Option 1: uvx

```jsonc
{
  "mcpServers": {
    "mnemo": {
      "command": "uvx",
      "args": ["--python", "3.13", "mnemo-mcp@latest"]
    }
  }
}
```

<details>
<summary>Other MCP clients (Cursor, Codex, Gemini CLI)</summary>

```jsonc
// Cursor (~/.cursor/mcp.json), Windsurf, Cline, Amp, OpenCode
{
  "mcpServers": {
    "mnemo": {
      "command": "uvx",
      "args": ["--python", "3.13", "mnemo-mcp@latest"]
    }
  }
}
```

```toml
# Codex (~/.codex/config.toml)
[mcp_servers.mnemo]
command = "uvx"
args = ["--python", "3.13", "mnemo-mcp@latest"]
```

</details>

#### Option 2: Docker

```jsonc
{
  "mcpServers": {
    "mnemo": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "--name", "mcp-mnemo",
        "-v", "mnemo-data:/data",
        "-e", "API_KEYS",
        "-e", "SYNC_ENABLED",
        "n24q02m/mnemo-mcp:latest"
      ]
    }
  }
}
```

Configure env vars in `~/.claude/settings.local.json` or your shell profile. See [Environment Variables](#environment-variables) below.

### Pre-install (optional)

Pre-download the embedding model (~570 MB) to avoid first-run delays.
Use the `setup` MCP tool after connecting:

```
setup(action="warmup")
```

### Sync setup

Sync is fully automatic. Just set `SYNC_ENABLED=true` and the server handles everything:

1. **First sync**: rclone is auto-downloaded, a browser opens for OAuth authentication
2. **Token saved**: OAuth token is stored locally at `~/.mnemo-mcp/tokens/` (600 permissions)
3. **Subsequent runs**: Token is loaded automatically -- no manual steps needed

For non-Google Drive providers, set `SYNC_PROVIDER` and `SYNC_REMOTE`:

```jsonc
{
  "SYNC_ENABLED": "true",
  "SYNC_PROVIDER": "dropbox",
  "SYNC_REMOTE": "dropbox"
}
```

## Tools

| Tool | Actions | Description |
|:-----|:--------|:------------|
| `memory` | `add`, `search`, `list`, `update`, `delete`, `export`, `import`, `stats`, `restore`, `archived`, `consolidate` | Core memory CRUD, hybrid search, import/export, archival, and LLM consolidation |
| `config` | `status`, `sync`, `set` | Server s