# 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 status, trigger sync, update settings |
| `setup` | `warmup`, `setup_sync` | Pre-download embedding model, authenticate sync provider |
| `help` | -- | Full documentation for any tool |

### MCP Resources

| URI | Description |
|:----|:------------|
| `mnemo://stats` | Database statistics and server status |
| `mnemo://recent` | 10 most recently updated memories |

### MCP Prompts

| Prompt | Parameters | Description |
|:-------|:-----------|:------------|
| `save_summary` | `summary` | Generate prompt to save a conversation summary as memory |
| `recall_context` | `topic` | Generate prompt to recall relevant memories about a topic |

## Configuration

| Variable | Required | Default | Description |
|:---------|:---------|:--------|:------------|
| `API_KEYS` | No | -- | API keys (`ENV:key,ENV:key`). Enables cloud embedding + reranking |
| `COHERE_API_KEY` | No | -- | Cohere API key (embedding + reranking) |
| `JINA_AI_API_KEY` | No | -- | Jina AI API key (embedding + reranking) |
| `GEMINI_API_KEY` | No | -- | Google Gemini API key (LLM + embedding) |
| `OPENAI_API_KEY` | No | -- | OpenAI API key (LLM + embedding) |
| `DB_PATH` | No | `~/.mnemo-mcp/memories.db` | Database location |
| `EMBEDDING_BACKEND` | No | auto-detect | `cloud` (auto-detect from API keys: Jina > Gemini > OpenAI > Cohere) or `local` (Qwen3) |
| `EMBEDDING_MODEL` | No | auto-detect | Cloud embedding model name |
| `EMBEDDING_DIMS` | No | `0` (auto=768) | Embedding dimensions |
| `RERANK_ENABLED` | No | `true` | Enable reranking (improves search precision) |
| `RERANK_BACKEND` | No | auto-detect | `cloud` (auto-detect from API keys: Jina > Cohere) or `local` (Qwen3) |
| `RERANK_MODEL` | No | auto-detect | Cloud reranker model name |
| `RERANK_TOP_N` | No | `10` | Number of top results to keep after reranking |
| `LLM_MODELS` | No | `gemini-3-flash-preview` | LLM model for graph extraction, importance scoring, consolidation (google-genai or openai) |
| `ARCHIVE_ENABLED` | No | `true` | Enable auto-archiving of old low-importance memories |
| `ARCHIVE_AFTER_DAYS` | No | `90` | Days before a memory is eligible for auto-archive |
| `ARCHIVE_IMPORTANCE_THRESHOLD` | No | `0.3` | Memories below this importance score are auto-archived |
| `DEDUP_THRESHOLD` | No | `0.9` | Similarity threshold to block duplicate memories |
| `DEDUP_WARN_THRESHOLD` | No | `0.7` | Similarity threshold to warn about similar memories |
| `RECENCY_HALF_LIFE_DAYS` | No | `7` | Half-life for temporal decay in search scoring |
| `SYNC_ENABLED` | No | `false` | Enable rclone sync |
| `SYNC_PROVIDER` | No | `drive` | rclone provider type (drive, dropbox, s3, etc.) |
| `SYNC_REMOTE` | No | `gdrive` | rclone remote name |
| `SYNC_FOLDER` | No | `mnemo-mcp` | Remote folder |
| `SYNC_INTERVAL` | No | `300` | Auto-sync interval in seconds (0=manual) |
| `LOG_LEVEL` | No | `INFO` | Logging level |

### Embedding & Reranking

Both embedding and reranking are **always available** -- local models are built-in and require no configuration.

- **Jina AI (recommended)**: A single `JINA_AI_API_KEY` enables both embedding and reranking
- **Embedding priority**: Jina AI > Gemini > OpenAI > Cohere. Local Qwen3 fallback always available
- **Reranking priority**: Jina AI > Cohere. Local Qwen3 fallback always available
- **GPU auto-detection**: CUDA/DirectML auto-detected, uses GGUF models for better performance
- All embeddings stored at **768 dims**. Switching providers never breaks the vector table

### Security

- **Graceful fallbacks** -- Cloud → Local embedding, no cross-mode fallback
- **Sync token security** -- OAuth tokens stored at `~/.mnemo-mcp/tokens/` with 600 permissions
- **Input validation** -- Sync provider, folder, remote validated against allowlists
- **Error sanitization** -- No credentials in error messages

## Build from Source

```bash
git clone https://github.com/n24q02m/mnemo-mcp.git
cd mnemo-mcp
uv sync
uv run mnemo-mcp
```

## Compatible With

[![Claude Code](https://img.shields.io/badge/Claude_Code-000000?logo=anthropic&logoColor=white)](#quick-start)
[![Claude Desktop](https://img.shields.io/badge/Claude_Desktop-F9DC7C?logo=anthropic&logoColor=black)](#quick-start)
[![Cursor](https://img.shields.io/badge/Cursor-000000?logo=cursor&logoColor=white)](#quick-start)
[![VS Code Copilot](https://img.shields.io/badge/VS_Code_Copilot-007ACC?logo=visualstudiocode&logoColor=white)](#quick-start)
[![Antigravity](https://img.shields.io/badge/Antigravity-4285F4?logo=google&logoColor=white)](#quick-start)
[![Gemini CLI](https://img.shields.io/badge/Gemini_CLI-8E75B2?logo=googlegemini&logoColor=white)](#quick-start)
[![OpenAI Codex](https://img.shields.io/badge/Codex-412991?logo=openai&logoColor=white)](#quick-start)
[![OpenCode](https://img.shields.io/badge/OpenCode-F7DF1E?logoColor=black)](#quick-start)

## Also by n24q02m

| Server | Description |
|--------|-------------|
| [wet-mcp](https://github.com/n24q02m/wet-mcp) | Web search, content extraction, and documentation indexing |
| [better-notion-mcp](https://github.com/n24q02m/better-notion-mcp) | Markdown-first Notion API with 9 composite tools |
| [better-email-mcp](https://github.com/n24q02m/better-email-mcp) | Email (IMAP/SMTP) with multi-account and auto-discovery |
| [better-godot-mcp](https://github.com/n24q02m/better-godot-mcp) | Godot Engine 4.x with 18 tools for scenes, scripts, and shaders |
| [better-telegram-mcp](https://github.com/n24q02m/better-telegram-mcp) | Telegram dual-mode (Bot API + MTProto) with 6 composite tools |
| [better-code-review-graph](https://github.com/n24q02m/better-code-review-graph) | Knowledge graph for token-efficient code reviews |

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md).

## License

MIT -- See [LICENSE](LICENSE).
