🧠 claude-memory-mcp

Give Claude (and any MCP client) a persistent long-term memory. 100% local, no API key, no cloud.

LLMs forget everything the moment a conversation ends. claude-memory-mcp is a tiny Model Context Protocol server that gives your assistant a durable memory it can write to and search across sessions — so you stop re-explaining your preferences, your stack, and your decisions every single time.

Everything runs on your machine. Memories live in a local SQLite file, and semantic search uses a local embedding model (all-MiniLM-L6-v2) that runs in-process. No API key. No data leaves your computer.

✨ Features

• Persistent memory across sessions, backed by a single SQLite file you own.
• Semantic search — recall by meaning, not just keywords, via local embeddings.
• Zero API keys / fully offline after the first model download.
• Works with any MCP client — Claude Desktop, Claude Code, Cursor, and more.
• Four simple tools: save_memory, search_memory, list_memories, delete_memory.
• Tiny & hackable — a few hundred lines of TypeScript.

🚀 Quick start

Claude Desktop

Add this to your claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json, Windows: %APPDATA%\Claude\claude_desktop_config.json):

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "claude-memory-mcp"],
      "env": {
        "MEMORY_DB_PATH": "~/.claude-memory/memories.db"
      }
    }
  }
}

Restart Claude Desktop. You'll see the memory tools appear in the tools menu.

Claude Code

claude mcp add memory -- npx -y claude-memory-mcp

From source

git clone https://github.com/<you>/claude-memory-mcp.git
cd claude-memory-mcp
npm install
npm run build
node dist/index.js   # speaks MCP over stdio

🛠️ Tools

Example prompts

• "Remember that I prefer TypeScript with 2-space indentation." → save_memory
• "What do you know about my coding preferences?" → search_memory
• "List everything tagged project-x." → list_memories

⚙️ Configuration

🧩 How it works

1. save_memory embeds the text with a local MiniLM model and stores the text, tags, and vector in SQLite.
2. search_memory embeds your query and ranks stored memories by cosine similarity — all in-process, no network calls.
3. The database is a plain SQLite file, so it's easy to back up, inspect, or sync yourself.

The first run downloads the embedding model (~25 MB) and caches it locally; every run after that is fully offline.

🔒 Privacy

Your memories never leave your machine. There is no telemetry and no external API. Delete the database file to wipe everything.

🤝 Contributing

Issues and PRs welcome! This project is intentionally small — good first issues include new storage backends, memory expiry/TTL, and export/import commands.

📄 License

MIT — see LICENSE.