MCP Local Memory

Lightweight, steerable, local memory tool for your AI (SQLite + local embedding model, optional OpenAI support).

Let your agent remember whatever you want: preferences, project details, or even prompts for reuse. From how memories are structured and retrieved to how they're updated and deleted -- it's all up to you and your prompt.

Designed for personal use with 2-3K stored memories.

Installation

Add this to your AI client's MCP config file:

Option A: Local

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "mcp-local-memory"]
    }
  }
}

Why go local?

• Free
• No API keys needed
• If your AI agent app also uses a locally-running model -- your data never leaves your computer

Trade-offs:

• Setup downloads a ~130MB model during configuration (one-time).
• Small performance overhead of using the local model.
• Your AI agent will need to be slightly more explicit when saving memories.
• Slightly lower retrieval accuracy

Option B: With OpenAI API key

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "mcp-local-memory"],
      "env": {
        "OPENAI_API_KEY": "your-key-here"
      }
    }
  }
}

OpenAI API key: platform.openai.com/api-keys

Why use OpenAI? Somewhat better performance due to:

• OpenAI embedding model is better than a locally-running one
• Memory tool will use a fast OpenAI model to better structure memory when saving

Cost: $5 will last you for months

Note: In both 'Local' and 'OpeanAI' mode, all your memories are stored locally. (default path is ~/Documents/mcp-personal-memory/memory.db)

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What Can It Do?

Once installed, your AI assistant can:

Remember things you tell it:

You: "Remember: I prefer TypeScript over JavaScript"
AI: ✓ Saved in memory

You: "Store key information about this project we just discussed"
AI: ✓ 8 memories saved

Recall information later:

You: "What programming language do I prefer?"
AI: [Searches memory] You prefer TypeScript over JavaScript

You: "What's the main Jira Epic for this project?"
AI: [Searches memory] It's [ABC-123](https://example.atlassian.net/browse/ABC-123)

Update/Organize Memories:

You: "Hey, pls retrieve all the memories we've had about project ABC. Now that 
     there is a clear outline, pls structure them by frontend and backend. 
     Remove redundant memories. If you find conflicting memories -- ask me 
     before proceeding."

AI: [Searches memories for "project ABC"]
    Found 8 memories. I've organized them by adding clear tags:
    
    - 3 memories didn't have tags → added "frontend" tag
    - 4 memories had vague tags → updated to "backend" tag  
    - 1 memory was redundant (duplicate deployment info) → deleted
    
    Now structured as:
    
    Frontend (3 memories):
    - React + TypeScript stack
    - UI design in Figma, link: [...]
    - Deployment: Vercel
    
    Backend (4 memories):
    - Node.js API with Express
    - Database APIs
    - Database performance considerations
    - Hosted on Railway
    - API docs at [...]
    
    ⚠️ Conflict found: Memory bnL9Df8a says backend uses MongoDB, but memory Asq8Y6kq 
    (created recently) mentions CosmosDB. Which is correct?

System Prompt Example

Customizing the system prompt will greatly affect how your agent uses the tool. Here are examples of what an adequate system prompt may include:

### Working with Memory

Your memory tool helps maintain context across conversations. Use it thoughtfully:

**When to retrieve from memory:**
- User mentions something that could benefit from past context
- Information from other sources seems incomplete or could be enriched
- You need account IDs, project details, or other recurring facts
- **User gives you a task without full context—search memory to understand connections and background**

**When to update memory:**
- Important facts or connections emerge from the conversation
- Actions or decisions result in changed understanding
- **Project phase transitions** (started → in development → testing → deployed → production)
- **Major milestones reached** (infrastructure deployed, API complete, feature shipped)
- **New features or capabilities added** that weren't in original scope
- **Corrections to outdated information** (especially when you're corrected by the user)
- **Key URLs, endpoints, or deployment locations** discovered or changed
- **Meta-patterns emerge** (e.g., "when user asks X, they typically mean Y" or common workflows/preferences)

**What NOT to store as updates:**
- Incremental progress percentages (40% → 60% done)
- Temporary states that will change soon ("currently debugging X")
- Information already well-captured elsewhere unless it provides quick-reference value
- Minor task status changes within the same phase

**Think of memory as "overall situational awareness":**
- Can we do X? (features/capabilities)
- Where is Y? (URLs, locations, resources)
- What phase is Z in? (not started / active development / testing / production)
- How does A relate to B? (architecture, dependencies, relationships)

If you've already retrieved relevant memories during this conversation, you can update them directly without re-checking. Otherwise, search first to see what exists.

💡 More advanced usage: See README-COMPLETE.md for technical details, tool call notes system, and advanced features.

Advanced Configuration

Separate Work & Personal Memories

Add CONTEXT_ID to your config:

{
  "memory-work": {
    "command": "npx",
    "args": ["-y", "mcp-local-memory"],
    "env": {
      "OPENAI_API_KEY": "your-key",
      "CONTEXT_ID": "work"
    }
  },
  "memory-personal": {
    "command": "npx",
    "args": ["-y", "mcp-local-memory"],
    "env": {
      "OPENAI_API_KEY": "your-key",
      "CONTEXT_ID": "personal"
    }
  }
}

Now you have two separate memory spaces that don't mix.

Custom Database Location

Add SQLITE_PATH to your config:

{
  "env": {
    "SQLITE_PATH": "/path/to/your/memory.db"
  }
}

Useful for:

• Syncing memories across devices (put DB in Dropbox/iCloud)
• Backing up to specific locations
• Multiple users on same computer

Switch Between OpenAI and Offline

Your AI can switch modes if needed:

• Ask: "Switch to offline memory mode"
• AI will use the switch_embedding_mode tool
• Useful if OpenAI API has issues or you want to go offline

For Developers

This is a user guide. For technical documentation, see:

• ARCHITECTURE.md - How it works internally
• SYSTEM_PROMPT_TEMPLATE.md - Ready-to-use prompt template for AI agents

More examples: Check the examples/ folder for different configuration options.

Privacy & Data

• Local storage: All memories stored on your computer
• OpenAI mode: Sends memory text to OpenAI for processing (subject to OpenAI's privacy policy)
• Your control: Delete memory.db to erase everything

License

MIT - Use freely for personal or commercial projects

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Questions or issues? Open an issue on GitHub or check the technical documentation above.