memorious-mcp

A 100% local & private semantic memory MCP (Model Context Protocol) server for AI assistants. Built with ChromaDB for vector similarity search and FastMCP 2. Runs entirely locally - no data ever leaves your machine.

Overview

memorious-mcp provides AI assistants with long-term memory capabilities through three core operations: store, recall, and forget. It uses ChromaDB's vector database to enable semantic similarity search, allowing assistants to retrieve relevant memories even when the exact wording differs from the original storage. All processing and storage happens locally on your machine - no data ever leaves your machine, ensuring complete privacy and security.

Key Features

• 🏠 100% Local & Private: All data processing and storage happens on your machine - nothing goes to the cloud
• 💾 Persistent Memory: Data persists across sessions using ChromaDB's disk-based storage
• 🔍 Semantic Search: Vector embeddings enable similarity-based memory retrieval
• ⚡ Simple API: Three intuitive tools for memory management
• 🚀 FastMCP Integration: Built on FastMCP for efficient MCP server implementation
• 🎯 Canonical Key Design: Optimized for short, embedding-friendly keys (1-5 words)
• 📂 Folder Scoped Storage: Per-project memory isolation.

Why This Project Exists

🔍 Gap in the MCP Ecosystem: Despite the growing popularity of memory MCP servers, there wasn't an existing memory server that combines both semantic similarity search and complete file based folder scoped local storage. Most memory solutions either:

• ☁️ Require cloud services and external API calls (compromising privacy) for either embeddings or storage or both
• 🔤 Only support exact key-value matching (no semantic understanding)
• 📁 Don't support folder scoped local storage

Use Cases

• Personal Assistant Memory: Remember user preferences, habits, and personal information
• Context Preservation: Maintain conversation context across sessions
• Knowledge Management: Store and retrieve project-specific information
• Personalization: Enable AI assistants to provide personalized responses based on stored preferences
• Privacy-First AI: Keep sensitive personal data local while still having persistent memory
• Folder-Scoped AI Agents: Perfect for VS Code Copilot Chat Modes and Claude Code agents with per-project memory isolation

Installation

For VS Code

Make sure you have uv and its its uvx command installed first.

For most MCP clients

Add to your MCP client configuration:

{
  "mcpServers": {
    "memorious": {
      "command": "uvx",
      "args": ["memorious-mcp"]
    }
  }
}

Development / Local Installation

uv sync

For development/local installation:

{
  "mcpServers": {
    "memorious": {
      "command": "uv",
      "args": ["run", "memorious-mcp"],
      "cwd": "/path/to/memorious-mcp"
    }
  }
}

Tools

store

Store facts, preferences, or information with short canonical keys optimized for vector similarity.

Parameters:

• key (string): Short, canonical key (1-5 words, space-separated)
• value (string): The actual information to store

recall

Retrieve stored memories using semantic similarity search.

Parameters:

• key (string): Query key for similarity search
• top_k (int, default: 3): Maximum number of results to return

forget

Delete memories matching a query key.

Parameters:

• key (string): Query key to find memories to delete
• top_k (int, default: 3): Number of nearest matches to consider

Claude CLI Configuration

To add memorious-mcp to Claude CLI, use the following commands:

# Add the MCP server using uvx (recommended)
claude mcp add memorious-mcp uvx memorious-mcp

# Alternative: for development/local installation
claude mcp add memorious-mcp uv run --project  memorious-mcp

You can then list your configured MCP servers:

claude mcp list

And remove the server if needed:

claude mcp remove memorious-mcp

Example Tool Signatures

• store(key: str, value: str) -> {"id": str}
• recall(key: str, top_k: int = 3) -> {"results": [...]} where each result includes id, key, value, distance, timestamp
• forget(key: str, top_k: int = 3) -> {"deleted_ids": [...]}

Testing

Run tests with:

# Using uv
uv run python -m pytest tests/ -v

# Or if pytest is available globally
pytest tests/ -v

Technical Details

• Backend: ChromaDB with persistent disk storage
• Embeddings: Uses ChromaDB's default embedding function (local processing)
• Storage Location: ./.memorious directory (configurable)
• Python Version: Requires Python ≥3.12
• License: MIT
• Privacy: No network requests, no cloud dependencies, all data stays local

Package Structure

The project follows the standard Python package layout:

memorious-mcp/
├── src/
│   └── memorious_mcp/
│       ├── __init__.py
│       ├── main.py                 # MCP server entry point
│       └── backends/
│           ├── __init__.py
│           ├── memory_backend.py   # Abstract base class
│           └── chroma_backend.py   # ChromaDB implementation
├── tests/
│   └── test_chroma_backend.py      # Integration tests
├── pyproject.toml                  # Package configuration
└── README.md

The server is designed for local/CLI integrations using stdio transport, making it suitable for personal AI assistants and development workflows where privacy and data security are paramount.

Limitations

⚠️ Important Security Considerations

While your data is 100% safe and private because it never leaves your local machine, you should still exercise caution about what you store:

• Data is stored unencrypted: All stored data is persisted to disk in unencrypted format in the .memorious directory
• Avoid storing secrets: Do NOT store passwords, API keys, private keys, personal identification numbers, financial information, or any other sensitive credentials
• Local file access: Anyone with access to your machine and the .memorious directory can read all stored memories
• Exercise caution: While the MCP server warns the client LLM to avoid storing sensitive information, you should not rely solely on this safeguard
• Backup considerations: Be mindful when backing up or syncing directories containing .memorious folders

Contributing

Contributions are welcome. Open a PR with tests.