MCP Memory

A Model Context Protocol (MCP) server implementing memory solutions for data-rich applications with efficient knowledge graph capabilities.

Overview

This MCP server implements a memory solution for data-rich applications that involve searching information from many sources including uploaded files. It uses HippoRAG internally to manage memory through an efficient knowledge graph.

Features

• Session-based Memory: Create and manage memory for specific chat sessions
• Efficient Knowledge Graph: Uses HippoRAG for advanced memory management
• Multiple Transport Support: Works with both stdio and SSE transports
• Search Capabilities: Search information from various sources including uploaded files

Installation

Install from PyPI:

pip install mcp-mem

Or install from source:

git clone https://github.com/ddkang1/mcp-mem.git
cd mcp-mem
pip install -e .

Usage

You can run the MCP server directly:

mcp-mem

By default, it uses stdio transport. To use SSE transport:

mcp-mem --sse

You can also specify host and port for SSE transport:

mcp-mem --sse --host 127.0.0.1 --port 3001

Configuration

Basic Configuration

To use this tool with Claude in Windsurf, add the following configuration to your MCP config file:

"memory": {
    "command": "/path/to/mcp-mem",
    "args": [],
    "type": "stdio",
    "pollingInterval": 30000,
    "startupTimeout": 30000,
    "restartOnFailure": true
}

The command field should point to the directory where you installed the python package using pip.

Environment Variable Configuration

You can configure the LLM and embedding models used by mcp-mem through environment variables:

• EMBEDDING_MODEL_NAME: Name of the embedding model to use (default: "text-embedding-3-large")
• EMBEDDING_BASE_URL: Base URL for the embedding API (optional)
• LLM_NAME: Name of the LLM model to use (default: "gpt-4o-mini")
• LLM_BASE_URL: Base URL for the LLM API (optional)
• OPENAI_API_KEY: OpenAI API key (required for HippoRAG to function properly)

Example usage:

EMBEDDING_MODEL_NAME="your-model" LLM_NAME="your-llm" mcp-mem

For convenience, you can use the provided example script:

./examples/run_with_env_vars.sh

Available Tools

The MCP server provides the following tools:

• create_memory: Create a new memory for a given chat session
• store_memory: Add memory to a specific session
• retrieve_memory: Retrieve memory from a specific session

Development

Installation for Development

git clone https://github.com/ddkang1/mcp-mem.git
cd mcp-mem
pip install -e ".[dev]"

Running Tests

pytest

Code Style

This project uses Black for formatting, isort for import sorting, and flake8 for linting:

black src tests
isort src tests
flake8 src tests

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.