Claude Memory
STDIOMCP server implementation providing persistent memory capabilities for Large Language Models.
MCP server implementation providing persistent memory capabilities for Large Language Models.
An MCP (Model Context Protocol) server implementation that provides persistent memory capabilities for Large Language Models, specifically designed to integrate with the Claude desktop application.
This project implements optimal memory techniques based on comprehensive research of current approaches in the field. It provides a standardized way for Claude to maintain persistent memory across conversations and sessions.
# Clone the repository git clone https://github.com/WhenMoon-afk/claude-memory-mcp.git cd claude-memory-mcp # Start with Docker Compose docker-compose up -d
Configure Claude Desktop to use the containerized MCP server (see Docker Usage Guide for details).
Prerequisites:
Installation:
# Clone the repository git clone https://github.com/WhenMoon-afk/claude-memory-mcp.git cd claude-memory-mcp # Install dependencies pip install -r requirements.txt # Run setup script chmod +x setup.sh ./setup.sh
Claude Desktop Integration:
Add the following to your Claude configuration file:
{ "mcpServers": { "memory": { "command": "python", "args": ["-m", "memory_mcp"], "env": { "MEMORY_FILE_PATH": "/path/to/your/memory.json" } } } }
The Memory MCP Server enables Claude to remember information across conversations without requiring explicit commands.
Automatic Memory: Claude will automatically:
Memory Recall: To see what Claude remembers, simply ask:
System Prompt: For optimal memory usage, add this to your Claude system prompt:
This Claude instance has been enhanced with persistent memory capabilities.
Claude will automatically remember important details about you across
conversations and recall them when relevant, without needing explicit commands.
See the User Guide for detailed usage instructions and examples.
The examples
directory contains scripts demonstrating how to interact with the Memory MCP Server:
store_memory_example.py
: Example of storing a memoryretrieve_memory_example.py
: Example of retrieving memoriesIf you encounter issues:
pip install "numpy>=1.20.0,<2.0.0"
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.