mcp-titan v0.1.8

Titan Memory MCP Server

A MCP server built with a three-tier memory architecture that handles storage as follows:

• Short-term memory: Holds the immediate conversational context in RAM.
• Long-term memory: Persists core patterns and knowledge over time. This state is saved automatically.
• Meta memory: Keeps higher-level abstractions that support context-aware responses.

🚀 Quick Start

1. Basic Installation (uses default memory path):

npx -y @smithery/cli@latest run @henryhawke/mcp-titan

2. With Custom Memory Path:

npx -y @smithery/cli@latest run @henryhawke/mcp-titan --config '{
  "memoryPath": "/path/to/your/memory/directory"
}'

The server will automatically:

• Initialize in the specified directory (or default location)
• Maintain persistent memory state
• Save model weights and configuration
• Learn from interactions

📂 Memory Storage

By default, the server stores memory files in:

• Windows: %APPDATA%\.mcp-titan
• MacOS/Linux: ~/.mcp-titan

You can customize the storage location using the memoryPath configuration:

# Example with all configuration options
npx -y @smithery/cli@latest run @henryhawke/mcp-titan --config '{
  "port": 3000,
  "memoryPath": "/custom/path/to/memory",
  "inputDim": 768,
  "outputDim": 768
}'

The following files will be created in the memory directory:

• memory.json: Current memory state
• model.json: Model architecture
• weights/: Model weights directory

🤖 LLM Integration

To integrate with your LLM:

1. Copy the contents of docs/llm-system-prompt.md into your LLM's system prompt
2. The LLM will automatically:
   • Use the memory system for every interaction
   • Learn from conversations
   • Provide context-aware responses
   • Maintain persistent knowledge

🔄 Automatic Features

• Self-initialization
• WebSocket and stdio transport support
• Automatic state persistence
• Real-time memory updates
• Error recovery and reconnection
• Resource cleanup

🧠 Memory Architecture

Three-tier memory system:

• Short-term memory for immediate context
• Long-term memory for persistent patterns
• Meta memory for high-level abstractions

🛠️ Configuration Options

📚 Technical Details

• Built with TensorFlow.js
• WebSocket and stdio transport support
• Automatic tensor cleanup
• Type-safe implementation
• Memory-efficient design

🔒 Security Considerations

When using a custom memory path:

• Ensure the directory has appropriate permissions
• Use a secure location not accessible to other users
• Consider encrypting sensitive memory data
• Backup memory files regularly

📝 License

MIT License - feel free to use and modify!

🙏 Acknowledgments

• Built with Model Context Protocol
• Uses TensorFlow.js
• Inspired by synthience/mcp-titan-cognitive-memory