Xano MCP Server

# 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. [](https://smithery.ai/server/@henryhawke/mcp-titan) ## 📦 Installation CHECK OUT docs/guides/how-to.md for more information on how to install and run the server. ## 🚀 Quick Start 1. Basic Installation (uses default memory path): ```bash npx -y @smithery/cli@latest run @henryhawke/mcp-titan ``` 2. With Custom Memory Path: ```bash 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: ```bash # 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 ## Example usage Usage Example: ```typescript const model = new TitanMemoryModel({ memorySlots: 10000, transformerLayers: 8, }); // Store semantic memory await model.storeMemory("User prefers dark mode and large text"); // Recall relevant memories const results = await model.recallMemory("interface preferences", 3); results.forEach((memory) => console.log(memory.arraySync())); // Continuous learning model.trainStep( wrapTensor(currentInput), wrapTensor(targetOutput), model.getMemoryState() ); ``` ## 🤖 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 | Option | Description | Default | | ------------ | ------------------------------ | -------------- | | `port` | HTTP/WebSocket port | `0` (disabled) | | `memoryPath` | Custom memory storage location | `~/.mcp-titan` | | `inputDim` | Size of input vectors | `768` | | `outputDim` | Size of memory state | `768` | ## 📚 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](https://modelcontextprotocol.io) - Uses [TensorFlow.js](https://tensorflow.org/js) - Inspired by [synthience/mcp-titan-cognitive-memory](https://github.com/synthience/mcp-titan-cognitive-memory/)