🧠 MCP - Titan Memory Server implementation

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An implementation inspired by Google Research’s paper “Generative AI for Programming: A Common Task Framework”. This server provides a neural memory system that can learn and predict sequences while maintaining state through a memory vector, following principles outlined in the research for improved code generation and understanding.

📚 Research Background

This implementation draws from the concepts presented in the Google Research paper (Muennighoff et al., 2024) which introduces a framework for evaluating and improving code generation models. The Titan Memory Server implements key concepts from the paper:

• Memory-augmented sequence learning
• Surprise metric for novelty detection
• Manifold optimization for stable learning
• State maintenance through memory vectors

These features align with the paper’s goals of improving code understanding and generation through better memory and state management.

🚀 Features

• Neural memory model with configurable dimensions
• Sequence learning and prediction
• Surprise metric calculation
• Model persistence (save/load)
• Memory state management
• Full MCP tool integration

Downloads, Releases, And Discovery

MCP memory server implementation for sequence learning, stateful memory vectors, model persistence, and memory-augmented tool workflows.

Clone, install dependencies, build, and connect it to an MCP-compatible client.

git clone https://github.com/ResearchForumOnline/mcp-titan-cognative-memory.git
cd mcp-titan-cognative-memory
npm install
npm run build

Search-friendly topics: MCP server, cognitive memory, Titan memory, model context protocol, local agent memory, AI memory server.

📦 Installation

# Install dependencies
npm install

# Build the project
npm run build

# Run tests
npm test

🛠️ Available MCP Tools

1. 🎯 init_model