What is Mcp Titan Cognative Memory

MCP - Titan Cognitive Memory is a neural memory server designed to enhance code generation and understanding by learning and predicting sequences while maintaining state through memory vectors. It is inspired by Google Research’s framework for evaluating and improving code generation models.

Use cases

Use cases include developing intelligent coding assistants, improving automated code generation tools, enhancing programming education platforms, and creating systems that require robust state management for sequence prediction.

How to use

To use MCP - Titan Cognitive Memory, install the necessary dependencies using ‘npm install’, build the project with ‘npm run build’, and run tests with ‘npm test’. You can initialize the model with custom configurations and perform training steps using provided methods.

Key features

Key features include a configurable neural memory model, sequence learning and prediction capabilities, surprise metric calculation for novelty detection, model persistence for saving/loading, and comprehensive memory state management.

Where to use

MCP - Titan Cognitive Memory can be utilized in fields such as software development, AI programming assistants, and any application requiring enhanced code generation and understanding through advanced memory management.

🧠 MCP - Titan Memory Server implementation

Colaboration between @jasonkneen and @ExpressionsBot

Follow us on X

• jasonkneen
• megaprompt

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

📦 Installation

# Install dependencies
npm install

# Build the project
npm run build

# Run tests
npm test

🛠️ Available MCP Tools

1. 🎯 init_model

Initialize the Titan Memory model with custom configuration.

{
  inputDim?: number;  // Input dimension (default: 64)
  outputDim?: number; // Output/Memory dimension (default: 64)
}

2. 📚 train_step

Perform a single training step with current and next state vectors.

{
  x_t: number[];    // Current state vector
  x_next: number[]; // Next state vector
}

3. 🔄 forward_pass

Run a forward pass through the model with an input vector.

{
  x: number[]; // Input vector
}

4. 💾 save_model

Save the model to a specified path.

{
  path: string; // Path to save the model
}

5. 📂 load_model

Load the model from a specified path.

{
  path: string; // Path to load the model from
}

6. ℹ️ get_status

Get current model status and configuration.

{} // No parameters required

7. 🔄 train_sequence

Train the model on a sequence of vectors.

{
  sequence: number[][]; // Array of vectors to train on
}

🌟 Example Usage

// Initialize model
await callTool('init_model', { inputDim: 64, outputDim: 64 });

// Train on a sequence
const sequence = [
  [1, 0, 0, /* ... */],
  [0, 1, 0, /* ... */],
  [0, 0, 1, /* ... */]
];
await callTool('train_sequence', { sequence });

// Run forward pass
const result = await callTool('forward_pass', {
  x: [1, 0, 0, /* ... */]
});

🔧 Technical Details

• Built with TensorFlow.js for efficient tensor operations
• Uses manifold optimization for stable learning
• Implements surprise metric for novelty detection
• Memory management with proper tensor cleanup
• Type-safe implementation with TypeScript
• Comprehensive error handling

🧪 Testing

The project includes comprehensive tests covering:

• Model initialization and configuration
• Training and forward pass operations
• Memory state management
• Model persistence
• Edge cases and error handling
• Tensor cleanup and memory management

Run tests with:

npm test

🔍 Implementation Notes

• All tensor operations are wrapped in tf.tidy() for proper memory management
• Implements proper error handling with detailed error messages
• Uses type-safe MCP tool definitions
• Maintains memory state between operations
• Handles floating-point precision issues with epsilon tolerance

📝 License

MIT License - feel free to use and modify as needed!