mcp-titan

Titan Memory MCP Server

A neural memory system for LLMs that can learn and predict sequences while maintaining state through a memory vector. This MCP (Model Context Protocol) server provides tools for Claude 3.7 Sonnet and other LLMs to maintain memory state across interactions.

I'm aware its broken right now, I'll fix it! Ideally this just runs in yolo mode in cursor (or claude desktop) without human intervention and creates a "brain" available independent of LLM version.

Features

• Perfect for Cursor: Now that Cursor automatically runs MCP in yolo mode, you can take your hands off the wheel with your LLM's new memory
• Neural Memory Architecture: Transformer-based memory system that can learn and predict sequences
• Memory Management: Efficient tensor operations with automatic memory cleanup
• MCP Integration: Fully compatible with Cursor and other MCP clients
• Text Encoding: Convert text inputs to tensor representations
• Memory Persistence: Save and load memory states between sessions

Installation

# Clone the repository
git clone https://github.com/yourusername/titan-memory.git
cd titan-memory
 
# Install dependencies
npm install
 
# Build the project
npm run build
 
# Start the server
npm start

Available Tools

The Titan Memory MCP server provides the following tools:

help

Get help about available tools.

Parameters:

• tool (optional): Specific tool name to get help for
• category (optional): Category of tools to explore
• showExamples (optional): Include usage examples
• verbose (optional): Include detailed descriptions

init_model

Initialize the Titan Memory model with custom configuration.

Parameters:

• inputDim: Input dimension size (default: 768)
• hiddenDim: Hidden dimension size (default: 512)
• memoryDim: Memory dimension size (default: 1024)
• transformerLayers: Number of transformer layers (default: 6)
• numHeads: Number of attention heads (default: 8)
• ffDimension: Feed-forward dimension (default: 2048)
• dropoutRate: Dropout rate (default: 0.1)
• maxSequenceLength: Maximum sequence length (default: 512)
• memorySlots: Number of memory slots (default: 5000)
• similarityThreshold: Similarity threshold (default: 0.65)
• surpriseDecay: Surprise decay rate (default: 0.9)
• pruningInterval: Pruning interval (default: 1000)
• gradientClip: Gradient clipping value (default: 1.0)

forward_pass

Perform a forward pass through the model to get predictions.

Parameters:

• x: Input vector or text
• memoryState (optional): Memory state to use

train_step

Execute a training step to update the model.

Parameters:

• x_t: Current input vector or text
• x_next: Next input vector or text

get_memory_state

Get the current memory state and statistics.

Parameters:

• type (optional): Optional memory type filter

manifold_step

Update memory along a manifold direction.

Parameters:

• base: Base memory state
• velocity: Update direction

prune_memory

Remove less relevant memories to free up space.

Parameters:

• threshold: Pruning threshold (0-1)

save_checkpoint

Save memory state to a file.

Parameters:

• path: Checkpoint file path

load_checkpoint

Load memory state from a file.

Parameters:

• path: Checkpoint file path

reset_gradients

Reset accumulated gradients to recover from training issues.

Parameters: None

Usage with Claude 3.7 Sonnet in Cursor

The Titan Memory MCP server is designed to work seamlessly with Claude 3.7 Sonnet in Cursor. Here's an example of how to use it:

// Initialize the model
const result = await callTool("init_model", {
  inputDim: 768,
  memorySlots: 10000,
  transformerLayers: 8,
});
 
// Perform a forward pass
const { predicted, memoryUpdate } = await callTool("forward_pass", {
  x: "const x = 5;", // or vector: [0.1, 0.2, ...]
  memoryState: currentMemory,
});
 
// Train the model
const result = await callTool("train_step", {
  x_t: "function hello() {",
  x_next: "  console.log('world');",
});
 
// Get memory state
const state = await callTool("get_memory_state", {});

Memory Management

The Titan Memory MCP server includes sophisticated memory management to prevent memory leaks and ensure efficient tensor operations:

1. Automatic Cleanup: Periodically cleans up unused tensors
2. Memory Encryption: Securely stores memory states
3. Tensor Validation: Ensures tensors have the correct shape
4. Error Recovery: Handles tensor errors gracefully

Architecture

The Titan Memory MCP server is built with a modular architecture:

• TitanMemoryServer: Main server class that registers tools and handles requests
• TitanMemoryModel: Neural memory model implementation
• VectorProcessor: Handles input processing and text encoding
• MemoryManager: Manages tensor operations and memory cleanup

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.