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.

• 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

# 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

The Titan Memory MCP server provides the following tools:

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

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)

Perform a forward pass through the model to get predictions.

Parameters:

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

Execute a training step to update the model.

Parameters:

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

Get the current memory state and statistics.

Parameters:

• type (optional): Optional memory type filter

Update memory along a manifold direction.

Parameters:

• base: Base memory state
• velocity: Update direction

Remove less relevant memories to free up space.

Parameters:

• threshold: Pruning threshold (0-1)

Save memory state to a file.

Parameters:

• path: Checkpoint file path

Load memory state from a file.

Parameters:

• path: Checkpoint file path

Reset accumulated gradients to recover from training issues.

Parameters: None

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", {});

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

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

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

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