This MCP server, developed by Henry Hawke, provides enhanced Titan Memory capabilities for AI agents. Built with TypeScript and leveraging TensorFlow.js, it offers improved context retention and retrieval through neural network-based memory encoding. The implementation focuses on optimizing long-term information storage and recall for conversational AI, enabling more coherent and contextually-aware interactions. It's particularly useful for applications requiring persistent memory across multiple conversations or complex, multi-step tasks where traditional context windows fall short.
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Displays the active tool registry and provides parameter hints.
Initializes the memory with a bootstrap configuration.
Initializes the model with optional configuration overrides.
Retrieves statistics regarding the current memory usage and status.
Executes a forward pass using the current memory state.
Performs a training step using the current model and memory.
Retrieves the current state of the memory.
Pulls metrics related to token flow within the system.
Fetches metrics related to hierarchical memory if enabled.
Resets the gradients in the model.
Performs a health check on the system to ensure functionality.
Prunes the memory when utilization exceeds a certain threshold.
Saves the current state of the memory to a checkpoint.
Loads a previously saved checkpoint into the memory.
Initializes the online learner system.
Pauses the online learner loop.
Resumes the online learner loop after being paused.
Retrieves statistics about the online learner's performance.
Adds a new training sample to the online learner's dataset.
