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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Call to confirm the active tool registry and read parameter hints.
Primarily used to initialize the memory state before running operations.
Initialize the model with any config overrides.
Retrieve current memory statistics.
Perform a forward pass through the model using the current memory state.
Execute a training step in conjunction with memory operations.
Inspect the current state of the memory.
Pull telemetry on token flow metrics.
Retrieve metrics related to hierarchical memory if enabled.
Reset the gradients used in training.
Perform a health check of the MCP server.
Prune the memory when utilization exceeds a certain threshold.
Persist the current state of the memory to a checkpoint file.
Restore the memory state from a previously saved checkpoint.
Initialize the online learning process.
Pause the online learning process.
Resume the online learning process after pausing.
Retrieve statistics related to the learner.
Feed data into the learner with a training sample.
