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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Accedi per unirti alla conversazione
Highlight AIInitialize or reconfigure memory system.
Quick-load context from text or URL.
Inspect current memory contents.
Get statistical summary of memory.
Clean up low-value memories.
Query memory and get prediction.
Explicit learning from example pair.
Clear training state.
Save memory to file.
Restore memory from checkpoint.
Start background learning service.
Pause the background learning service.
Resume the background learning service.
Monitor learning metrics.
Feed training data.
Analyze sequence patterns.
Check memory tier distribution.
Check system health and performance.
List all available tools with descriptions.
