MemoryAgent aligns user intent with persistent AI memory using Hypernym compression to create relevance-sorted slices ordered by LLM-perceived importance. Combined with Voyage’s domain embeddings, it reaches 81% fact-preservation correlation versus 78% for general models, enhancing both MongoDB and Voyage AI. Its 3-layer architecture (episodic, semantic, compressed) enables real-time learning with 75–80% similarity scores. MongoDB now captures critical info regardless of document size, rendering context windows irrelevant through intelligent slicing. Unlike static RAG systems, MemoryAgent learns and adapts. The demo shows 23 persistent memories, semantic clustering, and fact-preserving compression. Built with MongoDB Atlas Vector Search, Voyage AI embeddings, and Hypernym API, it scales to millions of users with adaptive compression efficiency.
Team
Placement
Participant
