Routemind

RouteMind is self-improving agent memory on MongoDB Atlas: a car teaches itself a real San Francisco drive, from Shack 15 to Ocean Beach, by learning from its own wrong turns. At each junction it recalls which way to turn from a memory of over a thousand vectors in Atlas, most of them wrong. After every trip it scores which recalled memories actually helped, keeps the useful ones, and deletes the ones that mislead. Over a handful of trips the route locks in, with no training and no retraining, while the same memory with learning switched off never leaves the first block. The point is a kind of memory that plain retrieval cannot provide. RAG ranks by similarity and never learns from what happened next, so an agent repeats the same mistake forever. In RouteMind, every possible turn is a document in MongoDB Atlas holding a Voyage AI embedding and a usefulness score; recall is one Atlas Vector Search query, and the agent writes the outcome back to Atlas and prunes the misleading memories. Because every candidate turn at a junction scores the same vector similarity, similarity alone cannot choose, and the usefulness learned and stored in Atlas is what picks the right turn. You can try it on any route: type a start and destination and it pulls the real roads from OpenStreetMap, builds the memory live, and drives it. It is built on MongoDB Atlas Vector Search (memory and recall), Voyage AI (embeddings), Raindrop (observability), Modelence (the deployed dashboard), and ElevenLabs (narration). Live demo: https://tets-mv4eiaze9dc-prod.cloud.modelence.app