What problem does it solve? Building distributed AI systems with vector databases requires solving cross-node synchronization latency, redundant search results, and the complexity of combining semantic similarity with metadata filtering across multiple databases. ## Core Features & Use Cases - QUIC Synchronization: Synchronize AgentDB instances across network nodes with sub-millisecond latency, multiplexed streams, and TLS 1.3 encryption. - Hybrid Search: Combine vector similarity (cosine, euclidean, dot product) with metadata filters and weighted scoring for precise retrieval. - Multi-Database Management & MMR: Shard databases by domain, pool connections, and use Maximal Marginal Relevance to diversify search results. - Use Case: Deploy a three-node AgentDB cluster where learned patterns sync across peers in under 1ms, then run hybrid searches that filter research papers by year, category, and citation count while ranking by semantic similarity. ## Quick Start Set up an AgentDB adapter with QUIC synchronization enabled across three peer nodes and run a hybrid vector search filtered by metadata.