AgentDB Advanced Features

To keep distributed vector memory consistent across multiple AI agent nodes, you can use QUIC synchronization. This protocol provides encrypted, multiplexed cross-node sync designed for sub-millisecond latency across peers.

Yes, you can combine semantic vector search with metadata filters using hybrid search. This approach allows you to apply structured constraints such as domains, recency, categories, price ranges, tags, and numeric thresholds alongside similarity scoring.

You balance relevance and diversity in vector retrieval by applying configurable ranking strategies like MMR diversification and custom distance metrics. These controls help refine search results for higher-level reasoning.

The best way to scale vector databases by domain in multi-agent systems is multi-database management. Separate databases by purpose, such as knowledge versus conversations, and shard by domain for scalable routing.

Yes, distributed vector search supports sub-millisecond peer sync through QUIC synchronization. You can configure this across peers during adapter creation to ensure consistent knowledge across multiple nodes.

You configure hybrid search with domain-scoped routing by running retrieval with hybrid weights and metadata filters. This returns precise results by combining vector similarity with structured domain constraints.