AgentDB Advanced Features

Hybrid search combines vector similarity with complex metadata filtering for precise information retrieval. It allows you to execute sophisticated queries that restrict vector matches using specific metadata constraints across your database.

QUIC-based synchronization is a protocol mechanism enabling high-performance, low-latency data syncing between distributed AgentDB nodes. You need it for sub-millisecond synchronization when building distributed AI systems across network boundaries.

Multi-database sharding distributes vector search workloads across multiple database instances. You can set it up using advanced configuration patterns to achieve horizontal scalability and performance monitoring for production-grade AI infrastructure.

Yes, you can use custom distance metrics and Maximal Marginal Relevance (MMR) to ensure diverse, non-redundant search results. This advanced feature supports diverse retrieval strategies within your vector search applications.

Yes, the agentic-flow dependency is required. It provides the foundational environment needed to implement production-grade error handling, performance monitoring, and database optimization techniques for complex AI system architectures.

QUIC synchronization is designed for low-latency cross-node communication but requires careful configuration. Limitations include managing complex network boundaries and ensuring production-grade error handling during multi-agent memory sharing operations.