AgentDB Advanced Features

What problem does it solve?

This Skill addresses the need for building sophisticated, distributed AI systems that require sub-millisecond cross-node communication, multi-database coordination, and advanced vector search capabilities beyond basic similarity.

Core Features & Use Cases

• QUIC Synchronization: Achieve <1ms latency synchronization between AgentDB instances across networks with built-in encryption and multiplexing.
• Custom Distance Metrics: Utilize Cosine Similarity, Euclidean Distance, Dot Product, or implement custom metrics for tailored vector similarity search.
• Hybrid Search: Combine vector similarity with powerful metadata filtering for highly precise and contextual search results.
• Multi-Database Management & Sharding: Manage separate databases for different domains or shard data horizontally for massive scalability.
• MMR (Maximal Marginal Relevance): Retrieve diverse results to avoid redundancy and enhance search exploration.
• Use Case: Build a distributed AI knowledge base where multiple AgentDB nodes sync in real-time using QUIC, allowing agents to perform hybrid searches for relevant information filtered by metadata and retrieve diverse results using MMR.

Quick Start

Enable QUIC synchronization for multi-node deployment: import { createAgentDBAdapter } from 'agentic-flow/reasoningbank'; const adapter = await createAgentDBAdapter({ dbPath: '.agentdb/distributed.db', enableQUICSync: true, syncPort: 4433, syncPeers: ['192.168.1.10:4433'], }); Perform a hybrid search with vector similarity and metadata filters: const result = await adapter.retrieveWithReasoning(queryEmbedding, { domain: 'research-papers', filters: { year: { $gte: 2023 }, category: 'machine-learning' }, });