What problem does it solve?
Building distributed AI systems requires high-performance, synchronized memory across multiple nodes, often with complex search and data management needs. This Skill provides advanced AgentDB capabilities for sub-millisecond QUIC synchronization, multi-database management, custom distance metrics, and hybrid vector search.
Core Features & Use Cases
- QUIC Synchronization: Enables sub-millisecond latency synchronization between AgentDB instances across networks, with built-in encryption, multiplexing, and automatic retry.
- Multi-Database Management: Orchestrates multiple AgentDB instances for specialized data storage, allowing agents to access different knowledge bases efficiently.
- Hybrid Search & Custom Metrics: Combines vector search with metadata filtering for precise retrieval and supports custom distance metrics for tailored similarity calculations.
- Use Case: Deploy a distributed AI system across several cloud instances. Each instance runs an AgentDB node, synchronized via QUIC, allowing agents on any node to access and update a shared, high-performance vector memory with near-instant consistency, enabling real-time collaborative learning.
Quick Start
Initialize AgentDB with QUIC synchronization, specifying the database path, sync port, and peer addresses.
import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
const adapter = await createAgentDBAdapter({ dbPath: '.agentdb/distributed.db', enableQUICSync: true, syncPort: 4433, syncPeers: ['192.168.1.10:4433', '192.168.1.11:4433', '192.168.1.12:4433'] });
await adapter.insertPattern({ /* ... pattern data ... */ });
