AgentDB Advanced Features

Configure QUIC synchronization, hybrid search, and multi-database management for AgentDB vector stores.

11|Updated Jan 20, 2026
One-click install
npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill agentdb-advanced-features-ishandutta2007
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: AgentDB Advanced Features
Source: https://github.com/ishandutta2007/claude-agent-orchestration/tree/main/.claude/skills/agentdb-advanced
Command: npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill agentdb-advanced-features-ishandutta2007

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Building distributed AI systems with vector databases requires solving cross-node synchronization, redundant search results, and metadata filtering challenges that basic vector search setups cannot handle. ## Core Features & Use Cases - QUIC Synchronization: Synchronize AgentDB instances across network nodes with sub-millisecond latency, TLS 1.3 encryption, and automatic retry. - Hybrid Search: Combine vector similarity with metadata filters (price ranges, categories, dates) 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 from AI agents replicate across all nodes in under 1ms, then query with filters like "ML papers from 2023+ with 50+ citations". ## Quick Start Set up AgentDB with QUIC synchronization enabled across two peer nodes and run a hybrid search combining vector similarity with metadata filters.

Frequently Asked Questions about AgentDB Advanced Features

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I synchronize AgentDB across multiple nodes?

Enable QUIC synchronization by setting enableQUICSync to true, specifying a syncPort (default 4433), and listing peer addresses in syncPeers. Patterns replicate across all peers in under 1ms with TLS 1.3 encryption and automatic retry.

How to combine vector search with metadata filters in AgentDB?

Use retrieveWithReasoning with a filters object supporting operators like $gte, $lte, $in, and $contains. You can also set hybridWeights to balance vector similarity against metadata match scores, such as 0.7 vector and 0.3 metadata.

Which distance metric should I use for vector search?

Cosine similarity works best for text embeddings and semantic search, euclidean distance suits spatial data and image embeddings, and dot product is fastest for pre-normalized vectors. AgentDB supports all three via the metric parameter.

Why is AgentDB QUIC sync not working between nodes?

QUIC uses UDP, so verify your firewall allows UDP traffic on port 4433, for example with ufw allow 4433/udp. Confirm peers are reachable via ping and enable debug logging with DEBUG=agentdb:quic to diagnose connection failures.

How do I avoid redundant results in vector search?

Enable Maximal Marginal Relevance by setting useMMR to true and tuning mmrLambda between 0 and 1. A value of 0.5 balances relevance and diversity, while higher values prioritize diverse, non-redundant results.

Can AgentDB shard data across multiple databases?

Yes, create separate adapter instances with different dbPath values per domain and route queries using a shard-key function. This supports horizontal scaling across knowledge, conversation, and code databases.