AgentDB Advanced Features

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

1|Updated Jun 3, 2026
One-click install
npx skills add https://github.com/KentwareDemo/RuView --skill agentdb-advanced-features-kentwaredemo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Advanced Features
Source: https://github.com/KentwareDemo/RuView/tree/main/.claude/skills/agentdb-advanced
Command: npx skills add https://github.com/KentwareDemo/RuView --skill agentdb-advanced-features-kentwaredemo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentic-flow, agentdb.

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: Sync 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. - Distance Metrics & MMR: Choose cosine, euclidean, or dot product metrics and apply Maximal Marginal Relevance to diversify results. - Use Case: Deploy a three-node AgentDB cluster where patterns inserted on one node replicate to peers within ~1ms, then run hybrid searches filtering research papers by year, category, and citation count. ## Quick Start Set up AgentDB with QUIC synchronization enabled across two peer nodes and run a hybrid vector search filtered by metadata.

Frequently Asked Questions about AgentDB Advanced Features

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

FAQPage Schema
How do I enable QUIC synchronization between AgentDB instances?▼

Enable QUIC sync by setting enableQUICSync to true in createAgentDBAdapter, specifying a syncPort and a list of syncPeers. Alternatively, set the AGENTDB_QUIC_SYNC, AGENTDB_QUIC_PORT, and AGENTDB_QUIC_PEERS environment variables before starting each node.

How to combine vector similarity with metadata filters in AgentDB?▼

Use retrieveWithReasoning with a filters object supporting operators like $gte, $lte, $in, and $contains alongside the query embedding. You can also set hybridWeights to balance vector similarity against metadata match scores.

Which distance metric should I use for vector search?▼

Cosine similarity suits text embeddings and semantic search, euclidean distance fits spatial data and image embeddings, and dot product works best for pre-normalized vectors. The choice depends on whether vector magnitude matters for your data.

Why is AgentDB QUIC sync not working between nodes?▼

QUIC sync commonly fails because UDP port 4433 is blocked by a firewall or peers are unreachable. Allow the port with your firewall tool, verify peer connectivity with ping, and run with DEBUG=agentdb:quic to inspect logs.

What does MMR do in vector search results?▼

Maximal Marginal Relevance diversifies retrieved results to reduce redundancy among similar embeddings. The mmrLambda parameter controls the trade-off: 0 maximizes relevance, 1 maximizes diversity, and 0.5 balances both.