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AgentDB Advanced Features

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

1|1|Updated Nov 28, 2025
Search Tags:#embeddings#multi-agent#distributed systems#agentdb#vector search#hybrid search#quic synchronization
One-click install
npx skills add https://github.com/33may/robotics --skill agentdb-advanced-features-33may
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Advanced Features
Source: https://github.com/33may/robotics/tree/main/humanoid/.claude/skills/agentdb-advanced
Command: npx skills add https://github.com/33may/robotics --skill agentdb-advanced-features-33may

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 cluster where learned patterns replicate across all peers in under 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. You can also configure it via environment variables AGENTDB_QUIC_SYNC, AGENTDB_QUIC_PORT, and AGENTDB_QUIC_PEERS.

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.

Which distance metric should I use for vector search?▼

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

Why is AgentDB QUIC sync not working between nodes?▼

QUIC sync commonly fails because the firewall blocks UDP port 4433 or peers are unreachable. Allow the port with ufw, verify connectivity with ping, and enable debug logging via DEBUG=agentdb:quic.

What does MMR do in vector search results?▼

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

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