AgentDB Advanced Features

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

Updated Feb 17, 2026
One-click install
npx skills add https://github.com/derbalimajd04-dot/al-wasat --skill agentdb-advanced-features-derbalimajd04-dot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Advanced Features
Source: https://github.com/derbalimajd04-dot/al-wasat/tree/main/.agents/skills/agentdb-advanced
Command: npx skills add https://github.com/derbalimajd04-dot/al-wasat --skill agentdb-advanced-features-derbalimajd04-dot

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 scaling beyond a single database, which involves complex configuration and tuning. ## Core Features & Use Cases - QUIC Synchronization: Synchronize AgentDB instances across network nodes with sub-millisecond latency, multiplexed streams, and TLS 1.3 encryption. - Hybrid Search: Combine vector similarity with metadata filters and weighted scoring to retrieve precisely filtered results. - Multi-Database Management: Run separate databases per domain or shard data horizontally for horizontal scaling. - Use Case: Deploy a three-node AgentDB cluster where learned patterns replicate across all peers within ~1ms, then run hybrid searches that filter research papers by year, category, and citation count alongside semantic similarity. ## 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, then specify syncPort and syncPeers with peer addresses. 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 works best for normalized text embeddings and semantic search, Euclidean distance suits spatial data and image embeddings, and dot product is fastest for pre-normalized vectors. Choose based on whether vector magnitude matters for your data.

Why is AgentDB QUIC sync not working between nodes?▼

QUIC sync commonly fails when the firewall blocks UDP traffic on port 4433, so allow it with a rule like ufw allow 4433/udp. Verify peers are reachable via ping and enable debug logging with DEBUG=agentdb:quic to diagnose further.

What does MMR do in vector search results?▼

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