| name | agentdb-advanced |
| description | Advanced AgentDB beyond single-database vector search: distributed QUIC sync across nodes, multi-database coordination and sharding, custom distance metrics, hybrid vector+metadata search, MMR diversity, context synthesis, and reinforcement-learning plugins. Use when an AgentDB deployment needs cross-node sync, cross-database routing, filtered/weighted hybrid retrieval, or self-improving RL agents. |
AgentDB Advanced Features
Distributed and advanced AgentDB patterns. For AgentDB architecture, performance
benchmarks, and common API patterns, see AgentDB Overview.
When to use
Reach for this skill when a plain AgentDB setup no longer covers the need:
- Distributed sync — keep patterns consistent across AgentDB instances on
different hosts (QUIC).
- Multiple / sharded databases — route or scale across separate
.db files.
- Advanced retrieval — custom distance metrics, hybrid vector+metadata
filtering, weighted scoring, MMR diversity, or synthesized context.
- Reinforcement learning — build self-improving agents that train on logged
experience (9 RL algorithms).
When not to use
- Basic vector search or single-database setups →
agentdb-vector-search.
- Simple agent memory (session, long-term) →
agentdb-memory-patterns.
- Performance tuning without distributed features →
agentdb-vector-search.
- Non-AgentDB vector databases (pgvector, Pinecone, Weaviate) — this skill is
AgentDB-specific.
Prerequisites: distributed-systems basics (for QUIC sync) and vector-search
fundamentals.
Quick path
import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
const adapter = await createAgentDBAdapter({
dbPath: '.agentdb/distributed.db',
enableQUICSync: true,
syncPort: 4433,
syncPeers: ['192.168.1.11:4433', '192.168.1.12:4433'],
});
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
metric: 'cosine',
k: 20,
useMMR: true,
filters: { year: { $gte: 2023 } },
});
Reference tiers (load on demand)
| Topic | File |
|---|
| QUIC synchronization — enable, config, multi-node deploy, env vars, troubleshooting | references/quic-sync.md |
| Search features — distance metrics, hybrid/weighted search, MMR, context synthesis | references/search-features.md |
| Deployment — multi-database, sharding, connection pooling, error handling, CLI import/export/optimise | references/deployment.md |
| Reinforcement-learning plugins — 9 algorithms, training API, decision-transformer config | references/reinforcement-learning.md |
Learn more
Category: Advanced / Distributed Systems · Difficulty: Advanced ·
Estimated Time: 45-60 minutes