AgentDB Advanced Features
What This Skill Does
Covers advanced AgentDB capabilities for distributed systems, multi-database coordination, custom distance metrics, hybrid search (vector + metadata), QUIC synchronization, and production deployment patterns. Enables building sophisticated AI systems with sub-millisecond cross-node communication and advanced search capabilities.
Performance: <1ms QUIC sync, hybrid search with filters, custom distance metrics.
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Understanding of distributed systems (for QUIC sync)
- Vector search fundamentals
---
QUIC Synchronization
What is QUIC Sync?
QUIC (Quick UDP Internet Connections) enables sub-millisecond latency synchronization between AgentDB instances across network boundaries with automatic retry, multiplexing, and encryption.
Benefits:
- <1ms latency between nodes
- Multiplexed streams (multiple operations simultaneously)
- Built-in encryption (TLS 1.3)
- Automatic retry and recovery
- Event-based broadcasting
Enable QUIC Sync
import { createAgentDBAdapter } from 'agentic-flow$reasoningbank';
// Initialize with QUIC synchronization
const adapter = await createAgentDBAdapter({
dbPath: '.agentdb$distributed.db',
enableQUICSync: true,
syncPort: 4433,
syncPeers: [
'192.168.1.10:4433',
'192.168.1.11:4433',
'192.168.1.12:4433',
],
});
// Patterns automatically sync across all peers
await adapter.insertPattern({
// ... pattern data
});
// Available on all peers within ~1msQUIC Configuration
const adapter = await createAgentDBAdapter({
enableQUICSync: true,
syncPort: 4433, // QUIC server port
syncPeers: ['host1:4433'], // Peer addresses
syncInterval: 1000, // Sync interval (ms)
syncBatchSize: 100, // Patterns per batch
maxRetries: 3, // Retry failed syncs
compression: true, // Enable compression
});Multi-Node Deployment
# Node 1 (192.168.1.10)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.11:4433,192.168.1.12:4433 \
node server.js
# Node 2 (192.168.1.11)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.12:4433 \
node server.js
# Node 3 (192.168.1.12)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.11:4433 \
node server.js---
Distance Metrics
Cosine Similarity (Default)
Best for normalized vectors, semantic similarity:
# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m cosine
# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
metric: 'cosine',
k: 10,
});Use Cases:
- Text embeddings (BERT, GPT, etc.)
- Semantic search
- Document similarity
- Most general-purpose applications
Formula: cos(θ) = (A · B) / (||A|| × ||B||) Range: [-1, 1] (1 = identical, -1 = opposite)
Euclidean Distance (L2)
Best for spatial data, geometric similarity:
# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m euclidean
# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
metric: 'euclidean',
k: 10,
});Use Cases:
- Image embeddings
- Spatial data
- Computer vision
- When vector magnitude matters
Formula: d = √(Σ(ai - bi)²) Range: [0, ∞] (0 = identical, ∞ = very different)
Dot Product
Best for pre-normalized vectors, fast computation:
# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m dot
# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
metric: 'dot',
k: 10,
});Use Cases:
- Pre-normalized embeddings
- Fast similarity computation
- When vectors are already unit-length
Formula: dot = Σ(ai × bi) Range: [-∞, ∞] (higher = more similar)
Custom Distance Metrics
// Implement custom distance function
function customDistance(vec1: number[], vec2: number[]): number {
// Weighted Euclidean distance
const weights = [1.0, 2.0, 1.5, ...];
let sum = 0;
for (let i = 0; i < vec1.length; i++) {
sum += weights[i] * Math.pow(vec1[i] - vec2[i], 2);
}
return Math.sqrt(sum);
}
// Use in search (requires custom implementation)---
Hybrid Search (Vector + Metadata)
Basic Hybrid Search
Combine vector similarity with metadata filtering:
// Store documents with metadata
await adapter.insertPattern({
id: '',
type: 'document',
domain: 'research-papers',
pattern_data: JSON.stringify({
embedding: documentEmbedding,
text: documentText,
metadata: {
author: 'Jane Smith',
year: 2025,
category: 'machine-learning',
citations: 150,
}
}),
confidence: 1.0,
usage_count: 0,
success_count: 0,
created_at: Date.now(),
last_used: Date.now(),
});
// Hybrid search: vector similarity + metadata filters
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
domain: 'research-papers',
k: 20,
filters: {
year: { $gte: 2023 }, // Published 2023 or later
category: 'machine-learning', // ML papers only
citations: { $gte: 50 }, // Highly cited
},
});Advanced Filtering
// Complex metadata queries
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
domain: 'products',
k: 50,
filters: {
price: { $gte: 10, $lte: 100 }, // Price range
category: { $in: ['electronics', 'gadgets'] }, // Multiple categories
rating: { $gte: 4.0 }, // High rated
inStock: true, // Available
tags: { $contains: 'wireless' }, // Has tag
},
});Weighted Hybrid Search
Combine vector and metadata scores:
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
domain: 'content',
k: 20,
hybridWeights: {
vectorSimilarity: 0.7, // 70% weight on semantic similarity
metadataScore: 0.3, // 30% weight on metadata match
},
filters: {
category: 'technology',
recency: { $gte: Date.now() - 30 * 24 * 3600000 }, // Last 30 days
},
});---
Multi-Database Management
Multiple Databases
// Separate databases for different domains
const knowledgeDB = await createAgentDBAdapter({
dbPath: '.agentdb$knowledge.db',
});
const conversationDB = await createAgentDBAdapter({
dbPath: '.agentdb$conversations.db',
});
const codeDB = await createAgentDBAdapter({
dbPath: '.agentdb$code.db',
});
// Use appropriate database for each task
await knowledgeDB.insertPattern({ /* knowledge */ });
await conversationDB.insertPattern({ /* conversation */ });
await codeDB.insertPattern({ /* code */ });Database Sharding
// Shard by domain for horizontal scaling
const shards = {
'domain-a': await createAgentDBAdapter({ dbPath: '.agentdb$shard-a.db' }),
'domain-b': await createAgentDBAdapter({ dbPath: '.agentdb$shard-b.db' }),
'domain-c': await createAgentDBAdapter({ dbPath: '.agentdb$shard-c.db' }),
};
// Route queries to appropriate shard
function getDBForDomain(domain: string) {
const shardKey = domain.split('-')[0]; // Extract shard key
return shards[shardKey] || shards['domain-a'];
}
// Insert to correct shard
const db = getDBForDomain('domain-a-task');
await db.insertPattern({ /* ... */ });---
MMR (Maximal Marginal Relevance)
Retrieve diverse results to avoid redundancy:
// Without MMR: Similar results may be redundant
const standardResults = await adapter.retrieveWithReasoning(queryEmbedding, {
k: 10,
useMMR: false,
});
// With MMR: Diverse, non-redundant results
const diverseResults = await adapter.retrieveWithReasoning(queryEmbedding, {
k: 10,
useMMR: true,
mmrLambda: 0.5, // Balance relevance (0) vs diversity (1)
});MMR Parameters:
mmrLambda = 0: Maximum relevance (may be redundant)mmrLambda = 0.5: Balanced (default)mmrLambda = 1: Maximum diversity (may be less relevant)
Use Cases:
- Search result diversification
- Recommendation systems
- Avoiding echo chambers
- Exploratory search
---
Context Synthesis
Generate rich context from multiple memories:
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
domain: 'problem-solving',
k: 10,
synthesizeContext: true, // Enable context synthesis
});
// ContextSynthesizer creates coherent narrative
console.log('Synthesized Context:', result.context);
// "Based on 10 similar problem-solving attempts, the most effective
// approach involves: 1) analyzing root cause, 2) brainstorming solutions,
// 3) evaluating trade-offs, 4) implementing incrementally. Success rate: 85%"
console.log('Patterns:', result.patterns);
// Extracted common patterns across memories---
Production Patterns
Connection Pooling
// Singleton pattern for shared adapter
class AgentDBPool {
private static instance: AgentDBAdapter;
static async getInstance() {
if (!this.instance) {
this.instance = await createAgentDBAdapter({
dbPath: '.agentdb$production.db',
quantizationType: 'scalar',
cacheSize: 2000,
});
}
return this.instance;
}
}
// Use in application
const db = await AgentDBPool.getInstance();
const results = await db.retrieveWithReasoning(queryEmbedding, { k: 10 });Error Handling
async function safeRetrieve(queryEmbedding: number[], options: any) {
try {
const result = await adapter.retrieveWithReasoning(queryEmbedding, options);
return result;
} catch (error) {
if (error.code === 'DIMENSION_MISMATCH') {
console.error('Query embedding dimension mismatch');
// Handle dimension error
} else if (error.code === 'DATABASE_LOCKED') {
// Retry with exponential backoff
await new Promise(resolve => setTimeout(resolve, 100));
return safeRetrieve(queryEmbedding, options);
}
throw error;
}
}Monitoring and Logging
// Performance monitoring
const startTime = Date.now();
const result = await adapter.retrieveWithReasoning(queryEmbedding, { k: 10 });
const latency = Date.now() - startTime;
if (latency > 100) {
console.warn('Slow query detected:', latency, 'ms');
}
// Log statistics
const stats = await adapter.getStats();
console.log('Database Stats:', {
totalPatterns: stats.totalPatterns,
dbSize: stats.dbSize,
cacheHitRate: stats.cacheHitRate,
avgSearchLatency: stats.avgSearchLatency,
});---
CLI Advanced Operations
Database Import/Export
# Export with compression
npx agentdb@latest export .$vectors.db .$backup.json.gz --compress
# Import from backup
npx agentdb@latest import .$backup.json.gz --decompress
# Merge databases
npx agentdb@latest merge .$db1.sqlite .$db2.sqlite .$merged.sqliteDatabase Optimization
# Vacuum database (reclaim space)
sqlite3 .agentdb$vectors.db "VACUUM;"
# Analyze for query optimization
sqlite3 .agentdb$vectors.db "ANALYZE;"
# Rebuild indices
npx agentdb@latest reindex .$vectors.db---
Environment Variables
# AgentDB configuration
AGENTDB_PATH=.agentdb$reasoningbank.db
AGENTDB_ENABLED=true
# Performance tuning
AGENTDB_QUANTIZATION=binary # binary|scalar|product|none
AGENTDB_CACHE_SIZE=2000
AGENTDB_HNSW_M=16
AGENTDB_HNSW_EF=100
# Learning plugins
AGENTDB_LEARNING=true
# Reasoning agents
AGENTDB_REASONING=true
# QUIC synchronization
AGENTDB_QUIC_SYNC=true
AGENTDB_QUIC_PORT=4433
AGENTDB_QUIC_PEERS=host1:4433,host2:4433---
Troubleshooting
Issue: QUIC sync not working
# Check firewall allows UDP port 4433
# NOTE: Requires administrator privileges - for reference only
sudo ufw allow 4433/udp
# Verify peers are reachable
ping host1
# Check QUIC logs
DEBUG=agentdb:quic node server.jsIssue: Hybrid search returns no results
// Relax filters
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
k: 100, // Increase k
filters: {
// Remove or relax filters
},
});Issue: Memory consolidation too aggressive
// Disable automatic optimization
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
optimizeMemory: false, // Disable auto-consolidation
k: 10,
});---
Learn More
- QUIC Protocol: docs$quic-synchronization.pdf
- Hybrid Search: docs$hybrid-search-guide.md
- GitHub: https:/$github.com$ruvnet$agentic-flow$tree$main$packages$agentdb
- Website: https:/$agentdb.ruv.io
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Category: Advanced / Distributed Systems Difficulty: Advanced Estimated Time: 45-60 minutes
