agentdb-advanced-features

name	agentdb-advanced-features
description	Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications. Use when this capability is needed.
metadata	{"author":"dnyoussef"}

---

LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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When NOT to Use This Skill

• Local-only operations with no vector search needs
• Simple key-value storage without semantic similarity
• Real-time streaming data without persistence requirements
• Operations that do not require embedding-based retrieval

Success Criteria

• Vector search query latency: <10ms for 99th percentile
• Embedding generation: <100ms per document
• Index build time: <1s per 1000 vectors
• Recall@10: >0.95 for similar documents
• Database connection success rate: >99.9%
• Memory footprint: <2GB for 1M vectors with quantization

Edge Cases & Error Handling

• Rate Limits: AgentDB local instances have no rate limits; cloud deployments may vary
• Connection Failures: Implement retry logic with exponential backoff (max 3 retries)
• Index Corruption: Maintain backup indices; rebuild from source if corrupted
• Memory Overflow: Use quantization (4-bit, 8-bit) to reduce memory by 4-32x
• Stale Embeddings: Implement TTL-based refresh for dynamic content
• Dimension Mismatch: Validate embedding dimensions (384 for sentence-transformers) before insertion

Guardrails & Safety

• NEVER expose database connection strings in logs or error messages
• ALWAYS validate vector dimensions before insertion
• ALWAYS sanitize metadata to prevent injection attacks
• NEVER store PII in vector metadata without encryption
• ALWAYS implement access control for multi-tenant deployments
• ALWAYS validate search results before returning to users

Evidence-Based Validation

• Verify database health: Check connection status and index integrity
• Validate search quality: Measure recall/precision on test queries
• Monitor performance: Track query latency, throughput, and memory usage
• Test failure recovery: Simulate connection drops and index corruption
• Benchmark improvements: Compare against baseline metrics (e.g., 150x speedup claim)

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

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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 ~1ms

QUIC 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

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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)

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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
    : { :  },       
  },
});

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.sqlite

Database 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
sudo ufw allow 4433/udp

# Verify peers are reachable
ping host1

# Check QUIC logs
DEBUG=agentdb:quic node server.js

Issue: 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,
});

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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

Core Principles

AgentDB Advanced Features operates on 3 fundamental principles:

Principle 1: Distributed Consistency Through QUIC Synchronization

Achieve sub-millisecond cross-node synchronization with automatic retry, multiplexing, and TLS 1.3 encryption for distributed vector databases.

In practice:

• QUIC enables <1ms pattern synchronization across network boundaries with UDP + reliability layer
• Multiplexed streams allow simultaneous operations (queries, inserts, syncs) without head-of-line blocking
• Event-based broadcasting ensures eventual consistency with configurable sync intervals (1s default)

Principle 2: Hybrid Search Combines Vector Similarity with Metadata Filtering

Merge semantic understanding (embeddings) with structured constraints (metadata filters) for precision retrieval beyond pure vector search.

In practice:

• Vector search finds semantically similar documents, metadata filters enforce business rules (date ranges, categories, permissions)
• MMR (Maximal Marginal Relevance) diversifies results to avoid redundancy while maintaining relevance
• Custom distance metrics (cosine, Euclidean, dot product) optimize for different embedding types (text vs images)

Principle 3: Multi-Database Sharding Enables Horizontal Scaling

Partition vector data across databases by domain or tenant for independent scaling and isolation.

In practice:

• Separate databases per domain (knowledge.db, conversations.db, code.db) prevent cross-contamination
• Sharding by tenant or region enables geographic distribution and compliance (GDPR data residency)
• Independent optimization per shard (different quantization, cache sizes) based on access patterns

Common Anti-Patterns

Anti-Pattern	Problem	Solution
Synchronous QUIC Sync	Blocking operations wait for sync completion, causing 10-100ms latency spikes	Enable async sync with configurable intervals (1s), batch sync operations (100 patterns), use fire-and-forget pattern
Over-Filtering Hybrid Search	Too many metadata filters return empty results despite semantic matches	Start with k=100 for vector search, then apply filters; progressively relax filters if results <5
Single Monolithic Database	One database for all domains causes index bloat, slow queries, and cross-domain contamination	Shard by domain or tenant; use separate databases with independent indices and optimization strategies

Conclusion

AgentDB Advanced Features unlocks production-grade distributed AI systems by extending core vector search with QUIC synchronization for multi-node deployments, hybrid search for combining semantic and structured queries, and flexible sharding for horizontal scaling. These capabilities transform AgentDB from a local vector database into a distributed platform capable of supporting multi-agent coordination, geographic distribution, and enterprise-scale applications.

Use this skill when building distributed AI systems requiring cross-node communication (<1ms QUIC sync), implementing RAG systems needing metadata filters beyond semantic search (hybrid search with date/category/permission constraints), or scaling beyond single-machine limits (multi-database sharding by domain/tenant). The key insight is architectural flexibility: QUIC enables distributed consistency, hybrid search adds precision to semantic retrieval, and sharding provides independent scaling per domain. Start with single-database deployment, add QUIC sync when distributing across nodes, enable hybrid search for complex filtering, and implement sharding only when hitting performance or isolation limits.

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