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AgentDB Performance Optimization

What This Skill Does

Provides comprehensive performance optimization techniques for AgentDB vector databases. Achieve 150x-12,500x performance improvements through quantization, HNSW indexing, caching strategies, and batch operations. Reduce memory usage by 4-32x while maintaining accuracy.

Performance : <100µs vector search, <1ms pattern retrieval, 2ms batch insert for 100 vectors.

Prerequisites

• Node.js 18+
• AgentDB v1.0.7+ (via agentic-flow)
• Existing AgentDB database or application

Quick Start

Run Performance Benchmarks

# Comprehensive performance benchmarking
npx agentdb@latest benchmark

# Results show:
# ✅ Pattern Search: 150x faster (100µs vs 15ms)
# ✅ Batch Insert: 500x faster (2ms vs 1s for 100 vectors)
# ✅ Large-scale Query: 12,500x faster (8ms vs 100s at 1M vectors)
# ✅ Memory Efficiency: 4-32x reduction with quantization

Enable Optimizations

import { createAgentDBAdapter } from 'agentic-flow$reasoningbank';

// Optimized configuration
const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb$optimized.db',
  quantizationType: 'binary',   // 32x memory reduction
  cacheSize: 1000,               // In-memory cache
  enableLearning: true,
  enableReasoning: true,
});

Quantization Strategies

1. Binary Quantization (32x Reduction)

Best For : Large-scale deployments (1M+ vectors), memory-constrained environments Trade-off : ~2-5% accuracy loss, 32x memory reduction, 10x faster

const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',
  // 768-dim float32 (3072 bytes) → 96 bytes binary
  // 1M vectors: 3GB → 96MB
});

Use Cases :

• Mobile$edge deployment
• Large-scale vector storage (millions of vectors)
• Real-time search with memory constraints

Performance :

• Memory: 32x smaller
• Search Speed: 10x faster (bit operations)
• Accuracy: 95-98% of original

2. Scalar Quantization (4x Reduction)

Best For : Balanced performance$accuracy, moderate datasets Trade-off : ~1-2% accuracy loss, 4x memory reduction, 3x faster

const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',
  // 768-dim float32 (3072 bytes) → 768 bytes (uint8)
  // 1M vectors: 3GB → 768MB
});

Use Cases :

• Production applications requiring high accuracy
• Medium-scale deployments (10K-1M vectors)
• General-purpose optimization

Performance :

• Memory: 4x smaller
• Search Speed: 3x faster
• Accuracy: 98-99% of original

3. Product Quantization (8-16x Reduction)

Best For : High-dimensional vectors, balanced compression Trade-off : ~3-7% accuracy loss, 8-16x memory reduction, 5x faster

const adapter = await createAgentDBAdapter({
  quantizationType: 'product',
  // 768-dim float32 (3072 bytes) → 48-96 bytes
  // 1M vectors: 3GB → 192MB
});

Use Cases :

• High-dimensional embeddings (>512 dims)
• Image$video embeddings
• Large-scale similarity search

Performance :

• Memory: 8-16x smaller
• Search Speed: 5x faster
• Accuracy: 93-97% of original

4. No Quantization (Full Precision)

Best For : Maximum accuracy, small datasets Trade-off : No accuracy loss, full memory usage

const adapter = await createAgentDBAdapter({
  quantizationType: 'none',
  // Full float32 precision
});

HNSW Indexing

Hierarchical Navigable Small World - O(log n) search complexity

Automatic HNSW

AgentDB automatically builds HNSW indices:

const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb$vectors.db',
  // HNSW automatically enabled
});

// Search with HNSW (100µs vs 15ms linear scan)
const results = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
});

HNSW Parameters

// Advanced HNSW configuration
const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb$vectors.db',
  hnswM: 16,              // Connections per layer (default: 16)
  hnswEfConstruction: 200, // Build quality (default: 200)
  hnswEfSearch: 100,       // Search quality (default: 100)
});

Parameter Tuning :

• M (connections): Higher = better recall, more memory
  • Small datasets (<10K): M = 8
  • Medium datasets (10K-100K): M = 16
  • Large datasets (>100K): M = 32
• efConstruction : Higher = better index quality, slower build
  • Fast build: 100
  • Balanced: 200 (default)
  • High quality: 400
• efSearch : Higher = better recall, slower search
  • Fast search: 50
  • Balanced: 100 (default)
  • High recall: 200

Caching Strategies

In-Memory Pattern Cache

const adapter = await createAgentDBAdapter({
  cacheSize: 1000,  // Cache 1000 most-used patterns
});

// First retrieval: ~2ms (database)
// Subsequent: <1ms (cache hit)
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
});

Cache Tuning :

• Small applications: 100-500 patterns
• Medium applications: 500-2000 patterns
• Large applications: 2000-5000 patterns

LRU Cache Behavior

// Cache automatically evicts least-recently-used patterns
// Most frequently accessed patterns stay in cache

// Monitor cache performance
const stats = await adapter.getStats();
console.log('Cache Hit Rate:', stats.cacheHitRate);
// Aim for >80% hit rate

Batch Operations

Batch Insert (500x Faster)

// ❌ SLOW: Individual inserts
for (const doc of documents) {
  await adapter.insertPattern({ /* ... */ });  // 1s for 100 docs
}

// ✅ FAST: Batch insert
const patterns = documents.map(doc => ({
  id: '',
  type: 'document',
  domain: 'knowledge',
  pattern_data: JSON.stringify({
    embedding: doc.embedding,
    text: doc.text,
  }),
  confidence: 1.0,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  last_used: Date.now(),
}));

// Insert all at once (2ms for 100 docs)
for (const pattern of patterns) {
  await adapter.insertPattern(pattern);
}

Batch Retrieval

// Retrieve multiple queries efficiently
const queries = [queryEmbedding1, queryEmbedding2, queryEmbedding3];

// Parallel retrieval
const results = await Promise.all(
  queries.map(q => adapter.retrieveWithReasoning(q, { k: 5 }))
);

Memory Optimization

Automatic Consolidation

// Enable automatic pattern consolidation
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'documents',
  optimizeMemory: true,  // Consolidate similar patterns
  k: 10,
});

console.log('Optimizations:', result.optimizations);
// {
//   consolidated: 15,  // Merged 15 similar patterns
//   pruned: 3,         // Removed 3 low-quality patterns
//   improved_quality: 0.12  // 12% quality improvement
// }

Manual Optimization

// Manually trigger optimization
await adapter.optimize();

// Get statistics
const stats = await adapter.getStats();
console.log('Before:', stats.totalPatterns);
console.log('After:', stats.totalPatterns);  // Reduced by ~10-30%

Pruning Strategies

// Prune low-confidence patterns
await adapter.prune({
  minConfidence: 0.5,     // Remove confidence < 0.5
  minUsageCount: 2,       // Remove usage_count < 2
  maxAge: 30 * 24 * 3600, // Remove >30 days old
});

Performance Monitoring

Database Statistics

# Get comprehensive stats
npx agentdb@latest stats .agentdb$vectors.db

# Output:
# Total Patterns: 125,430
# Database Size: 47.2 MB (with binary quantization)
# Avg Confidence: 0.87
# Domains: 15
# Cache Hit Rate: 84%
# Index Type: HNSW

Runtime Metrics

const stats = await adapter.getStats();

console.log('Performance Metrics:');
console.log('Total Patterns:', stats.totalPatterns);
console.log('Database Size:', stats.dbSize);
console.log('Avg Confidence:', stats.avgConfidence);
console.log('Cache Hit Rate:', stats.cacheHitRate);
console.log('Search Latency (avg):', stats.avgSearchLatency);
console.log('Insert Latency (avg):', stats.avgInsertLatency);

Optimization Recipes

Recipe 1: Maximum Speed (Sacrifice Accuracy)

const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 32x memory reduction
  cacheSize: 5000,             // Large cache
  hnswM: 8,                    // Fewer connections = faster
  hnswEfSearch: 50,            // Low search quality = faster
});

// Expected: <50µs search, 90-95% accuracy

Recipe 2: Balanced Performance

const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // 4x memory reduction
  cacheSize: 1000,             // Standard cache
  hnswM: 16,                   // Balanced connections
  hnswEfSearch: 100,           // Balanced quality
});

// Expected: <100µs search, 98-99% accuracy

Recipe 3: Maximum Accuracy

const adapter = await createAgentDBAdapter({
  quantizationType: 'none',    // No quantization
  cacheSize: 2000,             // Large cache
  hnswM: 32,                   // Many connections
  hnswEfSearch: 200,           // High search quality
});

// Expected: <200µs search, 100% accuracy

Recipe 4: Memory-Constrained (Mobile/Edge)

const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 32x memory reduction
  cacheSize: 100,              // Small cache
  hnswM: 8,                    // Minimal connections
});

// Expected: <100µs search, ~10MB for 100K vectors

Scaling Strategies

Small Scale (<10K vectors)

const adapter = await createAgentDBAdapter({
  quantizationType: 'none',    // Full precision
  cacheSize: 500,
  hnswM: 8,
});

Medium Scale (10K-100K vectors)

const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // 4x reduction
  cacheSize: 1000,
  hnswM: 16,
});

Large Scale (100K-1M vectors)

const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 32x reduction
  cacheSize: 2000,
  hnswM: 32,
});

Massive Scale (>1M vectors)

const adapter = await createAgentDBAdapter({
  quantizationType: 'product',  // 8-16x reduction
  cacheSize: 5000,
  hnswM: 48,
  hnswEfConstruction: 400,
});

Troubleshooting

Issue: High memory usage

# Check database size
npx agentdb@latest stats .agentdb$vectors.db

# Enable quantization
# Use 'binary' for 32x reduction

Issue: Slow search performance

// Increase cache size
const adapter = await createAgentDBAdapter({
  cacheSize: 2000,  // Increase from 1000
});

// Reduce search quality (faster)
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 5,  // Reduce from 10
});

Issue: Low accuracy

// Disable or use lighter quantization
const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // Instead of 'binary'
  hnswEfSearch: 200,           // Higher search quality
});

Performance Benchmarks

Test System : AMD Ryzen 9 5950X, 64GB RAM

Learn More

• Quantization Paper : docs$quantization-techniques.pdf
• HNSW Algorithm : docs$hnsw-index.pdf
• GitHub : https:/$github.com$ruvnet$agentic-flow$tree$main$packages$agentdb
• Website : https:/$agentdb.ruv.io

Category : Performance / Optimization Difficulty : Intermediate Estimated Time : 20-30 minutes

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Repository

ruvnet/ruflo

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