cldcde: Skill for Claude Code

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

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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,
});

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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)