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Skill

AgentDB Performance Optimization

From ruvnet

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

Facts

Repository
ruvnet/ruflo
Status
Actively maintained
Last commit
Source file
.agents/skills/agentdb-optimization/SKILL.md

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The instructions Claude Code reads when this skill runs.

# 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

```bash
# 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

```typescript
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**: La
View full source on GitHub →

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