AgentDB Performance Optimization
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.
Profile →npx skills add ruvnet/ruflo --skill agentdb-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- What This Skill Does
- Prerequisites
- Quick Start
- Run Performance Benchmarks
- Enable Optimizations
- Quantization Strategies
- 1. Binary Quantization (32x Reduction)
- 2. Scalar Quantization (4x Reduction)
- 3. Product Quantization (8-16x Reduction)
- 4. No Quantization (Full Precision)
- HNSW Indexing
- Automatic HNSW
- HNSW Parameters
- Caching Strategies
Comprehensive performance benchmarking npx agentdb@latest benchmark Results show: Get comprehensive stats npx agentdb@latest stats .agentdb$vectors.db Total Patterns: 125,430 Database Size: 47.2 MB (with binary quantization) Avg Confidence: 0.87 Cache Hit Rate: 84% Index Type: HNSW
What does the AgentDB Performance Optimization skill do?
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.
How do I install it?
Run `npx skills add ruvnet/ruflo --skill agentdb-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.