Agent skill · data analytics · proffesor-for-testing
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.
Why this skill is useful
Adds specific optimization techniques and executable scripts for AgentDB that significantly enhance performance and memory efficiency.
What it needs
Requires node installed locally. About 6k tokens when loaded. Last updated 2026-08-06. 434 stars on the source repository.
What this skill does
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 Enable Optimizations --- 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 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 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 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills proffesor-for-testing/agentdb-optimization