AgentDB Performance Optimization

You get quantization options that cut memory use by 4x to 32x (binary quantization drops 1M vectors from 3GB to 96MB), HNSW indexing for 150x faster searches, and caching for sub-millisecond retrieval. The benchmarks are solid: 100µs vector search, 2ms batch inserts for 100 vectors. Use it when you're hitting memory limits, need faster similarity search at scale, or deploying to edge devices. The quantization trade-offs are clearly documented, which matters since you're choosing between accuracy and compression. Works with AgentDB v1.0.7+ via agentic-flow.