AgentDB Performance Optimization

Quantization reduces AgentDB memory consumption by 4-32x using binary, scalar, or product quantization techniques. Enable binary quantization in your `createAgentDBAdapter` configuration to significantly lower memory footprint while preserving search accuracy across large deployments.

HNSW (Hierarchical Navigable Small World) indexing accelerates AgentDB vector search up to 150x faster. Combined with intelligent caching and batch operations, it achieves sub-100µs search times for real-time AI applications querying millions of vectors.

Yes. Enable binary quantization and set cache size to 1000 in your `createAgentDBAdapter` configuration. Run `npx agentdb@latest benchmark` first to measure current performance, then apply optimizations incrementally for faster search and reduced memory usage.

Batch insertion APIs improve AgentDB throughput for large-scale data loading, while real-time inserts suit continuous streaming workloads. Choose batch operations during initial indexing or bulk updates, and real-time inserts for dynamic vector addition in production AI applications.

Yes. Quantization maintains accuracy for vector similarity queries across millions of vectors in AgentDB. Combined with HNSW indexing and caching, quantized indexes deliver fast nearest-neighbor results with minimal memory overhead for pattern matching and semantic search.

Binary quantization offers maximum compression (32x reduction) with slight accuracy loss; scalar quantization balances speed and precision (8-16x reduction); product quantization provides fine-grained accuracy control. Choose based on your AgentDB scale, latency requirements, and acceptable accuracy margins.