AgentDB Performance Optimization

Quantization reduces AgentDB memory footprint by 4-32x with minimal accuracy loss, while HNSW indexing accelerates approximate nearest-neighbor search by 150x for high-dimensional embeddings, and in-memory caching boosts throughput for large-scale vector workloads.

Configure HNSW indexing in AgentDB by setting adapter parameters like hnswM and hnswEfSearch during createAgentDBAdapter initialization to balance search accuracy and latency for high-dimensional vector embeddings. Adjusting hnswM controls graph connectivity and hnswEfSearch tunes search depth.

Quantization in AgentDB achieves 4-32x memory reduction for millions of vectors with minimal accuracy loss, supporting configurable quantization types like binary. This enables scalable deployments handling large-scale embeddings while maintaining acceptable search accuracy within strict latency budgets.

Benchmark AgentDB performance by running npx agentdb@latest benchmark to measure vector search latency and memory usage improvements after applying quantization, HNSW indexing, and caching. This enables immediate experimentation to validate that optimizations meet your strict latency budgets.

AgentDB supports bulk inserts and in-memory caches to boost vector insert throughput and reduce latency for large-scale embeddings. Batch operations work alongside quantization and HNSW indexing to handle millions of vectors efficiently within production environments.