AgentDB Performance Optimization

Vector database memory usage is reduced through quantization, achieving 4x–32x compression with minimal accuracy loss. This optimization targets large-scale vector stores experiencing memory pressure, applying techniques like quantization to dramatically improve storage efficiency.

Vector search latency is accelerated by implementing HNSW indexing, caching, and batch operations. This approach achieves sub-millisecond searches and faster repeated pattern lookups on datasets containing millions of vectors.

Quantization reduces memory usage by 4x–32x with minimal loss of accuracy. It compresses vector data representations, allowing large-scale vector stores to fit into constrained memory environments without significantly degrading search result quality.

An existing AgentDB instance is required to apply performance optimization. The tuning process requires AgentDB v1.0.7+ via agentic-flow, configuring parameters like quantizationType, hnswM, hnswEfConstruction, and cacheSize to meet performance goals.

HNSW indexing parameters are configured by tuning hnswM and hnswEfConstruction values. Running the provided benchmark suite on your dataset after configuration helps observe improvements and validate settings for high-throughput search workloads.