AgentDB Performance Optimization

Memory reduction in AgentDB is achieved through quantization strategies—binary, scalar, and product quantization—that compress vector data while maintaining search accuracy. This Skill applies these techniques to deliver 4-32x memory savings depending on your data characteristics and acceptable precision trade-offs.

HNSW indexing combined with caching strategies accelerates vector search up to 12,500x faster in AgentDB. This Skill configures HNSW indexes and intelligent caching—with configurable cache sizes like 1000—to enable sub-100µs search latency across millions of vectors.

Yes. This Skill combines quantization, HNSW indexing, and batch operations to optimize AgentDB for real-time personalization workloads. The result is sub-millisecond recommendations on millions of vectors while reducing infrastructure costs through memory efficiency.

Batch operations in AgentDB insert vectors far faster than individual operations—around 2ms for 100 vectors. This Skill configures optimized batch insert and retrieval patterns alongside quantization and indexing to maximize data ingestion throughput.

You need Node.js 18 or higher and AgentDB v1.0.7 or later with an existing AgentDB deployment already running. This Skill applies optimization techniques to your live instance without requiring migration or data reformat.

Quantization trades precision for memory and speed; different strategies (binary, scalar, product) offer different accuracy profiles. This Skill provides benchmarking tools to measure the accuracy impact of each quantization strategy on your specific dataset before committing to production.