AgentDB Performance Optimization

Quantization reduces memory consumption 4-32x by compressing vector representations. Enable binary, scalar, or product quantization in your adapter config to cut storage while maintaining search precision for large-scale deployments.

HNSW indexing enables ultra-fast high-precision searches by building hierarchical graph structures. Tune M and efSearch parameters to balance speed and accuracy for real-time pattern retrieval across millions of vectors.

Batch insert workflows and in-memory caching dramatically improve throughput for bulk data ingestion. Combine these with quantization and HNSW to achieve dramatic performance gains on large-scale vector workloads.

Yes, quantization, HNSW indexing, caching, and batch operations require Node.js 18 or later and AgentDB v1.0.7 or higher. Verify your environment meets these prerequisites before deploying optimizations.

Binary quantization offers maximum compression; scalar quantization balances speed and precision; product quantization suits high-dimensional data. Run benchmarks with `npx agentdb@latest benchmark` to compare trade-offs for your workload.