AgentDB Performance Optimization

Optimize AgentDB vector databases using quantization, HNSW indexing, caching, and batch operations.

Updated May 8, 2026

Search Tags:#caching #vector database #performance optimization #agentdb #similarity search #quantization #hnsw indexing

Ready to run

npx skills add https://github.com/FrekiManagarm/d-chambaud --skill agentdb-performance-optimization-frekimanagarm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: AgentDB Performance Optimization
Source: https://github.com/FrekiManagarm/d-chambaud/tree/main/.agents/skills/agentdb-optimization
Command: npx skills add https://github.com/FrekiManagarm/d-chambaud --skill agentdb-performance-optimization-frekimanagarm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Vector databases consume excessive memory and suffer slow search latency at scale, making it difficult to run similarity search over thousands or millions of embeddings in production or memory-constrained environments. ## Core Features & Use Cases - Quantization Strategies: Apply binary (32x), scalar (4x), or product (8-16x) quantization to reduce memory usage while controlling accuracy trade-offs. - HNSW Indexing & Caching: Configure HNSW parameters (M, efConstruction, efSearch) and in-memory LRU caches to achieve sub-millisecond vector search. - Batch Operations & Pruning: Insert vectors in batches, consolidate similar patterns, and prune low-confidence or stale entries automatically. - Use Case: When deploying a semantic search application with 1M vectors, apply binary quantization to shrink storage from 3GB to 96MB and use HNSW indexing to cut query latency from 100 seconds to 8 milliseconds. ## Quick Start Configure my AgentDB adapter with binary quantization, a cache size of 1000, and HNSW indexing, then run the benchmark command to measure the performance improvement.

Frequently Asked Questions about AgentDB Performance Optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce AgentDB memory usage for large vector datasets?▼

Enable quantization when creating the AgentDB adapter. Binary quantization reduces memory 32x with 95-98% accuracy, scalar reduces 4x with 98-99% accuracy, and product quantization reduces 8-16x for high-dimensional vectors.

How do I speed up vector search in AgentDB?▼

AgentDB automatically builds HNSW indices for O(log n) search complexity. Tune hnswM, hnswEfConstruction, and hnswEfSearch parameters, and increase cacheSize so frequent queries are served from the in-memory cache in under 1ms.

Which quantization type should I use for vector search?▼

Use binary quantization for 1M+ vectors or memory-constrained deployments, scalar for balanced accuracy and performance on 10K-1M vectors, product for high-dimensional embeddings, and none when maximum accuracy matters on small datasets.

What HNSW parameters should I set for my dataset size?▼

For datasets under 10K vectors use M=8, for 10K-100K use M=16, and for over 100K use M=32. Set efConstruction to 200 for balanced builds and efSearch to 100 for balanced search, raising both for higher recall.

Why is my AgentDB search accuracy too low after optimization?▼

Low accuracy usually results from aggressive quantization or low search quality settings. Switch from binary to scalar quantization and raise hnswEfSearch to 200 to recover recall at the cost of some speed.

What are the limitations of binary quantization for embeddings?▼

Binary quantization trades roughly 2-5% accuracy for 32x memory reduction and 10x faster search. It is unsuitable when maximum precision is required, such as small datasets where full float32 precision is affordable.