
AgentDB Performance Optimization
This one's about squeezing serious performance out of AgentDB vector databases. You get quantization options that cut memory use by 4x to 32x (binary quantization drops 1M vectors from 3GB to 96MB), HNSW indexing for 150x faster searches, and caching for sub-millisecond retrieval. The benchmarks are solid: 100µs vector search, 2ms batch inserts for 100 vectors. Use it when you're hitting memory limits, need faster similarity search at scale, or deploying to edge devices. The quantization trade-offs are clearly documented, which matters since you're choosing between accuracy and compression. Works with AgentDB v1.0.7+ via agentic-flow.
- GitHub stars
- 240
- last updated
- Jun 11, 2026
Install
$ npx -y skills add spencermarx/open-code-review --skill 'AgentDB Performance Optimization' --agent claude-codeInstalls into .claude/skills of the current project.
Files
AgentDB Performance Optimization
What This Skill Does
Provides comprehensive performance optimization techniques for AgentDB vector databases. Achieve 150x-12,500x performance improvements through quantization, HNSW indexing, caching strategies, and batch operations. Reduce memory usage by 4-32x while maintaining accuracy.
Performance: <100µs vector search, <1ms pattern retrieval, 2ms batch insert for 100 vectors.
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Existing AgentDB database or application
Quick Start
Run Performance Benchmarks
# Comprehensive performance benchmarking
npx agentdb@latest benchmark
# Results show:
# ✅ Pattern Search: 150x faster (100µs vs 15ms)
# ✅ Batch Insert: 500x faster (2ms vs 1s for 100 vectors)
# ✅ Large-scale Query: 12,500x faster (8ms vs 100s at 1M vectors)
# ✅ Memory Efficiency: 4-32x reduction with quantization
Enable Optimizations
import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
Sponsors
Details
- Repository
- github.com/spencermarx/open-code-review
- Categories
- Git & Pull Requests, Code Review & Quality, Debugging, Automation & Workflows
- License
- Apache-2.0
- First seen
- Jun 11, 2026
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