Worker Benchmarks

by ruvnet60de638630abNo license74K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

Run comprehensive worker system benchmarks and performance analysis

Instructions onlySoftware Development
AI-generated overview

Runs performance benchmarks for the agentic-flow worker system and reports latency, throughput and memory metrics.

What it does
This skill documents how to run performance benchmarks for the agentic-flow worker system, covering trigger detection, worker registry CRUD, agent selection, model cache, concurrent workers and memory key generation. It lists the benchmark types, their p95 or total-time targets, iteration counts and the metrics collected. It also shows the console results format, threshold configuration in settings, programmatic TypeScript usage and optimization tips.
When to use it
Use it when you need to measure or compare the performance of the agentic-flow worker system, or to check whether latency targets are met. It suits performance regression checks and tuning of worker-related settings.
Requirements
Requires the agentic-flow package and npx or Node.js to run the benchmark commands; benchmark thresholds are read from .claude/settings.json. The skill ships no scripts of its own.

Worker Benchmarks Skill

Run comprehensive performance benchmarks for the agentic-flow worker system.

Quick Start

bash
# Run full benchmark suitenpx agentic-flow workers benchmark
# Run specific benchmarknpx agentic-flow workers benchmark --type trigger-detectionnpx agentic-flow workers benchmark --type registrynpx agentic-flow workers benchmark --type agent-selectionnpx agentic-flow workers benchmark --type concurrent

Benchmark Types

1. Trigger Detection (trigger-detection)

Tests keyword detection speed across 12 worker triggers.

  • Target: p95 < 5ms
  • Iterations: 1000
  • Metrics: latency, throughput, histogram

2. Worker Registry (registry)

Tests CRUD operations on worker entries.

  • Target: p95 < 10ms
  • Iterations: 500 creates, gets, updates
  • Metrics: per-operation latency breakdown

3. Agent Selection (agent-selection)

Tests performance-based agent selection.

  • Target: p95 < 1ms
  • Iterations: 1000
  • Metrics: selection confidence, agent scores

4. Model Cache (cache)

Tests model caching performance.

  • Target: p95 < 0.5ms
  • Metrics: hit rate, cache size, eviction stats

5. Concurrent Workers (concurrent)

Tests parallel worker creation and updates.

  • Target: < 1000ms for 10 workers
  • Metrics: per-worker latency, memory usage

6. Memory Key Generation (memory-keys)

Tests memory pattern key generation.

  • Target: p95 < 0.1ms
  • Iterations: 5000
  • Metrics: unique patterns, throughput

Output Format

═══════════════════════════════════════════════════════════📈 BENCHMARK RESULTS═══════════════════════════════════════════════════════════
✅ Trigger Detection   Operation: detect   Count: 1,000   Avg: 0.045ms | p95: 0.120ms (target: 5ms)   Throughput: 22,222 ops/s   Memory Δ: 0.12MB
✅ Worker Registry   Operation: crud   Count: 1,500   Avg: 1.234ms | p95: 3.456ms (target: 10ms)   Throughput: 810 ops/s   Memory Δ: 2.34MB
───────────────────────────────────────────────────────────📊 SUMMARY───────────────────────────────────────────────────────────Total Tests: 6Passed: 6 | Failed: 0Avg Latency: 0.567msTotal Duration: 2345msPeak Memory: 8.90MB═══════════════════════════════════════════════════════════

Integration with Settings

Benchmark thresholds are configured in .claude/settings.json:

json
{  "performance": {    "benchmarkThresholds": {      "triggerDetection": { "p95Ms": 5 },      "workerRegistry": { "p95Ms": 10 },      "agentSelection": { "p95Ms": 1 },      "memoryKeyGeneration": { "p95Ms": 0.1 },      "concurrentWorkers": { "totalMs": 1000 }    }  }}

Programmatic Usage

typescript
import { workerBenchmarks, runBenchmarks } from 'agentic-flow/workers/worker-benchmarks';
// Run full suiteconst suite = await runBenchmarks();console.log(suite.summary);
// Run individual benchmarksconst triggerResult = await workerBenchmarks.benchmarkTriggerDetection(1000);const registryResult = await workerBenchmarks.benchmarkRegistryOperations(500);

Performance Optimization Tips

  1. Model Cache: Enable with CLAUDE_FLOW_MODEL_CACHE_MB=512
  2. Parallel Workers: Enable with CLAUDE_FLOW_WORKER_PARALLEL=true
  3. Warning Suppression: Enable with CLAUDE_FLOW_SUPPRESS_WARNINGS=true
  4. SQLite WAL Mode: Automatic for better concurrent performance

Source and attribution

Source:ruvnet/rufloin.claude/skills/worker-benchmarksat commit60de638

License: No license

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