Worker Benchmarks

作者 ruvnet60de638630ab无许可证74K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库今天更新

Run comprehensive worker system benchmarks and performance analysis

AI 生成的概览

为 agentic-flow worker 系统运行性能基准测试,并报告延迟、吞吐量和内存指标。

功能
该技能说明如何为 agentic-flow worker 系统运行性能基准测试,涵盖触发器检测、worker 注册表 CRUD、代理选择、模型缓存、并发 worker 和内存键生成。它列出各基准类型、对应的 p95 或总耗时目标、迭代次数以及采集的指标。它还展示控制台结果格式、设置中的阈值配置、TypeScript 编程调用方式以及优化建议。
适用场景
当你需要测量或比较 agentic-flow worker 系统的性能,或检查是否达到延迟目标时使用。它适用于性能回归检查和 worker 相关设置的调优。
运行要求
需要 agentic-flow 包以及 npx 或 Node.js 来运行基准命令;基准阈值从 .claude/settings.json 读取。该技能本身不附带脚本。

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

来源与署名

来源:ruvnet/ruflo位于.claude/skills/worker-benchmarks提交60de638

许可证: 无许可证

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