OpenClaw Agent A/B测试框架

世界上有两种Agent：一种会犯错，另一种会通过A/B测试找到最优解。

📌 功能介绍

A/B测试框架是OpenClaw Agent优化的「科学实验室」。它允许你同时运行多个版本的Agent（不同的prompt、工具配置、决策策略），通过控制变量和统计分析，找出哪个版本表现最好。这就像让Agent参加「盲品大赛」，不看名字，只看结果。

🛠️ 使用方法

1. 创建实验

# 创建A/B测试实验
openclaw ab-test create \
  --name "prompt-optimization-v2" \
  --description "优化代码生成prompt" \
  --metric "code_quality_score" \
  --duration "7d" \
  --traffic-split 50 50

# 添加实验变体
openclaw ab-test variant add \
  --test-id "prompt-optimization-v2" \
  --variant-name "control" \
  --config "config/control-prompt.yaml"

openclaw ab-test variant add \
  --test-id "prompt-optimization-v2" \
  --variant-name "enhanced" \
  --config "config/enhanced-prompt.yaml"

# 启动实验
openclaw ab-test start --test-id "prompt-optimization-v2"

2. 流量分配策略

# traffic-config.yaml
traffic:
  strategy: "weighted"  # weighted | random | contextual | sequential
  
  weighted:
    variants:
      control: 40     # 40%流量
      variant-a: 30   # 30%流量
      variant-b: 30   # 30%流量
  
  contextual:
    rules:
      - condition: "user_type == 'premium'"
        weights:
          control: 20
          variant-a: 50
          variant-b: 30
      - condition: "task_type == 'code_generation'"
        weights:
          control: 30
          variant-a: 40
          variant-b: 30
  
  sequential:
    variants: ["control", "variant-a", "variant-b"]
    cycle: "24h"  # 每24小时切换一次

3. 指标定义

# metrics-config.yaml
metrics:
  primary:
    name: "success_rate"
    type: "binary"  # binary | continuous | count
    aggregation: "mean"
    threshold: 0.95
    
  secondary:
    - name: "response_time"
      type: "continuous"
      aggregation: "p95"
      target: "< 2s"
    
    - name: "token_usage"
      type: "continuous"
      aggregation: "mean"
      target: "< 1000"
    
    - name: "user_satisfaction"
      type: "binary"
      aggregation: "mean"
      target: "> 0.8"

  custom:
    - name: "code_quality"
      type: "continuous"
      aggregation: "mean"
      source: "code_quality_analyzer"
      weight: 1.5

🏆 最佳实践

实验设计原则

原则	说明
单一变量	每次只测试一个变量，避免混淆
足够样本量	确保统计显著性，避免随机误差
明确目标	定义清楚的成功指标
控制组	必须有对照组（当前版本）

💻 代码示例

实验管理SDK

const { OpenClabABTest } = require('@openclab/abtest');

async function runExperiment() {
  const abTest = new OpenClabABTest({
    experimentId: 'prompt-optimization-v2',
    config: {
      trafficSplit: { control: 0.4, variantA: 0.3, variantB: 0.3 },
      metrics: ['success_rate', 'response_time', 'token_usage'],
      duration: '7d'
    }
  });
  
  // 启动实验
  await abTest.start();
  
  // 监控实验进度
  const monitor = setInterval(async () => {
    const status = await abTest.getStatus();
    console.log(`实验进度: ${status.progress}%`);
    console.log(`样本数: ${status.samples.total}`);
    
    if (status.isComplete) {
      clearInterval(monitor);
      const result = await abTest.analyze();
      console.log('实验结果:', result);
      
      // 如果variantA获胜，自动部署
      if (result.winner === 'variantA') {
        await abTest.deployWinner();
        console.log('🎉 变体A已部署！');
      }
    }
  }, 60000); // 每分钟检查一次
}

多变量测试

async function multivariateTest() {
  const experiments = [
    {
      id: 'prompt-v1',
      variants: [
        { id: 'control', config: { prompt: 'basic' } },
        { id: 'enhanced', config: { prompt: 'detailed' } }
      ]
    },
    {
      id: 'tool-selection',
      variants: [
        { id: 'auto', config: { toolSelection: 'auto' } },
        { id: 'manual', config: { toolSelection: 'manual' } }
      ]
    }
  ];
  
  // 创建因子实验
  const factorialDesign = createFactorialDesign(experiments);
  
  // 执行实验
  const results = await Promise.all(
    factorialDesign.map(design => runSingleVariant(design))
  );
  
  // 分析交互效应
  const analysis = analyzeInteractionEffects(results);
  
  return {
    mainEffects: calculateMainEffects(results),
    interactions: analysis.interactions,
    recommendations: generateRecommendations(analysis)
  };
}

function createFactorialDesign(experiments) {
  const design = [];
  
  // 生成所有可能的组合
  const variants = experiments.map(exp => 
    exp.variants.map(v => ({ [exp.id]: v.id }))
  );
  
  // 交叉组合
  const combinations = cartesianProduct(variants);
  
  return combinations.map(comb => {
    const config = {};
    const variantId = [];
    
    Object.entries(comb).forEach(([expId, variant]) => {
      config[expId] = experiments.find(e => e.id === expId).variants.find(v => v.id === variant).config;
      variantId.push(`${expId}:${variant}`);
    });
    
    return {
      id: variantId.join('-'),
      config,
      name: `实验组合 ${variantId.join('-')}`
    };
  });
}

🔗 相关链接

📅 更新时间：2026-05-11 | 📖 更多OpenClaw教程请访问 工具教程索引