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# 在 CI 中运行 Scorer

在 CI Pipeline 中运行 Scorer，可以提供可量化的指标来衡量 Agent 质量随时间的变化。`runEvals` 函数会将多个测试用例交给 Agent 或 Workflow 处理，并返回聚合分数。

## 基础设置

可以使用任何支持 ESM 模块的测试框架，例如 [Vitest](https://vitest.dev/)、[Jest](https://jestjs.io/) 或 [Mocha](https://mochajs.org/)。

## 创建测试用例

使用 `runEvals` 针对多个测试用例评估 Agent。该函数接受数据项目数组，每个项目均包含 `input`，还可包含供 Scorer 验证使用的 `groundTruth`。

```typescript
import { describe, it, expect } from 'vitest'
import { createScorer, runEvals } from '@mastra/core/evals'
import { weatherAgent } from './weather-agent'
import { locationScorer } from '../scorers/location-scorer'

describe('Weather Agent Tests', () => {
  it('should correctly extract locations from queries', async () => {
    const result = await runEvals({
      data: [
        {
          input: 'weather in Berlin',
          groundTruth: { expectedLocation: 'Berlin', expectedCountry: 'DE' },
        },
        {
          input: 'weather in Berlin, Maryland',
          groundTruth: { expectedLocation: 'Berlin', expectedCountry: 'US' },
        },
        {
          input: 'weather in Berlin, Russia',
          groundTruth: { expectedLocation: 'Berlin', expectedCountry: 'RU' },
        },
      ],
      target: weatherAgent,
      scorers: [locationScorer],
    })

    // Assert aggregate score meets threshold
    expect(result.scores['location-accuracy']).toBe(1)
    expect(result.summary.totalItems).toBe(3)
  })
})
```

## 理解结果

`runEvals` 函数返回一个包含以下内容的对象：

- `scores`：每个 Scorer 在所有测试用例上的平均分数
- `summary.totalItems`：已处理的测试用例总数

```typescript
{
  scores: {
    'location-accuracy': 1.0,  // Average score across all items
    'another-scorer': 0.85
  },
  summary: {
    totalItems: 3
  }
}
```

## 多种测试场景

为不同评估场景创建单独的测试用例：

```typescript
describe('Weather Agent Tests', () => {
  const locationScorer = createScorer({/* ... */})

  it('should handle location disambiguation', async () => {
    const result = await runEvals({
      data: [
        {
          input: 'weather in Berlin',
          groundTruth: {/* ... */},
        },
        {
          input: 'weather in Berlin, Maryland',
          groundTruth: {/* ... */},
        },
      ],
      target: weatherAgent,
      scorers: [locationScorer],
    })

    expect(result.scores['location-accuracy']).toBe(1)
  })

  it('should handle typos and misspellings', async () => {
    const result = await runEvals({
      data: [
        {
          input: 'weather in Berln',
          groundTruth: { expectedLocation: 'Berlin', expectedCountry: 'DE' },
        },
        {
          input: 'weather in Parris',
          groundTruth: { expectedLocation: 'Paris', expectedCountry: 'FR' },
        },
      ],
      target: weatherAgent,
      scorers: [locationScorer],
    })

    expect(result.scores['location-accuracy']).toBe(1)
  })
})
```

## 后续步骤

- 了解如何[创建自定义 Scorer](https://mastra.zisheng.pro/docs/evals/custom-scorers)
- 探索[内置 Scorer](https://mastra.zisheng.pro/docs/evals/built-in-scorers)
- 针对[启用了 Memory 的 Agent](https://mastra.zisheng.pro/docs/evals/evals-with-memory)运行 Scorer
- 阅读 [runEvals API Reference](https://mastra.zisheng.pro/reference/evals/run-evals)