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# Scorer 实用函数

Mastra 提供工具函数，帮助从 Scorer 运行的输入和输出中提取并处理数据。这些工具函数在自定义 Scorer 的 `preprocess` 步骤中尤其有用。

## Import

```typescript
import {
  getAssistantMessageFromRunOutput,
  getReasoningFromRunOutput,
  getUserMessageFromRunInput,
  getSystemMessagesFromRunInput,
  getCombinedSystemPrompt,
  extractToolCalls,
  extractInputMessages,
  extractAgentResponseMessages,
  compareTrajectories,
  createTrajectoryTestRun,
} from '@mastra/evals/scorers/utils'
```

Trajectory 提取函数可从 `@mastra/core/evals` 导入：

```typescript
import {
  extractTrajectory,
  extractWorkflowTrajectory,
  extractTrajectoryFromTrace,
} from '@mastra/core/evals'
```

## 消息提取

### `getAssistantMessageFromRunOutput`

从运行输出的第一条 assistant 消息中提取文本内容。

```typescript
const scorer = createScorer({
  id: 'my-scorer',
  description: 'My scorer',
  type: 'agent',
})
  .preprocess(({ run }) => {
    const response = getAssistantMessageFromRunOutput(run.output)
    return { response }
  })
  .generateScore(({ results }) => {
    return results.preprocessStepResult?.response ? 1 : 0
  })
```

**output** (`ScorerRunOutputForAgent`): Scorer 运行输出 (array of MastraDBMessage)

**返回值：** `string | undefined` - The assistant message text, or undefined if no assistant message is found.

### `getUserMessageFromRunInput`

从运行输入的第一条 user 消息中提取文本内容。

```typescript
.preprocess(({ run }) => {
  const userMessage = getUserMessageFromRunInput(run.input);
  return { userMessage };
})
```

**input** (`ScorerRunInputForAgent`): Scorer 运行输入 containing input messages

**返回值：** `string | undefined` - The user message text, or undefined if no user message is found.

### `extractInputMessages`

以数组形式提取所有输入消息的文本内容。

```typescript
.preprocess(({ run }) => {
  const allUserMessages = extractInputMessages(run.input);
  return { conversationHistory: allUserMessages.join("\n") };
})
```

**返回值：** `string[]` - Array of text strings from each input message.

### `extractAgentResponseMessages`

以数组形式提取所有 assistant 响应消息的文本内容。

```typescript
.preprocess(({ run }) => {
  const allResponses = extractAgentResponseMessages(run.output);
  return { allResponses };
})
```

**返回值：** `string[]` - 每条 assistant 消息中的文本字符串数组。

## 推理内容提取

### `getReasoningFromRunOutput`

从运行输出中提取 reasoning 文本。这在评估 `deepseek-reasoner` 等会生成思维链 reasoning 的推理模型响应时尤其有用。

Reasoning 可能存储在两个位置：

1. `content.reasoning` - 消息内容中的字符串字段
2. `content.parts` - `type: 'reasoning'` 且包含 `details` 的 part

```typescript
import {
  getReasoningFromRunOutput,
  getAssistantMessageFromRunOutput,
} from '@mastra/evals/scorers/utils'

const reasoningQualityScorer = createScorer({
  id: 'reasoning-quality',
  name: 'Reasoning Quality',
  description: 'Evaluates the quality of model reasoning',
  type: 'agent',
})
  .preprocess(({ run }) => {
    const reasoning = getReasoningFromRunOutput(run.output)
    const response = getAssistantMessageFromRunOutput(run.output)
    return { reasoning, response }
  })
  .analyze(({ results }) => {
    const { reasoning } = results.preprocessStepResult || {}
    return {
      hasReasoning: !!reasoning,
      reasoningLength: reasoning?.length || 0,
      hasStepByStep: reasoning?.includes('step') || false,
    }
  })
  .generateScore(({ results }) => {
    const { hasReasoning, reasoningLength } = results.analyzeStepResult || {}
    if (!hasReasoning) return 0
    // Score based on reasoning length (normalized to 0-1)
    return Math.min(reasoningLength / 500, 1)
  })
  .generateReason(({ results, score }) => {
    const { hasReasoning, reasoningLength } = results.analyzeStepResult || {}
    if (!hasReasoning) {
      return 'No reasoning was provided by the model.'
    }
    return `Model provided ${reasoningLength} characters of reasoning. Score: ${score}`
  })
```

**output** (`ScorerRunOutputForAgent`): Scorer 运行输出 (array of MastraDBMessage)

**返回值：** `string | undefined` - The reasoning text, or undefined if no reasoning is present.

## system 消息提取

### `getSystemMessagesFromRunInput`

从运行输入中提取所有 system 消息，包括标准 system 消息和带标签的 system 消息（例如 memory 指令等专用 prompt）。

```typescript
.preprocess(({ run }) => {
  const systemMessages = getSystemMessagesFromRunInput(run.input);
  return {
    systemPromptCount: systemMessages.length,
    systemPrompts: systemMessages
  };
})
```

**返回值：** `string[]` - Array of system message strings.

### `getCombinedSystemPrompt`

将所有 system 消息合并为一个 prompt 字符串，并用两个换行符连接。

```typescript
.preprocess(({ run }) => {
  const fullSystemPrompt = getCombinedSystemPrompt(run.input);
  return { fullSystemPrompt };
})
```

**返回值：** `string` - Combined system prompt string.

## Tool 调用提取

### `extractToolCalls`

从运行输出中提取所有 Tool 调用的信息，包括 Tool 名称、调用 ID 及其在消息数组中的位置。

```typescript
const toolUsageScorer = createScorer({
  id: 'tool-usage',
  description: 'Evaluates tool usage patterns',
  type: 'agent',
})
  .preprocess(({ run }) => {
    const { tools, toolCallInfos } = extractToolCalls(run.output)
    return {
      toolsUsed: tools,
      toolCount: tools.length,
      toolDetails: toolCallInfos,
    }
  })
  .generateScore(({ results }) => {
    const { toolCount } = results.preprocessStepResult || {}
    // Score based on appropriate tool usage
    return toolCount > 0 ? 1 : 0
  })
```

**返回值：**

```typescript
{
  tools: string[];           // Array of tool names
  toolCallInfos: ToolCallInfo[];  // Detailed tool call information
}
```

其中 `ToolCallInfo` 为：

```typescript
type ToolCallInfo = {
  toolName: string // Name of the tool
  toolCallId: string // Unique call identifier
  messageIndex: number // Index in the output array
  invocationIndex: number // Index within message's tool invocations
}
```

## 测试工具函数

这些实用函数可帮助创建用于开发 Scorer 的测试数据。

### `createTestMessage`

创建用于测试的 `MastraDBMessage` 对象。

```typescript
import { createTestMessage } from '@mastra/evals/scorers/utils'

const userMessage = createTestMessage({
  content: 'What is the weather?',
  role: 'user',
})

const assistantMessage = createTestMessage({
  content: 'The weather is sunny.',
  role: 'assistant',
  toolInvocations: [
    {
      toolCallId: 'call-1',
      toolName: 'weatherTool',
      args: { location: 'London' },
      result: { temp: 20 },
      state: 'result',
    },
  ],
})
```

### `createAgentTestRun`

创建用于测试 Scorer 的完整测试运行对象。

```typescript
import { createAgentTestRun, createTestMessage } from '@mastra/evals/scorers/utils'

const testRun = createAgentTestRun({
  inputMessages: [createTestMessage({ content: 'Hello', role: 'user' })],
  output: [createTestMessage({ content: 'Hi there!', role: 'assistant' })],
})

// Run your scorer with the test data
const result = await myScorer.run({
  input: testRun.input,
  output: testRun.output,
})
```

## Trajectory 工具函数

### `extractTrajectory`

从 Agent 输出消息（`MastraDBMessage[]`）中提取 `Trajectory`。该函数会将 Tool 调用转换为 `ToolCallStep` 对象。`runEvals` pipeline 会为 Trajectory Scorer 自动调用此函数；仅在直接测试时才需要自行调用。

可从 `@mastra/core/evals` 导入。

```typescript
import { extractTrajectory } from '@mastra/core/evals'

const trajectory = extractTrajectory(agentOutputMessages)
// trajectory.steps — ToolCallStep[] extracted from toolInvocations
// trajectory.rawOutput — the original MastraDBMessage[] array
```

**返回值：** `Trajectory`: Contains `steps: TrajectoryStep[]`, `totalDurationMs`, and `rawOutput`.

### `extractWorkflowTrajectory`

从 Workflow 步骤结果中提取 `Trajectory`。该函数会按照执行路径顺序，将 `StepResult` 记录转换为 `WorkflowStepStep` 对象。

可从 `@mastra/core/evals` 导入。

```typescript
import { extractWorkflowTrajectory } from '@mastra/core/evals'

const trajectory = extractWorkflowTrajectory(
  workflowResult.steps, // Record<string, StepResult>
  workflowResult.stepExecutionPath, // string[] (optional)
)
// trajectory.steps — WorkflowStepStep[] in execution order
```

**返回值：** `Trajectory`: Contains `steps: TrajectoryStep[]`, `totalDurationMs`, and `rawWorkflowResult`.

### `extractTrajectoryFromTrace`

根据可观测性 trace span（`SpanRecord[]`）构建分层 `Trajectory`。该函数会重建父子 span 树，并将每个 span 映射到相应的 `TrajectoryStep` 可辨识联合类型，其中包含嵌套的 `children`。

存在可用 storage 时，首选此提取方法。当目标的 `Mastra` 实例配置了 storage 后端时，`runEvals` pipeline 会自动调用此函数。由于它会捕获完整执行树，包括嵌套的 Agent 运行、Tool 调用和模型生成，因此生成的 trajectory 比 `extractTrajectory` 或 `extractWorkflowTrajectory` 更丰富。

可从 `@mastra/core/evals` 导入。

```typescript
import { extractTrajectoryFromTrace } from '@mastra/core/evals'

// After fetching a trace from the observability store
const traceData = await observabilityStore.getTrace({ traceId })
const trajectory = extractTrajectoryFromTrace(traceData.spans, rootSpanId)
// trajectory.steps — hierarchical TrajectoryStep[] with children
```

**Parameters:**

- `spans` (`SpanRecord[]`)：Trace 查询返回的 Span record 数组。
- `rootSpanId`（`string`，可选）：用作起点的 Span ID。省略时，使用没有 parent 的 Span。

**返回值：** `Trajectory`：包含 `steps: TrajectoryStep[]`，其中具有递归的 `children` 和 `totalDurationMs`。

#### Span 类型映射

| Span 类型                | Trajectory step type   | 提取的关键字段                                                       |
| ---------------------- | ---------------------- | ------------------------------------------------------------- |
| `TOOL_CALL`            | `tool_call`            | `toolArgs`, `toolResult`, `success`                           |
| `MCP_TOOL_CALL`        | `mcp_tool_call`        | `toolArgs`, `toolResult`, `mcpServer`, `success`              |
| `MODEL_GENERATION`     | `model_generation`     | `modelId`, `promptTokens`, `completionTokens`, `finishReason` |
| `AGENT_RUN`            | `agent_run`            | `agentId`（来自 entity ID）                                       |
| `WORKFLOW_RUN`         | `workflow_run`         | `workflowId`（来自 entity ID）                                    |
| `WORKFLOW_STEP`        | `workflow_step`        | `output`                                                      |
| `WORKFLOW_CONDITIONAL` | `workflow_conditional` | `conditionCount`, `selectedSteps`                             |
| `WORKFLOW_PARALLEL`    | `workflow_parallel`    | `branchCount`, `parallelSteps`                                |
| `WORKFLOW_LOOP`        | `workflow_loop`        | `loopType`, `totalIterations`                                 |
| `WORKFLOW_SLEEP`       | `workflow_sleep`       | `sleepDurationMs`, `sleepType`                                |
| `WORKFLOW_WAIT_EVENT`  | `workflow_wait_event`  | `eventName`, `eventReceived`                                  |
| `PROCESSOR_RUN`        | `processor_run`        | `processorId`                                                 |

类型为 `GENERIC`、`MODEL_STEP`、`MODEL_CHUNK` 和 `WORKFLOW_CONDITIONAL_EVAL` 的 span 会被视为噪声并跳过。

### `compareTrajectories`

将实际 trajectory 与预期 trajectory 进行比较，并返回详细的比较结果。`createTrajectoryAccuracyScorerCode` 会在内部使用此函数。

`expected` 参数接受 `Trajectory`（实际 trajectory）或 `{ steps: ExpectedStep[] }`。使用 `ExpectedStep[]` 时，可以仅按名称匹配，也可以按名称 + stepType 匹配，还可以包含要比较的数据。有关详情，请参阅[预期步骤](https://mastra.zisheng.pro/reference/evals/trajectory-accuracy)。

```typescript
import { compareTrajectories } from '@mastra/evals/scorers/utils'

// Using ExpectedStep[] (recommended for expectations)
// Data fields (e.g. toolArgs) are auto-compared when present on expected steps
const result = compareTrajectories(
  actualTrajectory,
  { steps: [{ name: 'search' }, { name: 'summarize', stepType: 'tool_call' }] },
  { allowRepeatedSteps: true },
)
// result.score — 0.0 to 1.0
// result.missingSteps — step names not found
// result.extraSteps — unexpected step names
// result.outOfOrderSteps — steps found but in wrong order
```

**返回值：** `TrajectoryComparisonResult`

### `createTrajectoryTestRun`

为 Trajectory Scorer 创建测试运行对象。该函数会将 `Trajectory` 封装为预期的 `ScorerRun` 格式。

```typescript
import { createTrajectoryTestRun } from '@mastra/evals/scorers/utils'

const run = createTrajectoryTestRun({
  steps: [
    { stepType: 'tool_call', name: 'search', toolArgs: { q: 'test' } },
    { stepType: 'tool_call', name: 'summarize' },
  ],
})

const result = await trajectoryScorer.run(run)
```

### `checkTrajectoryEfficiency`

根据步骤、token 和时长预算评估 trajectory 效率，同时检测冗余调用（使用相同参数调用同一 Tool）。

```typescript
import { checkTrajectoryEfficiency } from '@mastra/evals/scorers/utils'

const result = checkTrajectoryEfficiency(trajectory, {
  maxSteps: 5,
  maxTotalTokens: 2000,
  maxTotalDurationMs: 5000,
  noRedundantCalls: true,
})
// result.score — 1.0 if within all budgets, lower with penalties
// result.redundantCalls — duplicate tool+args combos
// result.overStepBudget — true if maxSteps exceeded
// result.overTokenBudget — true if maxTotalTokens exceeded
// result.overDurationBudget — true if maxTotalDurationMs exceeded
```

**返回值：** `TrajectoryEfficiencyResult`

### `checkTrajectoryBlacklist`

检查 trajectory 是否包含禁止的 Tool 或 Tool 调用序列。

```typescript
import { checkTrajectoryBlacklist } from '@mastra/evals/scorers/utils'

const result = checkTrajectoryBlacklist(trajectory, {
  blacklistedTools: ['deleteAll', 'admin-override'],
  blacklistedSequences: [['escalate', 'admin-override']],
})
// result.score — 1.0 if no violations, 0.0 if any found
// result.violatedTools — blacklisted tools that were called
// result.violatedSequences — blacklisted sequences that were detected
```

**返回值：** `TrajectoryBlacklistResult`

### `analyzeToolFailures`

检测 Tool 失败模式，包括重试、回退和参数修正。

```typescript
import { analyzeToolFailures } from '@mastra/evals/scorers/utils'

const result = analyzeToolFailures(trajectory, {
  maxRetriesPerTool: 2,
})
// result.score — 1.0 if no failure patterns, lower if patterns detected
// result.patterns — detected patterns (retry, fallback, arg_correction)
```

**返回值：** `ToolFailureAnalysisResult`

## 完整示例

以下完整示例展示了如何组合使用多个实用函数：

```typescript
import { createScorer } from '@mastra/core/evals'
import {
  getAssistantMessageFromRunOutput,
  getReasoningFromRunOutput,
  getUserMessageFromRunInput,
  getCombinedSystemPrompt,
  extractToolCalls,
} from '@mastra/evals/scorers/utils'

const comprehensiveScorer = createScorer({
  id: 'comprehensive-analysis',
  name: 'Comprehensive Analysis',
  description: 'Analyzes all aspects of an agent response',
  type: 'agent',
})
  .preprocess(({ run }) => {
    // Extract all relevant data
    const userMessage = getUserMessageFromRunInput(run.input)
    const response = getAssistantMessageFromRunOutput(run.output)
    const reasoning = getReasoningFromRunOutput(run.output)
    const systemPrompt = getCombinedSystemPrompt(run.input)
    const { tools, toolCallInfos } = extractToolCalls(run.output)

    return {
      userMessage,
      response,
      reasoning,
      systemPrompt,
      toolsUsed: tools,
      toolCount: tools.length,
    }
  })
  .generateScore(({ results }) => {
    const { response, reasoning, toolCount } = results.preprocessStepResult || {}

    let score = 0
    if (response && response.length > 0) score += 0.4
    if (reasoning) score += 0.3
    if (toolCount > 0) score += 0.3

    return score
  })
  .generateReason(({ results, score }) => {
    const { response, reasoning, toolCount } = results.preprocessStepResult || {}

    const parts = []
    if (response) parts.push('provided a response')
    if (reasoning) parts.push('included reasoning')
    if (toolCount > 0) parts.push(`used ${toolCount} tool(s)`)

    return `Score: ${score}. The agent ${parts.join(', ')}.`
  })
```