@mastra/core/dist/evals/scoreTraces/index.cjs.map

{"version":3,"file":"index.cjs","names":["MastraError","createStep","z","MastraError","ErrorDomain","ErrorCategory","getEntityTypeForSpan","saveScorePayloadSchema","createWorkflow"],"sources":["../../../src/evals/scoreTraces/scoreTraces.ts","../../../src/evals/scoreTraces/utils.ts","../../../src/evals/scoreTraces/scoreTracesWorkflow.ts"],"sourcesContent":["import { MastraError } from '../../error';\nimport type { Mastra } from '../../mastra';\n\nexport async function scoreTraces({\n  scorerId,\n  targets,\n  mastra,\n}: {\n  scorerId: string;\n  targets: { traceId: string; spanId?: string }[];\n  mastra: Mastra;\n}) {\n  const workflow = mastra.__getInternalWorkflow('__batch-scoring-traces');\n  try {\n    const run = await workflow.createRun();\n\n    await run.start({ inputData: { targets, scorerId } });\n  } catch (error) {\n    const mastraError = new MastraError(\n      {\n        category: 'SYSTEM',\n        domain: 'SCORER',\n        id: 'MASTRA_SCORER_FAILED_TO_RUN_TRACE_SCORING',\n        details: {\n          scorerId,\n          targets: JSON.stringify(targets),\n        },\n      },\n      error,\n    );\n    mastra.getLogger()?.trackException(mastraError);\n  }\n}\n","import type { MastraDBMessage } from '../../agent';\nimport { SpanType } from '../../observability';\nimport type { SpanRecord, TraceRecord } from '../../storage';\nimport type { ScorerRunInputForAgent, ScorerRunOutputForAgent } from '../types';\n\n// Types for span input/output structures\ninterface SpanMessage {\n  role: string;\n  content: string | Array<{ type: string; text: string }>;\n}\n\ninterface SpanInputWithMessages {\n  messages: SpanMessage[];\n}\n\ninterface SpanOutputWithText {\n  text?: string;\n}\n\n// Type guards for span data\nfunction isSpanMessage(value: unknown): value is SpanMessage {\n  return (\n    typeof value === 'object' &&\n    value !== null &&\n    'role' in value &&\n    typeof (value as SpanMessage).role === 'string' &&\n    'content' in value\n  );\n}\n\nfunction hasMessagesArray(value: unknown): value is SpanInputWithMessages {\n  return (\n    typeof value === 'object' &&\n    value !== null &&\n    'messages' in value &&\n    Array.isArray((value as SpanInputWithMessages).messages)\n  );\n}\n\nfunction hasTextProperty(value: unknown): value is SpanOutputWithText {\n  return typeof value === 'object' && value !== null && 'text' in value;\n}\n\n// // Span tree structure for efficient lookups\ninterface SpanTree {\n  spanMap: Map<string, SpanRecord>;\n  childrenMap: Map<string, SpanRecord[]>;\n  rootSpans: SpanRecord[];\n}\n\n/**\n * Build a hierarchical span tree with efficient lookup maps\n */\nexport function buildSpanTree(spans: SpanRecord[]): SpanTree {\n  const spanMap = new Map<string, SpanRecord>();\n  const childrenMap = new Map<string, SpanRecord[]>();\n  const rootSpans: SpanRecord[] = [];\n\n  // First pass: build span map\n  for (const span of spans) {\n    spanMap.set(span.spanId, span);\n  }\n\n  // Second pass: build parent-child relationships\n  for (const span of spans) {\n    if (span.parentSpanId == null) {\n      // Root span (parentSpanId is null or undefined)\n      rootSpans.push(span);\n    } else {\n      const siblings = childrenMap.get(span.parentSpanId) || [];\n      siblings.push(span);\n      childrenMap.set(span.parentSpanId, siblings);\n    }\n  }\n\n  // Sort children by startedAt timestamp for temporal ordering\n  for (const children of childrenMap.values()) {\n    children.sort((a, b) => new Date(a.startedAt).getTime() - new Date(b.startedAt).getTime());\n  }\n\n  // Sort root spans by startedAt\n  rootSpans.sort((a, b) => new Date(a.startedAt).getTime() - new Date(b.startedAt).getTime());\n\n  return { spanMap, childrenMap, rootSpans };\n}\n\n/**\n * Extract children spans of a specific type\n */\nfunction getChildrenOfType<T extends SpanRecord>(spanTree: SpanTree, parentSpanId: string, spanType: SpanType): T[] {\n  const children = spanTree.childrenMap.get(parentSpanId) || [];\n  return children.filter(span => span.spanType === spanType) as T[];\n}\n\n/**\n * Normalize message content to string format\n * For arrays with multiple text parts, returns only the last text part (AI SDK convention)\n */\nfunction normalizeMessageContent(content: string | Array<{ type: string; text: string }>): string {\n  if (typeof content === 'string') {\n    return content;\n  }\n\n  // Extract text parts and return only the last one (AI SDK convention)\n  const textParts = content.filter(part => part.type === 'text');\n  return textParts.length > 0 ? textParts[textParts.length - 1]?.text || '' : '';\n}\n\n/**\n * Create MastraDBMessage directly from span message data\n */\nfunction createMastraDBMessage(\n  message: { role: string; content: string | Array<{ type: string; text: string }> },\n  createdAt: Date,\n  id: string = '',\n): MastraDBMessage {\n  const contentText = normalizeMessageContent(message.content);\n  const role = message.role as 'user' | 'assistant' | 'system';\n\n  return {\n    id,\n    role,\n    content: {\n      format: 2,\n      parts: [{ type: 'text', text: contentText }],\n      content: contentText,\n    },\n    createdAt: new Date(createdAt),\n  };\n}\n\n/**\n * Extract input messages from agent run span\n */\nfunction extractInputMessages(agentSpan: SpanRecord): MastraDBMessage[] {\n  const input = agentSpan.input;\n\n  // Handle different input formats\n  if (typeof input === 'string') {\n    return [\n      createMastraDBMessage(\n        {\n          role: 'user',\n          content: input,\n        },\n        agentSpan.startedAt,\n      ),\n    ];\n  }\n\n  if (Array.isArray(input)) {\n    const messages = input.filter(isSpanMessage) as SpanMessage[];\n    return messages.map(msg => createMastraDBMessage(msg, agentSpan.startedAt));\n  }\n\n  if (hasMessagesArray(input)) {\n    const messages = input.messages.filter(isSpanMessage) as SpanMessage[];\n    return messages.map(msg => createMastraDBMessage(msg, agentSpan.startedAt));\n  }\n  return [];\n}\n\n/**\n * Extract system messages from LLM span\n */\nfunction extractSystemMessages(llmSpan: SpanRecord): Array<{ role: 'system'; content: string }> {\n  const input = llmSpan.input;\n  if (!hasMessagesArray(input)) {\n    return [];\n  }\n  return input.messages\n    .filter((msg): msg is SpanMessage & { role: 'system' } => isSpanMessage(msg) && msg.role === 'system')\n    .map(msg => ({\n      role: 'system' as const,\n      content: normalizeMessageContent(msg.content),\n    }));\n}\n\n/**\n * Extract conversation history (remembered messages) from LLM span\n * Excludes system messages and the current input message\n */\nfunction extractRememberedMessages(llmSpan: SpanRecord, currentInputContent: string): MastraDBMessage[] {\n  const input = llmSpan.input;\n  if (!hasMessagesArray(input)) {\n    return [];\n  }\n  const filtered = input.messages.filter(isSpanMessage) as unknown as SpanMessage[];\n  const messages = filtered\n    .filter(msg => msg.role !== 'system')\n    .filter(msg => normalizeMessageContent(msg.content) !== currentInputContent);\n\n  return messages.map(msg => createMastraDBMessage(msg, llmSpan.startedAt));\n}\n\n/**\n * Reconstruct tool invocations from tool call spans\n */\nfunction reconstructToolInvocations(spanTree: SpanTree, parentSpanId: string) {\n  const toolSpans = getChildrenOfType<SpanRecord>(spanTree, parentSpanId, SpanType.TOOL_CALL);\n\n  return toolSpans.map(toolSpan => ({\n    toolCallId: toolSpan.spanId,\n    toolName: toolSpan.entityName ?? toolSpan.entityId ?? 'unknown',\n    toolId: toolSpan.entityId,\n    args: toolSpan.input || {},\n    result: toolSpan.output || {},\n    state: 'result' as const,\n  }));\n}\n\n/**\n * Validate trace structure and throw descriptive errors\n */\nexport function validateTrace(trace: TraceRecord): void {\n  if (!trace) {\n    throw new Error('Trace is null or undefined');\n  }\n\n  if (!trace.spans || !Array.isArray(trace.spans)) {\n    throw new Error('Trace must have a spans array');\n  }\n\n  if (trace.spans.length === 0) {\n    throw new Error('Trace has no spans');\n  }\n\n  // Check for circular references in parent-child relationships\n  const spanIds = new Set(trace.spans.map(span => span.spanId));\n  for (const span of trace.spans) {\n    if (span.parentSpanId && !spanIds.has(span.parentSpanId)) {\n      throw new Error(`Span ${span.spanId} references non-existent parent ${span.parentSpanId}`);\n    }\n  }\n}\n\n/**\n * Find the most recent model span that contains conversation history\n */\nfunction findPrimaryLLMSpan(spanTree: SpanTree, rootAgentSpan: SpanRecord): SpanRecord {\n  const directLLMSpans = getChildrenOfType<SpanRecord>(spanTree, rootAgentSpan.spanId, SpanType.MODEL_GENERATION);\n  if (directLLMSpans.length > 0) {\n    // There should only be one model generation span per agent run which is a direct child of the root agent span\n    return directLLMSpans[0]!;\n  }\n\n  throw new Error('No model generation span found in trace');\n}\n\n/**\n * Extract common trace validation and span tree building logic\n */\nfunction prepareTraceForTransformation(trace: TraceRecord) {\n  validateTrace(trace);\n  const spanTree = buildSpanTree(trace.spans);\n\n  // Find the root agent run span\n  const rootAgentSpan = spanTree.rootSpans.find(span => span.spanType === 'agent_run') as SpanRecord | undefined;\n\n  if (!rootAgentSpan) {\n    throw new Error('No root agent_run span found in trace');\n  }\n\n  return { spanTree, rootAgentSpan };\n}\n\nexport function transformTraceToScorerInputAndOutput(trace: TraceRecord): {\n  input: ScorerRunInputForAgent;\n  output: ScorerRunOutputForAgent;\n} {\n  const { spanTree, rootAgentSpan } = prepareTraceForTransformation(trace);\n\n  if (!rootAgentSpan.output) {\n    throw new Error('Root agent span has no output');\n  }\n\n  // Build input\n  const primaryLLMSpan = findPrimaryLLMSpan(spanTree, rootAgentSpan);\n  const inputMessages = extractInputMessages(rootAgentSpan);\n  const systemMessages = extractSystemMessages(primaryLLMSpan);\n\n  // Extract remembered messages from LLM span (excluding current input)\n  const currentInputContent = inputMessages[0]?.content.content || '';\n  const rememberedMessages = extractRememberedMessages(primaryLLMSpan, currentInputContent);\n\n  const input = {\n    inputMessages,\n    rememberedMessages,\n    systemMessages,\n    taggedSystemMessages: {}, // Todo: Support tagged system messages\n  };\n\n  // Build output\n  const toolInvocations = reconstructToolInvocations(spanTree, rootAgentSpan.spanId);\n  const responseText = hasTextProperty(rootAgentSpan.output) ? (rootAgentSpan.output.text ?? '') : '';\n\n  // Build parts array: tool invocations first, then text\n  const parts: Array<{ type: 'tool-invocation'; toolInvocation: any } | { type: 'text'; text: string }> = [];\n\n  // Add tool invocation parts\n  for (const toolInvocation of toolInvocations) {\n    parts.push({\n      type: 'tool-invocation',\n      toolInvocation,\n    });\n  }\n\n  // Add text part if present\n  if (responseText.trim()) {\n    parts.push({\n      type: 'text',\n      text: responseText,\n    });\n  }\n\n  const responseMessage: MastraDBMessage = {\n    id: '',\n    role: 'assistant',\n    content: {\n      format: 2,\n      parts: parts as any, // Type assertion needed due to providerMetadata optional field\n      content: responseText,\n      toolInvocations, // Always include, even if empty array\n    },\n    createdAt: new Date(rootAgentSpan.endedAt || rootAgentSpan.startedAt),\n  };\n\n  const output: MastraDBMessage[] = [responseMessage];\n\n  return {\n    input,\n    output,\n  };\n}\n","import pMap from 'p-map';\nimport { z } from 'zod/v4';\nimport { ErrorCategory, ErrorDomain, MastraError } from '../../error';\nimport { getEntityTypeForSpan, InternalSpans } from '../../observability';\nimport type { SpanRecord, TraceRecord, MastraStorage } from '../../storage';\nimport { createStep, createWorkflow } from '../../workflows/evented';\nimport type { MastraScorer, ScorerRun } from '../base';\nimport type { ScoreRowData } from '../types';\nimport { saveScorePayloadSchema } from '../types';\nimport { transformTraceToScorerInputAndOutput } from './utils';\n\nconst getTraceStep = createStep({\n  id: '__process-trace-scoring',\n  inputSchema: z.object({\n    targets: z.array(\n      z.object({\n        traceId: z.string(),\n        spanId: z.string().optional(),\n      }),\n    ),\n    scorerId: z.string(),\n  }),\n  outputSchema: z.any(),\n  execute: async ({ inputData, mastra }) => {\n    const logger = mastra.getLogger();\n    if (!logger) {\n      console.warn(\n        '[scoreTracesWorkflow] Logger not initialized: no debug or error logs will be recorded for scoring traces.',\n      );\n    }\n\n    const storage = mastra.getStorage();\n    if (!storage) {\n      const mastraError = new MastraError({\n        id: 'MASTRA_STORAGE_NOT_FOUND_FOR_TRACE_SCORING',\n        domain: ErrorDomain.STORAGE,\n        category: ErrorCategory.SYSTEM,\n        text: 'Storage not found for trace scoring',\n        details: {\n          scorerId: inputData.scorerId,\n        },\n      });\n      logger?.trackException(mastraError);\n      return;\n    }\n\n    let scorer: MastraScorer | undefined;\n    try {\n      scorer = mastra.getScorerById(inputData.scorerId);\n    } catch (error) {\n      const mastraError = new MastraError(\n        {\n          id: 'MASTRA_SCORER_NOT_FOUND_FOR_TRACE_SCORING',\n          domain: ErrorDomain.SCORER,\n          category: ErrorCategory.SYSTEM,\n          text: `Scorer not found for trace scoring`,\n          details: {\n            scorerId: inputData.scorerId,\n          },\n        },\n        error,\n      );\n      logger?.trackException(mastraError);\n      return;\n    }\n\n    await pMap(\n      inputData.targets,\n      async target => {\n        try {\n          await scoreTrace({ storage, scorer, target });\n        } catch (error) {\n          const mastraError = new MastraError(\n            {\n              id: 'MASTRA_SCORER_FAILED_TO_RUN_SCORER_ON_TRACE',\n              domain: ErrorDomain.SCORER,\n              category: ErrorCategory.SYSTEM,\n              details: {\n                scorerId: scorer.id,\n                spanId: target.spanId || '',\n                traceId: target.traceId,\n              },\n            },\n            error,\n          );\n          logger?.trackException(mastraError);\n        }\n      },\n      { concurrency: 3 },\n    );\n  },\n});\n\n/** Tenancy derived from a span, threaded into emitted scores. */\ntype ScoreTenancy = {\n  organizationId?: string;\n  projectId?: string;\n};\n\n/**\n * Derive score tenancy from a span. On spans, `resourceId` carries the project\n * scope, so it maps to the score's `projectId` field.\n */\nfunction getSpanTenancy(span: SpanRecord): ScoreTenancy {\n  const tenancy: ScoreTenancy = {};\n  if (span.organizationId) {\n    tenancy.organizationId = span.organizationId;\n  }\n  if (span.resourceId) {\n    tenancy.projectId = span.resourceId;\n  }\n  return tenancy;\n}\n\nexport type ScoreTraceTarget = { traceId: string; spanId?: string } | { trace: TraceRecord; spanId?: string };\n\nexport type ScoreTraceBatchTarget = {\n  traceId: string;\n  spanId?: string;\n  datasetItemId?: string;\n};\n\nexport type ScoreTraceBatchResult =\n  | {\n      ok: true;\n      index: number;\n      traceId: string;\n      spanId: string;\n      datasetItemId?: string;\n      score: ScoreRowData;\n    }\n  | {\n      ok: false;\n      index: number;\n      traceId: string;\n      spanId?: string;\n      datasetItemId?: string;\n      error: Error;\n    };\n\ntype ScoreTraceReferenceTarget = { traceId: string; spanId?: string };\n\nfunction isScoreTraceReferenceTarget(target: ScoreTraceTarget): target is ScoreTraceReferenceTarget {\n  return 'traceId' in target;\n}\n\nfunction resolveTargetSpan({ trace, spanId }: { trace: TraceRecord; spanId?: string }): SpanRecord {\n  const span = spanId\n    ? trace.spans.find(candidateSpan => candidateSpan.spanId === spanId)\n    : trace.spans.find(candidateSpan => candidateSpan.parentSpanId === null);\n\n  if (!span) {\n    throw new Error(`Span not found for scoring, traceId: ${trace.traceId}, spanId: ${spanId ?? 'Not provided'}`);\n  }\n\n  return span;\n}\n\n/** Resolve the target span for a trace/target pair. */\nasync function resolveTraceAndSpan({\n  storage,\n  target,\n}: {\n  storage: MastraStorage;\n  target: ScoreTraceTarget;\n}): Promise<{ trace: TraceRecord; span: SpanRecord }> {\n  if (!isScoreTraceReferenceTarget(target)) {\n    return {\n      trace: target.trace,\n      span: resolveTargetSpan({ trace: target.trace, spanId: target.spanId }),\n    };\n  }\n\n  // TODO: add storage api to get a single span\n  const observabilityStore = await storage.getStore('observability');\n  if (!observabilityStore) {\n    throw new MastraError({\n      id: 'MASTRA_OBSERVABILITY_STORAGE_NOT_AVAILABLE',\n      domain: ErrorDomain.STORAGE,\n      category: ErrorCategory.SYSTEM,\n      text: 'Observability storage domain is not available',\n    });\n  }\n  const trace = await observabilityStore.getTrace({ traceId: target.traceId });\n  if (!trace) {\n    throw new Error(`Trace not found for scoring, traceId: ${target.traceId}`);\n  }\n\n  return { trace, span: resolveTargetSpan({ trace, spanId: target.spanId }) };\n}\n\ntype TraceScoreResult = Awaited<ReturnType<MastraScorer['run']>>;\n\n/**\n * Run a scorer against an already-resolved trace + span.\n *\n * Span tenancy (`organizationId`, `resourceId` → `projectId`) is threaded into\n * the scorer run so any score the scorer emits is correctly multi-tenant.\n */\nasync function runScorerForTrace({\n  scorer,\n  trace,\n  span,\n}: {\n  scorer: MastraScorer;\n  trace: TraceRecord;\n  span: SpanRecord;\n}): Promise<TraceScoreResult> {\n  const tenancy = getSpanTenancy(span);\n\n  const scorerRun = buildScorerRun({\n    scorerType: scorer.type === 'agent' ? 'agent' : undefined,\n    trace,\n    targetSpan: span,\n  });\n\n  return scorer.run({\n    ...scorerRun,\n    scoreSource: 'trace',\n    targetScope: 'span',\n    targetEntityType: getEntityTypeForSpan(span),\n    targetTraceId: trace.traceId,\n    targetSpanId: span.spanId,\n    targetCorrelationContext: {\n      traceId: trace.traceId,\n      spanId: span.spanId,\n      ...(tenancy.organizationId ? { organizationId: tenancy.organizationId } : {}),\n      ...(tenancy.projectId ? { resourceId: tenancy.projectId } : {}),\n    },\n    targetMetadata: {\n      ...(tenancy.organizationId ? { organizationId: tenancy.organizationId } : {}),\n      ...(tenancy.projectId ? { projectId: tenancy.projectId } : {}),\n    },\n  });\n}\n\n/**\n * Resolve a trace/span target, run the scorer, and persist the resulting score.\n */\nexport async function scoreTrace({\n  storage,\n  scorer,\n  target,\n  batchId,\n  datasetId,\n  datasetItemId,\n}: {\n  storage: MastraStorage;\n  scorer: MastraScorer;\n  target: ScoreTraceTarget;\n  /** Optional batch handle stamped on the persisted score so all scores from one\n   * batch scoring call share a `batchId` (each keeps its own per-execution `runId`). */\n  batchId?: string;\n  /** Optional dataset provenance stamped on the persisted score so baseline scores\n   * can join back to the curated dataset item that produced them. Top-level handles,\n   * independent of how the trace is resolved. */\n  datasetId?: string;\n  datasetItemId?: string;\n}): Promise<ScoreRowData> {\n  const { trace, span } = await resolveTraceAndSpan({ storage, target });\n  const tenancy = getSpanTenancy(span);\n\n  const result = await runScorerForTrace({ scorer, trace, span });\n\n  const scorerResult = {\n    ...result,\n    scorer: {\n      id: scorer.id,\n      name: scorer.name || scorer.id,\n      description: scorer.description,\n      hasJudge: !!scorer.judge,\n    },\n    traceId: trace.traceId,\n    spanId: span.spanId,\n    entityId: span.entityId || span.entityName || 'unknown',\n    entityType: span.spanType,\n    entity: { traceId: span.traceId, spanId: span.spanId },\n    source: 'TEST',\n    scorerId: scorer.id,\n    ...(tenancy.organizationId ? { organizationId: tenancy.organizationId } : {}),\n    ...(tenancy.projectId ? { projectId: tenancy.projectId } : {}),\n    ...(batchId ? { batchId } : {}),\n    ...(datasetId ? { datasetId } : {}),\n    ...(datasetItemId ? { datasetItemId } : {}),\n  };\n\n  // Legacy score-store emission. This path is being deprecated.\n  const savedScoreRecord = await validateAndSaveScore({ storage, scorerResult });\n  await attachScoreToSpan({ storage, span, scoreRecord: savedScoreRecord });\n  return savedScoreRecord;\n}\n\nfunction toBatchResultError(error: unknown): Error {\n  if (error instanceof Error) {\n    return error;\n  }\n\n  return new Error(typeof error === 'string' ? error : 'Unknown scoreTraceBatch error');\n}\n\nexport async function scoreTraceBatch({\n  storage,\n  scorer,\n  targets,\n  batchId,\n  datasetId,\n  concurrency = 3,\n}: {\n  storage: MastraStorage;\n  scorer: MastraScorer;\n  targets: ScoreTraceBatchTarget[];\n  batchId?: string;\n  datasetId?: string;\n  concurrency?: number;\n}): Promise<{\n  batchId?: string;\n  datasetId?: string;\n  scoredCount: number;\n  failedCount: number;\n  results: ScoreTraceBatchResult[];\n}> {\n  const results = await pMap(\n    targets,\n    async (target, index): Promise<ScoreTraceBatchResult> => {\n      try {\n        const score = await scoreTrace({\n          storage,\n          scorer,\n          target,\n          batchId,\n          datasetId,\n          datasetItemId: target.datasetItemId,\n        });\n        const spanId = score.spanId ?? target.spanId;\n\n        if (!spanId) {\n          throw new Error(`Persisted score is missing spanId for traceId: ${target.traceId}`);\n        }\n\n        return {\n          ok: true,\n          index,\n          traceId: score.traceId ?? target.traceId,\n          spanId,\n          ...(target.datasetItemId ? { datasetItemId: target.datasetItemId } : {}),\n          score,\n        };\n      } catch (error) {\n        return {\n          ok: false,\n          index,\n          traceId: target.traceId,\n          ...(target.spanId ? { spanId: target.spanId } : {}),\n          ...(target.datasetItemId ? { datasetItemId: target.datasetItemId } : {}),\n          error: toBatchResultError(error),\n        };\n      }\n    },\n    { concurrency },\n  );\n\n  const scoredCount = results.filter(result => result.ok).length;\n\n  return {\n    ...(batchId ? { batchId } : {}),\n    ...(datasetId ? { datasetId } : {}),\n    scoredCount,\n    failedCount: results.length - scoredCount,\n    results,\n  };\n}\n\n/**\n * @deprecated Legacy scores-store path. New score emission should use `mastra.observability.addScore()`.\n */\nasync function validateAndSaveScore({ storage, scorerResult }: { storage: MastraStorage; scorerResult: ScorerRun }) {\n  const scoresStore = await storage.getStore('scores');\n  if (!scoresStore) {\n    throw new MastraError({\n      id: 'MASTRA_SCORES_STORAGE_NOT_AVAILABLE',\n      domain: ErrorDomain.STORAGE,\n      category: ErrorCategory.SYSTEM,\n      text: 'Scores storage domain is not available',\n    });\n  }\n  const payloadToSave = saveScorePayloadSchema.parse(scorerResult);\n  const result = await scoresStore.saveScore(payloadToSave);\n  return result.score;\n}\n\nfunction buildScorerRun({\n  scorerType,\n  trace,\n  targetSpan,\n}: {\n  scorerType?: string;\n  trace: TraceRecord;\n  targetSpan: SpanRecord;\n}): ScorerRun {\n  if (scorerType === 'agent') {\n    const { input, output } = transformTraceToScorerInputAndOutput(trace);\n    return { input, output };\n  }\n  return { input: targetSpan.input, output: targetSpan.output };\n}\n\n/**\n * @deprecated Legacy score-attach path. New score emission should use `mastra.observability.addScore()`\n * which autmatically attach scores to spans.\n */\nasync function attachScoreToSpan({\n  storage,\n  span,\n  scoreRecord,\n}: {\n  storage: MastraStorage;\n  span: SpanRecord;\n  scoreRecord: ScoreRowData;\n}) {\n  const observabilityStore = await storage.getStore('observability');\n  if (!observabilityStore) {\n    throw new MastraError({\n      id: 'MASTRA_OBSERVABILITY_STORAGE_NOT_AVAILABLE',\n      domain: ErrorDomain.STORAGE,\n      category: ErrorCategory.SYSTEM,\n      text: 'Observability storage domain is not available',\n    });\n  }\n\n  // Path 1: Legacy — attach score link to span\n  try {\n    const existingLinks = span.links || [];\n    const link = {\n      type: 'score',\n      scoreId: scoreRecord.id,\n      scorerId: scoreRecord.scorerId ?? scoreRecord.scorer?.id,\n      score: scoreRecord.score,\n      createdAt: scoreRecord.createdAt,\n    };\n    await observabilityStore.updateSpan({\n      spanId: span.spanId,\n      traceId: span.traceId,\n      updates: { links: [...existingLinks, link] },\n    });\n  } catch {\n    // Expected for event-sourced stores (e.g. DuckDB) that don't support updateSpan\n  }\n}\n\nexport const scoreTracesWorkflow = createWorkflow({\n  id: '__batch-scoring-traces',\n  inputSchema: z.object({\n    targets: z.array(\n      z.object({\n        traceId: z.string(),\n        spanId: z.string().optional(),\n      }),\n    ),\n    scorerId: z.string(),\n  }),\n  outputSchema: z.any(),\n  steps: [getTraceStep],\n  options: {\n    validateInputs: false,\n    // Internal batch-scoring plumbing — hide its workflow spans from exported\n    // traces. Any user-facing work invoked from steps (e.g. the scorer's own\n    // SCORER_RUN span) keeps its own policy and remains visible.\n    tracingPolicy: {\n      internal: InternalSpans.WORKFLOW,\n    },\n  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