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

Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.

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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      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 AISpanRecord>(\n  spanTree: SpanTree,\n  parentSpanId: string,\n  spanType: AISpanType,\n): T[] {\n  const children = 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if (typeof message.content === 'string') {\n    messageInput = {\n      id: 'temp',\n      role: message.role as 'user' | 'assistant' | 'system',\n      content: message.content,\n    };\n  } else {\n    messageInput = {\n      id: 'temp',\n      role: message.role as 'user' | 'assistant' | 'system',\n      parts: message.content.map(part => ({ type: part.type as 'text', text: part.text })),\n    };\n  }\n\n  const converted = convertMessages(messageInput).to('AIV4.UI');\n  const result = converted[0];\n\n  if (!result) {\n    throw new Error('Failed to convert message');\n  }\n\n  return {\n    ...result,\n    id: '', // Spans don't have message IDs\n    createdAt: new Date(createdAt), // Use span timestamp\n  };\n}\n\n/**\n * Extract input messages from agent run span\n */\nfunction extractInputMessages(agentSpan: AISpanRecord): TransformedUIMessage[] {\n  const input = agentSpan.input;\n\n  // Handle different input formats\n  if (typeof input === 'string') {\n    return [\n      {\n        role: 'user',\n        content: input,\n        createdAt: new Date(agentSpan.startedAt),\n        parts: [{ type: 'text', text: input }],\n        experimental_attachments: [],\n      },\n    ];\n  }\n\n  if (Array.isArray(input)) {\n    return input.map(msg => convertToUIMessage(msg, agentSpan.startedAt));\n  }\n\n  // @ts-ignore\n  if (input && typeof input === 'object' && Array.isArray(input.messages)) {\n    // @ts-ignore\n    return input.messages.map(msg => convertToUIMessage(msg, agentSpan.startedAt));\n  }\n  return [];\n}\n\n/**\n * Extract system messages from LLM span\n */\nfunction extractSystemMessages(llmSpan: AISpanRecord): Array<{ role: 'system'; content: string }> {\n  return (llmSpan.input?.messages || [])\n    .filter((msg: any) => msg.role === 'system')\n    .map((msg: any) => ({\n      role: 'system' as const,\n      content: normalizeMessageContent(msg.content),\n    }));\n}\n\n/**\n * Extract conversation history (remembered messages) from LLM span\n 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createMessageParts(toolInvocations: AISpanRecord[], textContent: string) {\n  const parts: { type: 'tool-invocation' | 'text'; toolInvocation?: AISpanRecord; text?: string }[] = [];\n  for (const toolInvocation of toolInvocations) {\n    parts.push({\n      type: 'tool-invocation',\n      toolInvocation,\n    });\n  }\n\n  if (textContent.trim()) {\n    parts.push({\n      type: 'text',\n      text: textContent,\n    });\n  }\n\n  return parts;\n}\n\n/**\n * Validate trace structure and throw descriptive errors\n */\nexport function validateTrace(trace: AITraceRecord): 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: AISpanRecord): AISpanRecord {\n  const directLLMSpans = getChildrenOfType<AISpanRecord>(spanTree, rootAgentSpan.spanId, AISpanType.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: AITraceRecord) {\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 AISpanRecord | 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: AITraceRecord): {\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 || '';\n  const rememberedMessages = extractRememberedMessages(primaryLLMSpan, currentInputContent);\n\n  const input = {\n    // We do not keep track of the tool call ids in traces, so we need to cast to UIMessageWithMetadata\n    inputMessages: inputMessages as UIMessageWithMetadata[],\n    rememberedMessages: rememberedMessages as UIMessageWithMetadata[],\n    systemMessages,\n    taggedSystemMessages: {}, // Todo: Support tagged system messages\n  };\n\n  // Build output\n  const toolInvocations = reconstructToolInvocations(spanTree, rootAgentSpan.spanId);\n  const responseText = rootAgentSpan.output.text || '';\n\n  const responseMessage: TransformedUIMessage = {\n    role: 'assistant',\n    content: responseText,\n    createdAt: new Date(rootAgentSpan.endedAt || rootAgentSpan.startedAt),\n    // @ts-ignore\n    parts: createMessageParts(toolInvocations, responseText),\n    experimental_attachments: [],\n    // Tool invocations are being deprecated however we need to support it for now\n    toolInvocations: toolInvocations as unknown as ToolInvocation[],\n  };\n\n  // We do not keep track of the tool call ids in traces, so we need to cast to UIMessageWithMetadata\n  const output = [responseMessage as UIMessageWithMetadata];\n\n  return {\n    input,\n    output,\n  };\n}\n","import pMap from 'p-map';\nimport z from 'zod';\nimport { InternalSpans } from '../../ai-tracing';\nimport type { TracingContext } from '../../ai-tracing';\nimport { ErrorCategory, ErrorDomain, MastraError } from '../../error';\nimport type { AISpanRecord, AITraceRecord, 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    scorerName: z.string(),\n  }),\n  outputSchema: z.any(),\n  execute: async ({ inputData, tracingContext, 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          scorerName: inputData.scorerName,\n        },\n      });\n      logger?.error(mastraError.toString());\n      logger?.trackException(mastraError);\n      return;\n    }\n\n    let scorer: MastraScorer | undefined;\n    try {\n      scorer = mastra.getScorerByName(inputData.scorerName);\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            scorerName: inputData.scorerName,\n          },\n        },\n        error,\n      );\n      logger?.error(mastraError.toString());\n      logger?.trackException(mastraError);\n      return;\n    }\n\n    await pMap(\n      inputData.targets,\n      async target => {\n        try {\n          await runScorerOnTarget({ storage, scorer, target, tracingContext });\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                scorerName: scorer.name,\n                spanId: target.spanId || '',\n                traceId: target.traceId,\n              },\n            },\n            error,\n          );\n          logger?.error(mastraError.toString());\n          logger?.trackException(mastraError);\n        }\n      },\n      { concurrency: 3 },\n    );\n  },\n});\n\nexport async function runScorerOnTarget({\n  storage,\n  scorer,\n  target,\n  tracingContext,\n}: {\n  storage: MastraStorage;\n  scorer: MastraScorer;\n  target: { traceId: string; spanId?: string };\n  tracingContext: TracingContext;\n}) {\n  // TODO: add storage api to get a single span\n  const trace = await storage.getAITrace(target.traceId);\n\n  if (!trace) {\n    throw new Error(`Trace not found for scoring, traceId: ${target.traceId}`);\n  }\n\n  let span: AISpanRecord | undefined;\n  if (target.spanId) {\n    span = trace.spans.find(span => span.spanId === target.spanId);\n  } else {\n    span = trace.spans.find(span => span.parentSpanId === null);\n  }\n\n  if (!span) {\n    throw new Error(\n      `Span not found for scoring, traceId: ${target.traceId}, spanId: ${target.spanId ?? 'Not provided'}`,\n    );\n  }\n\n  const scorerRun = buildScorerRun({\n    scorerType: scorer.type === 'agent' ? 'agent' : undefined,\n    tracingContext,\n    trace,\n    targetSpan: span,\n  });\n\n  const result = await scorer.run(scorerRun);\n  const scorerResult = {\n    ...result,\n    scorer: {\n      id: scorer.name,\n      name: scorer.name,\n      description: scorer.description,\n    },\n    traceId: target.traceId,\n    spanId: target.spanId,\n    entityId: span.name,\n    entityType: span.spanType,\n    entity: { traceId: span.traceId, spanId: span.spanId },\n    source: 'TEST',\n    scorerId: scorer.name,\n  };\n\n  const savedScoreRecord = await validateAndSaveScore({ storage, scorerResult });\n  await attachScoreToSpan({ storage, span, scoreRecord: savedScoreRecord });\n}\n\nasync function validateAndSaveScore({ storage, scorerResult }: { storage: MastraStorage; scorerResult: ScorerRun }) {\n  const payloadToSave = saveScorePayloadSchema.parse(scorerResult);\n  const result = await storage.saveScore(payloadToSave);\n  return result.score;\n}\n\nfunction buildScorerRun({\n  scorerType,\n  tracingContext,\n  trace,\n  targetSpan,\n}: {\n  scorerType?: string;\n  tracingContext: TracingContext;\n  trace: AITraceRecord;\n  targetSpan: AISpanRecord;\n}) {\n  let runPayload: ScorerRun;\n  if (scorerType === 'agent') {\n    const { input, output } = transformTraceToScorerInputAndOutput(trace);\n    runPayload = {\n      input,\n      output,\n    };\n  } else {\n    runPayload = { input: targetSpan.input, output: targetSpan.output };\n  }\n\n  runPayload.tracingContext = tracingContext;\n  return runPayload;\n}\n\nasync function attachScoreToSpan({\n  storage,\n  span,\n  scoreRecord,\n}: {\n  storage: MastraStorage;\n  span: AISpanRecord;\n  scoreRecord: ScoreRowData;\n}) {\n  const existingLinks = span.links || [];\n  const link = {\n    type: 'score',\n    scoreId: scoreRecord.id,\n    scorerName: scoreRecord.scorer.name,\n    score: scoreRecord.score,\n    createdAt: scoreRecord.createdAt,\n  };\n  await storage.updateAISpan({\n    spanId: span.spanId,\n    traceId: span.traceId,\n    updates: { links: [...existingLinks, link] },\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    scorerName: z.string(),\n  }),\n  outputSchema: z.any(),\n  steps: [getTraceStep],\n  options: {\n    tracingPolicy: {\n      internal: InternalSpans.ALL,\n    },\n  },\n});\n\nscoreTracesWorkflow.then(getTraceStep).commit();\n"]}