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@mastra/core

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

mastra-ai/mastra

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{ ErrorCategory, ErrorDomain, MastraError } from '../error';\nimport { saveScorePayloadSchema } from '../evals';\nimport type { ScoringHookInput } from '../evals/types';\nimport type { Mastra } from '../mastra';\nimport { EntityType } from '../observability';\nimport type { MastraStorage } from '../storage';\n\nfunction toScorerTargetEntityType(entityType: string): EntityType | undefined {\n switch (entityType) {\n case 'AGENT':\n return EntityType.AGENT;\n case 'WORKFLOW':\n return EntityType.WORKFLOW_RUN;\n default:\n return undefined;\n }\n}\n\nexport function createOnScorerHook(mastra: Mastra) {\n return async (hookData: ScoringHookInput) => {\n const storage = mastra.getStorage();\n\n if (!storage) {\n mastra.getLogger()?.warn('Storage not found, skipping score validation and saving');\n return;\n }\n\n const entityId = hookData.entity.id as string;\n const entityType = hookData.entityType;\n const scorer = hookData.scorer;\n const scorerId = scorer.id as string;\n\n if (!scorerId) {\n mastra.getLogger()?.warn('Scorer ID not found, skipping score validation and saving');\n return;\n }\n\n try {\n const scorerToUse = await findScorer(mastra, entityId, entityType, scorerId);\n\n if (!scorerToUse) {\n throw new MastraError({\n id: 'MASTRA_SCORER_NOT_FOUND',\n domain: ErrorDomain.MASTRA,\n category: ErrorCategory.USER,\n text: `Scorer with ID ${scorerId} not found`,\n });\n }\n\n let input = hookData.input;\n let output = hookData.output;\n\n const { structuredOutput, ...rest } = hookData;\n\n const currentSpan = hookData.tracingContext?.currentSpan;\n const traceId = currentSpan?.isValid ? currentSpan.traceId : undefined;\n const spanId = currentSpan?.isValid ? currentSpan.id : undefined;\n const targetCorrelationContext = currentSpan?.isValid ? currentSpan.getCorrelationContext?.() : undefined;\n const targetMetadata = currentSpan?.isValid && currentSpan.metadata ? { ...currentSpan.metadata } : undefined;\n const runResult = (await scorerToUse.scorer.run({\n ...rest,\n input,\n output,\n scoreSource: 'live',\n targetScope: 'span',\n targetEntityType: toScorerTargetEntityType(entityType),\n targetTraceId: traceId,\n targetSpanId: spanId,\n targetCorrelationContext,\n targetMetadata,\n } as any)) as Record<string, unknown>;\n\n const payload = {\n ...rest,\n ...runResult,\n entityId,\n scorerId: scorerId,\n spanId,\n traceId,\n scorer: {\n ...rest.scorer,\n hasJudge: !!scorerToUse.scorer.judge,\n },\n metadata: {\n structuredOutput: !!structuredOutput,\n },\n };\n // Legacy score-store emission. This path is being deprecated.\n // ScoreEvent emission already happens inside MastraScorer.run() (see\n // packages/core/src/evals/base.ts). The hook must not republish or every\n // exporter would receive the same score twice.\n await validateAndSaveScore(storage, payload);\n } catch (error) {\n const mastraError = new MastraError(\n {\n id: 'MASTRA_SCORER_FAILED_TO_RUN_HOOK',\n domain: ErrorDomain.SCORER,\n category: ErrorCategory.USER,\n details: {\n scorerId,\n entityId,\n entityType,\n },\n },\n error,\n );\n\n mastra.getLogger()?.trackException(mastraError);\n }\n };\n}\n\n/**\n * @deprecated Legacy scores-store path. New score emission should use `mastra.observability.addScore()`.\n */\nexport async function validateAndSaveScore(storage: MastraStorage, payload: unknown) {\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(payload);\n await scoresStore.saveScore(payloadToSave);\n}\n\nasync function findScorer(mastra: Mastra, entityId: string, entityType: string, scorerId: string) {\n let scorerToUse;\n if (entityType === 'AGENT') {\n // Try code-defined agents first\n try {\n const agent = mastra.getAgentById(entityId);\n const scorers = await agent.listScorers();\n for (const [_, scorer] of Object.entries(scorers)) {\n if (scorer.scorer.id === scorerId) {\n scorerToUse = scorer;\n break;\n }\n }\n } catch {\n // Agent not found in code-defined agents, try stored agents via editor\n try {\n const storedAgent = (await mastra.getEditor()?.agent.getById(entityId)) ?? null;\n if (storedAgent) {\n const scorers = await storedAgent.listScorers();\n for (const [_, scorer] of Object.entries(scorers) as [string, any][]) {\n if (scorer.scorer.id === scorerId) {\n scorerToUse = scorer;\n break;\n }\n }\n }\n } catch {\n // Stored agent also not found, will fall back to mastra-registered scorer\n }\n }\n } else if (entityType === 'WORKFLOW') {\n const scorers = await mastra.getWorkflowById(entityId).listScorers();\n for (const [_, scorer] of Object.entries(scorers)) {\n if (scorer.scorer.id === scorerId) {\n scorerToUse = scorer;\n break;\n }\n }\n }\n\n // Fallback to mastra-registered scorer\n if (!scorerToUse) {\n const mastraRegisteredScorer = mastra.getScorerById(scorerId);\n scorerToUse = mastraRegisteredScorer ? { scorer: mastraRegisteredScorer } : undefined;\n }\n\n return scorerToUse;\n}\n","export class ScoreAccumulator {\n private flatScores: Record<string, number[]> = {};\n private workflowScores: Record<string, number[]> = {};\n private stepScores: Record<string, Record<string, number[]>> = {};\n private agentScores: Record<string, number[]> = {};\n private trajectoryScores: Record<string, number[]> = {};\n\n addScores(scorerResults: Record<string, any>) {\n const isWorkflowScores = 'steps' in scorerResults || 'workflow' in scorerResults;\n const isAgentScores = 'agent' in scorerResults;\n const hasTrajectory = 'trajectory' in scorerResults;\n\n // Routing priority: workflow configs take precedence (they may also include\n // trajectory scores), then agent configs (agent or trajectory-only), then\n // flat scores for simple scorer arrays.\n if (isWorkflowScores) {\n this.addWorkflowScores(scorerResults);\n } else if (isAgentScores || hasTrajectory) {\n this.addAgentScores(scorerResults);\n } else {\n this.addFlatScores(scorerResults);\n }\n }\n\n private addFlatScores(scorerResults: Record<string, any>) {\n for (const [scorerName, result] of Object.entries(scorerResults)) {\n if (!this.flatScores[scorerName]) {\n this.flatScores[scorerName] = [];\n }\n this.flatScores[scorerName].push((result as { score: number }).score);\n }\n }\n\n private addWorkflowScores(scorerResults: Record<string, any>) {\n if ('workflow' in scorerResults && scorerResults.workflow) {\n for (const [scorerName, result] of Object.entries(scorerResults.workflow)) {\n if (!this.workflowScores[scorerName]) {\n this.workflowScores[scorerName] = [];\n }\n this.workflowScores[scorerName].push((result as { score: number }).score);\n }\n }\n\n if ('steps' in scorerResults && scorerResults.steps) {\n for (const [stepId, stepResults] of Object.entries(scorerResults.steps)) {\n if (!this.stepScores[stepId]) {\n this.stepScores[stepId] = {};\n }\n for (const [scorerName, result] of Object.entries(stepResults as Record<string, any>)) {\n if (!this.stepScores[stepId][scorerName]) {\n this.stepScores[stepId][scorerName] = [];\n }\n this.stepScores[stepId][scorerName].push((result as { score: number }).score);\n }\n }\n }\n\n // Trajectory scores can come from workflow scorer configs too\n if ('trajectory' in scorerResults && scorerResults.trajectory) {\n for (const [scorerName, result] of Object.entries(scorerResults.trajectory)) {\n if (!this.trajectoryScores[scorerName]) {\n this.trajectoryScores[scorerName] = [];\n }\n this.trajectoryScores[scorerName].push((result as { score: number }).score);\n }\n }\n }\n\n private addAgentScores(scorerResults: Record<string, any>) {\n if ('agent' in scorerResults && scorerResults.agent) {\n for (const [scorerName, result] of Object.entries(scorerResults.agent)) {\n if (!this.agentScores[scorerName]) {\n this.agentScores[scorerName] = [];\n }\n this.agentScores[scorerName].push((result as { score: number }).score);\n }\n }\n\n if ('trajectory' in scorerResults && scorerResults.trajectory) {\n for (const [scorerName, result] of Object.entries(scorerResults.trajectory)) {\n if (!this.trajectoryScores[scorerName]) {\n this.trajectoryScores[scorerName] = [];\n }\n this.trajectoryScores[scorerName].push((result as { score: number }).score);\n }\n }\n }\n\n addStepScores(stepScorerResults: Record<string, Record<string, any>>) {\n for (const [stepId, stepResults] of Object.entries(stepScorerResults)) {\n if (!this.stepScores[stepId]) {\n this.stepScores[stepId] = {};\n }\n for (const [scorerName, result] of Object.entries(stepResults)) {\n if (!this.stepScores[stepId][scorerName]) {\n this.stepScores[stepId][scorerName] = [];\n }\n this.stepScores[stepId][scorerName].push((result as { score: number }).score);\n }\n }\n }\n\n getAverageScores(): Record<string, any> {\n const result: Record<string, any> = {};\n\n for (const [scorerName, scoreArray] of Object.entries(this.flatScores)) {\n result[scorerName] = this.getAverageScore(scoreArray);\n }\n\n // Add workflow scores\n if (Object.keys(this.workflowScores).length > 0) {\n result.workflow = {};\n for (const [scorerName, scoreArray] of Object.entries(this.workflowScores)) {\n result.workflow[scorerName] = this.getAverageScore(scoreArray);\n }\n }\n\n if (Object.keys(this.stepScores).length > 0) {\n result.steps = {};\n for (const [stepId, stepScorers] of Object.entries(this.stepScores)) {\n result.steps[stepId] = {};\n for (const [scorerName, scoreArray] of Object.entries(stepScorers)) {\n result.steps[stepId][scorerName] = this.getAverageScore(scoreArray);\n }\n }\n }\n\n // Add agent scores\n if (Object.keys(this.agentScores).length > 0) {\n result.agent = {};\n for (const [scorerName, scoreArray] of Object.entries(this.agentScores)) {\n result.agent[scorerName] = this.getAverageScore(scoreArray);\n }\n }\n\n // Add trajectory scores\n if (Object.keys(this.trajectoryScores).length > 0) {\n result.trajectory = {};\n for (const [scorerName, scoreArray] of Object.entries(this.trajectoryScores)) {\n result.trajectory[scorerName] = this.getAverageScore(scoreArray);\n }\n }\n\n return result;\n }\n\n private getAverageScore(scoreArray: number[]): number {\n if (scoreArray.length > 0) {\n return scoreArray.reduce((a, b) => a + b, 0) / scoreArray.length;\n } else {\n return 0;\n }\n }\n}\n","import type { CoreMessage } from '@internal/ai-sdk-v4';\nimport type { Agent, AgentExecutionOptions, AiMessageType, UIMessageWithMetadata } from '../../agent';\nimport { isSupportedLanguageModel } from '../../agent';\nimport { MastraError } from '../../error';\nimport { validateAndSaveScore } from '../../mastra/hooks';\nimport type { ObservabilityContext } from '../../observability';\nimport { EntityType, resolveObservabilityContext } from '../../observability';\nimport type { RequestContext } from '../../request-context';\nimport type { MastraCompositeStore } from '../../storage';\nimport type { WorkflowResult, WorkflowRunStartOptions, StepResult } from '../../workflows/types';\nimport type { AnyWorkflow } from '../../workflows/workflow';\nimport { Workflow } from '../../workflows/workflow';\nimport type { MastraScorer } from '../base';\nimport { extractTrajectory, extractTrajectoryFromTrace, extractWorkflowTrajectory } from '../types';\nimport { ScoreAccumulator } from './scorerAccumulator';\n\ntype WorkflowRunOptions = WorkflowRunStartOptions & {\n initialState?: any;\n};\n\ntype RunEvalsDataItem<TTarget = unknown> = {\n input: TTarget extends Workflow<any, any>\n ? any\n : TTarget extends Agent\n ? string | string[] | CoreMessage[] | AiMessageType[] | UIMessageWithMetadata[]\n : unknown;\n groundTruth?: any;\n expectedTrajectory?: any;\n requestContext?: RequestContext;\n startOptions?: WorkflowRunOptions;\n} & Partial<ObservabilityContext>;\n\nexport type WorkflowScorerConfig = {\n /** Scorers that evaluate the overall workflow input/output */\n workflow?: MastraScorer<any, any, any, any>[];\n /** Scorers that evaluate individual workflow steps by step ID */\n steps?: Record<string, MastraScorer<any, any, any, any>[]>;\n /** Scorers that evaluate the workflow's step execution trajectory */\n trajectory?: MastraScorer<any, any, any, any>[];\n};\n\nexport type AgentScorerConfig = {\n /** Scorers that evaluate the full agent input/output */\n agent?: MastraScorer<any, any, any, any>[];\n /** Scorers that evaluate the agent's tool call trajectory */\n trajectory?: MastraScorer<any, any, any, any>[];\n};\n\ntype RunEvalsResult = {\n scores: Record<string, any>;\n summary: {\n totalItems: number;\n };\n};\n\n// Agent with scorers array\nexport function runEvals<TAgent extends Agent>(config: {\n data: RunEvalsDataItem<TAgent>[];\n scorers: MastraScorer<any, any, any, any>[];\n target: TAgent;\n targetOptions?: Omit<AgentExecutionOptions<any>, 'scorers' | 'returnScorerData' | 'requestContext'>;\n onItemComplete?: (params: {\n item: RunEvalsDataItem<TAgent>;\n targetResult: Awaited<ReturnType<Agent['generate']>>;\n scorerResults: Record<string, any>; // Flat structure: { scorerName: result }\n }) => void | Promise<void>;\n concurrency?: number;\n}): Promise<RunEvalsResult>;\n\n// Workflow with scorers array\nexport function runEvals<TWorkflow extends AnyWorkflow>(config: {\n data: RunEvalsDataItem<TWorkflow>[];\n scorers: MastraScorer<any, any, any, any>[];\n target: TWorkflow;\n targetOptions?: WorkflowRunOptions;\n onItemComplete?: (params: {\n item: RunEvalsDataItem<TWorkflow>;\n targetResult: WorkflowResult<any, any, any, any>;\n scorerResults: Record<string, any>; // Flat structure: { scorerName: result }\n }) => void | Promise<void>;\n concurrency?: number;\n}): Promise<RunEvalsResult>;\n\n// Workflow with workflow configuration\nexport function runEvals<TWorkflow extends AnyWorkflow>(config: {\n data: RunEvalsDataItem<TWorkflow>[];\n scorers: WorkflowScorerConfig;\n target: TWorkflow;\n targetOptions?: WorkflowRunOptions;\n onItemComplete?: (params: {\n item: RunEvalsDataItem<TWorkflow>;\n targetResult: WorkflowResult<any, any, any, any>;\n scorerResults: {\n workflow?: Record<string, any>;\n steps?: Record<string, Record<string, any>>;\n trajectory?: Record<string, any>;\n };\n }) => void | Promise<void>;\n concurrency?: number;\n}): Promise<RunEvalsResult>;\n\n// Agent with agent scorer configuration (agent-level + trajectory scorers)\nexport function runEvals<TAgent extends Agent>(config: {\n data: RunEvalsDataItem<TAgent>[];\n scorers: AgentScorerConfig;\n target: TAgent;\n targetOptions?: Omit<AgentExecutionOptions<any>, 'scorers' | 'returnScorerData' | 'requestContext'>;\n onItemComplete?: (params: {\n item: RunEvalsDataItem<TAgent>;\n targetResult: Awaited<ReturnType<Agent['generate']>>;\n scorerResults: {\n agent?: Record<string, any>;\n trajectory?: Record<string, any>;\n };\n }) => void | Promise<void>;\n concurrency?: number;\n}): Promise<RunEvalsResult>;\n\nexport async function runEvals(config: {\n data: RunEvalsDataItem<any>[];\n scorers: MastraScorer<any, any, any, any>[] | WorkflowScorerConfig | AgentScorerConfig;\n target: Agent | Workflow;\n targetOptions?:\n | Omit<AgentExecutionOptions<any>, 'scorers' | 'returnScorerData' | 'requestContext'>\n | WorkflowRunOptions;\n onItemComplete?: (params: {\n item: RunEvalsDataItem<any>;\n targetResult: any;\n scorerResults: any;\n }) => void | Promise<void>;\n concurrency?: number;\n}): Promise<RunEvalsResult> {\n const { data, scorers, target, targetOptions, onItemComplete, concurrency = 1 } = config;\n\n validateEvalsInputs(data, scorers, target);\n\n let totalItems = 0;\n const scoreAccumulator = new ScoreAccumulator();\n\n // Get storage from target's Mastra instance if available\n // Agent uses getMastraInstance(), Workflow uses .mastra getter\n const mastra = (target as any).getMastraInstance?.() || (target as any).mastra;\n const storage = mastra?.getStorage();\n\n const pMap = (await import('p-map')).default;\n await pMap(\n data,\n async (item: RunEvalsDataItem<any>) => {\n const targetResult = await executeTarget(target, item, targetOptions);\n const scorerResults = await runScorers(scorers, targetResult, item, storage);\n scoreAccumulator.addScores(scorerResults);\n\n // Save scores to storage if available\n if (storage) {\n await saveScoresToStorage({\n storage,\n scorerResults,\n target,\n item,\n mastra,\n });\n }\n\n if (onItemComplete) {\n await onItemComplete({\n item,\n targetResult: targetResult as any,\n scorerResults: scorerResults as any,\n });\n }\n\n totalItems++;\n },\n { concurrency },\n );\n\n return {\n scores: scoreAccumulator.getAverageScores(),\n summary: {\n totalItems,\n },\n };\n}\n\nfunction isWorkflow(target: Agent | Workflow): target is Workflow {\n return target instanceof Workflow;\n}\n\nfunction isWorkflowScorerConfig(scorers: any): scorers is WorkflowScorerConfig {\n return (\n typeof scorers === 'object' &&\n !Array.isArray(scorers) &&\n ('workflow' in scorers || 'steps' in scorers || ('trajectory' in scorers && !('agent' in scorers)))\n );\n}\n\nfunction isAgentScorerConfig(scorers: any): scorers is AgentScorerConfig {\n return (\n typeof scorers === 'object' &&\n !Array.isArray(scorers) &&\n ('agent' in scorers || ('trajectory' in scorers && !('workflow' in scorers) && !('steps' in scorers)))\n );\n}\n\nfunction validateEvalsInputs(\n data: RunEvalsDataItem<any>[],\n scorers: MastraScorer<any, any, any, any>[] | WorkflowScorerConfig | AgentScorerConfig,\n target: Agent | Workflow,\n): void {\n if (data.length === 0) {\n throw new MastraError({\n domain: 'SCORER',\n id: 'RUN_EXPERIMENT_FAILED_NO_DATA_PROVIDED',\n category: 'USER',\n text: 'Failed to run experiment: Data array is empty',\n });\n }\n\n for (let i = 0; i < data.length; i++) {\n const item = data[i];\n if (!item || typeof item !== 'object' || !('input' in item)) {\n throw new MastraError({\n domain: 'SCORER',\n id: 'INVALID_DATA_ITEM',\n category: 'USER',\n text: `Invalid data item at index ${i}: must have 'input' properties`,\n });\n }\n }\n\n // Validate scorers\n if (Array.isArray(scorers)) {\n if (scorers.length === 0) {\n throw new MastraError({\n domain: 'SCORER',\n id: 'NO_SCORERS_PROVIDED',\n category: 'USER',\n text: 'At least one scorer must be provided',\n });\n }\n } else if (isWorkflow(target) && isWorkflowScorerConfig(scorers)) {\n const hasScorers =\n (scorers.workflow && scorers.workflow.length > 0) ||\n (scorers.steps && Object.keys(scorers.steps).length > 0) ||\n (scorers.trajectory && scorers.trajectory.length > 0);\n\n if (!hasScorers) {\n throw new MastraError({\n domain: 'SCORER',\n id: 'NO_SCORERS_PROVIDED',\n category: 'USER',\n text: 'At least one workflow, step, or trajectory scorer must be provided',\n });\n }\n } else if (!isWorkflow(target) && isAgentScorerConfig(scorers)) {\n const hasScorers =\n (scorers.agent && scorers.agent.length > 0) || (scorers.trajectory && scorers.trajectory.length > 0);\n\n if (!hasScorers) {\n throw new MastraError({\n domain: 'SCORER',\n id: 'NO_SCORERS_PROVIDED',\n category: 'USER',\n text: 'At least one agent or trajectory scorer must be provided',\n });\n }\n } else if (!isWorkflow(target) && !Array.isArray(scorers) && !isAgentScorerConfig(scorers)) {\n throw new MastraError({\n domain: 'SCORER',\n id: 'INVALID_AGENT_SCORERS',\n category: 'USER',\n text: 'Agent scorers must be an array of scorers or an AgentScorerConfig',\n });\n }\n}\n\nasync function executeTarget(\n target: Agent | Workflow,\n item: RunEvalsDataItem<any>,\n targetOptions?:\n | Omit<AgentExecutionOptions<any>, 'scorers' | 'returnScorerData' | 'requestContext'>\n | WorkflowRunOptions,\n) {\n try {\n if (isWorkflow(target)) {\n return await executeWorkflow(target, item, targetOptions as WorkflowRunOptions);\n } else {\n return await executeAgent(\n target,\n item,\n targetOptions as Omit<AgentExecutionOptions<any>, 'scorers' | 'returnScorerData' | 'requestContext'>,\n );\n }\n } catch (error) {\n throw new MastraError(\n {\n domain: 'SCORER',\n id: 'RUN_EXPERIMENT_TARGET_FAILED_TO_GENERATE_RESULT',\n category: 'USER',\n text: 'Failed to run experiment: Error generating result from target',\n details: {\n item: JSON.stringify(item),\n },\n },\n error,\n );\n }\n}\n\nasync function executeWorkflow(target: Workflow, item: RunEvalsDataItem<any>, targetOptions?: WorkflowRunOptions) {\n const observabilityContext = resolveObservabilityContext(item);\n const run = await target.createRun({ disableScorers: true });\n const workflowResult = await run.start({\n ...targetOptions,\n ...item.startOptions,\n inputData: item.input,\n requestContext: item.requestContext,\n ...observabilityContext,\n });\n\n return {\n traceId: workflowResult.traceId,\n spanId: workflowResult.spanId,\n entityType: EntityType.WORKFLOW_RUN,\n scoringData: {\n input: item.input,\n output: workflowResult.status === 'success' ? workflowResult.result : undefined,\n stepResults: workflowResult.ste