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type { MastraDBMessage } from '@mastra/core/agent';\nimport type {\n ExpectedStep,\n ScorerRunInputForAgent,\n ScorerRunOutputForAgent,\n ScoringInput,\n TrajectoryExpectation,\n TrajectoryStep,\n Trajectory,\n} from '@mastra/core/evals';\nimport { RequestContext } from '@mastra/core/request-context';\n\nexport type ScorerRunInputForLLMJudge =\n | ScorerRunInputForAgent\n | string\n | {\n inputMessages?: unknown[];\n messages?: unknown[];\n prompt?: string;\n text?: string;\n content?: unknown;\n input?: unknown;\n user?: unknown;\n [key: string]: unknown;\n };\n\nexport type ScorerRunOutputForLLMJudge =\n | ScorerRunOutputForAgent\n | string\n | unknown[]\n | {\n text?: string;\n content?: unknown;\n role?: string;\n [key: string]: unknown;\n };\n\n/**\n * Extracts text content from a MastraDBMessage or ModelMessage-like object.\n *\n * @param message - The message to extract text from\n * @returns The extracted text content, or an empty string if no text is found\n *\n * @example\n * ```ts\n * const message: MastraDBMessage = {\n * id: 'msg-1',\n * role: 'assistant',\n * content: { format: 2, parts: [{ type: 'text', text: 'Hello!' }] },\n * createdAt: new Date(),\n * };\n * const text = getTextContentFromMastraDBMessage(message); // 'Hello!'\n * ```\n */\nexport function getTextContentFromMastraDBMessage(message: MastraDBMessage): string {\n const content = message.content as any;\n\n if (typeof content === 'string') {\n return content;\n }\n if (Array.isArray(content)) {\n const textParts = content.filter(p => p.type === 'text');\n return textParts.length > 0 ? textParts[textParts.length - 1]?.text || '' : '';\n }\n if (typeof content?.content === 'string' && content.content !== '') {\n return content.content;\n }\n if (typeof content?.text === 'string' && content.text !== '') {\n return content.text;\n }\n if (content?.parts && Array.isArray(content.parts)) {\n // Return only the last text part like AI SDK does\n const textParts = content.parts.filter((p: any) => p.type === 'text');\n return textParts.length > 0 ? textParts[textParts.length - 1]?.text || '' : '';\n }\n return '';\n}\n\nconst isRecord = (value: unknown): value is Record => {\n return typeof value === 'object' && value !== null;\n};\n\nconst getTextFromValue = (value: unknown): string | undefined => {\n if (typeof value === 'string') return value === '' ? undefined : value;\n if (Array.isArray(value)) {\n const textParts = value\n .filter(part => isRecord(part) && part.type === 'text' && typeof part.text === 'string')\n .map(part => part.text);\n return textParts.length > 0 ? textParts[textParts.length - 1] : undefined;\n }\n if (!isRecord(value)) return undefined;\n\n const fromParts = Array.isArray(value.parts) ? getTextFromValue(value.parts) : undefined;\n\n return (\n getTextFromValue(value.content) ??\n (typeof value.text === 'string' && value.text !== '' ? value.text : undefined) ??\n (typeof value.body === 'string' && value.body !== '' ? value.body : undefined) ??\n fromParts\n );\n};\n\nexport const isScorerRunInputForAgent = (input: unknown): input is ScorerRunInputForAgent => {\n return (\n isRecord(input) &&\n Array.isArray(input.inputMessages) &&\n Array.isArray(input.rememberedMessages) &&\n Array.isArray(input.systemMessages) &&\n isRecord(input.taggedSystemMessages)\n );\n};\n\nconst isMastraDBMessageLike = (message: unknown): message is MastraDBMessage => {\n return (\n isRecord(message) &&\n typeof message.id === 'string' &&\n typeof message.role === 'string' &&\n 'content' in message &&\n 'createdAt' in message\n );\n};\n\nexport const isScorerRunOutputForAgent = (output: unknown): output is ScorerRunOutputForAgent => {\n return Array.isArray(output) && output.every(isMastraDBMessageLike);\n};\n\nconst getTextFromMessages = (messages: unknown, role: string): string | undefined => {\n if (!Array.isArray(messages)) return undefined;\n\n const message = messages.find(message => isRecord(message) && message.role === role);\n return message ? getTextFromValue(message) : undefined;\n};\n\n/**\n * Rounds a number to two decimal places.\n *\n * Uses `Number.EPSILON` to handle floating-point precision issues.\n *\n * @param num - The number to round\n * @returns The number rounded to two decimal places\n *\n * @example\n * ```ts\n * roundToTwoDecimals(0.1 + 0.2); // 0.3\n * roundToTwoDecimals(1.005); // 1.01\n * ```\n */\nexport const roundToTwoDecimals = (num: number) => {\n return Math.round((num + Number.EPSILON) * 100) / 100;\n};\n\n/**\n * Determines if a value is closer to the first target than the second.\n *\n * @param value - The value to compare\n * @param target1 - The first target value\n * @param target2 - The second target value\n * @returns `true` if `value` is closer to `target1` than `target2`\n *\n * @example\n * ```ts\n * isCloserTo(0.6, 1, 0); // true (0.6 is closer to 1)\n * isCloserTo(0.3, 1, 0); // false (0.3 is closer to 0)\n * ```\n */\nexport function isCloserTo(value: number, target1: number, target2: number): boolean {\n return Math.abs(value - target1) < Math.abs(value - target2);\n}\n\n/**\n * Represents a test case for scorer evaluation.\n */\nexport type TestCase = {\n /** The input text to evaluate */\n input: string;\n /** The output text to evaluate */\n output: string;\n /** The expected result of the evaluation */\n expectedResult: {\n /** The expected score */\n score: number;\n /** The optional expected reason */\n reason?: string;\n };\n};\n\n/**\n * Represents a test case with additional context for scorer evaluation.\n */\nexport type TestCaseWithContext = TestCase & {\n /** Additional context strings for the evaluation */\n context: string[];\n};\n\n/**\n * Creates a scoring input object for testing purposes.\n *\n * @param input - The user input text\n * @param output - The assistant output text\n * @param additionalContext - Optional additional context data\n * @param requestContext - Optional request context data\n * @returns A ScoringInput object ready for use in scorer tests\n *\n * @example\n * ```ts\n * const run = createTestRun(\n * 'What is 2+2?',\n * 'The answer is 4.',\n * { topic: 'math' }\n * );\n * ```\n */\nexport const createTestRun = (\n input: string,\n output: string,\n additionalContext?: Record,\n requestContext?: Record,\n): ScoringInput => {\n return {\n input: [{ role: 'user', content: input }],\n output: { role: 'assistant', text: output },\n additionalContext: additionalContext ?? {},\n requestContext: requestContext ?? {},\n };\n};\n\n/**\n * Extracts the user message text from a scorer run input.\n *\n * Accepts the agent shape (`{ inputMessages }`), `ModelMessage[]`\n * (`{ messages }`), workflow input (`{ prompt }`), and a bare string.\n *\n * @param input - The scorer run input\n * @returns The user message text, or `undefined` if none can be extracted\n *\n * @example\n * ```ts\n * const scorer = createScorer({ ... })\n * .preprocess(({ run }) => {\n * const userText = getUserMessageFromRunInput(run.input);\n * return { userText };\n * });\n * ```\n */\nexport const getUserMessageFromRunInput = (input?: unknown): string | undefined => {\n if (typeof input === 'string') return input;\n if (!isRecord(input)) return undefined;\n\n return (\n getTextFromMessages(input.inputMessages, 'user') ??\n getTextFromMessages(input.messages, 'user') ??\n (typeof input.prompt === 'string' ? input.prompt : undefined) ??\n (typeof input.text === 'string' ? input.text : undefined) ??\n getTextFromValue(input.content) ??\n getTextFromValue(input.input) ??\n getTextFromValue(input.user)\n );\n};\n\n/**\n * Extracts all system messages from a scorer run input.\n *\n * Collects text from both standard system messages and tagged system messages\n * (specialized system prompts like memory instructions).\n *\n * @param input - The scorer run input containing system messages\n * @returns An array of system message strings\n *\n * @example\n * ```ts\n * const scorer = createScorer({ ... })\n * .preprocess(({ run }) => {\n * const systemMessages = getSystemMessagesFromRunInput(run.input);\n * return { systemPrompt: systemMessages.join('\\n') };\n * });\n * ```\n */\nexport const getSystemMessagesFromRunInput = (input?: unknown): string[] => {\n const systemMessages: string[] = [];\n if (!isRecord(input)) return systemMessages;\n\n // Add standard system messages\n if (Array.isArray(input.systemMessages)) {\n systemMessages.push(\n ...input.systemMessages\n .map(msg => {\n // Handle different content types - extract text if it's an array of parts\n if (typeof msg.content === 'string') {\n return msg.content;\n } else if (Array.isArray(msg.content)) {\n // Extract text from parts array\n return msg.content\n .filter((part: any) => part.type === 'text')\n .map((part: any) => part.text || '')\n .join(' ');\n }\n return '';\n })\n .filter(content => content),\n );\n }\n\n const addSystemMessages = (messages: unknown) => {\n if (!Array.isArray(messages)) return;\n\n systemMessages.push(\n ...messages\n .filter(message => isRecord(message) && message.role === 'system')\n .map(message => getTextFromValue(message))\n .filter((content): content is string => Boolean(content)),\n );\n };\n\n addSystemMessages(input.inputMessages);\n addSystemMessages(input.messages);\n\n // Add tagged system messages (these are specialized system prompts)\n if (isRecord(input.taggedSystemMessages)) {\n Object.values(input.taggedSystemMessages).forEach(messages => {\n if (!Array.isArray(messages)) return;\n messages.forEach(msg => {\n const content = getTextFromValue(msg);\n if (content) {\n systemMessages.push(content);\n }\n });\n });\n }\n\n return systemMessages;\n};\n\n/**\n * Combines all system messages into a single prompt string.\n *\n * Joins all system messages (standard and tagged) with double newlines.\n *\n * @param input - The scorer run input containing system messages\n * @returns A combined system prompt string\n *\n * @example\n * ```ts\n * const scorer = createScorer({ ... })\n * .preprocess(({ run }) => {\n * const systemPrompt = getCombinedSystemPrompt(run.input);\n * return { systemPrompt };\n * });\n * ```\n */\nexport const getCombinedSystemPrompt = (input?: unknown): string => {\n const systemMessages = getSystemMessagesFromRunInput(input);\n return systemMessages.join('\\n\\n');\n};\n\n/**\n * Extracts the assistant message text from a scorer run output.\n *\n * Accepts the agent shape (`MastraDBMessage[]` / `ModelMessage[]`), workflow\n * output (`{ text }`), task output (`{ content }`), a single assistant message\n * object, and a bare string.\n *\n * @param output - The scorer run output\n * @returns The assistant message text, or `undefined` if none can be extracted\n *\n * @example\n * ```ts\n * const scorer = createScorer({ ... })\n * .preprocess(({ run }) => {\n * const response = getAssistantMessageFromRunOutput(run.output);\n * return { response };\n * });\n * ```\n */\nexport const getAssistantMessageFromRunOutput = (output?: unknown) => {\n if (typeof output === 'string') return output;\n if (Array.isArray(output)) return getTextFromMessages(output, 'assistant');\n if (!isRecord(output)) return undefined;\n\n const isAssistantOutput = output.role === undefined || output.role === 'assistant';\n\n if (isAssistantOutput && typeof output.text === 'string') return output.text;\n if (isAssistantOutput && typeof output.content === 'string') return output.content;\n if (isAssistantOutput && (isRecord(output.content) || Array.isArray(output.content))) {\n return (\n getTextContentFromMastraDBMessage(output as MastraDBMessage) ||\n getTextContentFromMastraDBMessage(output.content as MastraDBMessage) ||\n undefined\n );\n }\n if (output.role === 'assistant') return getTextContentFromMastraDBMessage(output as MastraDBMessage) || undefined;\n\n return undefined;\n};\n\n/**\n * Extracts reasoning text from a scorer run output.\n *\n * This function extracts reasoning content from assistant messages, which is\n * produced by reasoning models like `deepseek-reasoner`. The reasoning can be\n * stored in two places:\n * 1. `content.reasoning` - a string field on the message content\n * 2. `content.parts` - as parts with `type: 'reasoning'` containing `details`\n *\n * @param output - The scorer run output (array of MastraDBMessage)\n * @returns The reasoning text, or `undefined` if no reasoning is present\n *\n * @example\n * ```ts\n * const reasoningScorer = createScorer({\n * id: 'reasoning-scorer',\n * name: 'Reasoning Quality',\n * description: 'Evaluates the quality of model reasoning',\n * type: 'agent',\n * })\n * .preprocess(({ run }) => {\n * const reasoning = getReasoningFromRunOutput(run.output);\n * const response = getAssistantMessageFromRunOutput(run.output);\n * return { reasoning, response };\n * })\n * .generateScore(({ results }) => {\n * // Score based on reasoning quality\n * return results.preprocessStepResult?.reasoning ? 1 : 0;\n * });\n * ```\n */\nexport const getReasoningFromRunOutput = (output?: ScorerRunOutputForAgent): string | undefined => {\n if (!output) return undefined;\n\n const message = output.find(({ role }) => role === 'assistant');\n if (!message) return undefined;\n\n // Check for reasoning in content.reasoning (string format)\n if (message.content.reasoning) {\n return message.content.reasoning;\n }\n\n // Check for reasoning in parts with type 'reasoning'\n // Reasoning models store reasoning in parts as { type: 'reasoning', details: [{ type: 'text', text: '...' }] }\n const reasoningParts = message.content.parts?.filter((p: any) => p.type === 'reasoning');\n if (reasoningParts && reasoningParts.length > 0) {\n const reasoningTexts = reasoningParts\n .map((p: any) => {\n // The reasoning text can be in p.reasoning or in p.details[].text\n if (p.details && Array.isArray(p.details)) {\n return p.details\n .filter((d: any) => d.type === 'text')\n .map((d: any) => d.text)\n .join('');\n }\n return p.reasoning || '';\n })\n .filter(Boolean);\n\n return reasoningTexts.length > 0 ? reasoningTexts.join('\\n') : undefined;\n }\n\n return undefined;\n};\n\n/**\n * Creates a tool invocation object for testing purposes.\n *\n * @param options - The tool invocation configuration\n * @param options.toolCallId - Unique identifier for the tool call\n * @param options.toolName - Name of the tool being called\n * @param options.args - Arguments passed to the tool\n * @param options.result - Result returned by the tool\n * @param options.state - State of the invocation (default: 'result')\n * @returns A tool invocation object\n *\n * @example\n * ```ts\n * const invocation = createToolInvocation({\n * toolCallId: 'call-123',\n * toolName: 'weatherTool',\n * args: { location: 'London' },\n * result: { temperature: 20, condition: 'sunny' },\n * });\n * ```\n */\nexport const createToolInvocation = ({\n toolCallId,\n toolName,\n args,\n result,\n state = 'result',\n}: {\n toolCallId: string;\n toolName: string;\n args: Record;\n result: Record;\n state?: 'call' | 'partial-call' | 'result';\n}): { toolCallId: string; toolName: string; args: Record; result: Record; state: string } => {\n return {\n toolCallId,\n toolName,\n args,\n result,\n state,\n };\n};\n\n/**\n * Creates a MastraDBMessage object for testing purposes.\n *\n * Supports optional tool invocations for testing tool call scenarios.\n *\n * @param options - The message configuration\n * @param options.content - The text content of the message\n * @param options.role - The role of the message sender ('user', 'assistant', or 'system')\n * @param options.id - Optional message ID (default: 'test-message')\n * @param options.toolInvocations - Optional array of tool invocations\n * @returns A MastraDBMessage object\n *\n * @example\n * ```ts\n * const message = createTestMessage({\n * content: 'Hello, how can I help?',\n * role: 'assistant',\n * });\n *\n * // With tool invocations\n * const messageWithTools = createTestMessage({\n * content: 'Let me check the weather.',\n * role: 'assistant',\n * toolInvocations: [{\n * toolCallId: 'call-1',\n * toolName: 'weatherTool',\n * args: { location: 'Paris' },\n * result: { temp: 22 },\n * state: 'result',\n * }],\n * });\n * ```\n */\nexport function createTestMessage({\n content,\n role,\n id = 'test-message',\n toolInvocations = [],\n}: {\n content: string;\n role: 'user' | 'assistant' | 'system';\n id?: string;\n toolInvocations?: Array<{\n toolCallId: string;\n toolName: string;\n args: Record;\n result: Record;\n state: any;\n }>;\n}): MastraDBMessage {\n return {\n id,\n role,\n content: {\n format: 2,\n parts: [{ type: 'text', text: content }],\n content,\n ...(toolInvocations.length > 0 && {\n toolInvocations: toolInvocations.map(ti => ({\n toolCallId: ti.toolCallId,\n toolName: ti.toolName,\n args: ti.args,\n result: ti.result,\n state: ti.state,\n })),\n }),\n },\n createdAt: new Date(),\n };\n}\n\n/**\n * Creates a complete agent test run object for testing scorers.\n *\n * Provides a convenient way to construct the full run object that scorers receive,\n * including input messages, output, system messages, and request context.\n *\n * @param options - The test run configuration\n * @param options.inputMessages - Array of input messages (default: [])\n * @param options.output - The output messages (required)\n * @param options.rememberedMessages - Array of remembered messages from memory (default: [])\n * @param options.systemMessages - Array of system messages (default: [])\n * @param options.taggedSystemMessages - Tagged system messages map (default: {})\n * @param options.requestContext - Request context (default: new RequestContext())\n * @param options.runId - Unique run ID (default: random UUID)\n * @returns A complete test run object\n *\n * @example\n * ```ts\n * const testRun = createAgentTestRun({\n * inputMessages: [createTestMessage({ content: 'Hello', role: 'user' })],\n * output: [createTestMessage({ content: 'Hi there!', role: 'assistant' })],\n * });\n *\n * const result = await scorer.run({\n * input: testRun.input,\n * output: testRun.output,\n * });\n * ```\n */\nexport const createAgentTestRun = ({\n inputMessages = [],\n output,\n rememberedMessages = [],\n systemMessages = [],\n taggedSystemMessages = {},\n requestContext = new RequestContext(),\n runId = crypto.randomUUID(),\n}: {\n inputMessages?: ScorerRunInputForAgent['inputMessages'];\n output: ScorerRunOutputForAgent;\n rememberedMessages?: ScorerRunInputForAgent['rememberedMessages'];\n systemMessages?: ScorerRunInputForAgent['systemMessages'];\n taggedSystemMessages?: ScorerRunInputForAgent['taggedSystemMessages'];\n requestContext?: RequestContext;\n runId?: string;\n}): {\n input: ScorerRunInputForAgent;\n output: ScorerRunOutputForAgent;\n requestContext: RequestContext;\n runId: string;\n} => {\n return {\n input: {\n inputMessages,\n rememberedMessages,\n systemMessages,\n taggedSystemMessages,\n },\n output,\n requestContext,\n runId,\n };\n};\n\n/**\n * Creates a test run for trajectory scorers where `output` is a `Trajectory`\n * (pre-extracted by the `runEvals` pipeline).\n *\n * @example\n * ```ts\n * const testRun = createTrajectoryTestRun({\n * inputMessages: [createTestMessage({ content: 'Do X', role: 'user', id: 'u1' })],\n * trajectory: {\n * steps: [\n * { stepType: 'tool_call', name: 'search', toolArgs: { q: 'test' } },\n * ],\n * },\n * });\n * ```\n */\nexport const createTrajectoryTestRun = ({\n inputMessages = [],\n trajectory,\n rememberedMessages = [],\n systemMessages = [],\n taggedSystemMessages = {},\n requestContext = new RequestContext(),\n runId = crypto.randomUUID(),\n expectedTrajectory,\n}: {\n inputMessages?: ScorerRunInputForAgent['inputMessages'];\n trajectory: Trajectory;\n rememberedMessages?: ScorerRunInputForAgent['rememberedMessages'];\n systemMessages?: ScorerRunInputForAgent['systemMessages'];\n taggedSystemMessages?: ScorerRunInputForAgent['taggedSystemMessages'];\n requestContext?: RequestContext;\n runId?: string;\n expectedTrajectory?: TrajectoryExpectation;\n}): {\n input: ScorerRunInputForAgent;\n output: Trajectory;\n requestContext: RequestContext;\n runId: string;\n expectedTrajectory?: TrajectoryExpectation;\n} => {\n return {\n input: {\n inputMessages,\n rememberedMessages,\n systemMessages,\n taggedSystemMessages,\n },\n output: trajectory,\n expectedTrajectory,\n requestContext,\n runId,\n };\n};\n\n/**\n * Information about a tool call extracted from scorer output.\n */\nexport type ToolCallInfo = {\n /** Name of the tool that was called */\n toolName: string;\n /** Unique identifier for the tool call */\n toolCallId: string;\n /** Index of the message containing this tool call */\n messageIndex: number;\n /** Index of the invocation within the message's tool invocations */\n invocationIndex: number;\n};\n\n/**\n * Extracts all tool calls from a scorer run output.\n *\n * Iterates through all messages and their tool invocations to collect\n * information about tools that were called (with state 'result' or 'call').\n *\n * @param output - The scorer run output (array of MastraDBMessage)\n * @returns An object containing tool names and detailed tool call info\n *\n * @example\n * ```ts\n * const scorer = createScorer({ ... })\n * .preprocess(({ run }) => {\n * const { tools, toolCallInfos } = extractToolCalls(run.output);\n * return {\n * toolsUsed: tools,\n * toolCount: tools.length,\n * };\n * });\n * ```\n */\nexport function extractToolCalls(output: ScorerRunOutputForAgent): { tools: string[]; toolCallInfos: ToolCallInfo[] } {\n const toolCalls: string[] = [];\n const toolCallInfos: ToolCallInfo[] = [];\n\n for (let messageIndex = 0; messageIndex < output.length; messageIndex++) {\n const message = output[messageIndex];\n // Prefer the legacy toolInvocations array when present; fall back to\n // V2 content.parts for messages that only store tool calls there.\n const legacy = message?.content?.toolInvocations;\n const fromParts = legacy\n ? undefined\n : message?.content?.parts\n ?.filter((p): p is Extract => p.type === 'tool-invocation')\n .map(p => p.toolInvocation);\n const toolInvocations = legacy ?? fromParts;\n\n if (!toolInvocations?.length) continue;\n\n for (let invocationIndex = 0; invocationIndex < toolInvocations.length; invocationIndex++) {\n const invocation = toolInvocations[invocationIndex];\n if (invocation && invocation.toolName && (invocation.state === 'result' || invocation.state === 'call')) {\n toolCalls.push(invocation.toolName);\n toolCallInfos.push({\n toolName: invocation.toolName,\n toolCallId: invocation.toolCallId || `${messageIndex}-${invocationIndex}`,\n messageIndex,\n invocationIndex,\n });\n }\n }\n }\n\n return { tools: toolCalls, toolCallInfos };\n}\n\n/**\n * Extracts text content from all input messages.\n *\n * @param runInput - The scorer run input\n * @returns An array of text strings from each input message\n *\n * @example\n * ```ts\n * const scorer = createScorer({ ... })\n * .preprocess(({ run }) => {\n * const messages = extractInputMessages(run.input);\n * return { allUserMessages: messages.join('\\n') };\n * });\n * ```\n */\nexport const extractInputMessages = (runInput: ScorerRunInputForAgent | undefined): string[] => {\n return runInput?.inputMessages?.map(msg => getTextContentFromMastraDBMessage(msg)) || [];\n};\n\n/**\n * Extracts text content from all assistant response messages.\n *\n * Filters for messages with role 'assistant' and extracts their text content.\n *\n * @param runOutput - The scorer run output (array of MastraDBMessage)\n * @returns An array of text strings from each assistant message\n *\n * @example\n * ```ts\n * const scorer = createScorer({ ... })\n * .preprocess(({ run }) => {\n * const responses = extractAgentResponseMessages(run.output);\n * return { allResponses: responses.join('\\n') };\n * });\n * ```\n */\nexport const extractAgentResponseMessages = (runOutput: ScorerRunOutputForAgent): string[] => {\n return runOutput.filter(msg => msg.role === 'assistant').map(msg => getTextContentFromMastraDBMessage(msg));\n};\n\n/**\n * Information about a tool result extracted from scorer output.\n */\nexport type ToolResultInfo = {\n /** Name of the tool that was called */\n toolName: string;\n /** Unique identifier for the tool call */\n toolCallId: string;\n /** Arguments passed to the tool */\n args: Record;\n /** Result returned by the tool */\n result: any;\n};\n\n/**\n * Extracts tool results from a scorer run output.\n *\n * Returns structured objects that can be used with the hallucination scorer's\n * `getContext` hook or for other scorer logic.\n *\n * @param output - The scorer run output (array of MastraDBMessage)\n * @returns An array of ToolResultInfo objects\n *\n * @example\n * ```ts\n * import { extractToolResults } from '@mastra/evals/scorers';\n * import { createHallucinationScorer } from '@mastra/evals/scorers/prebuilt';\n *\n * const scorer = createHallucinationScorer({\n * model: openai('gpt-4o'),\n * options: {\n * getContext: (run) => {\n * const toolResults = extractToolResults(run.output);\n * return toolResults.map(t => JSON.stringify({ tool: t.toolName, result: t.result }));\n * },\n * },\n * });\n * ```\n */\nexport function extractToolResults(output: ScorerRunOutputForAgent): ToolResultInfo[] {\n const results: ToolResultInfo[] = [];\n\n for (const message of output) {\n // Prefer the legacy toolInvocations array when present; fall back to\n // V2 content.parts for messages that only store tool calls there.\n const legacy = message?.content?.toolInvocations;\n const fromParts = legacy\n ? undefined\n : message?.content?.parts\n ?.filter((p): p is Extract => p.type === 'tool-invocation')\n .map(p => p.toolInvocation);\n const toolInvocations = legacy ?? fromParts;\n\n if (!toolInvocations?.length) continue;\n\n for (const invocation of toolInvocations) {\n if (invocation.state === 'result' && invocation.result !== undefined) {\n results.push({\n toolName: invocation.toolName,\n toolCallId: invocation.toolCallId || '',\n args: invocation.args || {},\n result: invocation.result,\n });\n }\n }\n }\n\n return results;\n}\n\n// Re-export extractTrajectory from core — it's called automatically by runEvals\n// for trajectory scorers, but users may still want it for custom use cases.\nexport { extractTrajectory } from '@mastra/core/evals';\n\n/**\n * Compares two trajectories and returns detailed comparison results.\n *\n * This is the core comparison logic used by trajectory scorers. It supports\n * strict and non-strict ordering, optional step data comparison, and loop detection.\n *\n * @param actual - The trajectory the agent actually took\n * @param expected - The expected trajectory to compare against\n * @param options - Comparison configuration options\n * @returns Detailed comparison results including match scores and diagnostics\n *\n * @example\n * ```ts\n * const result = compareTrajectories(\n * { steps: [{ stepType: 'tool_call', name: 'search' }, { stepType: 'tool_call', name: 'summarize' }] },\n * { steps: [{ stepType: 'tool_call', name: 'search' }, { stepType: 'tool_call', name: 'summarize' }] },\n * { ordering: 'strict' }\n * );\n * // result.score = 1.0\n * ```\n */\nexport function compareTrajectories(\n actual: Trajectory,\n expected: Trajectory | { steps: ExpectedStep[] },\n options: {\n ordering?: 'strict' | 'relaxed' | 'unordered';\n allowRepeatedSteps?: boolean;\n } = {},\n): TrajectoryComparisonResult {\n const { allowRepeatedSteps = true, ordering = 'relaxed' } = options;\n\n // Normalize expected to ExpectedStep[]. TrajectoryStep and ExpectedStep share\n // the same field names, so TrajectoryStep[] can be used directly as ExpectedStep[].\n // The only structural difference is `children` (TrajectoryStep[] vs TrajectoryExpectation),\n // but compareTrajectories doesn't recurse into children.\n const normalizedExpected: { steps: ExpectedStep[] } = {\n steps: expected.steps as ExpectedStep[],\n };\n\n if (normalizedExpected.steps.length === 0) {\n return {\n score: actual.steps.length === 0 ? 1 : 0,\n matchedSteps: 0,\n totalExpectedSteps: 0,\n totalActualSteps: actual.steps.length,\n missingSteps: [],\n extraSteps: actual.steps.map((s: TrajectoryStep) => s.name),\n outOfOrderSteps: [],\n repeatedSteps: [],\n };\n }\n\n const actualNames = actual.steps.map((s: TrajectoryStep) => s.name);\n\n // Detect repeated steps\n const nameCounts = new Map();\n for (const name of actualNames) {\n nameCounts.set(name, (nameCounts.get(name) || 0) + 1);\n }\n const repeatedSteps = [...nameCounts.entries()]\n .filter(([_, count]: [string, number]) => count > 1)\n .map(([name]: [string, number]) => name);\n\n if (ordering === 'strict') {\n return compareStrictOrder(actual, normalizedExpected, { allowRepeatedSteps, repeatedSteps });\n }\n\n if (ordering === 'unordered') {\n return compareUnorderedPresence(actual, normalizedExpected, { allowRepeatedSteps, repeatedSteps });\n }\n\n return compareRelaxedOrder(actual, normalizedExpected, { allowRepeatedSteps, repeatedSteps });\n}\n\n/**\n * Result of comparing two trajectories.\n */\nexport type TrajectoryComparisonResult = {\n /** Overall match score from 0 to 1 */\n score: number;\n /** Number of expected steps that were matched */\n matchedSteps: number;\n /** Total number of expected steps */\n totalExpectedSteps: number;\n /** Total number of actual steps taken */\n totalActualSteps: number;\n /** Expected steps that were not found in the actual trajectory */\n missingSteps: string[];\n /** Actual steps that were not in the expected trajectory */\n extraSteps: string[];\n /** Steps that appear but not in the expected position */\n outOfOrderSteps: string[];\n /** Steps that were repeated (appeared more than once) */\n repeatedSteps: string[];\n};\n\nfunction compareStrictOrder(\n actual: Trajectory,\n expected: { steps: ExpectedStep[] },\n opts: { allowRepeatedSteps: boolean; repeatedSteps: string[] },\n): TrajectoryComparisonResult {\n const actualNames: string[] = actual.steps.map((s: TrajectoryStep) => s.name);\n const expectedNames: string[] = expected.steps.map((s: ExpectedStep) => s.name);\n\n // Strict: exact same sequence\n let matchedSteps = 0;\n const outOfOrderSteps: string[] = [];\n const matchedExpectedIndices = new Set();\n const maxLen = Math.max(actualNames.length, expectedNames.length);\n\n for (let i = 0; i < maxLen; i++) {\n const actualName = actualNames[i];\n const expectedName = expectedNames[i];\n if (actualName === expectedName) {\n if (actual.steps[i] && expected.steps[i]) {\n if (expectedStepMatches(actual.steps[i]!, expected.steps[i]!)) {\n matchedSteps++;\n matchedExpectedIndices.add(i);\n }\n } else {\n matchedSteps++;\n matchedExpectedIndices.add(i);\n }\n } else if (actualName && expectedNames.includes(actualName)) {\n outOfOrderSteps.push(actualName);\n }\n }\n\n // Missing steps = expected steps that were not matched (accounts for stepType/data mismatches)\n const missingSteps: string[] = expectedNames.filter((_: string, i: number) => !matchedExpectedIndices.has(i));\n const extraSteps: string[] = actualNames.filter((name: string) => !expectedNames.includes(name));\n\n let score = matchedSteps / expected.steps.length;\n\n // Penalize extra steps in strict mode\n if (actualNames.length > expectedNames.length) {\n const extraPenalty = (actualNames.length - expectedNames.length) / expectedNames.length;\n score = Math.max(0, score - extraPenalty * 0.5);\n }\n\n // Penalize repeated steps if not allowed\n if (!opts.allowRepeatedSteps && opts.repeatedSteps.length > 0) {\n score = Math.max(0, score - opts.repeatedSteps.length * 0.1);\n }\n\n return {\n score: roundToTwoDecimals(Math.max(0, Math.min(1, score))),\n matchedSteps,\n totalExpectedSteps: expected.steps.length,\n totalActualSteps: actual.steps.length,\n missingSteps,\n extraSteps,\n outOfOrderSteps,\n repeatedSteps: opts.repeatedSteps,\n };\n}\n\nfunction compareRelaxedOrder(\n actual: Trajectory,\n expected: { steps: ExpectedStep[] },\n opts: { allowRepeatedSteps: boolean; repeatedSteps: string[] },\n): TrajectoryComparisonResult {\n const actualNames: string[] = actual.steps.map((s: TrajectoryStep) => s.name);\n const expectedNames: string[] = expected.steps.map((s: ExpectedStep) => s.name);\n\n // Relaxed: expected steps must appear in order but extra steps are allowed\n let matchedSteps = 0;\n let lastMatchedIndex = -1;\n const outOfOrderSteps: string[] = [];\n const matchedExpectedIndices = new Set();\n\n for (let i = 0; i < expectedNames.length; i++) {\n const expectedName = expectedNames[i];\n let found = false;\n\n for (let j = lastMatchedIndex + 1; j < actualNames.length; j++) {\n if (actualNames[j] === expectedName) {\n if (actual.steps[j] && expected.steps[i]) {\n if (expectedStepMatches(actual.steps[j]!, expected.steps[i]!)) {\n matchedSteps++;\n lastMatchedIndex = j;\n matchedExpectedIndices.add(i);\n found = true;\n break;\n }\n } else {\n matchedSteps++;\n lastMatchedIndex = j;\n matchedExpectedIndices.add(i);\n found = true;\n break;\n }\n }\n }\n\n if (!found) {\n // Check if the step exists but is out of order\n if (actualNames.includes(expectedName!)) {\n outOfOrderSteps.push(expectedName!);\n }\n }\n }\n\n // Missing steps = expected steps that were not matched (by name + stepType + data, not just name)\n const missingSteps = expectedNames.filter((_, i) => !matchedExpectedIndices.has(i));\n const expectedSet = new Set(expectedNames);\n const extraSteps = actualNames.filter(name => !expectedSet.has(name));\n\n let score = matchedSteps / expected.steps.length;\n\n // Penalize repeated steps if not allowed\n if (!opts.allowRepeatedSteps && opts.repeatedSteps.length > 0) {\n score = Math.max(0, score - opts.repeatedSteps.length * 0.1);\n }\n\n return {\n score: roundToTwoDecimals(Math.max(0, Math.min(1, score))),\n matchedSteps,\n totalExpectedSteps: expected.steps.length,\n totalActualSteps: actual.steps.length,\n missingSteps,\n extraSteps,\n outOfOrderSteps,\n repeatedSteps: opts.repeatedSteps,\n };\n}\n\n/**\n * Fields on each ExpectedStep variant that are comparable data (not structural).\n * Used by `expectedStepMatches` to know which fields to compare when `compareData` is true.\n */\nconst COMPARABLE_FIELDS_BY_TYPE: Record = {\n tool_call: ['toolArgs', 'toolResult', 'success'],\n mcp_tool_call: ['toolArgs', 'toolResult', 'mcpServer', 'success'],\n model_generation: ['modelId', 'promptTokens', 'completionTokens', 'finishReason'],\n agent_run: ['agentId'],\n workflow_step: ['stepId', 'status', 'output'],\n workflow_run: ['workflowId', 'status'],\n workflow_conditional: ['conditionCount', 'selectedSteps'],\n workflow_parallel: ['branchCount', 'parallelSteps'],\n workflow_loop: ['loopType', 'totalIterations'],\n workflow_sleep: ['sleepDurationMs', 'sleepType'],\n workflow_wait_event: ['eventName', 'eventReceived'],\n processor_run: ['processorId'],\n};\n\n/**\n * Check if an actual TrajectoryStep matches an ExpectedStep.\n * Matches by name, optionally by stepType, and auto-compares any variant-specific\n * fields that are present on the expected step.\n */\nfunction expectedStepMatches(actual: TrajectoryStep, expected: ExpectedStep): boolean {\n if (actual.name !== expected.name) return false;\n if (expected.stepType && actual.stepType !== expected.stepType) return false;\n\n if (expected.stepType) {\n const fields = COMPARABLE_FIELDS_BY_TYPE[expected.stepType] ?? [];\n for (const field of fields) {\n const expectedVal = (expected as any)[field];\n if (expectedVal === undefined) continue; // field not specified in expectation, skip\n const actualVal = (actual as any)[field];\n if (actualVal === undefined) return false;\n try {\n if (JSON.stringify(actualVal) !== JSON.stringify(expectedVal)) return false;\n } catch {\n return false;\n }\n }\n }\n\n return true;\n}\n\nfunction compareUnorderedPresence(\n actual: Trajectory,\n expected: { steps: ExpectedStep[] },\n opts: { allowRepeatedSteps: boolean; repeatedSteps: string[] },\n): TrajectoryComparisonResult {\n const actualNames: string[] = actual.steps.map((s: TrajectoryStep) => s.name);\n const expectedNames: string[] = expected.steps.map((s: ExpectedStep) => s.name);\n\n let matchedSteps = 0;\n const matchedExpectedIndices = new Set();\n const usedIndices = new Set();\n for (let i = 0; i < expected.steps.length; i++) {\n const expectedStep = expected.steps[i]!;\n for (let j = 0; j < actual.steps.length; j++) {\n if (!usedIndices.has(j) && expectedStepMatches(actual.steps[j]!, expectedStep)) {\n matchedSteps++;\n matchedExpectedIndices.add(i);\n usedIndices.add(j);\n break;\n }\n }\n }\n\n // Missing steps = expected steps that were not matched (accounts for stepType/data mismatches)\n const missingSteps = expectedNames.filter((_, i) => !matchedExpectedIndices.has(i));\n const expectedSet = new Set(expectedNames);\n const extraSteps = actualNames.filter(name => !expectedSet.has(name));\n\n let score = matchedSteps / expected.steps.length;\n\n // Penalize repeated steps if not allowed\n if (!opts.allowRepeatedSteps && opts.repeatedSteps.length > 0) {\n score = Math.max(0, score - opts.repeatedSteps.length * 0.1);\n }\n\n return {\n score: roundToTwoDecimals(Math.max(0, Math.min(1, score))),\n matchedSteps,\n totalExpectedSteps: expected.steps.length,\n totalActualSteps: actual.steps.length,\n missingSteps,\n extraSteps,\n outOfOrderSteps: [], // ordering not checked in unordered mode\n repeatedSteps: opts.repeatedSteps,\n };\n}\n\n// ─── Efficiency evaluation ───\n\n/**\n * Result of checking trajectory efficiency.\n */\nexport type TrajectoryEfficiencyResult = {\n /** Overall efficiency score from 0 to 1 */\n score: number;\n /** Total number of steps taken */\n totalSteps: number;\n /** Whether the step budget was exceeded */\n overStepBudget: boolean;\n /** Total tokens used across model_generation steps */\n totalTokens: number;\n /** Whether the token budget was exceeded */\n overTokenBudget: boolean;\n /** Total duration in milliseconds */\n totalDurationMs: number;\n /** Whether the duration budget was exceeded */\n overDurationBudget: boolean;\n /** Redundant calls detected (same tool + same args consecutively) */\n redundantCalls: Array<{ name: string; index: number }>;\n};\n\n/**\n * Evaluate trajectory efficiency against budgets and redundancy checks.\n */\nexport function checkTrajectoryEfficiency(\n trajectory: Trajectory,\n options: {\n maxSteps?: number;\n maxTotalTokens?: number;\n maxTotalDurationMs?: number;\n noRedundantCalls?: boolean;\n } = {},\n): TrajectoryEfficiencyResult {\n const { maxSteps, maxTotalTokens, maxTotalDurationMs, noRedundantCalls = true } = options;\n\n const totalSteps = trajectory.steps.length;\n\n // Calculate total tokens from model_generation steps\n let totalTokens = 0;\n for (const step of trajectory.steps) {\n if (step.stepType === 'model_generation') {\n totalTokens += (step.promptTokens ?? 0) + (step.completionTokens ?? 0);\n }\n }\n\n // Calculate total duration\n const totalDurationMs =\n trajectory.totalDurationMs ?? trajectory.steps.reduce((sum, s) => sum + (s.durationMs ?? 0), 0);\n\n // Detect redundant calls (same tool name + same args in consecutive calls)\n const redundantCalls: Array<{ name: string; index: number }> = [];\n if (noRedundantCalls) {\n for (let i = 1; i < trajectory.steps.length; i++) {\n const prev = trajectory.steps[i - 1]!;\n const curr = trajectory.steps[i]!;\n if (\n prev.name === curr.name &&\n prev.stepType === curr.stepType &&\n (prev.stepType === 'tool_call' || prev.stepType === 'mcp_tool_call')\n ) {\n const prevArgs = (prev as TrajectoryStep & { toolArgs?: Record }).toolArgs;\n const currArgs = (curr as TrajectoryStep & { toolArgs?: Record }).toolArgs;\n try {\n if (JSON.stringify(prevArgs) === JSON.stringify(currArgs)) {\n redundantCalls.push({ name: curr.name, index: i });\n }\n } catch {\n // If serialization fails, don't flag as redundant\n }\n }\n }\n }\n\n const overStepBudget = maxSteps !== undefined && totalSteps > maxSteps;\n const overTokenBudget = maxTotalTokens !== undefined && totalTokens > maxTotalTokens;\n const overDurationBudget = maxTotalDurationMs !== undefined && totalDurationMs > maxTotalDurationMs;\n\n // Calculate score: each dimension contributes equally\n const dimensions: number[] = [];\n\n if (maxSteps !== undefined) {\n dimensions.push(overStepBudget ? Math.max(0, 1 - (totalSteps - maxSteps) / maxSteps) : 1);\n }\n if (maxTotalTokens !== undefined) {\n dimensions.push(overTokenBudget ? Math.max(0, 1 - (totalTokens - maxTotalTokens) / maxTotalTokens) : 1);\n }\n if (maxTotalDurationMs !== undefined) {\n dimensions.push(\n overDurationBudget ? Math.max(0, 1 - (totalDurationMs - maxTotalDurationMs) / maxTotalDurationMs) : 1,\n );\n }\n if (noRedundantCalls) {\n dimensions.push(redundantCalls.length === 0 ? 1 : Math.max(0, 1 - redundantCalls.length * 0.2));\n }\n\n const score = dimensions.length > 0 ? dimensions.reduce((a, b) => a + b, 0) / dimensions.length : 1;\n\n return {\n score: roundToTwoDecimals(Math.max(0, Math.min(1, score))),\n totalSteps,\n overStepBudget,\n totalTokens,\n overTokenBudget,\n totalDurationMs,\n overDurationBudget,\n redundantCalls,\n };\n}\n\n// ─── Blacklist evaluation ───\n\n/**\n * Result of checking trajectory against a blacklist.\n */\nexport type TrajectoryBlacklistResult = {\n /** Score: 1.0 if clean, 0.0 if any violation found */\n score: number;\n /** Individual blacklisted tools that were found */\n violatedTools: string[];\n /** Blacklisted sequences that were found */\n violatedSequences: string[][];\n};\n\n/**\n * Check if a trajectory violates any blacklist rules.\n * Returns score 0.0 if any violation is found (hard fail).\n */\nexport function checkTrajectoryBlacklist(\n trajectory: Trajectory,\n options: {\n blacklistedTools?: string[];\n blacklistedSequences?: string[][];\n } = {},\n): TrajectoryBlacklistResult {\n const { blacklistedTools = [], blacklistedSequences = [] } = options;\n const violatedTools: string[] = [];\n const violatedSequences: string[][] = [];\n\n const stepNames = trajectory.steps.map(s => s.name);\n\n // Check blacklisted tools\n for (const forbidden of blacklistedTools) {\n if (stepNames.includes(forbidden)) {\n violatedTools.push(forbidden);\n }\n }\n\n // Check blacklisted sequences (contiguous subsequences)\n for (const sequence of blacklistedSequences) {\n if (sequence.length === 0) continue;\n for (let i = 0; i <= stepNames.length - sequence.length; i++) {\n let match = true;\n for (let j = 0; j < sequence.length; j++) {\n if (stepNames[i + j] !== sequence[j]) {\n match = false;\n break;\n }\n }\n if (match) {\n violatedSequences.push(sequence);\n break; // Only report each sequence once\n }\n }\n }\n\n const hasViolations = violatedTools.length > 0 || violatedSequences.length > 0;\n\n return {\n score: hasViolations ? 0 : 1,\n violatedTools,\n violatedSequences,\n };\n}\n\n// ─── Tool failure analysis ───\n\n/**\n * A detected tool failure pattern in the trajectory.\n */\nexport type ToolFailurePattern = {\n /** The tool name that experienced failure */\n toolName: string;\n /** Number of consecutive retries (same tool, same or similar args) */\n retryCount: number;\n /** Whether the agent fell back to a different tool after failures */\n fellBackToAlternative: boolean;\n /** The alternative tool used, if any */\n alternativeTool?: string;\n /** Whether any retry eventually succeeded */\n eventuallySucceeded: boolean;\n};\n\n/**\n * Result of analyzing tool failure patterns in a trajectory.\n */\nexport type ToolFailureAnalysisResult = {\n /** Score from 0 to 1 (lower = more failures/retries) */\n score: number;\n /** Tool failure patterns detected */\n patterns: ToolFailurePattern[];\n /** Total number of retries across all tools */\n totalRetries: number;\n /** Tools that exceeded the retry threshold */\n excessiveRetryTools: string[];\n};\n\n/**\n * Analyze tool failure and retry patterns in a trajectory.\n */\nexport function analyzeToolFailures(\n trajectory: Trajectory,\n options: {\n maxRetriesPerTool?: number;\n } = {},\n): ToolFailureAnalysisResult {\n const { maxRetriesPerTool = 2 } = options;\n const patterns: ToolFailurePattern[] = [];\n let totalRetries = 0;\n\n const toolCallSteps = trajectory.steps.filter(s => s.stepType === 'tool_call' || s.stepType === 'mcp_tool_call');\n\n if (toolCallSteps.length === 0) {\n return { score: 1, patterns: [], totalRetries: 0, excessiveRetryTools: [] };\n }\n\n // Group consecutive calls to the same tool as potential retry sequences\n let i = 0;\n while (i < toolCallSteps.length) {\n const currentTool = toolCallSteps[i]!;\n let retryCount = 0;\n let j = i + 1;\n\n // Count consecutive calls to the same tool\n // (toolCallSteps is pre-filtered to tool_call/mcp_tool_call, so no stepType checks needed)\n while (j < toolCallSteps.length && toolCallSteps[j]!.name === currentTool.name) {\n const prevStep = toolCallSteps[j - 1]! as TrajectoryStep & { success?: boolean };\n if (prevStep.success === false) {\n retryCount++;\n }\n j++;\n }\n\n if (retryCount > 0) {\n // Check if agent fell back to a different tool after retries\n const nextDifferentTool = j < toolCallSteps.length ? toolCallSteps[j] : undefined;\n const lastRetry = toolCallSteps[j - 1]! as TrajectoryStep & { success?: boolean };\n const lastSuccess = lastRetry.success !== false;\n\n patterns.push({\n toolName: currentTool.name,\n retryCount,\n fellBackToAlternative: nextDifferentTool !== undefined && !lastSuccess,\n alternativeTool: nextDifferentTool !== undefined && !lastSuccess ? nextDifferentTool.name : undefined,\n eventuallySucceeded: lastSuccess,\n });\n\n totalRetries += retryCount;\n }\n\n i = j;\n }\n\n // Score: penalize excessive retries\n const excessiveRetryTools = patterns.filter(p => p.retryCount > maxRetriesPerTool).map(p => p.toolName);\n\n let score = 1;\n if (toolCallSteps.length > 0) {\n // Each retry beyond the threshold costs more\n const excessRetries = patterns.reduce((sum, p) => sum + Math.max(0, p.retryCount - maxRetriesPerTool), 0);\n score = Math.max(0, 1 - excessRetries * 0.2);\n }\n\n return {\n score: roundToTwoDecimals(Math.max(0, Math.min(1, score))),\n patterns,\n totalRetries,\n excessiveRetryTools,\n };\n}\n"]}