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if (!part || typeof part !== 'object') {\n return null;\n }\n\n switch (part.type) {\n case 'text':\n return (part as AISDKTextPart).text || null;\n\n case 'image':\n // Represent image content with a placeholder\n return '[image]';\n\n case 'file': {\n // Represent file content with filename if available\n const filePart = part as AISDKFilePart;\n if (filePart.filename || filePart.name) {\n return `[file: ${filePart.filename || filePart.name}]`;\n }\n return '[file]';\n }\n\n case 'reasoning': {\n // Represent reasoning/thinking content\n const reasoningPart = part as AISDKReasoningPart;\n if (typeof reasoningPart.text === 'string' && reasoningPart.text.length > 0) {\n return `[reasoning: ${reasoningPart.text.substring(0, 100)}${reasoningPart.text.length > 100 ? '...' : ''}]`;\n }\n return '[reasoning]';\n }\n\n case 'tool-call':\n // Tool calls are handled separately in assistant messages\n return null;\n\n case 'tool-result':\n // Tool results are handled separately in tool messages\n return null;\n\n default: {\n // For unknown types, try to extract any text-like content\n const unknownPart = part as { type?: string; text?: string; content?: string };\n if (typeof unknownPart.text === 'string') {\n return unknownPart.text;\n }\n if (typeof unknownPart.content === 'string') {\n return unknownPart.content;\n }\n // Represent unknown content type\n return `[${unknownPart.type || 'unknown'}]`;\n }\n }\n}\n\n/**\n * Serializes tool result data to a string for OpenAI format.\n */\nfunction serializeToolResult(resultData: unknown): string {\n if (typeof resultData === 'string') {\n return resultData;\n }\n if (resultData && typeof resultData === 'object' && 'value' in resultData) {\n const valueData = (resultData as { value: unknown }).value;\n return typeof valueData === 'string' ? valueData : JSON.stringify(valueData);\n }\n if (resultData === undefined || resultData === null) {\n return '';\n }\n try {\n return JSON.stringify(resultData);\n } catch {\n return '[unserializable result]';\n }\n}\n\n/**\n * Converts AI SDK message format to OpenAI Chat Completion format for Braintrust.\n *\n * Supports both AI SDK v4 and v5 formats:\n * - v4 uses 'args' for tool calls and 'result' for tool results\n * - v5 uses 'input' for tool calls and 'output' for tool results\n *\n * AI SDK format:\n * { role: \"user\", content: [{ type: \"text\", text: \"hello\" }] }\n * { role: \"assistant\", content: [{ type: \"text\", text: \"...\" }, { type: \"tool-call\", toolCallId: \"...\", toolName: \"...\", args: {...} }] }\n * { role: \"tool\", content: [{ type: \"tool-result\", toolCallId: \"...\", result: {...} }] }\n *\n * OpenAI format (what Braintrust expects):\n * { role: \"user\", content: \"hello\" }\n * { role: \"assistant\", content: \"...\", tool_calls: [{ id: \"...\", type: \"function\", function: { name: \"...\", arguments: \"...\" } }] }\n * { role: \"tool\", content: \"result\", tool_call_id: \"...\" }\n */\nexport function convertAISDKMessage(message: AISDKMessage | OpenAIMessage | unknown): OpenAIMessage | unknown {\n if (!message || typeof message !== 'object') {\n return message;\n }\n\n const { role, content, ...rest } = message as AISDKMessage;\n\n // If content is already a string, return as-is (already in OpenAI format)\n if (typeof content === 'string') {\n return message;\n }\n\n // If content is an array (AI SDK format), convert based on role\n if (Array.isArray(content)) {\n // Handle empty content arrays\n if (content.length === 0) {\n return { role, content: '', ...rest };\n }\n\n // For user/system messages, extract text and represent non-text content\n if (role === 'user' || role === 'system') {\n const contentParts = content.map((part: AISDKContentPart) => convertContentPart(part)).filter(Boolean);\n\n return {\n role,\n content: contentParts.length > 0 ? contentParts.join('\\n') : '',\n ...rest,\n };\n }\n\n // For assistant messages, extract text, non-text content, AND tool calls\n if (role === 'assistant') {\n const contentParts = content\n .filter((part: AISDKContentPart) => part?.type !== 'tool-call')\n .map((part: AISDKContentPart) => convertContentPart(part))\n .filter(Boolean);\n\n const toolCallParts = content.filter((part: AISDKContentPart) => part?.type === 'tool-call');\n\n const result: OpenAIMessage = {\n role,\n content: contentParts.length > 0 ? (contentParts as string[]).join('\\n') : '',\n ...rest,\n };\n\n // Add tool_calls array if there are tool calls\n if (toolCallParts.length > 0) {\n result.tool_calls = toolCallParts.map((tc: AISDKContentPart) => {\n const toolCall = tc as AISDKToolCallPart;\n const toolCallId = toolCall.toolCallId;\n const toolName = toolCall.toolName;\n // Support both v4 'args' and v5 'input'\n const args = toolCall.args ?? toolCall.input;\n\n let argsString: string;\n if (typeof args === 'string') {\n argsString = args;\n } else if (args !== undefined && args !== null) {\n argsString = JSON.stringify(args);\n } else {\n argsString = '{}';\n }\n\n return {\n id: toolCallId,\n type: 'function' as const,\n function: {\n name: toolName,\n arguments: argsString,\n },\n };\n });\n }\n\n return result;\n }\n\n // For tool messages, convert to OpenAI tool message format\n if (role === 'tool') {\n const toolResult = content.find((part): part is AISDKToolResultPart => part?.type === 'tool-result');\n if (toolResult) {\n // Support both v4 'result' and v5 'output' fields\n const resultData = toolResult.output ?? toolResult.result;\n const resultContent = serializeToolResult(resultData);\n\n return {\n role: 'tool',\n content: resultContent,\n tool_call_id: toolResult.toolCallId,\n } as OpenAIMessage;\n }\n }\n }\n\n return message;\n}\n","import type { UsageStats } from '@mastra/core/observability';\n\n/**\n * BraintrustUsageMetrics\n *\n * Canonical metric keys expected by Braintrust for LLM usage accounting.\n */\nexport interface BraintrustUsageMetrics {\n prompt_tokens?: number;\n completion_tokens?: number;\n tokens?: number;\n completion_reasoning_tokens?: number;\n prompt_cached_tokens?: number;\n prompt_cache_creation_tokens?: number;\n}\n\n/**\n * Formats UsageStats to Braintrust's expected metric format.\n */\nexport function formatUsageMetrics(usage?: UsageStats): BraintrustUsageMetrics {\n const metrics: BraintrustUsageMetrics = {};\n\n if (usage?.inputTokens !== undefined) {\n metrics.prompt_tokens = usage.inputTokens;\n }\n\n if (usage?.outputTokens !== undefined) {\n metrics.completion_tokens = usage.outputTokens;\n }\n\n // Compute total if we have both\n if (metrics.prompt_tokens !== undefined && metrics.completion_tokens !== undefined) {\n metrics.tokens = metrics.prompt_tokens + metrics.completion_tokens;\n }\n\n if (usage?.outputDetails?.reasoning !== undefined) {\n metrics.completion_reasoning_tokens = usage.outputDetails.reasoning;\n }\n\n if (usage?.inputDetails?.cacheRead !== undefined) {\n metrics.prompt_cached_tokens = usage.inputDetails.cacheRead;\n }\n\n if (usage?.inputDetails?.cacheWrite !== undefined) {\n metrics.prompt_cache_creation_tokens = usage.inputDetails.cacheWrite;\n }\n\n return metrics;\n}\n","/**\n * Thread view reconstruction for Braintrust.\n *\n * Reconstructs LLM output in OpenAI Chat Completion format by examining\n * child MODEL_STEP and TOOL_CALL spans. This enables Braintrust's Thread\n * view to properly display the full conversation flow including tool calls.\n *\n * See THREAD_VIEW_RECONSTRUCTION.md for details.\n */\n\nimport { removeNullish } from './formatter';\nimport type { OpenAIMessage } from './formatter';\n\n// ==============================================================================\n// Thread view reconstruction types\n// ==============================================================================\n\n/**\n * Tool call data accumulated from MODEL_STEP and TOOL_CALL spans\n */\nexport interface ThreadToolCall {\n toolCallId: string;\n toolName: string;\n args: unknown;\n result?: unknown; // filled in when TOOL_CALL ends\n startTime?: Date; // from TOOL_CALL span, for ordering multiple tool calls within a step\n}\n\n/**\n * Step data accumulated from MODEL_STEP spans for Thread view reconstruction\n */\nexport interface ThreadStepData {\n stepSpanId: string;\n stepIndex: number; // for ordering steps correctly\n text?: string;\n toolCalls?: ThreadToolCall[];\n}\n\n/**\n * Accumulated data for reconstructing Braintrust Thread view.\n * Populated for MODEL_GENERATION spans as child MODEL_STEP and TOOL_CALL spans complete.\n */\nexport type ThreadData = ThreadStepData[];\n\n/**\n * Tool result data stored when TOOL_CALL spans end (before MODEL_STEP ends)\n */\nexport interface PendingToolResult {\n result: unknown;\n startTime: Date;\n}\n\n// ==============================================================================\n// Thread view reconstruction\n// ==============================================================================\n\n/**\n * Reconstruct the Thread view output from accumulated threadData.\n * Converts to OpenAI Chat Completion message format.\n */\nexport function reconstructThreadOutput(threadData: ThreadData, originalOutput: unknown): OpenAIMessage[] {\n const messages: OpenAIMessage[] = [];\n\n // Sort steps by stepIndex\n const sortedSteps = [...threadData].sort((a, b) => a.stepIndex - b.stepIndex);\n\n for (const step of sortedSteps) {\n // Sort tool calls by startTime within each step\n const sortedToolCalls = step.toolCalls\n ? [...step.toolCalls].sort((a, b) => {\n if (!a.startTime || !b.startTime) return 0;\n return a.startTime.getTime() - b.startTime.getTime();\n })\n : [];\n\n if (sortedToolCalls.length > 0) {\n // Add assistant message with tool_calls\n messages.push({\n role: 'assistant',\n content: step.text || '',\n tool_calls: sortedToolCalls.map(tc => {\n // Clean null/undefined values from args before stringifying\n const cleanArgs =\n tc.args && typeof tc.args === 'object' && !Array.isArray(tc.args)\n ? removeNullish(tc.args as Record)\n : tc.args;\n return {\n id: tc.toolCallId,\n type: 'function' as const,\n function: {\n name: tc.toolName,\n arguments: typeof cleanArgs === 'string' ? cleanArgs : JSON.stringify(cleanArgs),\n },\n };\n }),\n });\n\n // Add tool messages for each result\n for (const tc of sortedToolCalls) {\n if (tc.result !== undefined) {\n messages.push({\n role: 'tool',\n content: typeof tc.result === 'string' ? tc.result : JSON.stringify(tc.result),\n tool_call_id: tc.toolCallId,\n });\n }\n }\n } else if (step.text) {\n // Step with text only (final response)\n messages.push({\n role: 'assistant',\n content: step.text,\n });\n }\n }\n\n // If we have messages and the last one is a tool message,\n // add the final assistant text from original output\n if (messages.length > 0) {\n const lastMessage = messages[messages.length - 1]!;\n const originalText = (originalOutput as { text?: string })?.text;\n\n // If the last message is a tool response and we have final text, add it\n if (originalText && lastMessage.role === 'tool') {\n messages.push({\n role: 'assistant',\n content: originalText,\n });\n }\n }\n\n return messages;\n}\n","/**\n * Braintrust Exporter for Mastra Observability\n *\n * This exporter sends observability data to Braintrust.\n * Root spans become top-level Braintrust spans (no trace wrapper).\n * Events are handled as zero-duration spans with matching start/end times.\n */\n\nimport type { AnyExportedSpan, ModelGenerationAttributes, ScoreEvent, SpanErrorInfo } from '@mastra/core/observability';\nimport { SpanType } from '@mastra/core/observability';\nimport { omitKeys } from '@mastra/core/utils';\nimport { TrackingExporter } from '@mastra/observability';\nimport type { TraceData, TrackingExporterConfig } from '@mastra/observability';\nimport { initLogger, currentSpan } from 'braintrust';\nimport type { Span, Logger } from 'braintrust';\nimport { removeNullish, convertAISDKMessage } from './formatter';\nimport { formatUsageMetrics } from './metrics';\nimport { reconstructThreadOutput } from './thread-reconstruction';\nimport type { ThreadData, ThreadStepData, PendingToolResult } from './thread-reconstruction';\n\n/**\n * Extended Braintrust span data that includes span type and thread reconstruction data\n */\ninterface BraintrustSpanData {\n span: Span;\n spanType: SpanType;\n threadData?: ThreadData; // only populated for MODEL_GENERATION spans\n // Tool results stored when TOOL_CALL ends (may arrive before MODEL_STEP ends)\n pendingToolResults?: Map; // keyed by toolCallId\n}\n\nexport interface BraintrustExporterConfig extends TrackingExporterConfig {\n /**\n * Optional Braintrust logger instance.\n * When provided, enables integration with Braintrust contexts such as:\n * - Evals: Agent traces nest inside eval task spans\n * - logger.traced(): Agent traces nest inside traced spans\n * - Parent spans: Auto-detects and attaches to external Braintrust spans\n */\n braintrustLogger?: Logger;\n\n /**\n * Optional resolver for the active Braintrust span.\n *\n * Pass Braintrust's `currentSpan` from the same package instance that creates\n * `Eval()` or `logger.traced()` spans when your app and Mastra may resolve\n * different copies of the `braintrust` package.\n */\n currentSpan?: () => Span | undefined;\n\n /** Braintrust API key. Required if logger is not provided. */\n apiKey?: string;\n /** Optional custom endpoint */\n endpoint?: string;\n /** Braintrust project name (default: 'mastra-tracing') */\n projectName?: string;\n /** Support tuning parameters */\n tuningParameters?: Record;\n}\n\ntype BraintrustRoot = Logger | Span;\ntype BraintrustSpan = BraintrustSpanData;\ntype BraintrustEvent = Span;\ntype BraintrustMetadata = unknown;\ntype BraintrustTraceData = TraceData;\n\n// Default span type for all spans\nconst DEFAULT_SPAN_TYPE = 'task';\n\n// Exceptions to the default mapping\nconst SPAN_TYPE_EXCEPTIONS: Partial> = {\n [SpanType.MODEL_GENERATION]: 'llm',\n [SpanType.TOOL_CALL]: 'tool',\n [SpanType.MCP_TOOL_CALL]: 'tool',\n [SpanType.WORKFLOW_CONDITIONAL_EVAL]: 'function',\n [SpanType.WORKFLOW_WAIT_EVENT]: 'function',\n};\n\n// Mapping function - returns valid Braintrust span types\nfunction mapSpanType(spanType: SpanType): 'llm' | 'score' | 'function' | 'eval' | 'task' | 'tool' {\n return (SPAN_TYPE_EXCEPTIONS[spanType] as any) ?? DEFAULT_SPAN_TYPE;\n}\n\nexport class BraintrustExporter extends TrackingExporter<\n BraintrustRoot,\n BraintrustSpan,\n BraintrustEvent,\n BraintrustMetadata,\n BraintrustExporterConfig\n> {\n name = 'braintrust';\n\n // Flags and logger for context-aware mode\n #useProvidedLogger: boolean;\n #providedLogger?: Logger;\n #localLogger?: Logger;\n\n constructor(config: BraintrustExporterConfig = {}) {\n // Resolve env vars BEFORE calling super (config is readonly in base class)\n const resolvedApiKey = config.apiKey ?? process.env.BRAINTRUST_API_KEY;\n const resolvedEndpoint = config.endpoint ?? process.env.BRAINTRUST_ENDPOINT;\n\n super({\n ...config,\n apiKey: resolvedApiKey,\n endpoint: resolvedEndpoint,\n });\n\n this.#useProvidedLogger = !!config.braintrustLogger;\n\n if (this.#useProvidedLogger) {\n // Use provided logger - enables Braintrust context integration\n this.#providedLogger = config.braintrustLogger;\n } else {\n // Validate apiKey for creating loggers per trace\n if (!this.config.apiKey) {\n this.setDisabled(\n `Missing required API key. Set BRAINTRUST_API_KEY environment variable or pass apiKey in config.`,\n );\n return;\n }\n // lazy create logger on first rootSpan\n this.#localLogger = undefined;\n }\n }\n\n private async getLocalLogger(): Promise | undefined> {\n if (this.#localLogger) {\n return this.#localLogger;\n }\n try {\n const logger = await initLogger({\n projectName: this.config.projectName ?? 'mastra-tracing',\n apiKey: this.config.apiKey,\n appUrl: this.config.endpoint,\n ...this.config.tuningParameters,\n });\n this.#localLogger = logger;\n return logger;\n } catch (err) {\n this.logger.error('Braintrust exporter: Failed to initialize logger', { error: err });\n this.setDisabled('Failed to initialize Braintrust logger');\n }\n }\n\n async onScoreEvent(event: ScoreEvent): Promise {\n if (this.isDisabled) return;\n\n const { score } = event;\n const rowId = score.spanId ?? score.traceId;\n if (!rowId) {\n this.logger.debug('Braintrust exporter: skipping score with no spanId or traceId', {\n scorerId: score.scorerId,\n });\n return;\n }\n\n const logger = this.#useProvidedLogger ? this.#providedLogger : await this.getLocalLogger();\n if (!logger) return;\n\n const name = score.scorerName ?? score.scorerId;\n\n try {\n logger.logFeedback({\n id: rowId,\n scores: { [name]: score.score },\n ...(score.reason ? { comment: score.reason } : {}),\n metadata: {\n scorerId: score.scorerId,\n ...(score.scoreSource ? { scoreSource: score.scoreSource } : {}),\n ...(score.metadata ?? {}),\n },\n source: 'external',\n });\n } catch (err) {\n this.logger.error('Braintrust exporter: Failed to submit score', {\n error: err,\n traceId: score.traceId,\n spanId: score.spanId,\n scorerId: score.scorerId,\n });\n }\n }\n\n private startSpan(args: { parent: Span | Logger; span: AnyExportedSpan }): BraintrustSpanData {\n const { parent, span } = args;\n const payload = this.buildSpanPayload(span);\n\n // Braintrust's startSpan() accepts data properties via the `event` parameter\n // which maps to StartSpanEventArgs (ExperimentLogPartialArgs & Partial)\n // This includes: input, output, metadata, metrics, tags, scores, error, etc.\n const braintrustSpan = parent.startSpan({\n spanId: span.id,\n name: span.name,\n type: mapSpanType(span.type),\n startTime: span.startTime.getTime() / 1000,\n event: {\n id: span.id, // Use Mastra span ID as Braintrust row ID for logFeedback() compatibility\n ...payload,\n },\n });\n\n // Create BraintrustSpanData with span type for tree walking\n // Initialize threadData and pendingToolResults for MODEL_GENERATION spans (used for Thread view reconstruction)\n const isModelGeneration = span.type === SpanType.MODEL_GENERATION;\n return {\n span: braintrustSpan,\n spanType: span.type,\n threadData: isModelGeneration ? [] : undefined,\n pendingToolResults: isModelGeneration ? new Map() : undefined,\n };\n }\n\n protected override async _buildRoot(_args: {\n span: AnyExportedSpan;\n traceData: BraintrustTraceData;\n }): Promise {\n if (this.#useProvidedLogger) {\n // Try to find a Braintrust span to attach to:\n // 1. Auto-detect from Braintrust's current span (logger.traced(), Eval(), etc.)\n // 2. Fall back to the configured logger\n let externalSpan: Span | undefined;\n try {\n externalSpan = this.config.currentSpan?.();\n } catch (err) {\n this.logger.error('Braintrust exporter: Failed to resolve configured currentSpan', { error: err });\n }\n externalSpan ??= currentSpan();\n\n // Check if it's a valid span (not the NOOP_SPAN)\n if (externalSpan && externalSpan.id) {\n // External span detected - attach Mastra traces to it\n return externalSpan;\n } else {\n // No external span - use provided logger\n return this.#providedLogger!;\n }\n } else {\n // Use the local logger\n return this.getLocalLogger();\n }\n }\n\n protected override async _buildSpan(args: {\n span: AnyExportedSpan;\n traceData: BraintrustTraceData;\n }): Promise {\n const { span, traceData } = args;\n\n if (span.isRootSpan) {\n const root = traceData.getRoot();\n if (root) {\n return this.startSpan({ parent: root, span });\n }\n } else {\n const parent = traceData.getParent(args);\n if (parent) {\n // Parent could be BraintrustSpanData (has .span) or BraintrustRoot (Logger/Span, no .span)\n const parentSpan = 'span' in parent ? parent.span : parent;\n return this.startSpan({ parent: parentSpan, span });\n }\n }\n }\n\n protected override async _buildEvent(args: {\n span: AnyExportedSpan;\n traceData: BraintrustTraceData;\n }): Promise {\n const spanData = await this._buildSpan(args);\n\n if (!spanData) {\n // parent doesn't exist and not creating rootSpan, return early data\n return;\n }\n\n spanData.span.end({ endTime: args.span.startTime.getTime() / 1000 });\n return spanData.span;\n }\n\n protected override async _updateSpan(args: { span: AnyExportedSpan; traceData: BraintrustTraceData }): Promise {\n const { span, traceData } = args;\n\n const spanData = traceData.getSpan({ spanId: span.id });\n if (!spanData) {\n return;\n }\n spanData.span.log(this.buildSpanPayload(span, false));\n }\n\n protected override async _finishSpan(args: { span: AnyExportedSpan; traceData: BraintrustTraceData }): Promise {\n const { span, traceData } = args;\n\n const spanData = traceData.getSpan({ spanId: span.id });\n if (!spanData) {\n return;\n }\n\n // Handle thread data accumulation for MODEL_STEP and TOOL_CALL spans\n if (span.type === SpanType.MODEL_STEP) {\n this.accumulateModelStepData(span, traceData);\n } else if (span.type === SpanType.TOOL_CALL) {\n this.accumulateToolCallResult(span, traceData);\n }\n\n // Build payload - for MODEL_GENERATION, may reconstruct output from threadData\n const payload =\n span.type === SpanType.MODEL_GENERATION\n ? this.buildModelGenerationPayload(span, spanData)\n : this.buildSpanPayload(span, false);\n\n spanData.span.log(payload);\n\n if (span.endTime) {\n spanData.span.end({ endTime: span.endTime.getTime() / 1000 });\n } else {\n spanData.span.end();\n }\n }\n\n protected override async _abortSpan(args: { span: BraintrustSpan; reason: SpanErrorInfo }): Promise {\n const { span: spanData, reason } = args;\n spanData.span.log({\n error: reason.message,\n metadata: { errorDetails: reason },\n });\n spanData.span.end();\n }\n\n // ==============================================================================\n // Thread view reconstruction helpers\n // ==============================================================================\n\n /**\n * Walk up the tree to find the MODEL_GENERATION ancestor span.\n * Returns the BraintrustSpanData if found, undefined otherwise.\n */\n private findModelGenerationAncestor(spanId: string, traceData: BraintrustTraceData): BraintrustSpanData | undefined {\n let currentId: string | undefined = spanId;\n\n while (currentId) {\n const parentId = traceData.getParentId({ spanId: currentId });\n if (!parentId) return undefined;\n\n const parentSpanData = traceData.getSpan({ spanId: parentId });\n if (parentSpanData?.spanType === SpanType.MODEL_GENERATION) {\n return parentSpanData;\n }\n currentId = parentId;\n }\n\n return undefined;\n }\n\n /**\n * Accumulate MODEL_STEP data to the parent MODEL_GENERATION's threadData.\n * Called when a MODEL_STEP span ends.\n */\n private accumulateModelStepData(span: AnyExportedSpan, traceData: BraintrustTraceData): void {\n const modelGenSpanData = this.findModelGenerationAncestor(span.id, traceData);\n if (!modelGenSpanData?.threadData) {\n return;\n }\n\n // Extract step data from MODEL_STEP output and attributes\n const output = span.output as\n | { text?: string; toolCalls?: Array<{ toolCallId: string; toolName: string; args: unknown }> }\n | undefined;\n const attributes = span.attributes as { stepIndex?: number } | undefined;\n\n const stepData: ThreadStepData = {\n stepSpanId: span.id,\n stepIndex: attributes?.stepIndex ?? 0,\n text: output?.text,\n toolCalls: output?.toolCalls?.map(tc => ({\n toolCallId: tc.toolCallId,\n toolName: tc.toolName,\n args: tc.args,\n })),\n };\n\n modelGenSpanData.threadData.push(stepData);\n }\n\n /**\n * Store TOOL_CALL result in parent MODEL_GENERATION's pendingToolResults.\n * Called when a TOOL_CALL span ends.\n * Results are merged into threadData when MODEL_GENERATION ends.\n */\n private accumulateToolCallResult(span: AnyExportedSpan, traceData: BraintrustTraceData): void {\n const modelGenSpanData = this.findModelGenerationAncestor(span.id, traceData);\n if (!modelGenSpanData?.pendingToolResults) {\n return;\n }\n\n // Extract tool call ID from TOOL_CALL span input\n const input = span.input as { toolCallId?: string } | undefined;\n const toolCallId = input?.toolCallId;\n if (!toolCallId) {\n return;\n }\n\n // Store the result for later merging\n modelGenSpanData.pendingToolResults.set(toolCallId, {\n result: span.output,\n startTime: span.startTime,\n });\n }\n\n /**\n * Build the payload for MODEL_GENERATION span, reconstructing output from threadData if available.\n */\n private buildModelGenerationPayload(span: AnyExportedSpan, spanData: BraintrustSpanData): Record {\n const basePayload = this.buildSpanPayload(span, false);\n\n // Check if we have threadData with tool calls to reconstruct\n const threadData = spanData.threadData;\n if (!threadData || threadData.length === 0) {\n return basePayload;\n }\n\n // Merge pending tool results into threadData\n if (spanData.pendingToolResults && spanData.pendingToolResults.size > 0) {\n for (const step of threadData) {\n if (step.toolCalls) {\n for (const toolCall of step.toolCalls) {\n const pendingResult = spanData.pendingToolResults.get(toolCall.toolCallId);\n if (pendingResult) {\n toolCall.result = pendingResult.result;\n toolCall.startTime = pendingResult.startTime;\n }\n }\n }\n }\n }\n\n // Check if any step has tool calls\n const hasToolCalls = threadData.some(step => step.toolCalls && step.toolCalls.length > 0);\n if (!hasToolCalls) {\n return basePayload;\n }\n\n // Reconstruct output as OpenAI messages\n const reconstructedOutput = reconstructThreadOutput(threadData, span.output);\n return {\n ...basePayload,\n output: reconstructedOutput,\n };\n }\n\n /**\n * Transforms MODEL_GENERATION input to Braintrust Thread view format.\n * Converts AI SDK messages (v4/v5) to OpenAI Chat Completion format, which Braintrust requires\n * for proper rendering of threads (fixes #11023).\n */\n private transformInput(input: unknown, spanType: SpanType): unknown {\n if (spanType === SpanType.MODEL_GENERATION) {\n // If input is already an array of messages, convert AI SDK format to OpenAI format\n if (Array.isArray(input)) {\n return input.map((msg: unknown) => convertAISDKMessage(msg));\n }\n\n // If input has a messages array\n if (\n input &&\n typeof input === 'object' &&\n 'messages' in input &&\n Array.isArray((input as { messages: unknown[] }).messages)\n ) {\n return (input as { messages: unknown[] }).messages.map((msg: unknown) => convertAISDKMessage(msg));\n }\n }\n\n return input;\n }\n\n /**\n * Transforms MODEL_GENERATION output to Braintrust Thread view format.\n */\n private transformOutput(output: any, spanType: SpanType): any {\n if (spanType === SpanType.MODEL_GENERATION) {\n if (!output || typeof output !== 'object') {\n return output;\n }\n const { text, ...rest } = output;\n // Remove null/undefined values from rest to keep Thread view clean\n return { role: 'assistant', content: text, ...removeNullish(rest) };\n }\n\n return output;\n }\n\n private buildSpanPayload(span: AnyExportedSpan, isCreate = true): Record {\n const payload: Record = {};\n\n if (span.input !== undefined) {\n payload.input = this.transformInput(span.input, span.type);\n }\n\n if (span.output !== undefined) {\n payload.output = this.transformOutput(span.output, span.type);\n }\n\n if (isCreate && span.isRootSpan && span.tags?.length) {\n payload.tags = span.tags;\n }\n\n // Initialize metrics and metadata objects\n payload.metrics = {};\n // Spread span.metadata first, then set spanType to prevent accidental override\n payload.metadata = {\n ...span.metadata,\n spanType: span.type,\n };\n\n if (isCreate) {\n payload.metadata['mastra-trace-id'] = span.traceId;\n }\n\n const attributes = (span.attributes ?? {}) as Record;\n\n if (span.type === SpanType.MODEL_GENERATION) {\n const modelAttr = attributes as ModelGenerationAttributes;\n\n // Model goes to metadata\n if (modelAttr.model !== undefined) {\n payload.metadata.model = modelAttr.model;\n }\n\n // Provider goes to metadata (if provided by attributes)\n if (modelAttr.provider !== undefined) {\n payload.metadata.provider = modelAttr.provider;\n }\n\n // Prefer resolved model ID (e.g. \"claude-sonnet-4-5-20250929\") over\n // gateway aliases (e.g. \"claude-sonnet-4.5\") for accurate cost estimation\n if (modelAttr.responseModel !== undefined) {\n payload.metadata.model = modelAttr.responseModel;\n }\n\n // Usage/token info goes to metrics\n payload.metrics = formatUsageMetrics(modelAttr.usage);\n\n // Time to first token (TTFT) for streaming responses\n // Braintrust expects TTFT in seconds (not milliseconds)\n if (modelAttr.completionStartTime) {\n payload.metrics.time_to_first_token =\n (modelAttr.completionStartTime.getTime() - span.startTime.getTime()) / 1000;\n }\n\n // Model parameters go to metadata\n if (modelAttr.parameters !== undefined) {\n payload.metadata.modelParameters = modelAttr.parameters;\n }\n\n // Other LLM attributes go to metadata\n const otherAttributes = omitKeys(attributes, [\n 'model',\n 'responseModel',\n 'usage',\n 'parameters',\n 'completionStartTime',\n ]);\n payload.metadata = {\n ...payload.metadata,\n ...otherAttributes,\n };\n } else {\n // For non-LLM spans, put all attributes in metadata\n payload.metadata = {\n ...payload.metadata,\n ...attributes,\n };\n }\n\n // Handle errors\n if (span.errorInfo) {\n payload.error = span.errorInfo.message;\n payload.metadata.errorDetails = span.errorInfo;\n }\n\n // Clean up empty metrics object\n if (Object.keys(payload.metrics).length === 0) {\n delete payload.metrics;\n }\n\n // Remove null/undefined values from metadata to keep Braintrust UI clean\n payload.metadata = removeNullish(payload.metadata);\n\n return payload;\n }\n}\n"]}