{"version":3,"file":"model-PhgPlg24.cjs","names":["MastraBase","#model","#options","#mastra","OpenAIReasoningSchemaCompatLayer","OpenAISchemaCompatLayer","GoogleSchemaCompatLayer","AnthropicSchemaCompatLayer","DeepSeekSchemaCompatLayer","MetaSchemaCompatLayer","resolveObservabilityContext","isZodType","isZodArray","getZodDef","MastraError","ErrorDomain","ErrorCategory","delay","Output","executeWithContext","generateText","generateObject","executeWithContextSync","streamText","streamObject"],"sources":["../src/stream/aisdk/v4/usage.ts","../src/llm/model/model.ts"],"sourcesContent":["import type { UsageStats } from '../../../observability';\n\n/**\n * AI SDK v4 LanguageModelUsage type definition\n * (matches the ai package's LanguageModelUsage)\n */\ninterface LanguageModelUsageV4 {\n promptTokens: number;\n completionTokens: number;\n totalTokens?: number;\n}\n\n/**\n * Converts AI SDK v4 LanguageModelUsage to our UsageStats format.\n *\n * @param usage - The LanguageModelUsage from AI SDK v4\n * @returns Normalized UsageStats\n */\nexport function convertV4Usage(usage: LanguageModelUsageV4 | undefined): UsageStats {\n if (!usage) {\n return {};\n }\n\n return {\n inputTokens: usage.promptTokens,\n outputTokens: usage.completionTokens,\n };\n}\n","import { generateObject, generateText, Output, streamObject, streamText } from '@internal/ai-sdk-v4';\nimport type {\n CoreMessage,\n LanguageModelV1 as LanguageModel,\n StreamObjectOnFinishCallback,\n StreamTextOnFinishCallback,\n} from '@internal/ai-sdk-v4';\nimport {\n AnthropicSchemaCompatLayer,\n applyCompatLayer,\n DeepSeekSchemaCompatLayer,\n GoogleSchemaCompatLayer,\n MetaSchemaCompatLayer,\n OpenAIReasoningSchemaCompatLayer,\n OpenAISchemaCompatLayer,\n jsonSchema,\n} from '@mastra/schema-compat';\nimport type { JSONSchema7, Schema } from '@mastra/schema-compat';\nimport type { z } from 'zod/v4';\nimport type { MastraPrimitives } from '../../action';\nimport { MastraBase } from '../../base';\nimport { MastraError, ErrorDomain, ErrorCategory } from '../../error';\nimport type { Mastra } from '../../mastra';\nimport { SpanType, resolveObservabilityContext } from '../../observability';\nimport { executeWithContext, executeWithContextSync } from '../../observability/utils';\nimport { toStandardSchema, standardSchemaToJSONSchema, isStandardSchemaWithJSON } from '../../schema';\nimport type { ZodSchema } from '../../schema';\nimport { convertV4Usage } from '../../stream/aisdk/v4/usage';\nimport { delay, isZodType } from '../../utils';\nimport { isZodArray, getZodDef } from '../../utils/zod-utils';\n\nimport type {\n GenerateObjectWithMessagesArgs,\n GenerateTextResult,\n GenerateObjectResult,\n GenerateTextWithMessagesArgs,\n OriginalGenerateTextOptions,\n ToolSet,\n GenerateReturn,\n OriginalGenerateObjectOptions,\n StreamTextWithMessagesArgs,\n StreamTextResult,\n OriginalStreamTextOptions,\n StreamObjectWithMessagesArgs,\n OriginalStreamObjectOptions,\n StreamObjectResult,\n StreamReturn,\n} from './base.types';\nimport type { inferOutput, MastraModelOptions } from './shared.types';\n\nexport class MastraLLMV1 extends MastraBase {\n #model: LanguageModel;\n #mastra?: Mastra;\n #options?: MastraModelOptions;\n\n constructor({ model, mastra, options }: { model: LanguageModel; mastra?: Mastra; options?: MastraModelOptions }) {\n super({ name: 'aisdk' });\n\n this.#model = model;\n this.#options = options;\n\n if (mastra) {\n this.#mastra = mastra;\n if (mastra.getLogger()) {\n this.__setLogger(this.#mastra.getLogger());\n }\n }\n }\n\n __registerPrimitives(p: MastraPrimitives) {\n if (p.logger) {\n this.__setLogger(p.logger);\n }\n }\n\n __registerMastra(p: Mastra) {\n this.#mastra = p;\n }\n\n getProvider() {\n return this.#model.provider;\n }\n\n getModelId() {\n return this.#model.modelId;\n }\n\n getModel() {\n return this.#model;\n }\n\n private _applySchemaCompat(schema: ZodSchema | JSONSchema7): Schema {\n const model = this.#model;\n\n const schemaCompatLayers = [];\n\n if (model) {\n const modelInfo = {\n modelId: model.modelId,\n supportsStructuredOutputs: model.supportsStructuredOutputs ?? false,\n provider: model.provider,\n };\n schemaCompatLayers.push(\n new OpenAIReasoningSchemaCompatLayer(modelInfo),\n new OpenAISchemaCompatLayer(modelInfo),\n new GoogleSchemaCompatLayer(modelInfo),\n new AnthropicSchemaCompatLayer(modelInfo),\n new DeepSeekSchemaCompatLayer(modelInfo),\n new MetaSchemaCompatLayer(modelInfo),\n );\n }\n\n // \"Type instantiation is excessively deep\" error from complex ZodSchema generic inference\n return applyCompatLayer({\n schema: schema as any,\n compatLayers: schemaCompatLayers,\n mode: 'aiSdkSchema',\n });\n }\n\n async __text({\n runId,\n messages,\n maxSteps = 5,\n tools = {},\n temperature,\n toolChoice = 'auto',\n onStepFinish,\n experimental_output,\n threadId,\n resourceId,\n requestContext,\n ...rest\n }: GenerateTextWithMessagesArgs): Promise> {\n const model = this.#model;\n const observabilityContext = resolveObservabilityContext(rest);\n\n this.logger.debug('Generating text', {\n runId,\n messages,\n maxSteps,\n threadId,\n resourceId,\n tools: Object.keys(tools),\n });\n\n let schema: z.ZodType> | Schema> | undefined = undefined;\n\n if (experimental_output) {\n this.logger.debug('Using experimental output', {\n runId,\n });\n\n if (isZodType(experimental_output)) {\n schema = experimental_output as z.ZodType>;\n if (isZodArray(schema)) {\n schema = getZodDef(schema).type as z.ZodType>;\n }\n\n // Convert Zod schema to JSON Schema via standard schema interface\n const standardSchema = toStandardSchema(schema as any);\n const jsonSchemaToUse = standardSchemaToJSONSchema(standardSchema);\n\n schema = jsonSchema>(jsonSchemaToUse);\n } else {\n schema = jsonSchema>(experimental_output);\n }\n }\n\n // make json schema a ai sdk schema\n if (tools && Object.keys(tools).length > 0) {\n for (const tool of Object.values(tools)) {\n if (tool.parameters) {\n if ('validate' in tool.parameters) {\n tool.parameters = tool.parameters;\n } else if (isStandardSchemaWithJSON(tool.parameters)) {\n tool.parameters = jsonSchema(standardSchemaToJSONSchema(tool.parameters));\n } else {\n tool.parameters = jsonSchema(tool.parameters);\n }\n }\n }\n }\n\n const llmSpan = observabilityContext.tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: SpanType.MODEL_GENERATION,\n input: {\n messages,\n schema,\n },\n attributes: {\n model: model.modelId,\n provider: model.provider,\n parameters: {\n temperature,\n maxOutputTokens: rest.maxTokens,\n topP: rest.topP,\n frequencyPenalty: rest.frequencyPenalty,\n presencePenalty: rest.presencePenalty,\n },\n streaming: false,\n },\n metadata: {\n runId,\n threadId,\n resourceId,\n },\n tracingPolicy: this.#options?.tracingPolicy,\n requestContext,\n });\n\n const argsForExecute: OriginalGenerateTextOptions = {\n ...rest,\n messages,\n model,\n temperature,\n tools: {\n ...(tools as Tools),\n },\n toolChoice,\n maxSteps,\n onStepFinish: async props => {\n try {\n await onStepFinish?.({ ...props, runId: runId! });\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_TEXT_ON_STEP_FINISH_CALLBACK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.USER,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n finishReason: props?.finishReason,\n toolCalls: props?.toolCalls ? JSON.stringify(props.toolCalls) : '',\n toolResults: props?.toolResults ? JSON.stringify(props.toolResults) : '',\n usage: props?.usage ? JSON.stringify(props.usage) : '',\n },\n },\n e,\n );\n throw mastraError;\n }\n\n this.logger.debug('Text step change', {\n text: props?.text,\n toolCalls: props?.toolCalls,\n toolResults: props?.toolResults,\n finishReason: props?.finishReason,\n usage: props?.usage,\n runId,\n });\n\n const remainingTokens = parseInt(props?.response?.headers?.['x-ratelimit-remaining-tokens'] ?? '', 10);\n if (!isNaN(remainingTokens) && remainingTokens > 0 && remainingTokens < 2000) {\n this.logger.warn('Rate limit approaching, waiting 10 seconds', { runId, remainingTokens });\n const rateLimitSpan = llmSpan?.createChildSpan({\n name: 'rate-limit-sleep',\n type: SpanType.GENERIC,\n metadata: { remainingTokens, delayMs: 10_000 },\n });\n await delay(10 * 1000);\n rateLimitSpan?.end();\n }\n },\n experimental_output: schema\n ? Output.object({\n schema,\n })\n : undefined,\n };\n\n try {\n const result: GenerateTextResult = await executeWithContext({\n span: llmSpan,\n fn: () => generateText(argsForExecute),\n });\n\n if (schema && result.finishReason === 'stop') {\n result.object = (result as any).experimental_output;\n }\n llmSpan?.end({\n output: {\n text: result.text,\n object: result.object,\n reasoning: result.reasoningDetails,\n reasoningText: result.reasoning,\n files: result.files,\n sources: result.sources,\n toolCalls: result.toolCalls,\n warnings: result.warnings,\n },\n attributes: {\n finishReason: result.finishReason,\n responseId: result.response?.id,\n responseModel: result.response?.modelId,\n usage: convertV4Usage(result.usage),\n },\n });\n\n return result;\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_GENERATE_TEXT_AI_SDK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.THIRD_PARTY,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n e,\n );\n this.logger.error('Generate text failed', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n }\n\n async __textObject({\n messages,\n structuredOutput,\n runId,\n threadId,\n resourceId,\n requestContext,\n ...rest\n }: GenerateObjectWithMessagesArgs): Promise> {\n const model = this.#model;\n const observabilityContext = resolveObservabilityContext(rest);\n\n this.logger.debug('Generating text object', { runId });\n\n const llmSpan = observabilityContext.tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: SpanType.MODEL_GENERATION,\n input: {\n messages,\n },\n attributes: {\n model: model.modelId,\n provider: model.provider,\n parameters: {\n temperature: rest.temperature,\n maxOutputTokens: rest.maxTokens,\n topP: rest.topP,\n frequencyPenalty: rest.frequencyPenalty,\n presencePenalty: rest.presencePenalty,\n },\n streaming: false,\n },\n metadata: {\n runId,\n threadId,\n resourceId,\n },\n tracingPolicy: this.#options?.tracingPolicy,\n requestContext,\n });\n\n try {\n let output: 'object' | 'array' = 'object';\n if (isZodArray(structuredOutput)) {\n output = 'array';\n const zodDef = getZodDef(structuredOutput);\n if ('element' in zodDef) {\n structuredOutput = zodDef.element;\n } else {\n structuredOutput = zodDef.type;\n }\n }\n\n const processedSchema = this._applySchemaCompat(structuredOutput!);\n llmSpan?.update({\n input: {\n messages,\n schema: processedSchema,\n },\n });\n\n const argsForExecute: OriginalGenerateObjectOptions = {\n ...rest,\n messages,\n model,\n output,\n schema: processedSchema as Schema,\n };\n\n try {\n // @ts-expect-error - output in our implementation can only be object or array\n const result = await generateObject(argsForExecute);\n\n llmSpan?.end({\n output: {\n object: result.object,\n warnings: result.warnings,\n },\n attributes: {\n finishReason: result.finishReason,\n responseId: result.response?.id,\n responseModel: result.response?.modelId,\n usage: convertV4Usage(result.usage),\n },\n });\n\n // @ts-expect-error - output in our implementation can only be object or array\n return result;\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_GENERATE_OBJECT_AI_SDK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.THIRD_PARTY,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n e,\n );\n this.logger.error('Generate object failed', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n } catch (e: unknown) {\n if (e instanceof MastraError) {\n throw e;\n }\n\n const mastraError = new MastraError(\n {\n id: 'LLM_GENERATE_OBJECT_AI_SDK_SCHEMA_CONVERSION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.USER,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n e,\n );\n this.logger.error('Generate object schema conversion failed', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n }\n\n __stream({\n messages,\n onStepFinish,\n onFinish,\n maxSteps = 5,\n tools = {},\n runId,\n temperature,\n toolChoice = 'auto',\n experimental_output,\n threadId,\n resourceId,\n requestContext,\n ...rest\n }: StreamTextWithMessagesArgs): StreamTextResult {\n const model = this.#model;\n const observabilityContext = resolveObservabilityContext(rest);\n\n let schema: z.ZodType | Schema | undefined;\n if (experimental_output) {\n if (typeof (experimental_output as any).parse === 'function') {\n schema = experimental_output as z.ZodType;\n if (isZodArray(schema)) {\n schema = getZodDef(schema).type as z.ZodType;\n }\n } else {\n schema = jsonSchema(experimental_output as JSONSchema7) as Schema;\n }\n }\n\n const llmSpan = observabilityContext.tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: SpanType.MODEL_GENERATION,\n input: {\n messages,\n },\n attributes: {\n model: model.modelId,\n provider: model.provider,\n parameters: {\n temperature,\n maxOutputTokens: rest.maxTokens,\n topP: rest.topP,\n frequencyPenalty: rest.frequencyPenalty,\n presencePenalty: rest.presencePenalty,\n },\n streaming: true,\n },\n metadata: {\n runId,\n threadId,\n resourceId,\n },\n tracingPolicy: this.#options?.tracingPolicy,\n requestContext,\n });\n\n if (llmSpan) {\n executeWithContextSync({\n span: llmSpan,\n fn: () =>\n this.logger.debug('Streaming text', {\n runId,\n threadId,\n resourceId,\n messages,\n maxSteps,\n tools: Object.keys(tools || {}),\n }),\n });\n }\n\n // make json schema a ai sdk schema\n if (tools && Object.keys(tools).length > 0) {\n for (const tool of Object.values(tools)) {\n if (tool.parameters) {\n if ('validate' in tool.parameters) {\n tool.parameters = tool.parameters;\n } else if (isStandardSchemaWithJSON(tool.parameters)) {\n tool.parameters = jsonSchema(standardSchemaToJSONSchema(tool.parameters));\n } else {\n tool.parameters = jsonSchema(tool.parameters);\n }\n }\n }\n }\n\n const argsForExecute: OriginalStreamTextOptions = {\n model,\n temperature,\n tools: {\n ...(tools as Tools),\n },\n maxSteps,\n toolChoice,\n onStepFinish: async props => {\n try {\n await onStepFinish?.({ ...props, runId: runId! });\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_ON_STEP_FINISH_CALLBACK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.USER,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n finishReason: props?.finishReason,\n toolCalls: props?.toolCalls ? JSON.stringify(props.toolCalls) : '',\n toolResults: props?.toolResults ? JSON.stringify(props.toolResults) : '',\n usage: props?.usage ? JSON.stringify(props.usage) : '',\n },\n },\n e,\n );\n this.logger.trackException(mastraError);\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n\n this.logger.debug('Stream step change', {\n text: props?.text,\n toolCalls: props?.toolCalls,\n toolResults: props?.toolResults,\n finishReason: props?.finishReason,\n usage: props?.usage,\n runId,\n });\n\n const remainingTokens = parseInt(props?.response?.headers?.['x-ratelimit-remaining-tokens'] ?? '', 10);\n if (!isNaN(remainingTokens) && remainingTokens > 0 && remainingTokens < 2000) {\n this.logger.warn('Rate limit approaching, waiting 10 seconds', { runId, remainingTokens });\n const rateLimitSpan = llmSpan?.createChildSpan({\n name: 'rate-limit-sleep',\n type: SpanType.GENERIC,\n metadata: { remainingTokens, delayMs: 10_000 },\n });\n await delay(10 * 1000);\n rateLimitSpan?.end();\n }\n },\n onFinish: async props => {\n // End the model generation span BEFORE calling the user's onFinish callback\n // This ensures the model span ends before the agent span\n llmSpan?.end({\n output: {\n text: props?.text,\n reasoning: props?.reasoningDetails,\n reasoningText: props?.reasoning,\n files: props?.files,\n sources: props?.sources,\n toolCalls: props?.toolCalls,\n warnings: props?.warnings,\n },\n attributes: {\n finishReason: props?.finishReason,\n usage: convertV4Usage(props?.usage),\n },\n });\n\n try {\n await onFinish?.({ ...props, runId: runId! });\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_ON_FINISH_CALLBACK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.USER,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n finishReason: props?.finishReason,\n toolCalls: props?.toolCalls ? JSON.stringify(props.toolCalls) : '',\n toolResults: props?.toolResults ? JSON.stringify(props.toolResults) : '',\n usage: props?.usage ? JSON.stringify(props.usage) : '',\n },\n },\n e,\n );\n llmSpan?.error({ error: mastraError });\n this.logger.trackException(mastraError);\n throw mastraError;\n }\n\n this.logger.debug('Stream finished', {\n text: props?.text,\n toolCalls: props?.toolCalls,\n toolResults: props?.toolResults,\n finishReason: props?.finishReason,\n usage: props?.usage,\n runId,\n threadId,\n resourceId,\n });\n },\n onError: ({ error }) => {\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_TEXT_AI_SDK_STREAMING_ERROR',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.THIRD_PARTY,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n error,\n );\n this.logger.error('Stream text error', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n },\n ...rest,\n messages,\n experimental_output: schema\n ? (Output.object({\n schema,\n }) as any)\n : undefined,\n };\n\n try {\n return executeWithContextSync({ span: llmSpan, fn: () => streamText(argsForExecute) });\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_TEXT_AI_SDK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.THIRD_PARTY,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n e,\n );\n this.logger.error('Stream text failed', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n }\n\n __streamObject({\n messages,\n runId,\n requestContext,\n threadId,\n resourceId,\n onFinish,\n structuredOutput,\n ...rest\n }: StreamObjectWithMessagesArgs): StreamObjectResult {\n const model = this.#model;\n const observabilityContext = resolveObservabilityContext(rest);\n\n this.logger.debug('Streaming structured output', {\n runId,\n messages,\n });\n\n const llmSpan = observabilityContext.tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: SpanType.MODEL_GENERATION,\n input: {\n messages,\n },\n attributes: {\n model: model.modelId,\n provider: model.provider,\n parameters: {\n temperature: rest.temperature,\n maxOutputTokens: rest.maxTokens,\n topP: rest.topP,\n frequencyPenalty: rest.frequencyPenalty,\n presencePenalty: rest.presencePenalty,\n },\n streaming: true,\n },\n metadata: {\n runId,\n threadId,\n resourceId,\n },\n tracingPolicy: this.#options?.tracingPolicy,\n requestContext,\n });\n\n try {\n let output: 'object' | 'array' = 'object';\n if (isZodArray(structuredOutput)) {\n output = 'array';\n structuredOutput = getZodDef(structuredOutput).type;\n }\n\n const processedSchema = this._applySchemaCompat(structuredOutput!);\n llmSpan?.update({\n input: {\n messages,\n schema: processedSchema,\n },\n });\n\n const argsForExecute: OriginalStreamObjectOptions = {\n ...rest,\n model,\n onFinish: async (props: any) => {\n // End the model generation span BEFORE calling the user's onFinish callback\n // This ensures the model span ends before the agent span\n llmSpan?.end({\n output: {\n text: props?.text,\n object: props?.object,\n reasoning: props?.reasoningDetails,\n reasoningText: props?.reasoning,\n files: props?.files,\n sources: props?.sources,\n warnings: props?.warnings,\n },\n attributes: {\n finishReason: props?.finishReason,\n usage: props?.usage,\n },\n });\n\n try {\n await onFinish?.({ ...props, runId: runId! });\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_OBJECT_ON_FINISH_CALLBACK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.USER,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n toolCalls: '',\n toolResults: '',\n finishReason: '',\n usage: props?.usage ? JSON.stringify(props.usage) : '',\n },\n },\n e,\n );\n this.logger.trackException(mastraError);\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n\n this.logger.debug('Object stream finished', {\n usage: props?.usage,\n runId,\n threadId,\n resourceId,\n });\n },\n onError: ({ error }) => {\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_OBJECT_AI_SDK_STREAMING_ERROR',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.THIRD_PARTY,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n error,\n );\n this.logger.error('Stream object error', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n },\n messages,\n output,\n schema: processedSchema as Schema>,\n };\n\n try {\n return streamObject(argsForExecute as any);\n } catch (e: unknown) {\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_OBJECT_AI_SDK_EXECUTION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.THIRD_PARTY,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n e,\n );\n this.logger.error('Stream object failed', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n } catch (e: unknown) {\n if (e instanceof MastraError) {\n llmSpan?.error({ error: e });\n throw e;\n }\n\n const mastraError = new MastraError(\n {\n id: 'LLM_STREAM_OBJECT_AI_SDK_SCHEMA_CONVERSION_FAILED',\n domain: ErrorDomain.LLM,\n category: ErrorCategory.USER,\n details: {\n modelId: model.modelId,\n modelProvider: model.provider,\n runId: runId ?? 'unknown',\n threadId: threadId ?? 'unknown',\n resourceId: resourceId ?? 'unknown',\n },\n },\n e,\n );\n this.logger.error('Stream object schema conversion failed', {\n error: mastraError,\n runId,\n threadId,\n resourceId,\n modelId: model.modelId,\n modelProvider: model.provider,\n });\n llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n }\n\n convertToMessages(messages: string | string[] | CoreMessage[]): CoreMessage[] {\n if (Array.isArray(messages)) {\n return messages.map(m => {\n if (typeof m === 'string') {\n return {\n role: 'user',\n content: m,\n };\n }\n return m;\n });\n }\n\n return [\n {\n role: 'user',\n content: messages,\n },\n ];\n }\n\n async generate<\n Output extends ZodSchema | JSONSchema7 | undefined = undefined,\n StructuredOutput extends ZodSchema | JSONSchema7 | undefined = undefined,\n Tools extends ToolSet = ToolSet,\n >(\n messages: string | string[] | CoreMessage[],\n args?: Omit<\n Output extends undefined\n ? GenerateTextWithMessagesArgs\n : Omit>, 'structuredOutput' | 'output'>,\n 'messages'\n > & { output?: Output },\n ): Promise> {\n const msgs = this.convertToMessages(messages);\n const { output, ...rest } = args ?? ({} as NonNullable);\n\n if (!output) {\n return (await this.__text({\n messages: msgs,\n ...(rest as unknown as Omit, 'messages'>),\n })) as GenerateReturn;\n }\n\n return (await this.__textObject({\n messages: msgs,\n structuredOutput: output as NonNullable,\n ...(rest as unknown as Omit<\n GenerateObjectWithMessagesArgs>,\n 'messages' | 'structuredOutput'\n >),\n })) as GenerateReturn;\n }\n\n stream<\n Output extends ZodSchema | JSONSchema7 | undefined = undefined,\n StructuredOutput extends ZodSchema | JSONSchema7 | undefined = undefined,\n Tools extends ToolSet = ToolSet,\n >(\n messages: string | string[] | CoreMessage[],\n args?: Omit<\n Output extends undefined\n ? StreamTextWithMessagesArgs\n : Omit>, 'structuredOutput' | 'output'> & { maxSteps?: never },\n 'messages'\n > & { output?: Output },\n ): StreamReturn {\n const msgs = this.convertToMessages(messages);\n const { output, ...rest } = args ?? 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