@mastra/core
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Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.
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{\n AnthropicSchemaCompatLayer,\n applyCompatLayer,\n DeepSeekSchemaCompatLayer,\n GoogleSchemaCompatLayer,\n MetaSchemaCompatLayer,\n OpenAIReasoningSchemaCompatLayer,\n OpenAISchemaCompatLayer,\n} from '@mastra/schema-compat';\nimport { zodToJsonSchema } from '@mastra/schema-compat/zod-to-json';\nimport type { CoreMessage, LanguageModel, Schema, StreamObjectOnFinishCallback, StreamTextOnFinishCallback } from 'ai';\nimport { generateObject, generateText, jsonSchema, Output, streamObject, streamText } from 'ai';\nimport type { JSONSchema7 } from 'json-schema';\nimport type { ZodSchema } from 'zod';\nimport { z } from 'zod';\nimport type { MastraPrimitives } from '../../action';\nimport { AISpanType } from '../../ai-tracing';\nimport { MastraBase } from '../../base';\nimport { MastraError, ErrorDomain, ErrorCategory } from '../../error';\nimport type { Mastra } from '../../mastra';\nimport { delay, isZodType } from '../../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.telemetry) {\n 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schemaCompatLayers,\n mode: 'aiSdkSchema',\n });\n }\n\n async __text<Tools extends ToolSet, Z extends ZodSchema | JSONSchema7 | undefined>({\n runId,\n messages,\n maxSteps = 5,\n tools = {},\n temperature,\n toolChoice = 'auto',\n onStepFinish,\n experimental_output,\n telemetry,\n threadId,\n resourceId,\n runtimeContext,\n tracingContext,\n ...rest\n }: GenerateTextWithMessagesArgs<Tools, Z>): Promise<GenerateTextResult<Tools, Z>> {\n const model = this.#model;\n\n this.logger.debug(`[LLM] - Generating text`, {\n runId,\n messages,\n maxSteps,\n threadId,\n resourceId,\n tools: Object.keys(tools),\n });\n\n let schema: z.ZodType<inferOutput<Z>> | Schema<inferOutput<Z>> | undefined = undefined;\n\n if (experimental_output) {\n this.logger.debug('[LLM] - Using experimental output', {\n runId,\n });\n\n if (isZodType(experimental_output)) {\n schema = experimental_output as z.ZodType<inferOutput<Z>>;\n if (schema instanceof z.ZodArray) {\n schema = schema._def.type as z.ZodType<inferOutput<Z>>;\n }\n\n let jsonSchemaToUse;\n jsonSchemaToUse = zodToJsonSchema(schema, 'jsonSchema7') as JSONSchema7;\n\n schema = jsonSchema(jsonSchemaToUse) as Schema<inferOutput<Z>>;\n } else {\n schema = jsonSchema(experimental_output as JSONSchema7) as Schema<inferOutput<Z>>;\n }\n }\n\n const llmSpan = tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: AISpanType.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 });\n\n const argsForExecute: OriginalGenerateTextOptions<Tools, Z> = {\n ...rest,\n messages,\n model,\n temperature,\n tools: {\n ...(tools as 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'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('[LLM] - 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 });\n await delay(10 * 1000);\n }\n },\n experimental_telemetry: {\n ...this.experimental_telemetry,\n ...telemetry,\n },\n experimental_output: schema\n ? Output.object({\n schema,\n })\n : undefined,\n };\n\n try {\n const result: GenerateTextResult<Tools, Z> = await generateText(argsForExecute);\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 warnings: result.warnings,\n },\n attributes: {\n finishReason: result.finishReason,\n usage: 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 llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n }\n\n async __textObject<Z extends ZodSchema | JSONSchema7>({\n messages,\n structuredOutput,\n runId,\n telemetry,\n threadId,\n resourceId,\n runtimeContext,\n tracingContext,\n ...rest\n }: GenerateObjectWithMessagesArgs<Z>): Promise<GenerateObjectResult<Z>> {\n const model = this.#model;\n\n this.logger.debug(`[LLM] - Generating a text object`, { runId });\n\n const llmSpan = tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: AISpanType.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 });\n\n try {\n let output: 'object' | 'array' = 'object';\n if (structuredOutput instanceof z.ZodArray) {\n output = 'array';\n structuredOutput = structuredOutput._def.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: OriginalGenerateObjectOptions<Z> = {\n ...rest,\n messages,\n model,\n // @ts-expect-error - output in our implementation can only be object or array\n output,\n schema: processedSchema as Schema<Z>,\n experimental_telemetry: {\n ...this.experimental_telemetry,\n ...telemetry,\n },\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 usage: 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 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 llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n }\n\n __stream<Tools extends ToolSet, Z extends ZodSchema | JSONSchema7 | undefined = undefined>({\n messages,\n onStepFinish,\n onFinish,\n maxSteps = 5,\n tools = {},\n runId,\n temperature,\n toolChoice = 'auto',\n experimental_output,\n telemetry,\n threadId,\n resourceId,\n runtimeContext,\n tracingContext,\n ...rest\n }: StreamTextWithMessagesArgs<Tools, Z>): StreamTextResult<Tools, Z> {\n const model = this.#model;\n this.logger.debug(`[LLM] - Streaming text`, {\n runId,\n threadId,\n resourceId,\n messages,\n maxSteps,\n tools: Object.keys(tools || {}),\n });\n\n let schema: z.ZodType<Z> | Schema<Z> | undefined;\n if (experimental_output) {\n this.logger.debug('[LLM] - Using experimental output', {\n runId,\n });\n if (typeof (experimental_output as any).parse === 'function') {\n schema = experimental_output as z.ZodType<Z>;\n if (schema instanceof z.ZodArray) {\n schema = schema._def.type as z.ZodType<Z>;\n }\n } else {\n schema = jsonSchema(experimental_output as JSONSchema7) as Schema<Z>;\n }\n }\n\n const llmSpan = tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: AISpanType.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 });\n\n const argsForExecute: OriginalStreamTextOptions<Tools, Z> = {\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('[LLM] - 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 });\n await delay(10 * 1000);\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 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_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('[LLM] - 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 ...rest,\n messages,\n experimental_telemetry: {\n ...this.experimental_telemetry,\n ...telemetry,\n },\n experimental_output: schema\n ? (Output.object({\n schema,\n }) as any)\n : undefined,\n };\n\n try {\n return 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 llmSpan?.error({ error: mastraError });\n throw mastraError;\n }\n }\n\n __streamObject<T extends ZodSchema | JSONSchema7>({\n messages,\n runId,\n runtimeContext,\n threadId,\n resourceId,\n onFinish,\n structuredOutput,\n telemetry,\n tracingContext,\n ...rest\n }: StreamObjectWithMessagesArgs<T>): StreamObjectResult<T> {\n const model = this.#model;\n this.logger.debug(`[LLM] - Streaming structured output`, {\n runId,\n messages,\n });\n\n const llmSpan = tracingContext.currentSpan?.createChildSpan({\n name: `llm: '${model.modelId}'`,\n type: AISpanType.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 });\n\n try {\n let output: 'object' | 'array' = 'object';\n if (structuredOutput instanceof z.ZodArray) {\n output = 'array';\n structuredOutput = structuredOutput._def.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<T> = {\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('[LLM] - Object Stream Finished:', {\n usage: props?.usage,\n runId,\n threadId,\n resourceId,\n });\n },\n messages,\n // @ts-expect-error - output in our implementation can only be object or array\n output,\n experimental_telemetry: {\n ...this.experimental_telemetry,\n ...telemetry,\n },\n schema: processedSchema as Schema<inferOutput<T>>,\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 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 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 {\n output,\n ...rest\n }: Omit<\n Output extends undefined\n ? GenerateTextWithMessagesArgs<Tools, StructuredOutput>\n : Omit<GenerateObjectWithMessagesArgs<NonNullable<Output>>, 'structuredOutput' | 'output'>,\n 'messages'\n > & { output?: Output },\n ): Promise<GenerateReturn<Tools, Output, StructuredOutput>> {\n const msgs = this.convertToMessages(messages);\n\n if (!output) {\n const { maxSteps, onStepFinish, ...textOptions } = rest as Omit<\n GenerateTextWithMessagesArgs<Tools, StructuredOutput>,\n 'messages'\n >;\n return (await this.__text<Tools, StructuredOutput>({\n messages: msgs,\n maxSteps,\n onStepFinish,\n ...textOptions,\n })) as unknown as GenerateReturn<Tools, Output, StructuredOutput>;\n }\n\n return (await this.__textObject({\n messages: msgs,\n structuredOutput: output as NonNullable<Output>,\n ...rest,\n })) as unknown as GenerateReturn<Tools, Output, StructuredOutput>;\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 {\n maxSteps = 5,\n output,\n onFinish,\n ...rest\n }: Omit<\n Output extends undefined\n ? StreamTextWithMessagesArgs<Tools, StructuredOutput>\n : Omit<StreamObjectWithMessagesArgs<NonNullable<Output>>, 'structuredOutput' | 'output'> & { maxSteps?: never },\n 'messages'\n > & { output?: Output },\n ): StreamReturn<Tools, Output, StructuredOutput> {\n const msgs = this.convertToMessages(messages);\n\n if (!output) {\n return this.__stream({\n messages: msgs,\n maxSteps,\n onFinish: onFinish as StreamTextOnFinishCallback<Tools> | undefined,\n ...rest,\n }) as unknown as StreamReturn<Tools, Output, StructuredOutput>;\n }\n\n return this.__streamObject({\n messages: msgs,\n structuredOutput: output as NonNullable<Output>,\n onFinish: onFinish as StreamObjectOnFinishCallback<inferOutput<Output>> | undefined,\n ...rest,\n }) as unknown as StreamReturn<Tools, Output, StructuredOutput>;\n }\n}\n"]}