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type { CoreMessage } from 'ai';\nimport type { JSONSchema7 } from 'json-schema';\nimport type { z, ZodSchema } from 'zod';\n\nimport type { MastraPrimitives } from '../../action';\nimport { MastraBase } from '../../base';\nimport { RegisteredLogger } from '../../logger';\nimport type { Mastra } from '../../mastra';\nimport type {\n GenerateTextWithMessagesArgs,\n GenerateTextResult,\n ToolSet,\n GenerateReturn,\n GenerateObjectResult,\n GenerateObjectWithMessagesArgs,\n StreamReturn,\n StreamTextWithMessagesArgs,\n StreamTextResult,\n StreamObjectResult,\n StreamObjectWithMessagesArgs,\n} from './base.types';\n\nexport abstract class MastraLLMBase extends MastraBase {\n constructor({ name }: { name: string }) {\n super({\n component: RegisteredLogger.LLM,\n name,\n });\n }\n\n abstract getProvider(): string;\n abstract getModelId(): string;\n\n abstract __registerMastra(p: Mastra): void;\n\n abstract __text(\n input: GenerateTextWithMessagesArgs,\n ): Promise : unknown>>;\n\n abstract __textObject(\n input: GenerateObjectWithMessagesArgs,\n ): Promise>;\n\n abstract 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 options: Omit<\n Output extends undefined\n ? GenerateTextWithMessagesArgs\n : GenerateObjectWithMessagesArgs>,\n 'messages'\n >,\n ): Promise>;\n\n abstract __stream(\n input: StreamTextWithMessagesArgs,\n ): StreamTextResult : unknown>;\n\n abstract __streamObject(\n input: StreamObjectWithMessagesArgs,\n ): StreamObjectResult;\n\n abstract 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 options: Omit<\n Output extends undefined\n ? StreamTextWithMessagesArgs\n : StreamObjectWithMessagesArgs> & { maxSteps?: never },\n 'messages'\n >,\n ): StreamReturn;\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 __registerPrimitives(p: MastraPrimitives) {\n if (p.telemetry) {\n this.__setTelemetry(p.telemetry);\n }\n\n if (p.logger) {\n this.__setLogger(p.logger);\n }\n }\n}\n","import {\n AnthropicSchemaCompatLayer,\n applyCompatLayer,\n DeepSeekSchemaCompatLayer,\n GoogleSchemaCompatLayer,\n MetaSchemaCompatLayer,\n OpenAIReasoningSchemaCompatLayer,\n OpenAISchemaCompatLayer,\n} from '@mastra/schema-compat';\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';\n\nimport type { MastraPrimitives } from '../../action';\nimport { MastraError, ErrorDomain, ErrorCategory } from '../../error';\nimport type { Mastra } from '../../mastra';\nimport { delay } from '../../utils';\n\nimport { MastraLLMBase } from './base';\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 inferOutput,\n StreamObjectWithMessagesArgs,\n OriginalStreamObjectOptions,\n StreamObjectResult,\n StreamReturn,\n} from './base.types';\n\nexport class MastraLLM extends MastraLLMBase {\n #model: LanguageModel;\n #mastra?: Mastra;\n\n constructor({ model, mastra }: { model: LanguageModel; mastra?: Mastra }) {\n super({ name: 'aisdk' });\n\n this.#model = model;\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 this.__setTelemetry(p.telemetry);\n }\n\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 schemaCompatLayers.push(\n new OpenAIReasoningSchemaCompatLayer(model),\n new OpenAISchemaCompatLayer(model),\n new GoogleSchemaCompatLayer(model),\n new AnthropicSchemaCompatLayer(model),\n new DeepSeekSchemaCompatLayer(model),\n new MetaSchemaCompatLayer(model),\n );\n }\n\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 telemetry,\n threadId,\n resourceId,\n runtimeContext,\n ...rest\n }: GenerateTextWithMessagesArgs): Promise> {\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> | Schema> | undefined = undefined;\n\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>;\n if (schema instanceof z.ZodArray) {\n schema = schema._def.type as z.ZodType>;\n }\n } else {\n schema = jsonSchema(experimental_output as JSONSchema7) as Schema>;\n }\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('[LLM] - 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 if (\n props?.response?.headers?.['x-ratelimit-remaining-tokens'] &&\n parseInt(props?.response?.headers?.['x-ratelimit-remaining-tokens'], 10) < 2000\n ) {\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 = await generateText(argsForExecute);\n\n if (schema && result.finishReason === 'stop') {\n result.object = (result as any).experimental_output;\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 throw mastraError;\n }\n }\n\n async __textObject({\n messages,\n structuredOutput,\n runId,\n telemetry,\n threadId,\n resourceId,\n runtimeContext,\n ...rest\n }: GenerateObjectWithMessagesArgs): Promise> {\n const model = this.#model;\n\n this.logger.debug(`[LLM] - Generating a text object`, { runId });\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\n const argsForExecute: OriginalGenerateObjectOptions = {\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,\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 return await generateObject(argsForExecute);\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 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 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 telemetry,\n threadId,\n resourceId,\n runtimeContext,\n ...rest\n }: StreamTextWithMessagesArgs): StreamTextResult {\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 | Schema | 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;\n if (schema instanceof z.ZodArray) {\n schema = schema._def.type as z.ZodType;\n }\n } else {\n schema = jsonSchema(experimental_output as JSONSchema7) as Schema;\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 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 if (\n props?.response?.headers?.['x-ratelimit-remaining-tokens'] &&\n parseInt(props?.response?.headers?.['x-ratelimit-remaining-tokens'], 10) < 2000\n ) {\n this.logger.warn('Rate limit approaching, waiting 10 seconds', { runId });\n await delay(10 * 1000);\n }\n },\n onFinish: async props => {\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 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 throw mastraError;\n }\n }\n\n __streamObject({\n messages,\n runId,\n runtimeContext,\n threadId,\n resourceId,\n onFinish,\n structuredOutput,\n telemetry,\n ...rest\n }: StreamObjectWithMessagesArgs): StreamObjectResult {\n const model = this.#model;\n this.logger.debug(`[LLM] - Streaming structured output`, {\n runId,\n messages,\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\n const argsForExecute: OriginalStreamObjectOptions = {\n ...rest,\n model,\n onFinish: async props => {\n try {\n // @ts-expect-error - onFinish is not infered correctly\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 throw mastraError;\n }\n\n this.logger.debug('[LLM] - 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>,\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 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_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 throw mastraError;\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\n : Omit>, 'structuredOutput' | 'output'>,\n 'messages'\n > & { output?: Output },\n ): Promise> {\n const msgs = this.convertToMessages(messages);\n\n if (!output) {\n const { maxSteps, onStepFinish, ...textOptions } = rest as Omit<\n GenerateTextWithMessagesArgs,\n 'messages'\n >;\n return (await this.__text({\n messages: msgs,\n maxSteps,\n onStepFinish,\n ...textOptions,\n })) as unknown as GenerateReturn;\n }\n\n return (await this.__textObject({\n messages: msgs,\n structuredOutput: output as NonNullable,\n ...rest,\n })) as unknown 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 {\n maxSteps = 5,\n output,\n onFinish,\n ...rest\n }: 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\n if (!output) {\n return this.__stream({\n messages: msgs,\n maxSteps,\n onFinish: onFinish as StreamTextOnFinishCallback | undefined,\n ...rest,\n }) as unknown as StreamReturn;\n }\n\n return this.__streamObject({\n messages: msgs,\n structuredOutput: output as NonNullable,\n onFinish: onFinish as StreamObjectOnFinishCallback> | undefined,\n ...rest,\n }) as unknown as StreamReturn;\n }\n}\n"]}