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{ z } from 'zod/v4';\nimport { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\n\nexport const openaiErrorDataSchema = z.object({\n  error: z.object({\n    message: z.string(),\n\n    // The additional information below is handled loosely to support\n    // OpenAI-compatible providers that have slightly different error\n    // responses:\n    type: z.string().nullish(),\n    param: z.any().nullish(),\n    code: z.union([z.string(), z.number()]).nullish(),\n  }),\n});\n\nexport type OpenAIErrorData = z.infer<typeof openaiErrorDataSchema>;\n\nexport const openaiFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: openaiErrorDataSchema,\n  errorToMessage: data => data.error.message,\n});\n","export type OpenAILanguageModelCapabilities = {\n  isReasoningModel: boolean;\n  systemMessageMode: 'remove' | 'system' | 'developer';\n  supportsFlexProcessing: boolean;\n  supportsPriorityProcessing: boolean;\n\n  /**\n   * Allow temperature, topP, logProbs when reasoningEffort is none.\n   */\n  supportsNonReasoningParameters: boolean;\n};\n\nexport function getOpenAILanguageModelCapabilities(\n  modelId: string,\n): OpenAILanguageModelCapabilities {\n  const supportsFlexProcessing =\n    modelId.startsWith('o3') ||\n    modelId.startsWith('o4-mini') ||\n    (modelId.startsWith('gpt-5') && !modelId.startsWith('gpt-5-chat'));\n\n  const supportsPriorityProcessing =\n    modelId.startsWith('gpt-4') ||\n    (modelId.startsWith('gpt-5') &&\n      !modelId.startsWith('gpt-5-nano') &&\n      !modelId.startsWith('gpt-5-chat') &&\n      !modelId.startsWith('gpt-5.4-nano')) ||\n    modelId.startsWith('o3') ||\n    modelId.startsWith('o4-mini');\n\n  const isReasoningModel = !(\n    modelId.startsWith('gpt-3') ||\n    modelId.startsWith('gpt-4') ||\n    modelId.startsWith('chatgpt-4o') ||\n    modelId.startsWith('gpt-5-chat')\n  );\n\n  // https://platform.openai.com/docs/guides/latest-model#gpt-5-1-parameter-compatibility\n  // GPT-5.1, GPT-5.2, and GPT-5.4 support temperature, topP, logProbs when reasoningEffort is none\n  const supportsNonReasoningParameters =\n    modelId.startsWith('gpt-5.1') ||\n    modelId.startsWith('gpt-5.2') ||\n    modelId.startsWith('gpt-5.3') ||\n    modelId.startsWith('gpt-5.4');\n\n  const systemMessageMode = isReasoningModel ? 'developer' : 'system';\n\n  return {\n    supportsFlexProcessing,\n    supportsPriorityProcessing,\n    isReasoningModel,\n    systemMessageMode,\n    supportsNonReasoningParameters,\n  };\n}\n","import {\n  type LanguageModelV2CallWarning,\n  type LanguageModelV2Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport type { OpenAIChatPrompt } from './openai-chat-prompt';\nimport { convertToBase64 } from '@ai-sdk/provider-utils';\n\nexport function convertToOpenAIChatMessages({\n  prompt,\n  systemMessageMode = 'system',\n}: {\n  prompt: LanguageModelV2Prompt;\n  systemMessageMode?: 'system' | 'developer' | 'remove';\n}): {\n  messages: OpenAIChatPrompt;\n  warnings: Array<LanguageModelV2CallWarning>;\n} {\n  const messages: OpenAIChatPrompt = [];\n  const warnings: Array<LanguageModelV2CallWarning> = [];\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        switch (systemMessageMode) {\n          case 'system': {\n            messages.push({ role: 'system', content });\n            break;\n          }\n          case 'developer': {\n            messages.push({ role: 'developer', content });\n            break;\n          }\n          case 'remove': {\n            warnings.push({\n              type: 'other',\n              message: 'system messages are removed for this model',\n            });\n            break;\n          }\n          default: {\n            const _exhaustiveCheck: never = systemMessageMode;\n            throw new Error(\n              `Unsupported system message mode: ${_exhaustiveCheck}`,\n            );\n          }\n        }\n        break;\n      }\n\n      case 'user': {\n        if (content.length === 1 && content[0].type === 'text') {\n          messages.push({ role: 'user', content: content[0].text });\n          break;\n        }\n\n        messages.push({\n          role: 'user',\n          content: content.map((part, index) => {\n            switch (part.type) {\n              case 'text': {\n                return { type: 'text', text: part.text };\n              }\n              case 'file': {\n                if (part.mediaType.startsWith('image/')) {\n                  const mediaType =\n                    part.mediaType === 'image/*'\n                      ? 'image/jpeg'\n                      : part.mediaType;\n\n                  return {\n                    type: 'image_url',\n                    image_url: {\n                      url:\n                        part.data instanceof URL\n                          ? part.data.toString()\n                          : `data:${mediaType};base64,${convertToBase64(part.data)}`,\n\n                      // OpenAI specific extension: image detail\n                      detail: part.providerOptions?.openai?.imageDetail,\n                    },\n                  };\n                } else if (part.mediaType.startsWith('audio/')) {\n                  if (part.data instanceof URL) {\n                    throw new UnsupportedFunctionalityError({\n                      functionality: 'audio file parts with URLs',\n                    });\n                  }\n\n                  switch (part.mediaType) {\n                    case 'audio/wav': {\n                      return {\n                        type: 'input_audio',\n                        input_audio: {\n                          data: convertToBase64(part.data),\n                          format: 'wav',\n                        },\n                      };\n                    }\n                    case 'audio/mp3':\n                    case 'audio/mpeg': {\n                      return {\n                        type: 'input_audio',\n                        input_audio: {\n                          data: convertToBase64(part.data),\n                          format: 'mp3',\n                        },\n                      };\n                    }\n\n                    default: {\n                      throw new UnsupportedFunctionalityError({\n                        functionality: `audio content parts with media type ${part.mediaType}`,\n                      });\n                    }\n                  }\n                } else if (part.mediaType === 'application/pdf') {\n                  if (part.data instanceof URL) {\n                    throw new UnsupportedFunctionalityError({\n                      functionality: 'PDF file parts with URLs',\n                    });\n                  }\n\n                  return {\n                    type: 'file',\n                    file:\n                      typeof part.data === 'string' &&\n                      part.data.startsWith('file-')\n                        ? { file_id: part.data }\n                        : {\n                            filename: part.filename ?? `part-${index}.pdf`,\n                            file_data: `data:application/pdf;base64,${convertToBase64(part.data)}`,\n                          },\n                  };\n                } else {\n                  throw new UnsupportedFunctionalityError({\n                    functionality: `file part media type ${part.mediaType}`,\n                  });\n                }\n              }\n            }\n          }),\n        });\n\n        break;\n      }\n\n      case 'assistant': {\n        let text = '';\n        const toolCalls: Array<{\n          id: string;\n          type: 'function';\n          function: { name: string; arguments: string };\n        }> = [];\n\n        for (const part of content) {\n          switch (part.type) {\n            case 'text': {\n              text += part.text;\n              break;\n            }\n            case 'tool-call': {\n              toolCalls.push({\n                id: part.toolCallId,\n                type: 'function',\n                function: {\n                  name: part.toolName,\n                  arguments: JSON.stringify(part.input),\n                },\n              });\n              break;\n            }\n          }\n        }\n\n        messages.push({\n          role: 'assistant',\n          content: text,\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n        });\n\n        break;\n      }\n\n      case 'tool': {\n        for (const toolResponse of content) {\n          const output = toolResponse.output;\n\n          let contentValue: string;\n          switch (output.type) {\n            case 'text':\n            case 'error-text':\n              contentValue = output.value;\n              break;\n            case 'content':\n            case 'json':\n            case 'error-json':\n              contentValue = JSON.stringify(output.value);\n              break;\n          }\n\n          messages.push({\n            role: 'tool',\n            tool_call_id: toolResponse.toolCallId,\n            content: contentValue,\n          });\n        }\n        break;\n      }\n\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return { messages, warnings };\n}\n","export function getResponseMetadata({\n  id,\n  model,\n  created,\n}: {\n  id?: string | undefined | null;\n  created?: number | undefined | null;\n  model?: string | undefined | null;\n}) {\n  return {\n    id: id ?? undefined,\n    modelId: model ?? undefined,\n    timestamp: created ? new Date(created * 1000) : undefined,\n  };\n}\n","import type { LanguageModelV2FinishReason } from '@ai-sdk/provider';\n\nexport function mapOpenAIFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV2FinishReason {\n  switch (finishReason) {\n    case 'stop':\n      return 'stop';\n    case 'length':\n      return 'length';\n    case 'content_filter':\n      return 'content-filter';\n    case 'function_call':\n    case 'tool_calls':\n      return 'tool-calls';\n    default:\n      return 'unknown';\n  }\n}\n","import type { JSONSchema7 } from '@ai-sdk/provider';\nimport {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { openaiErrorDataSchema } from '../openai-error';\n\nexport interface OpenAIChatFunctionTool {\n  type: 'function';\n  function: {\n    name: string;\n    description: string | undefined;\n    parameters: JSONSchema7;\n    strict?: boolean;\n  };\n}\n\nexport type OpenAIChatToolChoice =\n  | 'auto'\n  | 'none'\n  | 'required'\n  | { type: 'function'; function: { name: string } };\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiChatResponseSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      id: z.string().nullish(),\n      created: z.number().nullish(),\n      model: z.string().nullish(),\n      choices: z.array(\n        z.object({\n          message: z.object({\n            role: z.literal('assistant').nullish(),\n            content: z.string().nullish(),\n            tool_calls: z\n              .array(\n                z.object({\n                  id: z.string().nullish(),\n                  type: z.literal('function'),\n                  function: z.object({\n                    name: z.string(),\n                    arguments: z.string(),\n                  }),\n                }),\n              )\n              .nullish(),\n            annotations: z\n              .array(\n                z.object({\n                  type: z.literal('url_citation'),\n                  url_citation: z.object({\n                    start_index: z.number(),\n                    end_index: z.number(),\n                    url: z.string(),\n                    title: z.string(),\n                  }),\n                }),\n              )\n              .nullish(),\n          }),\n          index: z.number(),\n          logprobs: z\n            .object({\n              content: z\n                .array(\n                  z.object({\n                    token: z.string(),\n                    logprob: z.number(),\n                    top_logprobs: z.array(\n                      z.object({\n                        token: z.string(),\n                        logprob: z.number(),\n                      }),\n                    ),\n                  }),\n                )\n                .nullish(),\n            })\n            .nullish(),\n          finish_reason: z.string().nullish(),\n        }),\n      ),\n      usage: z\n        .object({\n          prompt_tokens: z.number().nullish(),\n          completion_tokens: z.number().nullish(),\n          total_tokens: z.number().nullish(),\n          prompt_tokens_details: z\n            .object({\n              cached_tokens: z.number().nullish(),\n            })\n            .nullish(),\n          completion_tokens_details: z\n            .object({\n              reasoning_tokens: z.number().nullish(),\n              accepted_prediction_tokens: z.number().nullish(),\n              rejected_prediction_tokens: z.number().nullish(),\n            })\n            .nullish(),\n        })\n        .nullish(),\n    }),\n  ),\n);\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiChatChunkSchema = lazyValidator(() =>\n  zodSchema(\n    z.union([\n      z.object({\n        id: z.string().nullish(),\n        created: z.number().nullish(),\n        model: z.string().nullish(),\n        choices: z.array(\n          z.object({\n            delta: z\n              .object({\n                role: z.enum(['assistant']).nullish(),\n                content: z.string().nullish(),\n                tool_calls: z\n                  .array(\n                    z.object({\n                      index: z.number(),\n                      id: z.string().nullish(),\n                      type: z.literal('function').nullish(),\n                      function: z.object({\n                        name: z.string().nullish(),\n                        arguments: z.string().nullish(),\n                      }),\n                    }),\n                  )\n                  .nullish(),\n                annotations: z\n                  .array(\n                    z.object({\n                      type: z.literal('url_citation'),\n                      url_citation: z.object({\n                        start_index: z.number(),\n                        end_index: z.number(),\n                        url: z.string(),\n                        title: z.string(),\n                      }),\n                    }),\n                  )\n                  .nullish(),\n              })\n              .nullish(),\n            logprobs: z\n              .object({\n                content: z\n                  .array(\n                    z.object({\n                      token: z.string(),\n                      logprob: z.number(),\n                      top_logprobs: z.array(\n                        z.object({\n                          token: z.string(),\n                          logprob: z.number(),\n                        }),\n                      ),\n                    }),\n                  )\n                  .nullish(),\n              })\n              .nullish(),\n            finish_reason: z.string().nullish(),\n            index: z.number(),\n          }),\n        ),\n        usage: z\n          .object({\n            prompt_tokens: z.number().nullish(),\n            completion_tokens: z.number().nullish(),\n            total_tokens: z.number().nullish(),\n            prompt_tokens_details: z\n              .object({\n                cached_tokens: z.number().nullish(),\n              })\n              .nullish(),\n            completion_tokens_details: z\n              .object({\n                reasoning_tokens: z.number().nullish(),\n                accepted_prediction_tokens: z.number().nullish(),\n                rejected_prediction_tokens: z.number().nullish(),\n              })\n              .nullish(),\n          })\n          .nullish(),\n      }),\n      openaiErrorDataSchema,\n    ]),\n  ),\n);\n\nexport type OpenAIChatResponse = InferValidator<\n  typeof openaiChatResponseSchema\n>;\n\nexport type OpenAIChatChunk = InferValidator<typeof openaiChatChunkSchema>;\n","import {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://platform.openai.com/docs/models\nexport type OpenAIChatModelId =\n  | 'o1'\n  | 'o1-2024-12-17'\n  | 'o3-mini'\n  | 'o3-mini-2025-01-31'\n  | 'o3'\n  | 'o3-2025-04-16'\n  | 'gpt-4.1'\n  | 'gpt-4.1-2025-04-14'\n  | 'gpt-4.1-mini'\n  | 'gpt-4.1-mini-2025-04-14'\n  | 'gpt-4.1-nano'\n  | 'gpt-4.1-nano-2025-04-14'\n  | 'gpt-4o'\n  | 'gpt-4o-2024-05-13'\n  | 'gpt-4o-2024-08-06'\n  | 'gpt-4o-2024-11-20'\n  | 'gpt-4o-mini'\n  | 'gpt-4o-mini-2024-07-18'\n  | 'gpt-4-turbo'\n  | 'gpt-4-turbo-2024-04-09'\n  | 'gpt-4'\n  | 'gpt-4-0613'\n  | 'gpt-4.5-preview'\n  | 'gpt-4.5-preview-2025-02-27'\n  | 'gpt-3.5-turbo-0125'\n  | 'gpt-3.5-turbo'\n  | 'gpt-3.5-turbo-1106'\n  | 'chatgpt-4o-latest'\n  | 'gpt-5'\n  | 'gpt-5-2025-08-07'\n  | 'gpt-5-mini'\n  | 'gpt-5-mini-2025-08-07'\n  | 'gpt-5-nano'\n  | 'gpt-5-nano-2025-08-07'\n  | 'gpt-5-chat-latest'\n  | 'gpt-5.1'\n  | 'gpt-5.1-chat-latest'\n  | 'gpt-5.2'\n  | 'gpt-5.2-chat-latest'\n  | 'gpt-5.2-pro'\n  | 'gpt-5.2-pro-2025-12-11'\n  | 'gpt-5.3-chat-latest'\n  | 'gpt-5.4'\n  | 'gpt-5.4-2026-03-05'\n  | 'gpt-5.4-mini'\n  | 'gpt-5.4-mini-2026-03-17'\n  | 'gpt-5.4-nano'\n  | 'gpt-5.4-nano-2026-03-17'\n  | 'gpt-5.4-pro'\n  | 'gpt-5.4-pro-2026-03-05'\n  | (string & {});\n\nexport const openaiChatLanguageModelOptions = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      /**\n       * Modify the likelihood of specified tokens appearing in the completion.\n       *\n       * Accepts a JSON object that maps tokens (specified by their token ID in\n       * the GPT tokenizer) to an associated bias value from -100 to 100.\n       */\n      logitBias: z.record(z.coerce.number<string>(), z.number()).optional(),\n\n      /**\n       * Return the log probabilities of the tokens.\n       *\n       * Setting to true will return the log probabilities of the tokens that\n       * were generated.\n       *\n       * Setting to a number will return the log probabilities of the top n\n       * tokens that were generated.\n       */\n      logprobs: z.union([z.boolean(), z.number()]).optional(),\n\n      /**\n       * Whether to enable parallel function calling during tool use. Default to true.\n       */\n      parallelToolCalls: z.boolean().optional(),\n\n      /**\n       * A unique identifier representing your end-user, which can help OpenAI to\n       * monitor and detect abuse.\n       */\n      user: z.string().optional(),\n\n      /**\n       * Reasoning effort for reasoning models. Defaults to `medium`.\n       */\n      reasoningEffort: z\n        .enum(['none', 'minimal', 'low', 'medium', 'high', 'xhigh'])\n        .optional(),\n\n      /**\n       * Maximum number of completion tokens to generate. Useful for reasoning models.\n       */\n      maxCompletionTokens: z.number().optional(),\n\n      /**\n       * Whether to enable persistence in responses API.\n       */\n      store: z.boolean().optional(),\n\n      /**\n       * Metadata to associate with the request.\n       */\n      metadata: z.record(z.string().max(64), z.string().max(512)).optional(),\n\n      /**\n       * Parameters for prediction mode.\n       */\n      prediction: z.record(z.string(), z.any()).optional(),\n\n      /**\n       * Whether to use structured outputs.\n       *\n       * @default true\n       */\n      structuredOutputs: z.boolean().optional(),\n\n      /**\n       * Service tier for the request.\n       * - 'auto': Default service tier. The request will be processed with the service tier configured in the\n       *           Project settings. Unless otherwise configured, the Project will use 'default'.\n       * - 'flex': 50% cheaper processing at the cost of increased latency. Only available for o3 and o4-mini models.\n       * - 'priority': Higher-speed processing with predictably low latency at premium cost. Available for Enterprise customers.\n       * - 'default': The request will be processed with the standard pricing and performance for the selected model.\n       *\n       * @default 'auto'\n       */\n      serviceTier: z.enum(['auto', 'flex', 'priority', 'default']).optional(),\n\n      /**\n       * Whether to use strict JSON schema validation.\n       *\n       * @default false\n       */\n      strictJsonSchema: z.boolean().optional(),\n\n      /**\n       * Controls the verbosity of the model's responses.\n       * Lower values will result in more concise responses, while higher values will result in more verbose responses.\n       */\n      textVerbosity: z.enum(['low', 'medium', 'high']).optional(),\n\n      /**\n       * A cache key for prompt caching. Allows manual control over prompt caching behavior.\n       * Useful for improving cache hit rates and working around automatic caching issues.\n       */\n      promptCacheKey: z.string().optional(),\n\n      /**\n       * The retention policy for the prompt cache.\n       * - 'in_memory': Default. Standard prompt caching behavior.\n       * - '24h': Extended prompt caching that keeps cached prefixes active for up to 24 hours.\n       *          Currently only available for 5.1 series models.\n       *\n       * @default 'in_memory'\n       */\n      promptCacheRetention: z.enum(['in_memory', '24h']).optional(),\n\n      /**\n       * A stable identifier used to help detect users of your application\n       * that may be violating OpenAI's usage policies. The IDs should be a\n       * string that uniquely identifies each user. We recommend hashing their\n       * username or email address, in order to avoid sending us any identifying\n       * information.\n       */\n      safetyIdentifier: z.string().optional(),\n    }),\n  ),\n);\n\nexport type OpenAIChatLanguageModelOptions = InferValidator<\n  typeof openaiChatLanguageModelOptions\n>;\n","import {\n  type LanguageModelV2CallOptions,\n  type LanguageModelV2CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport type {\n  OpenAIChatToolChoice,\n  OpenAIChatFunctionTool,\n} from './openai-chat-api';\n\nexport function prepareChatTools({\n  tools,\n  toolChoice,\n  structuredOutputs,\n  strictJsonSchema,\n}: {\n  tools: LanguageModelV2CallOptions['tools'];\n  toolChoice?: LanguageModelV2CallOptions['toolChoice'];\n  structuredOutputs: boolean;\n  strictJsonSchema: boolean;\n}): {\n  tools?: OpenAIChatFunctionTool[];\n  toolChoice?: OpenAIChatToolChoice;\n  toolWarnings: Array<LanguageModelV2CallWarning>;\n} {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  tools = tools?.length ? tools : undefined;\n\n  const toolWarnings: LanguageModelV2CallWarning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, toolChoice: undefined, toolWarnings };\n  }\n\n  const openaiTools: OpenAIChatFunctionTool[] = [];\n\n  for (const tool of tools) {\n    switch (tool.type) {\n      case 'function':\n        openaiTools.push({\n          type: 'function',\n          function: {\n            name: tool.name,\n            description: tool.description,\n            parameters: tool.inputSchema,\n            strict: structuredOutputs ? strictJsonSchema : undefined,\n          },\n        });\n        break;\n      default:\n        toolWarnings.push({ type: 'unsupported-tool', tool });\n        break;\n    }\n  }\n\n  if (toolChoice == null) {\n    return { tools: openaiTools, toolChoice: undefined, toolWarnings };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n    case 'none':\n    case 'required':\n      return { tools: openaiTools, toolChoice: type, toolWarnings };\n    case 'tool':\n      return {\n        tools: openaiTools,\n        toolChoice: {\n          type: 'function',\n          function: {\n            name: toolChoice.toolName,\n          },\n        },\n        toolWarnings,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  type LanguageModelV2,\n  type LanguageModelV2CallOptions,\n  type LanguageModelV2CallWarning,\n  type LanguageModelV2Content,\n  type LanguageModelV2FinishReason,\n  type LanguageModelV2StreamPart,\n  type LanguageModelV2Usage,\n  type SharedV2ProviderMetadata,\n  InvalidResponseDataError,\n} from '@ai-sdk/provider';\nimport {\n  type FetchFunction,\n  type ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  isParsableJson,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport { getOpenAILanguageModelCapabilities } from '../openai-language-model-capabilities';\nimport { convertToOpenAIChatMessages } from './convert-to-openai-chat-messages';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport {\n  type OpenAIChatChunk,\n  openaiChatChunkSchema,\n  openaiChatResponseSchema,\n} from './openai-chat-api';\nimport {\n  type OpenAIChatModelId,\n  openaiChatLanguageModelOptions,\n} from './openai-chat-options';\nimport { prepareChatTools } from './openai-chat-prepare-tools';\n\ntype OpenAIChatConfig = {\n  provider: string;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n};\n\nexport class OpenAIChatLanguageModel implements LanguageModelV2 {\n  readonly specificationVersion = 'v2';\n\n  readonly modelId: OpenAIChatModelId;\n\n  readonly supportedUrls = {\n    'image/*': [/^https?:\\/\\/.*$/],\n  };\n\n  private readonly config: OpenAIChatConfig;\n\n  constructor(modelId: OpenAIChatModelId, config: OpenAIChatConfig) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    tools,\n    toolChoice,\n    providerOptions,\n  }: LanguageModelV2CallOptions) {\n    const warnings: LanguageModelV2CallWarning[] = [];\n\n    // Parse provider options\n    const openaiOptions =\n      (await parseProviderOptions({\n        provider: 'openai',\n        providerOptions,\n        schema: openaiChatLanguageModelOptions,\n      })) ?? {};\n\n    const structuredOutputs = openaiOptions.structuredOutputs ?? true;\n    const modelCapabilities = getOpenAILanguageModelCapabilities(this.modelId);\n\n    if (topK != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'topK',\n      });\n    }\n\n    if (\n      responseFormat?.type === 'json' &&\n      responseFormat.schema != null &&\n      !structuredOutputs\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details:\n          'JSON response format schema is only supported with structuredOutputs',\n      });\n    }\n\n    const { messages, warnings: messageWarnings } = convertToOpenAIChatMessages(\n      {\n        prompt,\n        systemMessageMode: modelCapabilities.systemMessageMode,\n      },\n    );\n\n    warnings.push(...messageWarnings);\n\n    const strictJsonSchema = openaiOptions.strictJsonSchema ?? false;\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      logit_bias: openaiOptions.logitBias,\n      logprobs:\n        openaiOptions.logprobs === true ||\n        typeof openaiOptions.logprobs === 'number'\n          ? true\n          : undefined,\n      top_logprobs:\n        typeof openaiOptions.logprobs === 'number'\n          ? openaiOptions.logprobs\n          : typeof openaiOptions.logprobs === 'boolean'\n            ? openaiOptions.logprobs\n              ? 0\n              : undefined\n            : undefined,\n      user: openaiOptions.user,\n      parallel_tool_calls: openaiOptions.parallelToolCalls,\n\n      // standardized settings:\n      max_tokens: maxOutputTokens,\n      temperature,\n      top_p: topP,\n      frequency_penalty: frequencyPenalty,\n      presence_penalty: presencePenalty,\n      response_format:\n        responseFormat?.type === 'json'\n          ? structuredOutputs && responseFormat.schema != null\n            ? {\n                type: 'json_schema',\n                json_schema: {\n                  schema: responseFormat.schema,\n                  strict: strictJsonSchema,\n                  name: responseFormat.name ?? 'response',\n                  description: responseFormat.description,\n                },\n              }\n            : { type: 'json_object' }\n          : undefined,\n      stop: stopSequences,\n      seed,\n      verbosity: openaiOptions.textVerbosity,\n\n      // openai specific settings:\n      // TODO AI SDK 6: remove, we auto-map maxOutputTokens now\n      max_completion_tokens: openaiOptions.maxCompletionTokens,\n      store: openaiOptions.store,\n      metadata: openaiOptions.metadata,\n      prediction: openaiOptions.prediction,\n      reasoning_effort: openaiOptions.reasoningEffort,\n      service_tier: openaiOptions.serviceTier,\n      prompt_cache_key: openaiOptions.promptCacheKey,\n      prompt_cache_retention: openaiOptions.promptCacheRetention,\n      safety_identifier: openaiOptions.safetyIdentifier,\n\n      // messages:\n      messages,\n    };\n\n    // remove unsupported settings for reasoning models\n    // see https://platform.openai.com/docs/guides/reasoning#limitations\n    if (modelCapabilities.isReasoningModel) {\n      // when reasoning effort is none, gpt-5.1 models allow temperature, topP, logprobs\n      //  https://platform.openai.com/docs/guides/latest-model#gpt-5-1-parameter-compatibility\n      if (\n        openaiOptions.reasoningEffort !== 'none' ||\n        !modelCapabilities.supportsNonReasoningParameters\n      ) {\n        if (baseArgs.temperature != null) {\n          baseArgs.temperature = undefined;\n          warnings.push({\n            type: 'unsupported-setting',\n            setting: 'temperature',\n            details: 'temperature is not supported for reasoning models',\n          });\n        }\n        if (baseArgs.top_p != null) {\n          baseArgs.top_p = undefined;\n          warnings.push({\n            type: 'unsupported-setting',\n            setting: 'topP',\n            details: 'topP is not supported for reasoning models',\n          });\n        }\n        if (baseArgs.logprobs != null) {\n          baseArgs.logprobs = undefined;\n          warnings.push({\n            type: 'other',\n            message: 'logprobs is not supported for reasoning models',\n          });\n        }\n      }\n\n      if (baseArgs.frequency_penalty != null) {\n        baseArgs.frequency_penalty = undefined;\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'frequencyPenalty',\n          details: 'frequencyPenalty is not supported for reasoning models',\n        });\n      }\n      if (baseArgs.presence_penalty != null) {\n        baseArgs.presence_penalty = undefined;\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'presencePenalty',\n          details: 'presencePenalty is not supported for reasoning models',\n        });\n      }\n      if (baseArgs.logit_bias != null) {\n        baseArgs.logit_bias = undefined;\n        warnings.push({\n          type: 'other',\n          message: 'logitBias is not supported for reasoning models',\n        });\n      }\n\n      if (baseArgs.top_logprobs != null) {\n        baseArgs.top_logprobs = undefined;\n        warnings.push({\n          type: 'other',\n          message: 'topLogprobs is not supported for reasoning models',\n        });\n      }\n\n      // reasoning models use max_completion_tokens instead of max_tokens:\n      if (baseArgs.max_tokens != null) {\n        if (baseArgs.max_completion_tokens == null) {\n          baseArgs.max_completion_tokens = baseArgs.max_tokens;\n        }\n        baseArgs.max_tokens = undefined;\n      }\n    } else if (\n      this.modelId.startsWith('gpt-4o-search-preview') ||\n      this.modelId.startsWith('gpt-4o-mini-search-preview')\n    ) {\n      if (baseArgs.temperature != null) {\n        baseArgs.temperature = undefined;\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'temperature',\n          details:\n            'temperature is not supported for the search preview models and has been removed.',\n        });\n      }\n    }\n\n    // Validate flex processing support\n    if (\n      openaiOptions.serviceTier === 'flex' &&\n      !modelCapabilities.supportsFlexProcessing\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'serviceTier',\n        details:\n          'flex processing is only available for o3, o4-mini, and gpt-5 models',\n      });\n      baseArgs.service_tier = undefined;\n    }\n\n    // Validate priority processing support\n    if (\n      openaiOptions.serviceTier === 'priority' &&\n      !modelCapabilities.supportsPriorityProcessing\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'serviceTier',\n        details:\n          'priority processing is only available for supported models (gpt-4, gpt-5, gpt-5-mini, o3, o4-mini) and requires Enterprise access. gpt-5-nano is not supported',\n      });\n      baseArgs.service_tier = undefined;\n    }\n\n    const {\n      tools: openaiTools,\n      toolChoice: openaiToolChoice,\n      toolWarnings,\n    } = prepareChatTools({\n      tools,\n      toolChoice,\n      structuredOutputs,\n      strictJsonSchema,\n    });\n\n    return {\n      args: {\n        ...baseArgs,\n        tools: openaiTools,\n        tool_choice: openaiToolChoice,\n      },\n      warnings: [...warnings, ...toolWarnings],\n    };\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doGenerate']>>> {\n    const { args: body, warnings } = await this.getArgs(options);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: this.config.url({\n        path: '/chat/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const choice = response.choices[0];\n    const content: Array<LanguageModelV2Content> = [];\n\n    // text content:\n    const text = choice.message.content;\n    if (text != null && text.length > 0) {\n      content.push({ type: 'text', text });\n    }\n\n    // tool calls:\n    for (const toolCall of choice.message.tool_calls ?? []) {\n      content.push({\n        type: 'tool-call' as const,\n        toolCallId: toolCall.id ?? generateId(),\n        toolName: toolCall.function.name,\n        input: toolCall.function.arguments!,\n      });\n    }\n\n    // annotations/citations:\n    for (const annotation of choice.message.annotations ?? []) {\n      content.push({\n        type: 'source',\n        sourceType: 'url',\n        id: generateId(),\n        url: annotation.url_citation.url,\n        title: annotation.url_citation.title,\n      });\n    }\n\n    // provider metadata:\n    const completionTokenDetails = response.usage?.completion_tokens_details;\n    const promptTokenDetails = response.usage?.prompt_tokens_details;\n    const providerMetadata: SharedV2ProviderMetadata = { openai: {} };\n    if (completionTokenDetails?.accepted_prediction_tokens != null) {\n      providerMetadata.openai.acceptedPredictionTokens =\n        completionTokenDetails?.accepted_prediction_tokens;\n    }\n    if (completionTokenDetails?.rejected_prediction_tokens != null) {\n      providerMetadata.openai.rejectedPredictionTokens =\n        completionTokenDetails?.rejected_prediction_tokens;\n    }\n    if (choice.logprobs?.content != null) {\n      providerMetadata.openai.logprobs = choice.logprobs.content;\n    }\n\n    return {\n      content,\n      finishReason: mapOpenAIFinishReason(choice.finish_reason),\n      usage: {\n        inputTokens: response.usage?.prompt_tokens ?? undefined,\n        outputTokens: response.usage?.completion_tokens ?? undefined,\n        totalTokens: response.usage?.total_tokens ?? undefined,\n        reasoningTokens: completionTokenDetails?.reasoning_tokens ?? undefined,\n        cachedInputTokens: promptTokenDetails?.cached_tokens ?? undefined,\n      },\n      request: { body },\n      response: {\n        ...getResponseMetadata(response),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n      providerMetadata,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV2['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doStream']>>> {\n    const { args, warnings } = await this.getArgs(options);\n\n    const body = {\n      ...args,\n      stream: true,\n      stream_options: {\n        include_usage: true,\n      },\n    };\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: this.config.url({\n        path: '/chat/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        openaiChatChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const toolCalls: Array<{\n      id: string;\n      type: 'function';\n      function: {\n        name: string;\n        arguments: string;\n      };\n      hasFinished: boolean;\n    }> = [];\n\n    let finishReason: LanguageModelV2FinishReason = 'unknown';\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n    let metadataExtracted = false;\n    let isActiveText = false;\n\n    const providerMetadata: SharedV2ProviderMetadata = { openai: {} };\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<OpenAIChatChunk>,\n          LanguageModelV2StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = 'error';\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            // handle error chunks:\n            if ('error' in value) {\n              finishReason = 'error';\n              controller.enqueue({ type: 'error', error: value.error });\n              return;\n            }\n\n            // extract and emit response metadata once. Usually it comes in the first chunk.\n            // Azure may prepend a chunk with a `\"prompt_filter_results\"` key which does not contain other metadata,\n            // https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/content-filter-annotations?tabs=powershell\n            if (!metadataExtracted) {\n              const metadata = getResponseMetadata(value);\n              if (Object.values(metadata).some(Boolean)) {\n                metadataExtracted = true;\n                controller.enqueue({\n                  type: 'response-metadata',\n                  ...getResponseMetadata(value),\n                });\n              }\n            }\n\n            if (value.usage != null) {\n              usage.inputTokens = value.usage.prompt_tokens ?? undefined;\n              usage.outputTokens = value.usage.completion_tokens ?? undefined;\n              usage.totalTokens = value.usage.total_tokens ?? undefined;\n              usage.reasoningTokens =\n                value.usage.completion_tokens_details?.reasoning_tokens ??\n                undefined;\n              usage.cachedInputTokens =\n                value.usage.prompt_tokens_details?.cached_tokens ?? undefined;\n\n              if (\n                value.usage.completion_tokens_details\n                  ?.accepted_prediction_tokens != null\n              ) {\n                providerMetadata.openai.acceptedPredictionTokens =\n                  value.usage.completion_tokens_details?.accepted_prediction_tokens;\n              }\n              if (\n                value.usage.completion_tokens_details\n                  ?.rejected_prediction_tokens != null\n              ) {\n                providerMetadata.openai.rejectedPredictionTokens =\n                  value.usage.completion_tokens_details?.rejected_prediction_tokens;\n              }\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = mapOpenAIFinishReason(choice.finish_reason);\n            }\n\n            if (choice?.logprobs?.content != null) {\n              providerMetadata.openai.logprobs = choice.logprobs.content;\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            if (delta.content != null) {\n              if (!isActiveText) {\n                controller.enqueue({ type: 'text-start', id: '0' });\n                isActiveText = true;\n              }\n\n              controller.enqueue({\n                type: 'text-delta',\n                id: '0',\n                delta: delta.content,\n              });\n            }\n\n            if (delta.tool_calls != null) {\n              for (const toolCallDelta of delta.tool_calls) {\n                const index = toolCallDelta.index;\n\n                // Tool call start. OpenAI returns all information except the arguments in the first chunk.\n                if (toolCalls[index] == null) {\n                  if (\n                    toolCallDelta.type != null &&\n                    toolCallDelta.type !== 'function'\n                  ) {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'function' type.`,\n                    });\n                  }\n\n                  if (toolCallDelta.id == null) {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'id' to be a string.`,\n                    });\n                  }\n\n                  if (toolCallDelta.function?.name == null) {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'function.name' to be a string.`,\n                    });\n                  }\n\n                  controller.enqueue({\n                    type: 'tool-input-start',\n                    id: toolCallDelta.id,\n                    toolName: toolCallDelta.function.name,\n                  });\n\n                  toolCalls[index] = {\n                    id: toolCallDelta.id,\n                    type: 'function',\n                    function: {\n                      name: toolCallDelta.function.name,\n                      arguments: toolCallDelta.function.arguments ?? '',\n                    },\n                    hasFinished: false,\n                  };\n\n                  const toolCall = toolCalls[index];\n\n                  if (\n                    toolCall.function?.name != null &&\n                    toolCall.function?.arguments != null\n                  ) {\n                    // send delta if the argument text has already started:\n                    if (toolCall.function.arguments.length > 0) {\n                      controller.enqueue({\n                        type: 'tool-input-delta',\n                        id: toolCall.id,\n                        delta: toolCall.function.arguments,\n                      });\n                    }\n\n                    // check if tool call is complete\n                    // (some providers send the full tool call in one chunk):\n                    if (isParsableJson(toolCall.function.arguments)) {\n                      controller.enqueue({\n                        type: 'tool-input-end',\n                        id: toolCall.id,\n                      });\n\n                      controller.enqueue({\n                        type: 'tool-call',\n                        toolCallId: toolCall.id ?? generateId(),\n                        toolName: toolCall.function.name,\n                        input: toolCall.function.arguments,\n                      });\n                      toolCall.hasFinished = true;\n                    }\n                  }\n\n                  continue;\n                }\n\n                // existing tool call, merge if not finished\n                const toolCall = toolCalls[index];\n\n                if (toolCall.hasFinished) {\n                  continue;\n                }\n\n                if (toolCallDelta.function?.arguments != null) {\n                  toolCall.function!.arguments +=\n                    toolCallDelta.function?.arguments ?? '';\n                }\n\n                // send delta\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: toolCall.id,\n                  delta: toolCallDelta.function.arguments ?? '',\n                });\n\n                // check if tool call is complete\n                if (\n                  toolCall.function?.name != null &&\n                  toolCall.function?.arguments != null &&\n                  isParsableJson(toolCall.function.arguments)\n                ) {\n                  controller.enqueue({\n                    type: 'tool-input-end',\n                    id: toolCall.id,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId: toolCall.id ?? generateId(),\n                    toolName: toolCall.function.name,\n                    input: toolCall.function.arguments,\n                  });\n                  toolCall.hasFinished = true;\n                }\n              }\n            }\n\n            // annotations/citations:\n            if (delta.annotations != null) {\n              for (const annotation of delta.annotations) {\n                controller.enqueue({\n                  type: 'source',\n                  sourceType: 'url',\n                  id: generateId(),\n                  url: annotation.url_citation.url,\n                  title: annotation.url_citation.title,\n                });\n              }\n            }\n          },\n\n          flush(controller) {\n            if (isActiveText) {\n              controller.enqueue({ type: 'text-end', id: '0' });\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage,\n              ...(providerMetadata != null ? { providerMetadata } : {}),\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n","import {\n  type LanguageModelV2Prompt,\n  InvalidPromptError,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\n\nexport function convertToOpenAICompletionPrompt({\n  prompt,\n  user = 'user',\n  assistant = 'assistant',\n}: {\n  prompt: LanguageModelV2Prompt;\n  user?: string;\n  assistant?: string;\n}): {\n  prompt: string;\n  stopSequences?: string[];\n} {\n  // transform to a chat message format:\n  let text = '';\n\n  // if first message is a system message, add it to the text:\n  if (prompt[0].role === 'system') {\n    text += `${prompt[0].content}\\n\\n`;\n    prompt = prompt.slice(1);\n  }\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        throw new InvalidPromptError({\n          message: 'Unexpected system message in prompt: ${content}',\n          prompt,\n        });\n      }\n\n      case 'user': {\n        const userMessage = content\n          .map(part => {\n            switch (part.type) {\n              case 'text': {\n                return part.text;\n              }\n            }\n          })\n          .filter(Boolean)\n          .join('');\n\n        text += `${user}:\\n${userMessage}\\n\\n`;\n        break;\n      }\n\n      case 'assistant': {\n        const assistantMessage = content\n          .map(part => {\n            switch (part.type) {\n              case 'text': {\n                return part.text;\n              }\n              case 'tool-call': {\n                throw new UnsupportedFunctionalityError({\n                  functionality: 'tool-call messages',\n                });\n              }\n            }\n          })\n          .join('');\n\n        text += `${assistant}:\\n${assistantMessage}\\n\\n`;\n        break;\n      }\n\n      case 'tool': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'tool messages',\n        });\n      }\n\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  // Assistant message prefix:\n  text += `${assistant}:\\n`;\n\n  return {\n    prompt: text,\n    stopSequences: [`\\n${user}:`],\n  };\n}\n","export function getResponseMetadata({\n  id,\n  model,\n  created,\n}: {\n  id?: string | undefined | null;\n  created?: number | undefined | null;\n  model?: string | undefined | null;\n}) {\n  return {\n    id: id ?? undefined,\n    modelId: model ?? undefined,\n    timestamp: created != null ? new Date(created * 1000) : undefined,\n  };\n}\n","import type { LanguageModelV2FinishReason } from '@ai-sdk/provider';\n\nexport function mapOpenAIFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV2FinishReason {\n  switch (finishReason) {\n    case 'stop':\n      return 'stop';\n    case 'length':\n      return 'length';\n    case 'content_filter':\n      return 'content-filter';\n    case 'function_call':\n    case 'tool_calls':\n      return 'tool-calls';\n    default:\n      return 'unknown';\n  }\n}\n","import { z } from 'zod/v4';\nimport { openaiErrorDataSchema } from '../openai-error';\nimport {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiCompletionResponseSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      id: z.string().nullish(),\n      created: z.number().nullish(),\n      model: z.string().nullish(),\n      choices: z.array(\n        z.object({\n          text: z.string(),\n          finish_reason: z.string(),\n          logprobs: z\n            .object({\n              tokens: z.array(z.string()),\n              token_logprobs: z.array(z.number()),\n              top_logprobs: z.array(z.record(z.string(), z.number())).nullish(),\n            })\n            .nullish(),\n        }),\n      ),\n      usage: z\n        .object({\n          prompt_tokens: z.number(),\n          completion_tokens: z.number(),\n          total_tokens: z.number(),\n        })\n        .nullish(),\n    }),\n  ),\n);\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiCompletionChunkSchema = lazyValidator(() =>\n  zodSchema(\n    z.union([\n      z.object({\n        id: z.string().nullish(),\n        created: z.number().nullish(),\n        model: z.string().nullish(),\n        choices: z.array(\n          z.object({\n            text: z.string(),\n            finish_reason: z.string().nullish(),\n            index: z.number(),\n            logprobs: z\n              .object({\n                tokens: z.array(z.string()),\n                token_logprobs: z.array(z.number()),\n                top_logprobs: z\n                  .array(z.record(z.string(), z.number()))\n                  .nullish(),\n              })\n              .nullish(),\n          }),\n        ),\n        usage: z\n          .object({\n            prompt_tokens: z.number(),\n            completion_tokens: z.number(),\n            total_tokens: z.number(),\n          })\n          .nullish(),\n      }),\n      openaiErrorDataSchema,\n    ]),\n  ),\n);\n\nexport type OpenAICompletionChunk = InferValidator<\n  typeof openaiCompletionChunkSchema\n>;\n\nexport type OpenAICompletionResponse = InferValidator<\n  typeof openaiCompletionResponseSchema\n>;\n","import {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://platform.openai.com/docs/models\nexport type OpenAICompletionModelId = 'gpt-3.5-turbo-instruct' | (string & {});\n\nexport const openaiCompletionProviderOptions = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      /**\nEcho back the prompt in addition to the completion.\n   */\n      echo: z.boolean().optional(),\n\n      /**\nModify the likelihood of specified tokens appearing in the completion.\n\nAccepts a JSON object that maps tokens (specified by their token ID in\nthe GPT tokenizer) to an associated bias value from -100 to 100. You\ncan use this tokenizer tool to convert text to token IDs. Mathematically,\nthe bias is added to the logits generated by the model prior to sampling.\nThe exact effect will vary per model, but values between -1 and 1 should\ndecrease or increase likelihood of selection; values like -100 or 100\nshould result in a ban or exclusive selection of the relevant token.\n\nAs an example, you can pass {\"50256\": -100} to prevent the <|endoftext|>\ntoken from being generated.\n */\n      logitBias: z.record(z.string(), z.number()).optional(),\n\n      /**\nThe suffix that comes after a completion of inserted text.\n */\n      suffix: z.string().optional(),\n\n      /**\nA unique identifier representing your end-user, which can help OpenAI to\nmonitor and detect abuse. Learn more.\n */\n      user: z.string().optional(),\n\n      /**\nReturn the log probabilities of the tokens. Including logprobs will increase\nthe response size and can slow down response times. However, it can\nbe useful to better understand how the model is behaving.\nSetting to true will return the log probabilities of the tokens that\nwere generated.\nSetting to a number will return the log probabilities of the top n\ntokens that were generated.\n   */\n      logprobs: z.union([z.boolean(), z.number()]).optional(),\n    }),\n  ),\n);\n\nexport type OpenAICompletionProviderOptions = InferValidator<\n  typeof openaiCompletionProviderOptions\n>;\n","import type {\n  LanguageModelV2,\n  LanguageModelV2CallWarning,\n  LanguageModelV2FinishReason,\n  LanguageModelV2StreamPart,\n  LanguageModelV2Usage,\n  SharedV2ProviderMetadata,\n} from '@ai-sdk/provider';\nimport {\n  type FetchFunction,\n  type ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport { convertToOpenAICompletionPrompt } from './convert-to-openai-completion-prompt';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport {\n  type OpenAICompletionChunk,\n  openaiCompletionChunkSchema,\n  openaiCompletionResponseSchema,\n} from './openai-completion-api';\nimport {\n  type OpenAICompletionModelId,\n  openaiCompletionProviderOptions,\n} from './openai-completion-options';\n\ntype OpenAICompletionConfig = {\n  provider: string;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n};\n\nexport class OpenAICompletionLanguageModel implements LanguageModelV2 {\n  readonly specificationVersion = 'v2';\n\n  readonly modelId: OpenAICompletionModelId;\n\n  private readonly config: OpenAICompletionConfig;\n\n  private get providerOptionsName(): string {\n    return this.config.provider.split('.')[0].trim();\n  }\n\n  constructor(\n    modelId: OpenAICompletionModelId,\n    config: OpenAICompletionConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  readonly supportedUrls: Record<string, RegExp[]> = {\n    // No URLs are supported for completion models.\n  };\n\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences: userStopSequences,\n    responseFormat,\n    tools,\n    toolChoice,\n    seed,\n    providerOptions,\n  }: Parameters<LanguageModelV2['doGenerate']>[0]) {\n    const warnings: LanguageModelV2CallWarning[] = [];\n\n    // Parse provider options\n    const openaiOptions = {\n      ...(await parseProviderOptions({\n        provider: 'openai',\n        providerOptions,\n        schema: openaiCompletionProviderOptions,\n      })),\n      ...(await parseProviderOptions({\n        provider: this.providerOptionsName,\n        providerOptions,\n        schema: openaiCompletionProviderOptions,\n      })),\n    };\n\n    if (topK != null) {\n      warnings.push({ type: 'unsupported-setting', setting: 'topK' });\n    }\n\n    if (tools?.length) {\n      warnings.push({ type: 'unsupported-setting', setting: 'tools' });\n    }\n\n    if (toolChoice != null) {\n      warnings.push({ type: 'unsupported-setting', setting: 'toolChoice' });\n    }\n\n    if (responseFormat != null && responseFormat.type !== 'text') {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details: 'JSON response format is not supported.',\n      });\n    }\n\n    const { prompt: completionPrompt, stopSequences } =\n      convertToOpenAICompletionPrompt({ prompt });\n\n    const stop = [...(stopSequences ?? []), ...(userStopSequences ?? [])];\n\n    return {\n      args: {\n        // model id:\n        model: this.modelId,\n\n        // model specific settings:\n        echo: openaiOptions.echo,\n        logit_bias: openaiOptions.logitBias,\n        logprobs:\n          openaiOptions?.logprobs === true\n            ? 0\n            : openaiOptions?.logprobs === false\n              ? undefined\n              : openaiOptions?.logprobs,\n        suffix: openaiOptions.suffix,\n        user: openaiOptions.user,\n\n        // standardized settings:\n        max_tokens: maxOutputTokens,\n        temperature,\n        top_p: topP,\n        frequency_penalty: frequencyPenalty,\n        presence_penalty: presencePenalty,\n        seed,\n\n        // prompt:\n        prompt: completionPrompt,\n\n        // stop sequences:\n        stop: stop.length > 0 ? stop : undefined,\n      },\n      warnings,\n    };\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doGenerate']>>> {\n    const { args, warnings } = await this.getArgs(options);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: this.config.url({\n        path: '/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: args,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiCompletionResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const choice = response.choices[0];\n\n    const providerMetadata: SharedV2ProviderMetadata = { openai: {} };\n\n    if (choice.logprobs != null) {\n      providerMetadata.openai.logprobs = choice.logprobs;\n    }\n\n    return {\n      content: [{ type: 'text', text: choice.text }],\n      usage: {\n        inputTokens: response.usage?.prompt_tokens,\n        outputTokens: response.usage?.completion_tokens,\n        totalTokens: response.usage?.total_tokens,\n      },\n      finishReason: mapOpenAIFinishReason(choice.finish_reason),\n      request: { body: args },\n      response: {\n        ...getResponseMetadata(response),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      providerMetadata,\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV2['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doStream']>>> {\n    const { args, warnings } = await this.getArgs(options);\n\n    const body = {\n      ...args,\n      stream: true,\n\n      stream_options: {\n        include_usage: true,\n      },\n    };\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: this.config.url({\n        path: '/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        openaiCompletionChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    let finishReason: LanguageModelV2FinishReason = 'unknown';\n    const providerMetadata: SharedV2ProviderMetadata = { openai: {} };\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n    let isFirstChunk = true;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<OpenAICompletionChunk>,\n          LanguageModelV2StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = 'error';\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            // handle error chunks:\n            if ('error' in value) {\n              finishReason = 'error';\n              controller.enqueue({ type: 'error', error: value.error });\n              return;\n            }\n\n            if (isFirstChunk) {\n              isFirstChunk = false;\n\n              controller.enqueue({\n                type: 'response-metadata',\n                ...getResponseMetadata(value),\n              });\n\n              controller.enqueue({ type: 'text-start', id: '0' });\n            }\n\n            if (value.usage != null) {\n              usage.inputTokens = value.usage.prompt_tokens;\n              usage.outputTokens = value.usage.completion_tokens;\n              usage.totalTokens = value.usage.total_tokens;\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = mapOpenAIFinishReason(choice.finish_reason);\n            }\n\n            if (choice?.logprobs != null) {\n              providerMetadata.openai.logprobs = choice.logprobs;\n            }\n\n            if (choice?.text != null && choice.text.length > 0) {\n              controller.enqueue({\n                type: 'text-delta',\n                id: '0',\n                delta: choice.text,\n              });\n            }\n          },\n\n          flush(controller) {\n            if (!isFirstChunk) {\n              controller.enqueue({ type: 'text-end', id: '0' });\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              providerMetadata,\n              usage,\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n","import {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type OpenAIEmbeddingModelId =\n  | 'text-embedding-3-small'\n  | 'text-embedding-3-large'\n  | 'text-embedding-ada-002'\n  | (string & {});\n\nexport const openaiEmbeddingProviderOptions = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      /**\nThe number of dimensions the resulting output embeddings should have.\nOnly supported in text-embedding-3 and later models.\n   */\n      dimensions: z.number().optional(),\n\n      /**\nA unique identifier representing your end-user, which can help OpenAI to\nmonitor and detect abuse. Learn more.\n*/\n      user: z.string().optional(),\n    }),\n  ),\n);\n\nexport type OpenAIEmbeddingProviderOptions = InferValidator<\n  typeof openaiEmbeddingProviderOptions\n>;\n","import { lazyValidator, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiTextEmbeddingResponseSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      data: z.array(z.object({ embedding: z.array(z.number()) })),\n      usage: z.object({ prompt_tokens: z.number() }).nullish(),\n    }),\n  ),\n);\n","import {\n  type EmbeddingModelV2,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport type { OpenAIConfig } from '../openai-config';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport {\n  type OpenAIEmbeddingModelId,\n  openaiEmbeddingProviderOptions,\n} from './openai-embedding-options';\nimport { openaiTextEmbeddingResponseSchema } from './openai-embedding-api';\n\nexport class OpenAIEmbeddingModel implements EmbeddingModelV2<string> {\n  readonly specificationVersion = 'v2';\n  readonly modelId: OpenAIEmbeddingModelId;\n  readonly maxEmbeddingsPerCall = 2048;\n  readonly supportsParallelCalls = true;\n\n  private readonly config: OpenAIConfig;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  constructor(modelId: OpenAIEmbeddingModelId, config: OpenAIConfig) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  async doEmbed({\n    values,\n    headers,\n    abortSignal,\n    providerOptions,\n  }: Parameters<EmbeddingModelV2<string>['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV2<string>['doEmbed']>>\n  > {\n    if (values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        provider: this.provider,\n        modelId: this.modelId,\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        values,\n      });\n    }\n\n    // Parse provider options\n    const openaiOptions =\n      (await parseProviderOptions({\n        provider: 'openai',\n        providerOptions,\n        schema: openaiEmbeddingProviderOptions,\n      })) ?? {};\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      url: this.config.url({\n        path: '/embeddings',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), headers),\n      body: {\n        model: this.modelId,\n        input: values,\n        encoding_format: 'float',\n        dimensions: openaiOptions.dimensions,\n        user: openaiOptions.user,\n      },\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiTextEmbeddingResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      embeddings: response.data.map(item => item.embedding),\n      usage: response.usage\n        ? { tokens: response.usage.prompt_tokens }\n        : undefined,\n      response: { headers: responseHeaders, body: rawValue },\n    };\n  }\n}\n","import { lazyValidator, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// minimal version of the schema, focused on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiImageResponseSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      created: z.number().nullish(),\n      data: z.array(\n        z.object({\n          b64_json: z.string(),\n          revised_prompt: z.string().nullish(),\n        }),\n      ),\n      background: z.string().nullish(),\n      output_format: z.string().nullish(),\n      size: z.string().nullish(),\n      quality: z.string().nullish(),\n      usage: z\n        .object({\n          input_tokens: z.number().nullish(),\n          output_tokens: z.number().nullish(),\n          total_tokens: z.number().nullish(),\n          input_tokens_details: z\n            .object({\n              image_tokens: z.number().nullish(),\n              text_tokens: z.number().nullish(),\n            })\n            .nullish(),\n        })\n        .nullish(),\n    }),\n  ),\n);\n","import {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type OpenAIImageModelId =\n  | 'dall-e-3'\n  | 'dall-e-2'\n  | 'gpt-image-1'\n  | 'gpt-image-1-mini'\n  | 'gpt-image-1.5'\n  | 'gpt-image-2'\n  | (string & {});\n\n// https://platform.openai.com/docs/guides/images\nexport const modelMaxImagesPerCall: Record<OpenAIImageModelId, number> = {\n  'dall-e-3': 1,\n  'dall-e-2': 10,\n  'gpt-image-1': 10,\n  'gpt-image-1-mini': 10,\n  'gpt-image-1.5': 10,\n  'gpt-image-2': 10,\n};\n\nexport const hasDefaultResponseFormat = new Set([\n  'gpt-image-1',\n  'gpt-image-1-mini',\n  'gpt-image-1.5',\n  'gpt-image-2',\n]);\n\nconst baseImageModelOptionsObject = z.object({\n  /**\n   * Quality of the generated image(s).\n   *\n   * Valid values: `standard`, `hd`, `low`, `medium`, `high`, `auto`.\n   */\n  quality: z\n    .enum(['standard', 'hd', 'low', 'medium', 'high', 'auto'])\n    .optional(),\n\n  /**\n   * Background behavior for the generated image(s).\n   *\n   * If `transparent`, the output format must support transparency\n   * (i.e. `png` or `webp`).\n   */\n  background: z.enum(['transparent', 'opaque', 'auto']).optional(),\n\n  /**\n   * Format in which the generated image(s) are returned.\n   */\n  outputFormat: z.enum(['png', 'jpeg', 'webp']).optional(),\n\n  /**\n   * Compression level (0-100) for the generated image(s). Applies to the\n   * `jpeg` and `webp` output formats.\n   */\n  outputCompression: z.number().int().min(0).max(100).optional(),\n\n  /**\n   * A unique identifier representing your end-user, which can help OpenAI\n   * to monitor and detect abuse.\n   */\n  user: z.string().optional(),\n});\n\nexport const openaiImageModelOptions = lazyValidator(() =>\n  zodSchema(baseImageModelOptionsObject),\n);\n\nexport type OpenAIImageModelOptions = InferValidator<\n  typeof openaiImageModelOptions\n>;\n\nexport const openaiImageModelGenerationOptions = lazyValidator(() =>\n  zodSchema(\n    baseImageModelOptionsObject.extend({\n      /**\n       * Style of the generated image. `vivid` produces hyper-real and\n       * dramatic images; `natural` produces more subdued, less hyper-real\n       * looking images.\n       */\n      style: z.enum(['vivid', 'natural']).optional(),\n\n      /**\n       * Content moderation level for the generated image(s). `low` applies\n       * less restrictive filtering.\n       */\n      moderation: z.enum(['auto', 'low']).optional(),\n    }),\n  ),\n);\n\nexport type OpenAIImageModelGenerationOptions = InferValidator<\n  typeof openaiImageModelGenerationOptions\n>;\n","import type { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport type { OpenAIConfig } from '../openai-config';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport { openaiImageResponseSchema } from './openai-image-api';\nimport {\n  type OpenAIImageModelId,\n  hasDefaultResponseFormat,\n  modelMaxImagesPerCall,\n  openaiImageModelGenerationOptions,\n} from './openai-image-options';\n\ninterface OpenAIImageModelConfig extends OpenAIConfig {\n  _internal?: {\n    currentDate?: () => Date;\n  };\n}\n\nexport class OpenAIImageModel implements ImageModelV2 {\n  readonly specificationVersion = 'v2';\n\n  get maxImagesPerCall(): number {\n    return modelMaxImagesPerCall[this.modelId] ?? 1;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  constructor(\n    readonly modelId: OpenAIImageModelId,\n    private readonly config: OpenAIImageModelConfig,\n  ) {}\n\n  async doGenerate({\n    prompt,\n    n,\n    size,\n    aspectRatio,\n    seed,\n    providerOptions,\n    headers,\n    abortSignal,\n  }: Parameters<ImageModelV2['doGenerate']>[0]): Promise<\n    Awaited<ReturnType<ImageModelV2['doGenerate']>>\n  > {\n    const warnings: Array<ImageModelV2CallWarning> = [];\n\n    if (aspectRatio != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'aspectRatio',\n        details:\n          'This model does not support aspect ratio. Use `size` instead.',\n      });\n    }\n\n    if (seed != null) {\n      warnings.push({ type: 'unsupported-setting', setting: 'seed' });\n    }\n\n    const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n\n    const openaiOptions =\n      (await parseProviderOptions({\n        provider: 'openai',\n        providerOptions,\n        schema: openaiImageModelGenerationOptions,\n      })) ?? {};\n\n    const { value: response, responseHeaders } = await postJsonToApi({\n      url: this.config.url({\n        path: '/images/generations',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), headers),\n      body: {\n        model: this.modelId,\n        prompt,\n        n,\n        size,\n        quality: openaiOptions.quality,\n        style: openaiOptions.style,\n        background: openaiOptions.background,\n        moderation: openaiOptions.moderation,\n        output_format: openaiOptions.outputFormat,\n        output_compression: openaiOptions.outputCompression,\n        user: openaiOptions.user,\n        ...(!hasDefaultResponseFormat.has(this.modelId)\n          ? { response_format: 'b64_json' }\n          : {}),\n      },\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiImageResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      images: response.data.map(item => item.b64_json),\n      warnings,\n      response: {\n        timestamp: currentDate,\n        modelId: this.modelId,\n        headers: responseHeaders,\n      },\n      providerMetadata: {\n        openai: {\n          images: response.data.map((item, index) => ({\n            ...(item.revised_prompt\n              ? { revisedPrompt: item.revised_prompt }\n              : {}),\n            ...(response.created != null ? { created: response.created } : {}),\n            ...(response.size != null ? { size: response.size } : {}),\n            ...(response.quality != null ? { quality: response.quality } : {}),\n            ...(response.background != null\n              ? { background: response.background }\n              : {}),\n            ...(response.output_format != null\n              ? { outputFormat: response.output_format }\n              : {}),\n            ...distributeTokenDetails(\n              response.usage,\n              index,\n              response.data.length,\n            ),\n          })),\n        },\n      },\n    };\n  }\n}\n\n/**\n * Distributes token usage details evenly across images, with the remainder\n * assigned to the last image so that summing across all entries gives the\n * exact total.\n */\nfunction distributeTokenDetails(\n  usage:\n    | {\n        input_tokens?: number | null;\n        output_tokens?: number | null;\n        total_tokens?: number | null;\n        input_tokens_details?: {\n          image_tokens?: number | null;\n          text_tokens?: number | null;\n        } | null;\n      }\n    | null\n    | undefined,\n  index: number,\n  total: number,\n): {\n  imageTokens?: number;\n  textTokens?: number;\n  inputTokens?: number;\n  outputTokens?: number;\n} {\n  if (usage == null) {\n    return {};\n  }\n\n  const result: {\n    imageTokens?: number;\n    textTokens?: number;\n    inputTokens?: number;\n    outputTokens?: number;\n  } = {};\n\n  const details = usage.input_tokens_details;\n\n  if (details?.image_tokens != null) {\n    const base = Math.floor(details.image_tokens / total);\n    const remainder = details.image_tokens - base * (total - 1);\n    result.imageTokens = index === total - 1 ? remainder : base;\n  }\n\n  if (details?.text_tokens != null) {\n    const base = Math.floor(details.text_tokens / total);\n    const remainder = details.text_tokens - base * (total - 1);\n    result.textTokens = index === total - 1 ? remainder : base;\n  }\n\n  if (usage.input_tokens != null) {\n    const base = Math.floor(usage.input_tokens / total);\n    const remainder = usage.input_tokens - base * (total - 1);\n    result.inputTokens = index === total - 1 ? remainder : base;\n  }\n\n  if (usage.output_tokens != null) {\n    const base = Math.floor(usage.output_tokens / total);\n    const remainder = usage.output_tokens - base * (total - 1);\n    result.outputTokens = index === total - 1 ? remainder : base;\n  }\n\n  return result;\n}\n","import { lazyValidator, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport const openaiTranscriptionResponseSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      text: z.string(),\n      language: z.string().nullish(),\n      duration: z.number().nullish(),\n      words: z\n        .array(\n          z.object({\n            word: z.string(),\n            start: z.number(),\n            end: z.number(),\n          }),\n        )\n        .nullish(),\n      segments: z\n        .array(\n          z.object({\n            id: z.number(),\n            seek: z.number(),\n            start: z.number(),\n            end: z.number(),\n            text: z.string(),\n            tokens: z.array(z.number()),\n            temperature: z.number(),\n            avg_logprob: z.number(),\n            compression_ratio: z.number(),\n            no_speech_prob: z.number(),\n          }),\n        )\n        .nullish(),\n    }),\n  ),\n);\n","import {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type OpenAITranscriptionModelId =\n  | 'whisper-1'\n  | 'gpt-4o-mini-transcribe'\n  | 'gpt-4o-transcribe'\n  | (string & {});\n\n// https://platform.openai.com/docs/api-reference/audio/createTranscription\nexport const openAITranscriptionProviderOptions = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      /**\n       * Additional information to include in the transcription response.\n       */\n\n      include: z.array(z.string()).optional(),\n\n      /**\n       * The language of the input audio in ISO-639-1 format.\n       */\n      language: z.string().optional(),\n\n      /**\n       * An optional text to guide the model's style or continue a previous audio segment.\n       */\n      prompt: z.string().optional(),\n\n      /**\n       * The sampling temperature, between 0 and 1.\n       * @default 0\n       */\n      temperature: z.number().min(0).max(1).default(0).optional(),\n\n      /**\n       * The timestamp granularities to populate for this transcription.\n       * @default ['segment']\n       */\n      timestampGranularities: z\n        .array(z.enum(['word', 'segment']))\n        .default(['segment'])\n        .optional(),\n    }),\n  ),\n);\n\nexport type OpenAITranscriptionProviderOptions = InferValidator<\n  typeof openAITranscriptionProviderOptions\n>;\n","import type {\n  TranscriptionModelV2,\n  TranscriptionModelV2CallOptions,\n  TranscriptionModelV2CallWarning,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  convertBase64ToUint8Array,\n  createJsonResponseHandler,\n  mediaTypeToExtension,\n  parseProviderOptions,\n  postFormDataToApi,\n} from '@ai-sdk/provider-utils';\nimport type { OpenAIConfig } from '../openai-config';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport { openaiTranscriptionResponseSchema } from './openai-transcription-api';\nimport {\n  type OpenAITranscriptionModelId,\n  type OpenAITranscriptionProviderOptions,\n  openAITranscriptionProviderOptions,\n} from './openai-transcription-options';\n\nexport type OpenAITranscriptionCallOptions = Omit<\n  TranscriptionModelV2CallOptions,\n  'providerOptions'\n> & {\n  providerOptions?: {\n    openai?: OpenAITranscriptionProviderOptions;\n  };\n};\n\ninterface OpenAITranscriptionModelConfig extends OpenAIConfig {\n  _internal?: {\n    currentDate?: () => Date;\n  };\n}\n\n// https://platform.openai.com/docs/guides/speech-to-text#supported-languages\nconst languageMap = {\n  afrikaans: 'af',\n  arabic: 'ar',\n  armenian: 'hy',\n  azerbaijani: 'az',\n  belarusian: 'be',\n  bosnian: 'bs',\n  bulgarian: 'bg',\n  catalan: 'ca',\n  chinese: 'zh',\n  croatian: 'hr',\n  czech: 'cs',\n  danish: 'da',\n  dutch: 'nl',\n  english: 'en',\n  estonian: 'et',\n  finnish: 'fi',\n  french: 'fr',\n  galician: 'gl',\n  german: 'de',\n  greek: 'el',\n  hebrew: 'he',\n  hindi: 'hi',\n  hungarian: 'hu',\n  icelandic: 'is',\n  indonesian: 'id',\n  italian: 'it',\n  japanese: 'ja',\n  kannada: 'kn',\n  kazakh: 'kk',\n  korean: 'ko',\n  latvian: 'lv',\n  lithuanian: 'lt',\n  macedonian: 'mk',\n  malay: 'ms',\n  marathi: 'mr',\n  maori: 'mi',\n  nepali: 'ne',\n  norwegian: 'no',\n  persian: 'fa',\n  polish: 'pl',\n  portuguese: 'pt',\n  romanian: 'ro',\n  russian: 'ru',\n  serbian: 'sr',\n  slovak: 'sk',\n  slovenian: 'sl',\n  spanish: 'es',\n  swahili: 'sw',\n  swedish: 'sv',\n  tagalog: 'tl',\n  tamil: 'ta',\n  thai: 'th',\n  turkish: 'tr',\n  ukrainian: 'uk',\n  urdu: 'ur',\n  vietnamese: 'vi',\n  welsh: 'cy',\n};\n\nexport class OpenAITranscriptionModel implements TranscriptionModelV2 {\n  readonly specificationVersion = 'v2';\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  constructor(\n    readonly modelId: OpenAITranscriptionModelId,\n    private readonly config: OpenAITranscriptionModelConfig,\n  ) {}\n\n  private async getArgs({\n    audio,\n    mediaType,\n    providerOptions,\n  }: OpenAITranscriptionCallOptions) {\n    const warnings: TranscriptionModelV2CallWarning[] = [];\n\n    // Parse provider options\n    const openAIOptions = await parseProviderOptions({\n      provider: 'openai',\n      providerOptions,\n      schema: openAITranscriptionProviderOptions,\n    });\n\n    // Create form data with base fields\n    const formData = new FormData();\n    const blob =\n      audio instanceof Uint8Array\n        ? new Blob([audio])\n        : new Blob([convertBase64ToUint8Array(audio)]);\n\n    formData.append('model', this.modelId);\n    const fileExtension = mediaTypeToExtension(mediaType);\n    formData.append(\n      'file',\n      new File([blob], 'audio', { type: mediaType }),\n      `audio.${fileExtension}`,\n    );\n\n    // Add provider-specific options\n    if (openAIOptions) {\n      const transcriptionModelOptions = {\n        include: openAIOptions.include,\n        language: openAIOptions.language,\n        prompt: openAIOptions.prompt,\n        // https://platform.openai.com/docs/api-reference/audio/createTranscription#audio_createtranscription-response_format\n        // prefer verbose_json to get segments for models that support it\n        response_format: [\n          'gpt-4o-transcribe',\n          'gpt-4o-mini-transcribe',\n        ].includes(this.modelId)\n          ? 'json'\n          : 'verbose_json',\n        temperature: openAIOptions.temperature,\n        timestamp_granularities: openAIOptions.timestampGranularities,\n      };\n\n      for (const [key, value] of Object.entries(transcriptionModelOptions)) {\n        if (value != null) {\n          if (Array.isArray(value)) {\n            for (const item of value) {\n              formData.append(`${key}[]`, String(item));\n            }\n          } else {\n            formData.append(key, String(value));\n          }\n        }\n      }\n    }\n\n    return {\n      formData,\n      warnings,\n    };\n  }\n\n  async doGenerate(\n    options: OpenAITranscriptionCallOptions,\n  ): Promise<Awaited<ReturnType<TranscriptionModelV2['doGenerate']>>> {\n    const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n    const { formData, warnings } = await this.getArgs(options);\n\n    const {\n      value: response,\n      responseHeaders,\n      rawValue: rawResponse,\n    } = await postFormDataToApi({\n      url: this.config.url({\n        path: '/audio/transcriptions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      formData,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiTranscriptionResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const language =\n      response.language != null && response.language in languageMap\n        ? languageMap[response.language as keyof typeof languageMap]\n        : undefined;\n\n    return {\n      text: response.text,\n      segments:\n        response.segments?.map(segment => ({\n          text: segment.text,\n          startSecond: segment.start,\n          endSecond: segment.end,\n        })) ??\n        response.words?.map(word => ({\n          text: word.word,\n          startSecond: word.start,\n          endSecond: word.end,\n        })) ??\n        [],\n      language,\n      durationInSeconds: response.duration ?? undefined,\n      warnings,\n      response: {\n        timestamp: currentDate,\n        modelId: this.modelId,\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n    };\n  }\n}\n","import {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type OpenAISpeechModelId =\n  | 'tts-1'\n  | 'tts-1-hd'\n  | 'gpt-4o-mini-tts'\n  | (string & {});\n\n// https://platform.openai.com/docs/api-reference/audio/createSpeech\nexport const openaiSpeechProviderOptionsSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      instructions: z.string().nullish(),\n      speed: z.number().min(0.25).max(4.0).default(1.0).nullish(),\n    }),\n  ),\n);\n\nexport type OpenAISpeechCallOptions = InferValidator<\n  typeof openaiSpeechProviderOptionsSchema\n>;\n","import type { SpeechModelV2, SpeechModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createBinaryResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport type { OpenAIConfig } from '../openai-config';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport type { OpenAISpeechAPITypes } from './openai-speech-api';\nimport {\n  type OpenAISpeechModelId,\n  openaiSpeechProviderOptionsSchema,\n} from './openai-speech-options';\n\ninterface OpenAISpeechModelConfig extends OpenAIConfig {\n  _internal?: {\n    currentDate?: () => Date;\n  };\n}\n\nexport class OpenAISpeechModel implements SpeechModelV2 {\n  readonly specificationVersion = 'v2';\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  constructor(\n    readonly modelId: OpenAISpeechModelId,\n    private readonly config: OpenAISpeechModelConfig,\n  ) {}\n\n  private async getArgs({\n    text,\n    voice = 'alloy',\n    outputFormat = 'mp3',\n    speed,\n    instructions,\n    language,\n    providerOptions,\n  }: Parameters<SpeechModelV2['doGenerate']>[0]) {\n    const warnings: SpeechModelV2CallWarning[] = [];\n\n    // Parse provider options\n    const openAIOptions = await parseProviderOptions({\n      provider: 'openai',\n      providerOptions,\n      schema: openaiSpeechProviderOptionsSchema,\n    });\n\n    // Create request body\n    const requestBody: Record<string, unknown> = {\n      model: this.modelId,\n      input: text,\n      voice,\n      response_format: 'mp3',\n      speed,\n      instructions,\n    };\n\n    if (outputFormat) {\n      if (['mp3', 'opus', 'aac', 'flac', 'wav', 'pcm'].includes(outputFormat)) {\n        requestBody.response_format = outputFormat;\n      } else {\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'outputFormat',\n          details: `Unsupported output format: ${outputFormat}. Using mp3 instead.`,\n        });\n      }\n    }\n\n    // Add provider-specific options\n    if (openAIOptions) {\n      const speechModelOptions: OpenAISpeechAPITypes = {};\n\n      for (const key in speechModelOptions) {\n        const value = speechModelOptions[key as keyof OpenAISpeechAPITypes];\n        if (value !== undefined) {\n          requestBody[key] = value;\n        }\n      }\n    }\n\n    if (language) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'language',\n        details: `OpenAI speech models do not support language selection. Language parameter \"${language}\" was ignored.`,\n      });\n    }\n\n    return {\n      requestBody,\n      warnings,\n    };\n  }\n\n  async doGenerate(\n    options: Parameters<SpeechModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<SpeechModelV2['doGenerate']>>> {\n    const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n    const { requestBody, warnings } = await this.getArgs(options);\n\n    const {\n      value: audio,\n      responseHeaders,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: this.config.url({\n        path: '/audio/speech',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: requestBody,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createBinaryResponseHandler(),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      audio,\n      warnings,\n      request: {\n        body: JSON.stringify(requestBody),\n      },\n      response: {\n        timestamp: currentDate,\n        modelId: this.modelId,\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n    };\n  }\n}\n","import {\n  createProviderDefinedToolFactoryWithOutputSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport const localShellInputSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      action: z.object({\n        type: z.literal('exec'),\n        command: z.array(z.string()),\n        timeoutMs: z.number().optional(),\n        user: z.string().optional(),\n        workingDirectory: z.string().optional(),\n        env: z.record(z.string(), z.string()).optional(),\n      }),\n    }),\n  ),\n);\n\nexport const localShellOutputSchema = lazySchema(() =>\n  zodSchema(z.object({ output: z.string() })),\n);\n\nexport const localShell = createProviderDefinedToolFactoryWithOutputSchema<\n  {\n    /**\n     * Execute a shell command on the server.\n     */\n    action: {\n      type: 'exec';\n\n      /**\n       * The command to run.\n       */\n      command: string[];\n\n      /**\n       * Optional timeout in milliseconds for the command.\n       */\n      timeoutMs?: number;\n\n      /**\n       * Optional user to run the command as.\n       */\n      user?: string;\n\n      /**\n       * Optional working directory to run the command in.\n       */\n      workingDirectory?: string;\n\n      /**\n       * Environment variables to set for the command.\n       */\n      env?: Record<string, string>;\n    };\n  },\n  {\n    /**\n     * The output of local shell tool call.\n     */\n    output: string;\n  },\n  {}\n>({\n  id: 'openai.local_shell',\n  name: 'local_shell',\n  inputSchema: localShellInputSchema,\n  outputSchema: localShellOutputSchema,\n});\n","import {\n  type LanguageModelV2CallWarning,\n  type LanguageModelV2Prompt,\n  type LanguageModelV2ToolCallPart,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport {\n  convertToBase64,\n  parseProviderOptions,\n  validateTypes,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport {\n  localShellInputSchema,\n  localShellOutputSchema,\n} from '../tool/local-shell';\nimport { webSearchOutputSchema } from '../tool/web-search';\nimport type {\n  OpenAIResponsesFunctionCallOutput,\n  OpenAIResponsesInput,\n  OpenAIResponsesReasoning,\n} from './openai-responses-api';\n\n/**\n * Check if a string is a file ID based on the given prefixes\n * Returns false if prefixes is undefined (disables file ID detection)\n */\nfunction isFileId(data: string, prefixes?: readonly string[]): boolean {\n  if (!prefixes) return false;\n  return prefixes.some(prefix => data.startsWith(prefix));\n}\n\nexport async function convertToOpenAIResponsesInput({\n  prompt,\n  systemMessageMode,\n  fileIdPrefixes,\n  store,\n  hasLocalShellTool = false,\n}: {\n  prompt: LanguageModelV2Prompt;\n  systemMessageMode: 'system' | 'developer' | 'remove';\n  fileIdPrefixes?: readonly string[];\n  store: boolean;\n  hasLocalShellTool?: boolean;\n}): Promise<{\n  input: OpenAIResponsesInput;\n  warnings: Array<LanguageModelV2CallWarning>;\n}> {\n  let input: OpenAIResponsesInput = [];\n  const warnings: Array<LanguageModelV2CallWarning> = [];\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        switch (systemMessageMode) {\n          case 'system': {\n            input.push({ role: 'system', content });\n            break;\n          }\n          case 'developer': {\n            input.push({ role: 'developer', content });\n            break;\n          }\n          case 'remove': {\n            warnings.push({\n              type: 'other',\n              message: 'system messages are removed for this model',\n            });\n            break;\n          }\n          default: {\n            const _exhaustiveCheck: never = systemMessageMode;\n            throw new Error(\n              `Unsupported system message mode: ${_exhaustiveCheck}`,\n            );\n          }\n        }\n        break;\n      }\n\n      case 'user': {\n        input.push({\n          role: 'user',\n          content: content.map((part, index) => {\n            switch (part.type) {\n              case 'text': {\n                return { type: 'input_text', text: part.text };\n              }\n              case 'file': {\n                if (part.mediaType.startsWith('image/')) {\n                  const mediaType =\n                    part.mediaType === 'image/*'\n                      ? 'image/jpeg'\n                      : part.mediaType;\n\n                  return {\n                    type: 'input_image',\n                    ...(part.data instanceof URL\n                      ? { image_url: part.data.toString() }\n                      : typeof part.data === 'string' &&\n                          isFileId(part.data, fileIdPrefixes)\n                        ? { file_id: part.data }\n                        : {\n                            image_url: `data:${mediaType};base64,${convertToBase64(part.data)}`,\n                          }),\n                    detail: part.providerOptions?.openai?.imageDetail,\n                  };\n                } else if (part.mediaType === 'application/pdf') {\n                  if (part.data instanceof URL) {\n                    return {\n                      type: 'input_file',\n                      file_url: part.data.toString(),\n                    };\n                  }\n                  return {\n                    type: 'input_file',\n                    ...(typeof part.data === 'string' &&\n                    isFileId(part.data, fileIdPrefixes)\n                      ? { file_id: part.data }\n                      : {\n                          filename: part.filename ?? `part-${index}.pdf`,\n                          file_data: `data:application/pdf;base64,${convertToBase64(part.data)}`,\n                        }),\n                  };\n                } else {\n                  throw new UnsupportedFunctionalityError({\n                    functionality: `file part media type ${part.mediaType}`,\n                  });\n                }\n              }\n            }\n          }),\n        });\n\n        break;\n      }\n\n      case 'assistant': {\n        const reasoningMessages: Record<string, OpenAIResponsesReasoning> = {};\n        const toolCallParts: Record<string, LanguageModelV2ToolCallPart> = {};\n\n        for (const part of content) {\n          switch (part.type) {\n            case 'text': {\n              const id = part.providerOptions?.openai?.itemId as\n                | string\n                | undefined;\n              const phase = part.providerOptions?.openai?.phase as\n                | 'commentary'\n                | 'final_answer'\n                | null\n                | undefined;\n\n              // item references reduce the payload size\n              if (store && id != null) {\n                input.push({ type: 'item_reference', id });\n                break;\n              }\n\n              input.push({\n                role: 'assistant',\n                content: [{ type: 'output_text', text: part.text }],\n                id,\n                ...(phase != null && { phase }),\n              });\n\n              break;\n            }\n            case 'tool-call': {\n              toolCallParts[part.toolCallId] = part;\n\n              if (part.providerExecuted) {\n                break;\n              }\n\n              const id = part.providerOptions?.openai?.itemId as\n                | string\n                | undefined;\n\n              // item references reduce the payload size\n              if (store && id != null) {\n                input.push({ type: 'item_reference', id });\n                break;\n              }\n\n              if (hasLocalShellTool && part.toolName === 'local_shell') {\n                const parsedInput = await validateTypes({\n                  value: part.input,\n                  schema: localShellInputSchema,\n                });\n                input.push({\n                  type: 'local_shell_call',\n                  call_id: part.toolCallId,\n                  id: id!,\n                  action: {\n                    type: 'exec',\n                    command: parsedInput.action.command,\n                    timeout_ms: parsedInput.action.timeoutMs,\n                    user: parsedInput.action.user,\n                    working_directory: parsedInput.action.workingDirectory,\n                    env: parsedInput.action.env,\n                  },\n                });\n\n                break;\n              }\n\n              input.push({\n                type: 'function_call',\n                call_id: part.toolCallId,\n                name: part.toolName,\n                arguments: JSON.stringify(part.input),\n                id,\n              });\n              break;\n            }\n\n            // assistant tool result parts are from provider-executed tools:\n            case 'tool-result': {\n              if (store) {\n                // use item references to refer to tool results from built-in tools\n                input.push({ type: 'item_reference', id: part.toolCallId });\n              } else {\n                warnings.push({\n                  type: 'other',\n                  message: `Results for OpenAI tool ${part.toolName} are not sent to the API when store is false`,\n                });\n              }\n\n              break;\n            }\n\n            case 'reasoning': {\n              const providerOptions = await parseProviderOptions({\n                provider: 'openai',\n                providerOptions: part.providerOptions,\n                schema: openaiResponsesReasoningProviderOptionsSchema,\n              });\n\n              const reasoningId = providerOptions?.itemId;\n\n              if (reasoningId != null) {\n                const reasoningMessage = reasoningMessages[reasoningId];\n\n                if (store) {\n                  // use item references to refer to reasoning (single reference)\n                  // when the first part is encountered\n                  if (reasoningMessage === undefined) {\n                    input.push({ type: 'item_reference', id: reasoningId });\n\n                    // store unused reasoning message to mark id as used\n                    reasoningMessages[reasoningId] = {\n                      type: 'reasoning',\n                      id: reasoningId,\n                      summary: [],\n                    };\n                  }\n                } else {\n                  const summaryParts: Array<{\n                    type: 'summary_text';\n                    text: string;\n                  }> = [];\n\n                  if (part.text.length > 0) {\n                    summaryParts.push({\n                      type: 'summary_text',\n                      text: part.text,\n                    });\n                  } else if (reasoningMessage !== undefined) {\n                    warnings.push({\n                      type: 'other',\n                      message: `Cannot append empty reasoning part to existing reasoning sequence. Skipping reasoning part: ${JSON.stringify(part)}.`,\n                    });\n                  }\n\n                  if (reasoningMessage === undefined) {\n                    reasoningMessages[reasoningId] = {\n                      type: 'reasoning',\n                      id: reasoningId,\n                      encrypted_content:\n                        providerOptions?.reasoningEncryptedContent,\n                      summary: summaryParts,\n                    };\n                    input.push(reasoningMessages[reasoningId]);\n                  } else {\n                    reasoningMessage.summary.push(...summaryParts);\n\n                    // updated encrypted content to enable setting it in the last summary part:\n                    if (providerOptions?.reasoningEncryptedContent != null) {\n                      reasoningMessage.encrypted_content =\n                        providerOptions.reasoningEncryptedContent;\n                    }\n                  }\n                }\n              } else {\n                warnings.push({\n                  type: 'other',\n                  message: `Non-OpenAI reasoning parts are not supported. Skipping reasoning part: ${JSON.stringify(part)}.`,\n                });\n              }\n              break;\n            }\n          }\n        }\n\n        break;\n      }\n\n      case 'tool': {\n        for (const part of content) {\n          const output = part.output;\n\n          if (\n            hasLocalShellTool &&\n            part.toolName === 'local_shell' &&\n            output.type === 'json'\n          ) {\n            const parsedOutput = await validateTypes({\n              value: output.value,\n              schema: localShellOutputSchema,\n            });\n\n            input.push({\n              type: 'local_shell_call_output',\n              call_id: part.toolCallId,\n              output: parsedOutput.output,\n            });\n            break;\n          }\n\n          let contentValue: OpenAIResponsesFunctionCallOutput['output'];\n          switch (output.type) {\n            case 'text':\n            case 'error-text':\n              contentValue = output.value;\n              break;\n            case 'json':\n            case 'error-json':\n              contentValue = JSON.stringify(output.value);\n              break;\n            case 'content':\n              contentValue = output.value.map(item => {\n                switch (item.type) {\n                  case 'text': {\n                    return { type: 'input_text' as const, text: item.text };\n                  }\n                  case 'media': {\n                    return item.mediaType.startsWith('image/')\n                      ? {\n                          type: 'input_image' as const,\n                          image_url: `data:${item.mediaType};base64,${item.data}`,\n                        }\n                      : {\n                          type: 'input_file' as const,\n                          filename: 'data',\n                          file_data: `data:${item.mediaType};base64,${item.data}`,\n                        };\n                  }\n                }\n              });\n              break;\n          }\n\n          input.push({\n            type: 'function_call_output',\n            call_id: part.toolCallId,\n            output: contentValue,\n          });\n        }\n\n        break;\n      }\n\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  // when store is false, remove reasoning parts without encrypted content\n  if (\n    !store &&\n    input.some(\n      item =>\n        'type' in item &&\n        item.type === 'reasoning' &&\n        item.encrypted_content == null,\n    )\n  ) {\n    warnings.push({\n      type: 'other',\n      message:\n        'Reasoning parts without encrypted content are not supported when store is false. Skipping reasoning parts.',\n    });\n    input = input.filter(\n      item =>\n        !('type' in item) ||\n        item.type !== 'reasoning' ||\n        item.encrypted_content != null,\n    );\n  }\n\n  return { input, warnings };\n}\n\nconst openaiResponsesReasoningProviderOptionsSchema = z.object({\n  itemId: z.string().nullish(),\n  reasoningEncryptedContent: z.string().nullish(),\n});\n\nexport type OpenAIResponsesReasoningProviderOptions = z.infer<\n  typeof openaiResponsesReasoningProviderOptionsSchema\n>;\n","import type { LanguageModelV2FinishReason } from '@ai-sdk/provider';\n\nexport function mapOpenAIResponseFinishReason({\n  finishReason,\n  hasFunctionCall,\n}: {\n  finishReason: string | null | undefined;\n  // flag that checks if there have been client-side tool calls (not executed by openai)\n  hasFunctionCall: boolean;\n}): LanguageModelV2FinishReason {\n  switch (finishReason) {\n    case undefined:\n    case null:\n      return hasFunctionCall ? 'tool-calls' : 'stop';\n    case 'max_output_tokens':\n      return 'length';\n    case 'content_filter':\n      return 'content-filter';\n    default:\n      return hasFunctionCall ? 'tool-calls' : 'unknown';\n  }\n}\n","import type { JSONSchema7 } from '@ai-sdk/provider';\nimport {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type OpenAIResponsesInput = Array<OpenAIResponsesInputItem>;\n\nexport type OpenAIResponsesInputItem =\n  | OpenAIResponsesSystemMessage\n  | OpenAIResponsesUserMessage\n  | OpenAIResponsesAssistantMessage\n  | OpenAIResponsesFunctionCall\n  | OpenAIResponsesFunctionCallOutput\n  | OpenAIResponsesComputerCall\n  | OpenAIResponsesLocalShellCall\n  | OpenAIResponsesLocalShellCallOutput\n  | OpenAIResponsesReasoning\n  | OpenAIResponsesItemReference;\n\nexport type OpenAIResponsesIncludeValue =\n  | 'web_search_call.action.sources'\n  | 'code_interpreter_call.outputs'\n  | 'computer_call_output.output.image_url'\n  | 'file_search_call.results'\n  | 'message.input_image.image_url'\n  | 'message.output_text.logprobs'\n  | 'reasoning.encrypted_content';\n\nexport type OpenAIResponsesIncludeOptions =\n  | Array<OpenAIResponsesIncludeValue>\n  | undefined\n  | null;\n\nexport type OpenAIResponsesSystemMessage = {\n  role: 'system' | 'developer';\n  content: string;\n};\n\nexport type OpenAIResponsesUserMessage = {\n  role: 'user';\n  content: Array<\n    | { type: 'input_text'; text: string }\n    | { type: 'input_image'; image_url: string }\n    | { type: 'input_image'; file_id: string }\n    | { type: 'input_file'; file_url: string }\n    | { type: 'input_file'; filename: string; file_data: string }\n    | { type: 'input_file'; file_id: string }\n  >;\n};\n\nexport type OpenAIResponsesAssistantMessage = {\n  role: 'assistant';\n  content: Array<{ type: 'output_text'; text: string }>;\n  id?: string;\n  phase?: 'commentary' | 'final_answer' | null;\n};\n\nexport type OpenAIResponsesFunctionCall = {\n  type: 'function_call';\n  call_id: string;\n  name: string;\n  arguments: string;\n  id?: string;\n};\n\nexport type OpenAIResponsesFunctionCallOutput = {\n  type: 'function_call_output';\n  call_id: string;\n  output:\n    | string\n    | Array<\n        | { type: 'input_text'; text: string }\n        | { type: 'input_image'; image_url: string }\n        | { type: 'input_file'; filename: string; file_data: string }\n      >;\n};\n\nexport type OpenAIResponsesComputerCall = {\n  type: 'computer_call';\n  id: string;\n  status?: string;\n};\n\nexport type OpenAIResponsesLocalShellCall = {\n  type: 'local_shell_call';\n  id: string;\n  call_id: string;\n  action: {\n    type: 'exec';\n    command: string[];\n    timeout_ms?: number;\n    user?: string;\n    working_directory?: string;\n    env?: Record<string, string>;\n  };\n};\n\nexport type OpenAIResponsesLocalShellCallOutput = {\n  type: 'local_shell_call_output';\n  call_id: string;\n  output: string;\n};\n\nexport type OpenAIResponsesItemReference = {\n  type: 'item_reference';\n  id: string;\n};\n\n/**\n * A filter used to compare a specified attribute key to a given value using a defined comparison operation.\n */\nexport type OpenAIResponsesFileSearchToolComparisonFilter = {\n  /**\n   * The key to compare against the value.\n   */\n  key: string;\n\n  /**\n   * Specifies the comparison operator: eq, ne, gt, gte, lt, lte, in, nin.\n   */\n  type: 'eq' | 'ne' | 'gt' | 'gte' | 'lt' | 'lte' | 'in' | 'nin';\n\n  /**\n   * The value to compare against the attribute key; supports string, number, boolean, or array of string types.\n   */\n  value: string | number | boolean | string[];\n};\n\n/**\n * Combine multiple filters using and or or.\n */\nexport type OpenAIResponsesFileSearchToolCompoundFilter = {\n  /**\n   * Type of operation: and or or.\n   */\n  type: 'and' | 'or';\n\n  /**\n   * Array of filters to combine. Items can be ComparisonFilter or CompoundFilter.\n   */\n  filters: Array<\n    | OpenAIResponsesFileSearchToolComparisonFilter\n    | OpenAIResponsesFileSearchToolCompoundFilter\n  >;\n};\n\nexport type OpenAIResponsesTool =\n  | {\n      type: 'function';\n      name: string;\n      description: string | undefined;\n      parameters: JSONSchema7;\n      strict: boolean | undefined;\n    }\n  | {\n      type: 'web_search';\n      external_web_access: boolean | undefined;\n      filters: { allowed_domains: string[] | undefined } | undefined;\n      search_context_size: 'low' | 'medium' | 'high' | undefined;\n      user_location:\n        | {\n            type: 'approximate';\n            city?: string;\n            country?: string;\n            region?: string;\n            timezone?: string;\n          }\n        | undefined;\n    }\n  | {\n      type: 'web_search_preview';\n      search_context_size: 'low' | 'medium' | 'high' | undefined;\n      user_location:\n        | {\n            type: 'approximate';\n            city?: string;\n            country?: string;\n            region?: string;\n            timezone?: string;\n          }\n        | undefined;\n    }\n  | {\n      type: 'code_interpreter';\n      container: string | { type: 'auto'; file_ids: string[] | undefined };\n    }\n  | {\n      type: 'file_search';\n      vector_store_ids: string[];\n      max_num_results: number | undefined;\n      ranking_options:\n        | { ranker?: string; score_threshold?: number }\n        | undefined;\n      filters:\n        | OpenAIResponsesFileSearchToolComparisonFilter\n        | OpenAIResponsesFileSearchToolCompoundFilter\n        | undefined;\n    }\n  | {\n      type: 'image_generation';\n      background: 'auto' | 'opaque' | 'transparent' | undefined;\n      input_fidelity: 'low' | 'high' | undefined;\n      input_image_mask:\n        | {\n            file_id: string | undefined;\n            image_url: string | undefined;\n          }\n        | undefined;\n      model: string | undefined;\n      moderation: 'auto' | undefined;\n      output_compression: number | undefined;\n      output_format: 'png' | 'jpeg' | 'webp' | undefined;\n      quality: 'auto' | 'low' | 'medium' | 'high' | undefined;\n      size: 'auto' | '1024x1024' | '1024x1536' | '1536x1024' | undefined;\n    }\n  | {\n      type: 'local_shell';\n    };\n\nexport type OpenAIResponsesReasoning = {\n  type: 'reasoning';\n  id: string;\n  encrypted_content?: string | null;\n  summary: Array<{\n    type: 'summary_text';\n    text: string;\n  }>;\n};\n\nexport const openaiResponsesChunkSchema = lazyValidator(() =>\n  zodSchema(\n    z.union([\n      z.object({\n        type: z.literal('response.output_text.delta'),\n        item_id: z.string(),\n        delta: z.string(),\n        logprobs: z\n          .array(\n            z.object({\n              token: z.string(),\n              logprob: z.number(),\n              top_logprobs: z.array(\n                z.object({\n                  token: z.string(),\n                  logprob: z.number(),\n                }),\n              ),\n            }),\n          )\n          .nullish(),\n      }),\n      z.object({\n        type: z.enum(['response.completed', 'response.incomplete']),\n        response: z.object({\n          incomplete_details: z.object({ reason: z.string() }).nullish(),\n          usage: z.object({\n            input_tokens: z.number(),\n            input_tokens_details: z\n              .object({ cached_tokens: z.number().nullish() })\n              .nullish(),\n            output_tokens: z.number(),\n            output_tokens_details: z\n              .object({ reasoning_tokens: z.number().nullish() })\n              .nullish(),\n          }),\n          service_tier: z.string().nullish(),\n        }),\n      }),\n      z.object({\n        type: z.literal('response.created'),\n        response: z.object({\n          id: z.string(),\n          created_at: z.number(),\n          model: z.string(),\n          service_tier: z.string().nullish(),\n        }),\n      }),\n      z.object({\n        type: z.literal('response.output_item.added'),\n        output_index: z.number(),\n        item: z.discriminatedUnion('type', [\n          z.object({\n            type: z.literal('message'),\n            id: z.string(),\n            phase: z.enum(['commentary', 'final_answer']).nullish(),\n          }),\n          z.object({\n            type: z.literal('reasoning'),\n            id: z.string(),\n            encrypted_content: z.string().nullish(),\n          }),\n          z.object({\n            type: z.literal('function_call'),\n            id: z.string(),\n            call_id: z.string(),\n            name: z.string(),\n            arguments: z.string(),\n          }),\n          z.object({\n            type: z.literal('web_search_call'),\n            id: z.string(),\n            status: z.string(),\n          }),\n          z.object({\n            type: z.literal('computer_call'),\n            id: z.string(),\n            status: z.string(),\n          }),\n          z.object({\n            type: z.literal('file_search_call'),\n            id: z.string(),\n          }),\n          z.object({\n            type: z.literal('image_generation_call'),\n            id: z.string(),\n          }),\n          z.object({\n            type: z.literal('code_interpreter_call'),\n            id: z.string(),\n            container_id: z.string(),\n            code: z.string().nullable(),\n            outputs: z\n              .array(\n                z.discriminatedUnion('type', [\n                  z.object({ type: z.literal('logs'), logs: z.string() }),\n                  z.object({ type: z.literal('image'), url: z.string() }),\n                ]),\n              )\n              .nullable(),\n            status: z.string(),\n          }),\n        ]),\n      }),\n      z.object({\n        type: z.literal('response.output_item.done'),\n        output_index: z.number(),\n        item: z.discriminatedUnion('type', [\n          z.object({\n            type: z.literal('message'),\n            id: z.string(),\n            phase: z.enum(['commentary', 'final_answer']).nullish(),\n          }),\n          z.object({\n            type: z.literal('reasoning'),\n            id: z.string(),\n            encrypted_content: z.string().nullish(),\n          }),\n          z.object({\n            type: z.literal('function_call'),\n            id: z.string(),\n            call_id: z.string(),\n            name: z.string(),\n            arguments: z.string(),\n            status: z.literal('completed'),\n          }),\n          z.object({\n            type: z.literal('code_interpreter_call'),\n            id: z.string(),\n            code: z.string().nullable(),\n            container_id: z.string(),\n            outputs: z\n              .array(\n                z.discriminatedUnion('type', [\n                  z.object({ type: z.literal('logs'), logs: z.string() }),\n                  z.object({ type: z.literal('image'), url: z.string() }),\n                ]),\n              )\n              .nullable(),\n          }),\n          z.object({\n            type: z.literal('image_generation_call'),\n            id: z.string(),\n            result: z.string(),\n          }),\n          z.object({\n            type: z.literal('web_search_call'),\n            id: z.string(),\n            status: z.string(),\n            action: z\n              .discriminatedUnion('type', [\n                z.object({\n                  type: z.literal('search'),\n                  query: z.string().nullish(),\n                  sources: z\n                    .array(\n                      z.discriminatedUnion('type', [\n                        z.object({ type: z.literal('url'), url: z.string() }),\n                        z.object({ type: z.literal('api'), name: z.string() }),\n                      ]),\n                    )\n                    .nullish(),\n                }),\n                z.object({\n                  type: z.literal('open_page'),\n                  url: z.string().nullish(),\n                }),\n                z.object({\n                  type: z.literal('find_in_page'),\n                  url: z.string().nullish(),\n                  pattern: z.string().nullish(),\n                }),\n              ])\n              .nullish(),\n          }),\n          z.object({\n            type: z.literal('file_search_call'),\n            id: z.string(),\n            queries: z.array(z.string()),\n            results: z\n              .array(\n                z.object({\n                  attributes: z.record(z.string(), z.unknown()),\n                  file_id: z.string(),\n                  filename: z.string(),\n                  score: z.number(),\n                  text: z.string(),\n                }),\n              )\n              .nullish(),\n          }),\n          z.object({\n            type: z.literal('local_shell_call'),\n            id: z.string(),\n            call_id: z.string(),\n            action: z.object({\n              type: z.literal('exec'),\n              command: z.array(z.string()),\n              timeout_ms: z.number().optional(),\n              user: z.string().optional(),\n              working_directory: z.string().optional(),\n              env: z.record(z.string(), z.string()).optional(),\n            }),\n          }),\n          z.object({\n            type: z.literal('computer_call'),\n            id: z.string(),\n            status: z.literal('completed'),\n          }),\n        ]),\n      }),\n      z.object({\n        type: z.literal('response.function_call_arguments.delta'),\n        item_id: z.string(),\n        output_index: z.number(),\n        delta: z.string(),\n      }),\n      z.object({\n        type: z.literal('response.image_generation_call.partial_image'),\n        item_id: z.string(),\n        output_index: z.number(),\n        partial_image_b64: z.string(),\n      }),\n      z.object({\n        type: z.literal('response.code_interpreter_call_code.delta'),\n        item_id: z.string(),\n        output_index: z.number(),\n        delta: z.string(),\n      }),\n      z.object({\n        type: z.literal('response.code_interpreter_call_code.done'),\n        item_id: z.string(),\n        output_index: z.number(),\n        code: z.string(),\n      }),\n      z.object({\n        type: z.literal('response.output_text.annotation.added'),\n        annotation: z.discriminatedUnion('type', [\n          z.object({\n            type: z.literal('url_citation'),\n            start_index: z.number(),\n            end_index: z.number(),\n            url: z.string(),\n            title: z.string(),\n          }),\n          z.object({\n            type: z.literal('file_citation'),\n            file_id: z.string(),\n            filename: z.string().nullish(),\n            index: z.number().nullish(),\n            start_index: z.number().nullish(),\n            end_index: z.number().nullish(),\n            quote: z.string().nullish(),\n          }),\n        ]),\n      }),\n      z.object({\n        type: z.literal('response.reasoning_summary_part.added'),\n        item_id: z.string(),\n        summary_index: z.number(),\n      }),\n      z.object({\n        type: z.literal('response.reasoning_summary_text.delta'),\n        item_id: z.string(),\n        summary_index: z.number(),\n        delta: z.string(),\n      }),\n      z.object({\n        type: z.literal('response.reasoning_summary_part.done'),\n        item_id: z.string(),\n        summary_index: z.number(),\n      }),\n      z.object({\n        type: z.literal('error'),\n        sequence_number: z.number(),\n        error: z.object({\n          type: z.string(),\n          code: z.string(),\n          message: z.string(),\n          param: z.string().nullish(),\n        }),\n      }),\n      z\n        .object({ type: z.string() })\n        .loose()\n        .transform(value => ({\n          type: 'unknown_chunk' as const,\n          message: value.type,\n        })), // fallback for unknown chunks\n    ]),\n  ),\n);\n\nexport type OpenAIResponsesChunk = InferValidator<\n  typeof openaiResponsesChunkSchema\n>;\n\nexport type OpenAIResponsesLogprobs = NonNullable<\n  (OpenAIResponsesChunk & {\n    type: 'response.output_text.delta';\n  })['logprobs']\n> | null;\n\nexport type OpenAIResponsesWebSearchAction = NonNullable<\n  ((OpenAIResponsesChunk & {\n    type: 'response.output_item.done';\n  })['item'] & {\n    type: 'web_search_call';\n  })['action']\n>;\n\nexport const openaiResponsesResponseSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      id: z.string().optional(),\n      created_at: z.number().optional(),\n      error: z\n        .object({\n          message: z.string(),\n          type: z.string(),\n          param: z.string().nullish(),\n          code: z.string(),\n        })\n        .nullish(),\n      model: z.string().optional(),\n      output: z\n        .array(\n          z.discriminatedUnion('type', [\n            z.object({\n              type: z.literal('message'),\n              role: z.literal('assistant'),\n              id: z.string(),\n              phase: z.enum(['commentary', 'final_answer']).nullish(),\n              content: z.array(\n                z.object({\n                  type: z.literal('output_text'),\n                  text: z.string(),\n                  logprobs: z\n                    .array(\n                      z.object({\n                        token: z.string(),\n                        logprob: z.number(),\n                        top_logprobs: z.array(\n                          z.object({\n                            token: z.string(),\n                            logprob: z.number(),\n                          }),\n                        ),\n                      }),\n                    )\n                    .nullish(),\n                  annotations: z.array(\n                    z.discriminatedUnion('type', [\n                      z.object({\n                        type: z.literal('url_citation'),\n                        start_index: z.number(),\n                        end_index: z.number(),\n                        url: z.string(),\n                        title: z.string(),\n                      }),\n                      z.object({\n                        type: z.literal('file_citation'),\n                        file_id: z.string(),\n                        filename: z.string().nullish(),\n                        index: z.number().nullish(),\n                        start_index: z.number().nullish(),\n                        end_index: z.number().nullish(),\n                        quote: z.string().nullish(),\n                      }),\n                      z.object({\n                        type: z.literal('container_file_citation'),\n                        container_id: z.string(),\n                        file_id: z.string(),\n                        filename: z.string().nullish(),\n                        start_index: z.number().nullish(),\n                        end_index: z.number().nullish(),\n                        index: z.number().nullish(),\n                      }),\n                      z.object({\n                        type: z.literal('file_path'),\n                        file_id: z.string(),\n                        index: z.number().nullish(),\n                      }),\n                    ]),\n                  ),\n                }),\n              ),\n            }),\n            z.object({\n              type: z.literal('web_search_call'),\n              id: z.string(),\n              status: z.string(),\n              action: z\n                .discriminatedUnion('type', [\n                  z.object({\n                    type: z.literal('search'),\n                    query: z.string().nullish(),\n                    sources: z\n                      .array(\n                        z.discriminatedUnion('type', [\n                          z.object({ type: z.literal('url'), url: z.string() }),\n                          z.object({\n                            type: z.literal('api'),\n                            name: z.string(),\n                          }),\n                        ]),\n                      )\n                      .nullish(),\n                  }),\n                  z.object({\n                    type: z.literal('open_page'),\n                    url: z.string().nullish(),\n                  }),\n                  z.object({\n                    type: z.literal('find_in_page'),\n                    url: z.string().nullish(),\n                    pattern: z.string().nullish(),\n                  }),\n                ])\n                .nullish(),\n            }),\n            z.object({\n              type: z.literal('file_search_call'),\n              id: z.string(),\n              queries: z.array(z.string()),\n              results: z\n                .array(\n                  z.object({\n                    attributes: z.record(\n                      z.string(),\n                      z.union([z.string(), z.number(), z.boolean()]),\n                    ),\n                    file_id: z.string(),\n                    filename: z.string(),\n                    score: z.number(),\n                    text: z.string(),\n                  }),\n                )\n                .nullish(),\n            }),\n            z.object({\n              type: z.literal('code_interpreter_call'),\n              id: z.string(),\n              code: z.string().nullable(),\n              container_id: z.string(),\n              outputs: z\n                .array(\n                  z.discriminatedUnion('type', [\n                    z.object({ type: z.literal('logs'), logs: z.string() }),\n                    z.object({ type: z.literal('image'), url: z.string() }),\n                  ]),\n                )\n                .nullable(),\n            }),\n            z.object({\n              type: z.literal('image_generation_call'),\n              id: z.string(),\n              result: z.string(),\n            }),\n            z.object({\n              type: z.literal('local_shell_call'),\n              id: z.string(),\n              call_id: z.string(),\n              action: z.object({\n                type: z.literal('exec'),\n                command: z.array(z.string()),\n                timeout_ms: z.number().optional(),\n                user: z.string().optional(),\n                working_directory: z.string().optional(),\n                env: z.record(z.string(), z.string()).optional(),\n              }),\n            }),\n            z.object({\n              type: z.literal('function_call'),\n              call_id: z.string(),\n              name: z.string(),\n              arguments: z.string(),\n              id: z.string(),\n            }),\n            z.object({\n              type: z.literal('computer_call'),\n              id: z.string(),\n              status: z.string().optional(),\n            }),\n            z.object({\n              type: z.literal('reasoning'),\n              id: z.string(),\n              encrypted_content: z.string().nullish(),\n              summary: z.array(\n                z.object({\n                  type: z.literal('summary_text'),\n                  text: z.string(),\n                }),\n              ),\n            }),\n          ]),\n        )\n        .optional(),\n      service_tier: z.string().nullish(),\n      incomplete_details: z.object({ reason: z.string() }).nullish(),\n      usage: z\n        .object({\n          input_tokens: z.number(),\n          input_tokens_details: z\n            .object({ cached_tokens: z.number().nullish() })\n            .nullish(),\n          output_tokens: z.number(),\n          output_tokens_details: z\n            .object({ reasoning_tokens: z.number().nullish() })\n            .nullish(),\n        })\n        .optional(),\n    }),\n  ),\n);\n","import {\n  type InferValidator,\n  lazyValidator,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n/**\n * `top_logprobs` request body argument can be set to an integer between\n * 0 and 20 specifying the number of most likely tokens to return at each\n * token position, each with an associated log probability.\n *\n * @see https://platform.openai.com/docs/api-reference/responses/create#responses_create-top_logprobs\n */\nexport const TOP_LOGPROBS_MAX = 20;\n\nexport const openaiResponsesReasoningModelIds = [\n  'o1',\n  'o1-2024-12-17',\n  'o3',\n  'o3-2025-04-16',\n  'o3-deep-research',\n  'o3-deep-research-2025-06-26',\n  'o3-mini',\n  'o3-mini-2025-01-31',\n  'o4-mini',\n  'o4-mini-2025-04-16',\n  'o4-mini-deep-research',\n  'o4-mini-deep-research-2025-06-26',\n  'codex-mini-latest',\n  'computer-use-preview',\n  'gpt-5',\n  'gpt-5-2025-08-07',\n  'gpt-5-codex',\n  'gpt-5-mini',\n  'gpt-5-mini-2025-08-07',\n  'gpt-5-nano',\n  'gpt-5-nano-2025-08-07',\n  'gpt-5-pro',\n  'gpt-5-pro-2025-10-06',\n  'gpt-5.1',\n  'gpt-5.1-chat-latest',\n  'gpt-5.1-codex-mini',\n  'gpt-5.1-codex',\n  'gpt-5.1-codex-max',\n  'gpt-5.2',\n  'gpt-5.2-chat-latest',\n  'gpt-5.2-pro',\n  'gpt-5.2-codex',\n  'gpt-5.3-chat-latest',\n  'gpt-5.3-codex',\n  'gpt-5.4',\n  'gpt-5.4-2026-03-05',\n  'gpt-5.4-mini',\n  'gpt-5.4-mini-2026-03-17',\n  'gpt-5.4-nano',\n  'gpt-5.4-nano-2026-03-17',\n  'gpt-5.4-pro',\n  'gpt-5.4-pro-2026-03-05',\n] as const;\n\nexport const openaiResponsesModelIds = [\n  'gpt-4.1',\n  'gpt-4.1-2025-04-14',\n  'gpt-4.1-mini',\n  'gpt-4.1-mini-2025-04-14',\n  'gpt-4.1-nano',\n  'gpt-4.1-nano-2025-04-14',\n  'gpt-4o',\n  'gpt-4o-2024-05-13',\n  'gpt-4o-2024-08-06',\n  'gpt-4o-2024-11-20',\n  'gpt-4o-audio-preview',\n  'gpt-4o-audio-preview-2024-10-01',\n  'gpt-4o-audio-preview-2024-12-17',\n  'gpt-4o-search-preview',\n  'gpt-4o-search-preview-2025-03-11',\n  'gpt-4o-mini-search-preview',\n  'gpt-4o-mini-search-preview-2025-03-11',\n  'gpt-4o-mini',\n  'gpt-4o-mini-2024-07-18',\n  'gpt-4-turbo',\n  'gpt-4-turbo-2024-04-09',\n  'gpt-4-turbo-preview',\n  'gpt-4-0125-preview',\n  'gpt-4-1106-preview',\n  'gpt-4',\n  'gpt-4-0613',\n  'gpt-4.5-preview',\n  'gpt-4.5-preview-2025-02-27',\n  'gpt-3.5-turbo-0125',\n  'gpt-3.5-turbo',\n  'gpt-3.5-turbo-1106',\n  'chatgpt-4o-latest',\n  'gpt-5-chat-latest',\n  ...openaiResponsesReasoningModelIds,\n] as const;\n\nexport type OpenAIResponsesModelId =\n  | 'chatgpt-4o-latest'\n  | 'gpt-3.5-turbo-0125'\n  | 'gpt-3.5-turbo-1106'\n  | 'gpt-3.5-turbo'\n  | 'gpt-4-0613'\n  | 'gpt-4-turbo-2024-04-09'\n  | 'gpt-4-turbo'\n  | 'gpt-4.1-2025-04-14'\n  | 'gpt-4.1-mini-2025-04-14'\n  | 'gpt-4.1-mini'\n  | 'gpt-4.1-nano-2025-04-14'\n  | 'gpt-4.1-nano'\n  | 'gpt-4.1'\n  | 'gpt-4'\n  | 'gpt-4o-2024-05-13'\n  | 'gpt-4o-2024-08-06'\n  | 'gpt-4o-2024-11-20'\n  | 'gpt-4o-mini-2024-07-18'\n  | 'gpt-4o-mini'\n  | 'gpt-4o'\n  | 'gpt-5.1'\n  | 'gpt-5.1-chat-latest'\n  | 'gpt-5.1-codex-mini'\n  | 'gpt-5.1-codex'\n  | 'gpt-5.1-codex-max'\n  | 'gpt-5.2'\n  | 'gpt-5.2-chat-latest'\n  | 'gpt-5.2-pro'\n  | 'gpt-5.2-codex'\n  | 'gpt-5.3-chat-latest'\n  | 'gpt-5.3-codex'\n  | 'gpt-5.4'\n  | 'gpt-5.4-2026-03-05'\n  | 'gpt-5.4-mini'\n  | 'gpt-5.4-mini-2026-03-17'\n  | 'gpt-5.4-nano'\n  | 'gpt-5.4-nano-2026-03-17'\n  | 'gpt-5.4-pro'\n  | 'gpt-5.4-pro-2026-03-05'\n  | 'gpt-5-2025-08-07'\n  | 'gpt-5-chat-latest'\n  | 'gpt-5-codex'\n  | 'gpt-5-mini-2025-08-07'\n  | 'gpt-5-mini'\n  | 'gpt-5-nano-2025-08-07'\n  | 'gpt-5-nano'\n  | 'gpt-5-pro-2025-10-06'\n  | 'gpt-5-pro'\n  | 'gpt-5'\n  | 'o1-2024-12-17'\n  | 'o1'\n  | 'o3-2025-04-16'\n  | 'o3-mini-2025-01-31'\n  | 'o3-mini'\n  | 'o3'\n  | (string & {});\n\n// TODO AI SDK 6: use optional here instead of nullish\nexport const openaiResponsesProviderOptionsSchema = lazyValidator(() =>\n  zodSchema(\n    z.object({\n      conversation: z.string().nullish(),\n      include: z\n        .array(\n          z.enum([\n            'reasoning.encrypted_content', // handled internally by default, only needed for unknown reasoning models\n            'file_search_call.results',\n            'message.output_text.logprobs',\n          ]),\n        )\n        .nullish(),\n      instructions: z.string().nullish(),\n\n      /**\n       * Return the log probabilities of the tokens.\n       *\n       * Setting to true will return the log probabilities of the tokens that\n       * were generated.\n       *\n       * Setting to a number will return the log probabilities of the top n\n       * tokens that were generated.\n       *\n       * @see https://platform.openai.com/docs/api-reference/responses/create\n       * @see https://cookbook.openai.com/examples/using_logprobs\n       */\n      logprobs: z\n        .union([z.boolean(), z.number().min(1).max(TOP_LOGPROBS_MAX)])\n        .optional(),\n\n      /**\n       * The maximum number of total calls to built-in tools that can be processed in a response.\n       * This maximum number applies across all built-in tool calls, not per individual tool.\n       * Any further attempts to call a tool by the model will be ignored.\n       */\n      maxToolCalls: z.number().nullish(),\n\n      metadata: z.any().nullish(),\n      parallelToolCalls: z.boolean().nullish(),\n      previousResponseId: z.string().nullish(),\n      promptCacheKey: z.string().nullish(),\n\n      /**\n       * The retention policy for the prompt cache.\n       * - 'in_memory': Default. Standard prompt caching behavior.\n       * - '24h': Extended prompt caching that keeps cached prefixes active for up to 24 hours.\n       *          Currently only available for 5.1 series models.\n       *\n       * @default 'in_memory'\n       */\n      promptCacheRetention: z.enum(['in_memory', '24h']).nullish(),\n\n      /**\n       * Reasoning effort for reasoning models. Defaults to `medium`. If you use\n       * `providerOptions` to set the `reasoningEffort` option, this model setting will be ignored.\n       * Valid values: 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh'\n       *\n       * The 'none' type for `reasoningEffort` is only available for OpenAI's GPT-5.1\n       * models. Also, the 'xhigh' type for `reasoningEffort` is only available for\n       * OpenAI's GPT-5.1-Codex-Max model. Setting `reasoningEffort` to 'none' or 'xhigh' with unsupported models will result in\n       * an error.\n       */\n      reasoningEffort: z.string().nullish(),\n      reasoningSummary: z.string().nullish(),\n      safetyIdentifier: z.string().nullish(),\n      serviceTier: z.enum(['auto', 'flex', 'priority', 'default']).nullish(),\n      store: z.boolean().nullish(),\n      strictJsonSchema: z.boolean().nullish(),\n      textVerbosity: z.enum(['low', 'medium', 'high']).nullish(),\n      truncation: z.enum(['auto', 'disabled']).nullish(),\n      user: z.string().nullish(),\n    }),\n  ),\n);\n\nexport type OpenAIResponsesProviderOptions = InferValidator<\n  typeof openaiResponsesProviderOptionsSchema\n>;\n","import {\n  createProviderDefinedToolFactoryWithOutputSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport const codeInterpreterInputSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      code: z.string().nullish(),\n      containerId: z.string(),\n    }),\n  ),\n);\n\nexport const codeInterpreterOutputSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      outputs: z\n        .array(\n          z.discriminatedUnion('type', [\n            z.object({ type: z.literal('logs'), logs: z.string() }),\n            z.object({ type: z.literal('image'), url: z.string() }),\n          ]),\n        )\n        .nullish(),\n    }),\n  ),\n);\n\nexport const codeInterpreterArgsSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      container: z\n        .union([\n          z.string(),\n          z.object({\n            fileIds: z.array(z.string()).optional(),\n          }),\n        ])\n        .optional(),\n    }),\n  ),\n);\n\ntype CodeInterpreterArgs = {\n  /**\n   * The code interpreter container.\n   * Can be a container ID\n   * or an object that specifies uploaded file IDs to make available to your code.\n   */\n  container?: string | { fileIds?: string[] };\n};\n\nexport const codeInterpreterToolFactory =\n  createProviderDefinedToolFactoryWithOutputSchema<\n    {\n      /**\n       * The code to run, or null if not available.\n       */\n      code?: string | null;\n\n      /**\n       * The ID of the container used to run the code.\n       */\n      containerId: string;\n    },\n    {\n      /**\n       * The outputs generated by the code interpreter, such as logs or images.\n       * Can be null if no outputs are available.\n       */\n      outputs?: Array<\n        | {\n            type: 'logs';\n\n            /**\n             * The logs output from the code interpreter.\n             */\n            logs: string;\n          }\n        | {\n            type: 'image';\n\n            /**\n             * The URL of the image output from the code interpreter.\n             */\n            url: string;\n          }\n      > | null;\n    },\n    CodeInterpreterArgs\n  >({\n    id: 'openai.code_interpreter',\n    name: 'code_interpreter',\n    inputSchema: codeInterpreterInputSchema,\n    outputSchema: codeInterpreterOutputSchema,\n  });\n\nexport const codeInterpreter = (\n  args: CodeInterpreterArgs = {}, // default\n) => {\n  return codeInterpreterToolFactory(args);\n};\n","import {\n  createProviderDefinedToolFactoryWithOutputSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport type {\n  OpenAIResponsesFileSearchToolComparisonFilter,\n  OpenAIResponsesFileSearchToolCompoundFilter,\n} from '../responses/openai-responses-api';\n\nconst comparisonFilterSchema = z.object({\n  key: z.string(),\n  type: z.enum(['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'nin']),\n  value: z.union([z.string(), z.number(), z.boolean(), z.array(z.string())]),\n});\n\nconst compoundFilterSchema: z.ZodType<any> = z.object({\n  type: z.enum(['and', 'or']),\n  filters: z.array(\n    z.union([comparisonFilterSchema, z.lazy(() => compoundFilterSchema)]),\n  ),\n});\n\nexport const fileSearchArgsSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      vectorStoreIds: z.array(z.string()),\n      maxNumResults: z.number().optional(),\n      ranking: z\n        .object({\n          ranker: z.string().optional(),\n          scoreThreshold: z.number().optional(),\n        })\n        .optional(),\n      filters: z\n        .union([comparisonFilterSchema, compoundFilterSchema])\n        .optional(),\n    }),\n  ),\n);\n\nexport const fileSearchOutputSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      queries: z.array(z.string()),\n      results: z\n        .array(\n          z.object({\n            attributes: z.record(z.string(), z.unknown()),\n            fileId: z.string(),\n            filename: z.string(),\n            score: z.number(),\n            text: z.string(),\n          }),\n        )\n        .nullable(),\n    }),\n  ),\n);\n\nexport const fileSearch = createProviderDefinedToolFactoryWithOutputSchema<\n  {},\n  {\n    /**\n     * The search query to execute.\n     */\n    queries: string[];\n\n    /**\n     * The results of the file search tool call.\n     */\n    results:\n      | null\n      | {\n          /**\n           * Set of 16 key-value pairs that can be attached to an object.\n           * This can be useful for storing additional information about the object\n           * in a structured format, and querying for objects via API or the dashboard.\n           * Keys are strings with a maximum length of 64 characters.\n           * Values are strings with a maximum length of 512 characters, booleans, or numbers.\n           */\n          attributes: Record<string, unknown>;\n\n          /**\n           * The unique ID of the file.\n           */\n          fileId: string;\n\n          /**\n           * The name of the file.\n           */\n          filename: string;\n\n          /**\n           * The relevance score of the file - a value between 0 and 1.\n           */\n          score: number;\n\n          /**\n           * The text that was retrieved from the file.\n           */\n          text: string;\n        }[];\n  },\n  {\n    /**\n     * List of vector store IDs to search through.\n     */\n    vectorStoreIds: string[];\n\n    /**\n     * Maximum number of search results to return. Defaults to 10.\n     */\n    maxNumResults?: number;\n\n    /**\n     * Ranking options for the search.\n     */\n    ranking?: {\n      /**\n       * The ranker to use for the file search.\n       */\n      ranker?: string;\n\n      /**\n       * The score threshold for the file search, a number between 0 and 1.\n       * Numbers closer to 1 will attempt to return only the most relevant results,\n       * but may return fewer results.\n       */\n      scoreThreshold?: number;\n    };\n\n    /**\n     * A filter to apply.\n     */\n    filters?:\n      | OpenAIResponsesFileSearchToolComparisonFilter\n      | OpenAIResponsesFileSearchToolCompoundFilter;\n  }\n>({\n  id: 'openai.file_search',\n  name: 'file_search',\n  inputSchema: z.object({}),\n  outputSchema: fileSearchOutputSchema,\n});\n","import {\n  createProviderDefinedToolFactoryWithOutputSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport const webSearchArgsSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      externalWebAccess: z.boolean().optional(),\n      filters: z\n        .object({ allowedDomains: z.array(z.string()).optional() })\n        .optional(),\n      searchContextSize: z.enum(['low', 'medium', 'high']).optional(),\n      userLocation: z\n        .object({\n          type: z.literal('approximate'),\n          country: z.string().optional(),\n          city: z.string().optional(),\n          region: z.string().optional(),\n          timezone: z.string().optional(),\n        })\n        .optional(),\n    }),\n  ),\n);\n\nconst webSearchInputSchema = lazySchema(() => zodSchema(z.object({})));\n\nexport const webSearchOutputSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      action: z\n        .discriminatedUnion('type', [\n          z.object({\n            type: z.literal('search'),\n            query: z.string().optional(),\n          }),\n          z.object({\n            type: z.literal('openPage'),\n            url: z.string().nullish(),\n          }),\n          z.object({\n            type: z.literal('findInPage'),\n            url: z.string().nullish(),\n            pattern: z.string().nullish(),\n          }),\n        ])\n        .optional(),\n      sources: z\n        .array(\n          z.discriminatedUnion('type', [\n            z.object({ type: z.literal('url'), url: z.string() }),\n            z.object({ type: z.literal('api'), name: z.string() }),\n          ]),\n        )\n        .optional(),\n    }),\n  ),\n);\n\nexport const webSearchToolFactory =\n  createProviderDefinedToolFactoryWithOutputSchema<\n    {\n      // Web search doesn't take input parameters - it's controlled by the prompt\n    },\n    {\n      /**\n       * An object describing the specific action taken in this web search call.\n       * Includes details on how the model used the web (search, open_page, findInPage).\n       */\n      action?:\n        | {\n            /**\n             * Action type \"search\" - Performs a web search query.\n             */\n            type: 'search';\n\n            /**\n             * The search query.\n             */\n            query?: string;\n          }\n        | {\n            /**\n             * Action type \"openPage\" - Opens a specific URL from search results.\n             */\n            type: 'openPage';\n\n            /**\n             * The URL opened by the model.\n             */\n            url?: string | null;\n          }\n        | {\n            /**\n             * Action type \"findInPage\": Searches for a pattern within a loaded page.\n             */\n            type: 'findInPage';\n\n            /**\n             * The URL of the page searched for the pattern.\n             */\n            url?: string | null;\n\n            /**\n             * The pattern or text to search for within the page.\n             */\n            pattern?: string | null;\n          };\n\n      /**\n       * Optional sources cited by the model for the web search call.\n       */\n      sources?: Array<\n        { type: 'url'; url: string } | { type: 'api'; name: string }\n      >;\n    },\n    {\n      /**\n       * Whether to use external web access for fetching live content.\n       * - true: Fetch live web content (default)\n       * - false: Use cached/indexed results\n       */\n      externalWebAccess?: boolean;\n\n      /**\n       * Filters for the search.\n       */\n      filters?: {\n        /**\n         * Allowed domains for the search.\n         * If not provided, all domains are allowed.\n         * Subdomains of the provided domains are allowed as well.\n         */\n        allowedDomains?: string[];\n      };\n\n      /**\n       * Search context size to use for the web search.\n       * - high: Most comprehensive context, highest cost, slower response\n       * - medium: Balanced context, cost, and latency (default)\n       * - low: Least context, lowest cost, fastest response\n       */\n      searchContextSize?: 'low' | 'medium' | 'high';\n\n      /**\n       * User location information to provide geographically relevant search results.\n       */\n      userLocation?: {\n        /**\n         * Type of location (always 'approximate')\n         */\n        type: 'approximate';\n        /**\n         * Two-letter ISO country code (e.g., 'US', 'GB')\n         */\n        country?: string;\n        /**\n         * City name (free text, e.g., 'Minneapolis')\n         */\n        city?: string;\n        /**\n         * Region name (free text, e.g., 'Minnesota')\n         */\n        region?: string;\n        /**\n         * IANA timezone (e.g., 'America/Chicago')\n         */\n        timezone?: string;\n      };\n    }\n  >({\n    id: 'openai.web_search',\n    name: 'web_search',\n    inputSchema: webSearchInputSchema,\n    outputSchema: webSearchOutputSchema,\n  });\n\nexport const webSearch = (\n  args: Parameters<typeof webSearchToolFactory>[0] = {}, // default\n) => webSearchToolFactory(args);\n","import {\n  createProviderDefinedToolFactoryWithOutputSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport const webSearchPreviewArgsSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      searchContextSize: z.enum(['low', 'medium', 'high']).optional(),\n      userLocation: z\n        .object({\n          type: z.literal('approximate'),\n          country: z.string().optional(),\n          city: z.string().optional(),\n          region: z.string().optional(),\n          timezone: z.string().optional(),\n        })\n        .optional(),\n    }),\n  ),\n);\n\nexport const webSearchPreviewInputSchema = lazySchema(() =>\n  zodSchema(z.object({})),\n);\n\nconst webSearchPreviewOutputSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      action: z\n        .discriminatedUnion('type', [\n          z.object({\n            type: z.literal('search'),\n            query: z.string().optional(),\n          }),\n          z.object({\n            type: z.literal('openPage'),\n            url: z.string().nullish(),\n          }),\n          z.object({\n            type: z.literal('findInPage'),\n            url: z.string().nullish(),\n            pattern: z.string().nullish(),\n          }),\n        ])\n        .optional(),\n    }),\n  ),\n);\n\nexport const webSearchPreview =\n  createProviderDefinedToolFactoryWithOutputSchema<\n    {\n      // Web search preview doesn't take input parameters - it's controlled by the prompt\n    },\n    {\n      /**\n       * An object describing the specific action taken in this web search call.\n       * Includes details on how the model used the web (search, open_page, findInPage).\n       */\n      action?:\n        | {\n            /**\n             * Action type \"search\" - Performs a web search query.\n             */\n            type: 'search';\n\n            /**\n             * The search query.\n             */\n            query?: string;\n          }\n        | {\n            /**\n             * Action type \"openPage\" - Opens a specific URL from search results.\n             */\n            type: 'openPage';\n\n            /**\n             * The URL opened by the model.\n             */\n            url?: string | null;\n          }\n        | {\n            /**\n             * Action type \"findInPage\": Searches for a pattern within a loaded page.\n             */\n            type: 'findInPage';\n\n            /**\n             * The URL of the page searched for the pattern.\n             */\n            url?: string | null;\n            /**\n             * The pattern or text to search for within the page.\n             */\n            pattern?: string | null;\n          };\n    },\n    {\n      /**\n       * Search context size to use for the web search.\n       * - high: Most comprehensive context, highest cost, slower response\n       * - medium: Balanced context, cost, and latency (default)\n       * - low: Least context, lowest cost, fastest response\n       */\n      searchContextSize?: 'low' | 'medium' | 'high';\n\n      /**\n       * User location information to provide geographically relevant search results.\n       */\n      userLocation?: {\n        /**\n         * Type of location (always 'approximate')\n         */\n        type: 'approximate';\n        /**\n         * Two-letter ISO country code (e.g., 'US', 'GB')\n         */\n        country?: string;\n        /**\n         * City name (free text, e.g., 'Minneapolis')\n         */\n        city?: string;\n        /**\n         * Region name (free text, e.g., 'Minnesota')\n         */\n        region?: string;\n        /**\n         * IANA timezone (e.g., 'America/Chicago')\n         */\n        timezone?: string;\n      };\n    }\n  >({\n    id: 'openai.web_search_preview',\n    name: 'web_search_preview',\n    inputSchema: webSearchPreviewInputSchema,\n    outputSchema: webSearchPreviewOutputSchema,\n  });\n","import {\n  createProviderDefinedToolFactoryWithOutputSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport const imageGenerationArgsSchema = lazySchema(() =>\n  zodSchema(\n    z\n      .object({\n        background: z.enum(['auto', 'opaque', 'transparent']).optional(),\n        inputFidelity: z.enum(['low', 'high']).optional(),\n        inputImageMask: z\n          .object({\n            fileId: z.string().optional(),\n            imageUrl: z.string().optional(),\n          })\n          .optional(),\n        model: z.string().optional(),\n        moderation: z.enum(['auto']).optional(),\n        outputCompression: z.number().int().min(0).max(100).optional(),\n        outputFormat: z.enum(['png', 'jpeg', 'webp']).optional(),\n        partialImages: z.number().int().min(0).max(3).optional(),\n        quality: z.enum(['auto', 'low', 'medium', 'high']).optional(),\n        size: z\n          .enum(['1024x1024', '1024x1536', '1536x1024', 'auto'])\n          .optional(),\n      })\n      .strict(),\n  ),\n);\n\nconst imageGenerationInputSchema = lazySchema(() => zodSchema(z.object({})));\n\nexport const imageGenerationOutputSchema = lazySchema(() =>\n  zodSchema(z.object({ result: z.string() })),\n);\n\ntype ImageGenerationArgs = {\n  /**\n   * Background type for the generated image. Default is 'auto'.\n   */\n  background?: 'auto' | 'opaque' | 'transparent';\n\n  /**\n   * Input fidelity for the generated image. Default is 'low'.\n   */\n  inputFidelity?: 'low' | 'high';\n\n  /**\n   * Optional mask for inpainting.\n   * Contains image_url (string, optional) and file_id (string, optional).\n   */\n  inputImageMask?: {\n    /**\n     * File ID for the mask image.\n     */\n    fileId?: string;\n\n    /**\n     * Base64-encoded mask image.\n     */\n    imageUrl?: string;\n  };\n\n  /**\n   * The image generation model to use. Default: gpt-image-1.\n   */\n  model?: string;\n\n  /**\n   * Moderation level for the generated image. Default: auto.\n   */\n  moderation?: 'auto';\n\n  /**\n   * Compression level for the output image. Default: 100.\n   */\n  outputCompression?: number;\n\n  /**\n   * The output format of the generated image. One of png, webp, or jpeg.\n   * Default: png\n   */\n  outputFormat?: 'png' | 'jpeg' | 'webp';\n\n  /**\n   * The quality of the generated image.\n   * One of low, medium, high, or auto. Default: auto.\n   */\n  quality?: 'auto' | 'low' | 'medium' | 'high';\n\n  /**\n   * The size of the generated image.\n   * One of 1024x1024, 1024x1536, 1536x1024, or auto.\n   * Default: auto.\n   */\n  size?: 'auto' | '1024x1024' | '1024x1536' | '1536x1024';\n};\n\nconst imageGenerationToolFactory =\n  createProviderDefinedToolFactoryWithOutputSchema<\n    {},\n    {\n      /**\n       * The generated image encoded in base64.\n       */\n      result: string;\n    },\n    ImageGenerationArgs\n  >({\n    id: 'openai.image_generation',\n    name: 'image_generation',\n    inputSchema: imageGenerationInputSchema,\n    outputSchema: imageGenerationOutputSchema,\n  });\n\nexport const imageGeneration = (\n  args: ImageGenerationArgs = {}, // default\n) => {\n  return imageGenerationToolFactory(args);\n};\n","import {\n  type LanguageModelV2CallOptions,\n  type LanguageModelV2CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { codeInterpreterArgsSchema } from '../tool/code-interpreter';\nimport { fileSearchArgsSchema } from '../tool/file-search';\nimport { webSearchArgsSchema } from '../tool/web-search';\nimport { webSearchPreviewArgsSchema } from '../tool/web-search-preview';\nimport { imageGenerationArgsSchema } from '../tool/image-generation';\nimport type { OpenAIResponsesTool } from './openai-responses-api';\nimport { validateTypes } from '@ai-sdk/provider-utils';\n\nexport async function prepareResponsesTools({\n  tools,\n  toolChoice,\n  strictJsonSchema,\n}: {\n  tools: LanguageModelV2CallOptions['tools'];\n  toolChoice?: LanguageModelV2CallOptions['toolChoice'];\n  strictJsonSchema: boolean;\n}): Promise<{\n  tools?: Array<OpenAIResponsesTool>;\n  toolChoice?:\n    | 'auto'\n    | 'none'\n    | 'required'\n    | { type: 'file_search' }\n    | { type: 'web_search_preview' }\n    | { type: 'web_search' }\n    | { type: 'function'; name: string }\n    | { type: 'code_interpreter' }\n    | { type: 'image_generation' };\n  toolWarnings: LanguageModelV2CallWarning[];\n}> {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  tools = tools?.length ? tools : undefined;\n\n  const toolWarnings: LanguageModelV2CallWarning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, toolChoice: undefined, toolWarnings };\n  }\n\n  const openaiTools: Array<OpenAIResponsesTool> = [];\n\n  for (const tool of tools) {\n    switch (tool.type) {\n      case 'function':\n        openaiTools.push({\n          type: 'function',\n          name: tool.name,\n          description: tool.description,\n          parameters: tool.inputSchema,\n          strict: strictJsonSchema,\n        });\n        break;\n      case 'provider-defined': {\n        switch (tool.id) {\n          case 'openai.file_search': {\n            const args = await validateTypes({\n              value: tool.args,\n              schema: fileSearchArgsSchema,\n            });\n\n            openaiTools.push({\n              type: 'file_search',\n              vector_store_ids: args.vectorStoreIds,\n              max_num_results: args.maxNumResults,\n              ranking_options: args.ranking\n                ? {\n                    ranker: args.ranking.ranker,\n                    score_threshold: args.ranking.scoreThreshold,\n                  }\n                : undefined,\n              filters: args.filters,\n            });\n\n            break;\n          }\n          case 'openai.local_shell': {\n            openaiTools.push({\n              type: 'local_shell',\n            });\n            break;\n          }\n          case 'openai.web_search_preview': {\n            const args = await validateTypes({\n              value: tool.args,\n              schema: webSearchPreviewArgsSchema,\n            });\n            openaiTools.push({\n              type: 'web_search_preview',\n              search_context_size: args.searchContextSize,\n              user_location: args.userLocation,\n            });\n            break;\n          }\n          case 'openai.web_search': {\n            const args = await validateTypes({\n              value: tool.args,\n              schema: webSearchArgsSchema,\n            });\n            openaiTools.push({\n              type: 'web_search',\n              filters:\n                args.filters != null\n                  ? { allowed_domains: args.filters.allowedDomains }\n                  : undefined,\n              external_web_access: args.externalWebAccess,\n              search_context_size: args.searchContextSize,\n              user_location: args.userLocation,\n            });\n            break;\n          }\n          case 'openai.code_interpreter': {\n            const args = await validateTypes({\n              value: tool.args,\n              schema: codeInterpreterArgsSchema,\n            });\n\n            openaiTools.push({\n              type: 'code_interpreter',\n              container:\n                args.container == null\n                  ? { type: 'auto', file_ids: undefined }\n                  : typeof args.container === 'string'\n                    ? args.container\n                    : { type: 'auto', file_ids: args.container.fileIds },\n            });\n            break;\n          }\n          case 'openai.image_generation': {\n            const args = await validateTypes({\n              value: tool.args,\n              schema: imageGenerationArgsSchema,\n            });\n\n            openaiTools.push({\n              type: 'image_generation',\n              background: args.background,\n              input_fidelity: args.inputFidelity,\n              input_image_mask: args.inputImageMask\n                ? {\n                    file_id: args.inputImageMask.fileId,\n                    image_url: args.inputImageMask.imageUrl,\n                  }\n                : undefined,\n              model: args.model,\n              size: args.size,\n              quality: args.quality,\n              moderation: args.moderation,\n              output_format: args.outputFormat,\n              output_compression: args.outputCompression,\n            });\n            break;\n          }\n        }\n        break;\n      }\n      default:\n        toolWarnings.push({ type: 'unsupported-tool', tool });\n        break;\n    }\n  }\n\n  if (toolChoice == null) {\n    return { tools: openaiTools, toolChoice: undefined, toolWarnings };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n    case 'none':\n    case 'required':\n      return { tools: openaiTools, toolChoice: type, toolWarnings };\n    case 'tool':\n      return {\n        tools: openaiTools,\n        toolChoice:\n          toolChoice.toolName === 'code_interpreter' ||\n          toolChoice.toolName === 'file_search' ||\n          toolChoice.toolName === 'image_generation' ||\n          toolChoice.toolName === 'web_search_preview' ||\n          toolChoice.toolName === 'web_search'\n            ? { type: toolChoice.toolName }\n            : { type: 'function', name: toolChoice.toolName },\n        toolWarnings,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  type LanguageModelV2,\n  type LanguageModelV2CallWarning,\n  type LanguageModelV2Content,\n  type LanguageModelV2FinishReason,\n  type LanguageModelV2ProviderDefinedTool,\n  type LanguageModelV2StreamPart,\n  type LanguageModelV2Usage,\n  type SharedV2ProviderMetadata,\n  APICallError,\n} from '@ai-sdk/provider';\nimport {\n  type InferValidator,\n  type ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport type { OpenAIConfig } from '../openai-config';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport type {\n  codeInterpreterInputSchema,\n  codeInterpreterOutputSchema,\n} from '../tool/code-interpreter';\nimport type { fileSearchOutputSchema } from '../tool/file-search';\nimport type { imageGenerationOutputSchema } from '../tool/image-generation';\nimport type { localShellInputSchema } from '../tool/local-shell';\nimport type { webSearchOutputSchema } from '../tool/web-search';\nimport { convertToOpenAIResponsesInput } from './convert-to-openai-responses-input';\nimport { mapOpenAIResponseFinishReason } from './map-openai-responses-finish-reason';\nimport {\n  type OpenAIResponsesChunk,\n  type OpenAIResponsesIncludeOptions,\n  type OpenAIResponsesIncludeValue,\n  type OpenAIResponsesLogprobs,\n  type OpenAIResponsesWebSearchAction,\n  openaiResponsesChunkSchema,\n  openaiResponsesResponseSchema,\n} from './openai-responses-api';\nimport {\n  type OpenAIResponsesModelId,\n  openaiResponsesProviderOptionsSchema,\n  TOP_LOGPROBS_MAX,\n} from './openai-responses-options';\nimport { prepareResponsesTools } from './openai-responses-prepare-tools';\nimport { getOpenAILanguageModelCapabilities } from '../openai-language-model-capabilities';\n\nexport class OpenAIResponsesLanguageModel implements LanguageModelV2 {\n  readonly specificationVersion = 'v2';\n\n  readonly modelId: OpenAIResponsesModelId;\n\n  private readonly config: OpenAIConfig;\n\n  constructor(modelId: OpenAIResponsesModelId, config: OpenAIConfig) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  readonly supportedUrls: Record<string, RegExp[]> = {\n    'image/*': [/^https?:\\/\\/.*$/],\n    'application/pdf': [/^https?:\\/\\/.*$/],\n  };\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private async getArgs({\n    maxOutputTokens,\n    temperature,\n    stopSequences,\n    topP,\n    topK,\n    presencePenalty,\n    frequencyPenalty,\n    seed,\n    prompt,\n    providerOptions,\n    tools,\n    toolChoice,\n    responseFormat,\n  }: Parameters<LanguageModelV2['doGenerate']>[0]) {\n    const warnings: LanguageModelV2CallWarning[] = [];\n    const modelCapabilities = getOpenAILanguageModelCapabilities(this.modelId);\n\n    if (topK != null) {\n      warnings.push({ type: 'unsupported-setting', setting: 'topK' });\n    }\n\n    if (seed != null) {\n      warnings.push({ type: 'unsupported-setting', setting: 'seed' });\n    }\n\n    if (presencePenalty != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'presencePenalty',\n      });\n    }\n\n    if (frequencyPenalty != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'frequencyPenalty',\n      });\n    }\n\n    if (stopSequences != null) {\n      warnings.push({ type: 'unsupported-setting', setting: 'stopSequences' });\n    }\n\n    const openaiOptions = await parseProviderOptions({\n      provider: 'openai',\n      providerOptions,\n      schema: openaiResponsesProviderOptionsSchema,\n    });\n\n    if (openaiOptions?.conversation && openaiOptions?.previousResponseId) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'conversation',\n        details: 'conversation and previousResponseId cannot be used together',\n      });\n    }\n\n    const { input, warnings: inputWarnings } =\n      await convertToOpenAIResponsesInput({\n        prompt,\n        systemMessageMode: modelCapabilities.systemMessageMode,\n        fileIdPrefixes: this.config.fileIdPrefixes,\n        store: openaiOptions?.store ?? true,\n        hasLocalShellTool: hasOpenAITool('openai.local_shell'),\n      });\n\n    warnings.push(...inputWarnings);\n\n    const strictJsonSchema = openaiOptions?.strictJsonSchema ?? false;\n\n    let include: OpenAIResponsesIncludeOptions = openaiOptions?.include;\n\n    function addInclude(key: OpenAIResponsesIncludeValue) {\n      if (include == null) {\n        include = [key];\n      } else if (!include.includes(key)) {\n        include = [...include, key];\n      }\n    }\n\n    function hasOpenAITool(id: string) {\n      return (\n        tools?.find(\n          tool => tool.type === 'provider-defined' && tool.id === id,\n        ) != null\n      );\n    }\n\n    // when logprobs are requested, automatically include them:\n    const topLogprobs =\n      typeof openaiOptions?.logprobs === 'number'\n        ? openaiOptions?.logprobs\n        : openaiOptions?.logprobs === true\n          ? TOP_LOGPROBS_MAX\n          : undefined;\n\n    if (topLogprobs) {\n      addInclude('message.output_text.logprobs');\n    }\n\n    // when a web search tool is present, automatically include the sources:\n    const webSearchToolName = (\n      tools?.find(\n        tool =>\n          tool.type === 'provider-defined' &&\n          (tool.id === 'openai.web_search' ||\n            tool.id === 'openai.web_search_preview'),\n      ) as LanguageModelV2ProviderDefinedTool | undefined\n    )?.name;\n\n    if (webSearchToolName) {\n      addInclude('web_search_call.action.sources');\n    }\n\n    // when a code interpreter tool is present, automatically include the outputs:\n    if (hasOpenAITool('openai.code_interpreter')) {\n      addInclude('code_interpreter_call.outputs');\n    }\n\n    const store = openaiOptions?.store;\n\n    // store defaults to true in the OpenAI responses API, so check for false exactly:\n    if (store === false && modelCapabilities.isReasoningModel) {\n      addInclude('reasoning.encrypted_content');\n    }\n\n    const baseArgs = {\n      model: this.modelId,\n      input,\n      temperature,\n      top_p: topP,\n      max_output_tokens: maxOutputTokens,\n\n      ...((responseFormat?.type === 'json' || openaiOptions?.textVerbosity) && {\n        text: {\n          ...(responseFormat?.type === 'json' && {\n            format:\n              responseFormat.schema != null\n                ? {\n                    type: 'json_schema',\n                    strict: strictJsonSchema,\n                    name: responseFormat.name ?? 'response',\n                    description: responseFormat.description,\n                    schema: responseFormat.schema,\n                  }\n                : { type: 'json_object' },\n          }),\n          ...(openaiOptions?.textVerbosity && {\n            verbosity: openaiOptions.textVerbosity,\n          }),\n        },\n      }),\n\n      // provider options:\n      conversation: openaiOptions?.conversation,\n      max_tool_calls: openaiOptions?.maxToolCalls,\n      metadata: openaiOptions?.metadata,\n      parallel_tool_calls: openaiOptions?.parallelToolCalls,\n      previous_response_id: openaiOptions?.previousResponseId,\n      store,\n      user: openaiOptions?.user,\n      instructions: openaiOptions?.instructions,\n      service_tier: openaiOptions?.serviceTier,\n      include,\n      prompt_cache_key: openaiOptions?.promptCacheKey,\n      prompt_cache_retention: openaiOptions?.promptCacheRetention,\n      safety_identifier: openaiOptions?.safetyIdentifier,\n      top_logprobs: topLogprobs,\n      truncation: openaiOptions?.truncation,\n\n      // model-specific settings:\n      ...(modelCapabilities.isReasoningModel &&\n        (openaiOptions?.reasoningEffort != null ||\n          openaiOptions?.reasoningSummary != null) && {\n          reasoning: {\n            ...(openaiOptions?.reasoningEffort != null && {\n              effort: openaiOptions.reasoningEffort,\n            }),\n            ...(openaiOptions?.reasoningSummary != null && {\n              summary: openaiOptions.reasoningSummary,\n            }),\n          },\n        }),\n    };\n\n    // remove unsupported settings for reasoning models\n    // see https://platform.openai.com/docs/guides/reasoning#limitations\n    if (modelCapabilities.isReasoningModel) {\n      // when reasoning effort is none, gpt-5.1 models allow temperature, topP, logprobs\n      //  https://platform.openai.com/docs/guides/latest-model#gpt-5-1-parameter-compatibility\n      if (\n        !(\n          openaiOptions?.reasoningEffort === 'none' &&\n          modelCapabilities.supportsNonReasoningParameters\n        )\n      ) {\n        if (baseArgs.temperature != null) {\n          baseArgs.temperature = undefined;\n          warnings.push({\n            type: 'unsupported-setting',\n            setting: 'temperature',\n            details: 'temperature is not supported for reasoning models',\n          });\n        }\n\n        if (baseArgs.top_p != null) {\n          baseArgs.top_p = undefined;\n          warnings.push({\n            type: 'unsupported-setting',\n            setting: 'topP',\n            details: 'topP is not supported for reasoning models',\n          });\n        }\n      }\n    } else {\n      if (openaiOptions?.reasoningEffort != null) {\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'reasoningEffort',\n          details: 'reasoningEffort is not supported for non-reasoning models',\n        });\n      }\n\n      if (openaiOptions?.reasoningSummary != null) {\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'reasoningSummary',\n          details: 'reasoningSummary is not supported for non-reasoning models',\n        });\n      }\n    }\n\n    // Validate flex processing support\n    if (\n      openaiOptions?.serviceTier === 'flex' &&\n      !modelCapabilities.supportsFlexProcessing\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'serviceTier',\n        details:\n          'flex processing is only available for o3, o4-mini, and gpt-5 models',\n      });\n      // Remove from args if not supported\n      delete (baseArgs as any).service_tier;\n    }\n\n    // Validate priority processing support\n    if (\n      openaiOptions?.serviceTier === 'priority' &&\n      !modelCapabilities.supportsPriorityProcessing\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'serviceTier',\n        details:\n          'priority processing is only available for supported models (gpt-4, gpt-5, gpt-5-mini, o3, o4-mini) and requires Enterprise access. gpt-5-nano is not supported',\n      });\n      // Remove from args if not supported\n      delete (baseArgs as any).service_tier;\n    }\n\n    const {\n      tools: openaiTools,\n      toolChoice: openaiToolChoice,\n      toolWarnings,\n    } = await prepareResponsesTools({\n      tools,\n      toolChoice,\n      strictJsonSchema,\n    });\n\n    return {\n      webSearchToolName,\n      args: {\n        ...baseArgs,\n        tools: openaiTools,\n        tool_choice: openaiToolChoice,\n      },\n      warnings: [...warnings, ...toolWarnings],\n      store,\n    };\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doGenerate']>>> {\n    const {\n      args: body,\n      warnings,\n      webSearchToolName,\n    } = await this.getArgs(options);\n    const url = this.config.url({\n      path: '/responses',\n      modelId: this.modelId,\n    });\n\n    const providerKey = this.config.provider.replace('.responses', ''); // can be 'openai' or 'azure'. provider is 'openai.responses' or 'azure.responses'.\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiResponsesResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    if (response.error) {\n      throw new APICallError({\n        message: response.error.message,\n        url,\n        requestBodyValues: body,\n        statusCode: 400,\n        responseHeaders,\n        responseBody: rawResponse as string,\n        isRetryable: false,\n      });\n    }\n\n    const content: Array<LanguageModelV2Content> = [];\n    const logprobs: Array<OpenAIResponsesLogprobs> = [];\n\n    // flag that checks if there have been client-side tool calls (not executed by openai)\n    let hasFunctionCall = false;\n\n    // map response content to content array (defined when there is no error)\n    for (const part of response.output!) {\n      switch (part.type) {\n        case 'reasoning': {\n          // when there are no summary parts, we need to add an empty reasoning part:\n          if (part.summary.length === 0) {\n            part.summary.push({ type: 'summary_text', text: '' });\n          }\n\n          for (const summary of part.summary) {\n            content.push({\n              type: 'reasoning' as const,\n              text: summary.text,\n              providerMetadata: {\n                [providerKey]: {\n                  itemId: part.id,\n                  reasoningEncryptedContent: part.encrypted_content ?? null,\n                },\n              },\n            });\n          }\n          break;\n        }\n\n        case 'image_generation_call': {\n          content.push({\n            type: 'tool-call',\n            toolCallId: part.id,\n            toolName: 'image_generation',\n            input: '{}',\n            providerExecuted: true,\n          });\n\n          content.push({\n            type: 'tool-result',\n            toolCallId: part.id,\n            toolName: 'image_generation',\n            result: {\n              result: part.result,\n            } satisfies InferValidator<typeof imageGenerationOutputSchema>,\n            providerExecuted: true,\n          });\n\n          break;\n        }\n\n        case 'local_shell_call': {\n          content.push({\n            type: 'tool-call',\n            toolCallId: part.call_id,\n            toolName: 'local_shell',\n            input: JSON.stringify({\n              action: part.action,\n            } satisfies InferValidator<typeof localShellInputSchema>),\n            providerMetadata: {\n              [providerKey]: {\n                itemId: part.id,\n              },\n            },\n          });\n\n          break;\n        }\n\n        case 'message': {\n          for (const contentPart of part.content) {\n            if (\n              options.providerOptions?.openai?.logprobs &&\n              contentPart.logprobs\n            ) {\n              logprobs.push(contentPart.logprobs);\n            }\n\n            content.push({\n              type: 'text',\n              text: contentPart.text,\n              providerMetadata: {\n                [providerKey]: {\n                  itemId: part.id,\n                  ...(part.phase != null && { phase: part.phase }),\n                },\n              },\n            });\n\n            for (const annotation of contentPart.annotations) {\n              if (annotation.type === 'url_citation') {\n                content.push({\n                  type: 'source',\n                  sourceType: 'url',\n                  id: this.config.generateId?.() ?? generateId(),\n                  url: annotation.url,\n                  title: annotation.title,\n                });\n              } else if (annotation.type === 'file_citation') {\n                content.push({\n                  type: 'source',\n                  sourceType: 'document',\n                  id: this.config.generateId?.() ?? generateId(),\n                  mediaType: 'text/plain',\n                  title: annotation.quote ?? annotation.filename ?? 'Document',\n                  filename: annotation.filename ?? annotation.file_id,\n                  ...(annotation.file_id\n                    ? {\n                        providerMetadata: {\n                          [providerKey]: {\n                            fileId: annotation.file_id,\n                          },\n                        },\n                      }\n                    : {}),\n                });\n              } else if (annotation.type === 'container_file_citation') {\n                content.push({\n                  type: 'source',\n                  sourceType: 'document',\n                  id: this.config.generateId?.() ?? generateId(),\n                  mediaType: 'text/plain',\n                  title:\n                    annotation.filename ?? annotation.file_id ?? 'Document',\n                  filename: annotation.filename ?? annotation.file_id,\n                  providerMetadata: {\n                    [providerKey]: {\n                      fileId: annotation.file_id,\n                      containerId: annotation.container_id,\n                      ...(annotation.index != null\n                        ? { index: annotation.index }\n                        : {}),\n                    },\n                  },\n                });\n              } else if (annotation.type === 'file_path') {\n                content.push({\n                  type: 'source',\n                  sourceType: 'document',\n                  id: this.config.generateId?.() ?? generateId(),\n                  mediaType: 'application/octet-stream',\n                  title: annotation.file_id,\n                  filename: annotation.file_id,\n                  providerMetadata: {\n                    [providerKey]: {\n                      fileId: annotation.file_id,\n                      ...(annotation.index != null\n                        ? { index: annotation.index }\n                        : {}),\n                    },\n                  },\n                });\n              }\n            }\n          }\n\n          break;\n        }\n\n        case 'function_call': {\n          hasFunctionCall = true;\n\n          content.push({\n            type: 'tool-call',\n            toolCallId: part.call_id,\n            toolName: part.name,\n            input: part.arguments,\n            providerMetadata: {\n              [providerKey]: {\n                itemId: part.id,\n              },\n            },\n          });\n          break;\n        }\n\n        case 'web_search_call': {\n          content.push({\n            type: 'tool-call',\n            toolCallId: part.id,\n            toolName: webSearchToolName ?? 'web_search',\n            input: JSON.stringify({}),\n            providerExecuted: true,\n          });\n\n          content.push({\n            type: 'tool-result',\n            toolCallId: part.id,\n            toolName: webSearchToolName ?? 'web_search',\n            result: mapWebSearchOutput(part.action),\n            providerExecuted: true,\n          });\n\n          break;\n        }\n\n        case 'computer_call': {\n          content.push({\n            type: 'tool-call',\n            toolCallId: part.id,\n            toolName: 'computer_use',\n            input: '',\n            providerExecuted: true,\n          });\n\n          content.push({\n            type: 'tool-result',\n            toolCallId: part.id,\n            toolName: 'computer_use',\n            result: {\n              type: 'computer_use_tool_result',\n              status: part.status || 'completed',\n            },\n            providerExecuted: true,\n          });\n          break;\n        }\n\n        case 'file_search_call': {\n          content.push({\n            type: 'tool-call',\n            toolCallId: part.id,\n            toolName: 'file_search',\n            input: '{}',\n            providerExecuted: true,\n          });\n\n          content.push({\n            type: 'tool-result',\n            toolCallId: part.id,\n            toolName: 'file_search',\n            result: {\n              queries: part.queries,\n              results:\n                part.results?.map(result => ({\n                  attributes: result.attributes,\n                  fileId: result.file_id,\n                  filename: result.filename,\n                  score: result.score,\n                  text: result.text,\n                })) ?? null,\n            } satisfies InferValidator<typeof fileSearchOutputSchema>,\n            providerExecuted: true,\n          });\n          break;\n        }\n\n        case 'code_interpreter_call': {\n          content.push({\n            type: 'tool-call',\n            toolCallId: part.id,\n            toolName: 'code_interpreter',\n            input: JSON.stringify({\n              code: part.code,\n              containerId: part.container_id,\n            } satisfies InferValidator<typeof codeInterpreterInputSchema>),\n            providerExecuted: true,\n          });\n\n          content.push({\n            type: 'tool-result',\n            toolCallId: part.id,\n            toolName: 'code_interpreter',\n            result: {\n              outputs: part.outputs,\n            } satisfies InferValidator<typeof codeInterpreterOutputSchema>,\n            providerExecuted: true,\n          });\n          break;\n        }\n      }\n    }\n\n    const providerMetadata: SharedV2ProviderMetadata = {\n      [providerKey]: {\n        ...(response.id != null ? { responseId: response.id } : {}),\n      },\n    };\n\n    if (logprobs.length > 0) {\n      providerMetadata[providerKey].logprobs = logprobs;\n    }\n\n    if (typeof response.service_tier === 'string') {\n      providerMetadata[providerKey].serviceTier = response.service_tier;\n    }\n\n    const usage = response.usage!; // defined when there is no error\n\n    return {\n      content,\n      finishReason: mapOpenAIResponseFinishReason({\n        finishReason: response.incomplete_details?.reason,\n        hasFunctionCall,\n      }),\n      usage: {\n        inputTokens: usage.input_tokens,\n        outputTokens: usage.output_tokens,\n        totalTokens: usage.input_tokens + usage.output_tokens,\n        reasoningTokens:\n          usage.output_tokens_details?.reasoning_tokens ?? undefined,\n        cachedInputTokens:\n          usage.input_tokens_details?.cached_tokens ?? undefined,\n      },\n      request: { body },\n      response: {\n        id: response.id,\n        timestamp: new Date(response.created_at! * 1000),\n        modelId: response.model,\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      providerMetadata,\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV2['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doStream']>>> {\n    const {\n      args: body,\n      warnings,\n      webSearchToolName,\n      store,\n    } = await this.getArgs(options);\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: this.config.url({\n        path: '/responses',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: {\n        ...body,\n        stream: true,\n      },\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        openaiResponsesChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const self = this;\n    const providerKey = this.config.provider.replace('.responses', ''); // can be 'openai' or 'azure'. provider is 'openai.responses' or 'azure.responses'.\n\n    let finishReason: LanguageModelV2FinishReason = 'unknown';\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n    const logprobs: Array<OpenAIResponsesLogprobs> = [];\n    let responseId: string | null = null;\n    const ongoingToolCalls: Record<\n      number,\n      | {\n          toolName: string;\n          toolCallId: string;\n          codeInterpreter?: {\n            containerId: string;\n          };\n        }\n      | undefined\n    > = {};\n\n    // set annotations in 'text-end' part providerMetadata.\n    const ongoingAnnotations: Array<\n      Extract<\n        OpenAIResponsesChunk,\n        { type: 'response.output_text.annotation.added' }\n      >['annotation']\n    > = [];\n\n    // track the phase of the current message being streamed\n    let activeMessagePhase: 'commentary' | 'final_answer' | undefined;\n\n    // flag that checks if there have been client-side tool calls (not executed by openai)\n    let hasFunctionCall = false;\n\n    const activeReasoning: Record<\n      string,\n      {\n        encryptedContent?: string | null;\n        // summary index as string to reasoning part state:\n        summaryParts: Record<string, 'active' | 'can-conclude' | 'concluded'>;\n      }\n    > = {};\n\n    let serviceTier: string | undefined;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<OpenAIResponsesChunk>,\n          LanguageModelV2StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = 'error';\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            if (isResponseOutputItemAddedChunk(value)) {\n              if (value.item.type === 'function_call') {\n                ongoingToolCalls[value.output_index] = {\n                  toolName: value.item.name,\n                  toolCallId: value.item.call_id,\n                };\n\n                controller.enqueue({\n                  type: 'tool-input-start',\n                  id: value.item.call_id,\n                  toolName: value.item.name,\n                });\n              } else if (value.item.type === 'web_search_call') {\n                ongoingToolCalls[value.output_index] = {\n                  toolName: webSearchToolName ?? 'web_search',\n                  toolCallId: value.item.id,\n                };\n\n                controller.enqueue({\n                  type: 'tool-input-start',\n                  id: value.item.id,\n                  toolName: webSearchToolName ?? 'web_search',\n                  providerExecuted: true,\n                });\n\n                controller.enqueue({\n                  type: 'tool-input-end',\n                  id: value.item.id,\n                });\n\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: value.item.id,\n                  toolName: webSearchToolName ?? 'web_search',\n                  input: JSON.stringify({}),\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'computer_call') {\n                ongoingToolCalls[value.output_index] = {\n                  toolName: 'computer_use',\n                  toolCallId: value.item.id,\n                };\n\n                controller.enqueue({\n                  type: 'tool-input-start',\n                  id: value.item.id,\n                  toolName: 'computer_use',\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'code_interpreter_call') {\n                ongoingToolCalls[value.output_index] = {\n                  toolName: 'code_interpreter',\n                  toolCallId: value.item.id,\n                  codeInterpreter: {\n                    containerId: value.item.container_id,\n                  },\n                };\n\n                controller.enqueue({\n                  type: 'tool-input-start',\n                  id: value.item.id,\n                  toolName: 'code_interpreter',\n                  providerExecuted: true,\n                });\n\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: value.item.id,\n                  delta: `{\"containerId\":\"${value.item.container_id}\",\"code\":\"`,\n                });\n              } else if (value.item.type === 'file_search_call') {\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: value.item.id,\n                  toolName: 'file_search',\n                  input: '{}',\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'image_generation_call') {\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: value.item.id,\n                  toolName: 'image_generation',\n                  input: '{}',\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'message') {\n                ongoingAnnotations.splice(0, ongoingAnnotations.length);\n                activeMessagePhase = value.item.phase ?? undefined;\n                controller.enqueue({\n                  type: 'text-start',\n                  id: value.item.id,\n                  providerMetadata: {\n                    [providerKey]: {\n                      itemId: value.item.id,\n                      ...(value.item.phase != null && {\n                        phase: value.item.phase,\n                      }),\n                    },\n                  },\n                });\n              } else if (\n                isResponseOutputItemAddedChunk(value) &&\n                value.item.type === 'reasoning'\n              ) {\n                activeReasoning[value.item.id] = {\n                  encryptedContent: value.item.encrypted_content,\n                  summaryParts: { 0: 'active' },\n                };\n\n                controller.enqueue({\n                  type: 'reasoning-start',\n                  id: `${value.item.id}:0`,\n                  providerMetadata: {\n                    [providerKey]: {\n                      itemId: value.item.id,\n                      reasoningEncryptedContent:\n                        value.item.encrypted_content ?? null,\n                    },\n                  },\n                });\n              }\n            } else if (isResponseOutputItemDoneChunk(value)) {\n              if (value.item.type === 'message') {\n                const phase = value.item.phase ?? activeMessagePhase;\n                activeMessagePhase = undefined;\n                controller.enqueue({\n                  type: 'text-end',\n                  id: value.item.id,\n                  providerMetadata: {\n                    [providerKey]: {\n                      itemId: value.item.id,\n                      ...(phase != null && { phase }),\n                      ...(ongoingAnnotations.length > 0 && {\n                        annotations: ongoingAnnotations,\n                      }),\n                    },\n                  },\n                });\n              } else if (value.item.type === 'function_call') {\n                ongoingToolCalls[value.output_index] = undefined;\n                hasFunctionCall = true;\n\n                controller.enqueue({\n                  type: 'tool-input-end',\n                  id: value.item.call_id,\n                });\n\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: value.item.call_id,\n                  toolName: value.item.name,\n                  input: value.item.arguments,\n                  providerMetadata: {\n                    [providerKey]: {\n                      itemId: value.item.id,\n                    },\n                  },\n                });\n              } else if (value.item.type === 'web_search_call') {\n                ongoingToolCalls[value.output_index] = undefined;\n\n                controller.enqueue({\n                  type: 'tool-result',\n                  toolCallId: value.item.id,\n                  toolName: webSearchToolName ?? 'web_search',\n                  result: mapWebSearchOutput(value.item.action),\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'computer_call') {\n                ongoingToolCalls[value.output_index] = undefined;\n\n                controller.enqueue({\n                  type: 'tool-input-end',\n                  id: value.item.id,\n                });\n\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: value.item.id,\n                  toolName: 'computer_use',\n                  input: '',\n                  providerExecuted: true,\n                });\n\n                controller.enqueue({\n                  type: 'tool-result',\n                  toolCallId: value.item.id,\n                  toolName: 'computer_use',\n                  result: {\n                    type: 'computer_use_tool_result',\n                    status: value.item.status || 'completed',\n                  },\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'file_search_call') {\n                ongoingToolCalls[value.output_index] = undefined;\n\n                controller.enqueue({\n                  type: 'tool-result',\n                  toolCallId: value.item.id,\n                  toolName: 'file_search',\n                  result: {\n                    queries: value.item.queries,\n                    results:\n                      value.item.results?.map(result => ({\n                        attributes: result.attributes,\n                        fileId: result.file_id,\n                        filename: result.filename,\n                        score: result.score,\n                        text: result.text,\n                      })) ?? null,\n                  } satisfies InferValidator<typeof fileSearchOutputSchema>,\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'code_interpreter_call') {\n                ongoingToolCalls[value.output_index] = undefined;\n\n                controller.enqueue({\n                  type: 'tool-result',\n                  toolCallId: value.item.id,\n                  toolName: 'code_interpreter',\n                  result: {\n                    outputs: value.item.outputs,\n                  } satisfies InferValidator<\n                    typeof codeInterpreterOutputSchema\n                  >,\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'image_generation_call') {\n                controller.enqueue({\n                  type: 'tool-result',\n                  toolCallId: value.item.id,\n                  toolName: 'image_generation',\n                  result: {\n                    result: value.item.result,\n                  } satisfies InferValidator<\n                    typeof imageGenerationOutputSchema\n                  >,\n                  providerExecuted: true,\n                });\n              } else if (value.item.type === 'local_shell_call') {\n                ongoingToolCalls[value.output_index] = undefined;\n\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: value.item.call_id,\n                  toolName: 'local_shell',\n                  input: JSON.stringify({\n                    action: {\n                      type: 'exec',\n                      command: value.item.action.command,\n                      timeoutMs: value.item.action.timeout_ms,\n                      user: value.item.action.user,\n                      workingDirectory: value.item.action.working_directory,\n                      env: value.item.action.env,\n                    },\n                  } satisfies InferValidator<typeof localShellInputSchema>),\n                  providerMetadata: {\n                    [providerKey]: { itemId: value.item.id },\n                  },\n                });\n              } else if (value.item.type === 'reasoning') {\n                const activeReasoningPart = activeReasoning[value.item.id];\n\n                // get all active or can-conclude summary parts' ids\n                // to conclude ongoing reasoning parts:\n                const summaryPartIndices = Object.entries(\n                  activeReasoningPart.summaryParts,\n                )\n                  .filter(\n                    ([_, status]) =>\n                      status === 'active' || status === 'can-conclude',\n                  )\n                  .map(([summaryIndex]) => summaryIndex);\n\n                for (const summaryIndex of summaryPartIndices) {\n                  controller.enqueue({\n                    type: 'reasoning-end',\n                    id: `${value.item.id}:${summaryIndex}`,\n                    providerMetadata: {\n                      [providerKey]: {\n                        itemId: value.item.id,\n                        reasoningEncryptedContent:\n                          value.item.encrypted_content ?? null,\n                      },\n                    },\n                  });\n                }\n\n                delete activeReasoning[value.item.id];\n              }\n            } else if (isResponseFunctionCallArgumentsDeltaChunk(value)) {\n              const toolCall = ongoingToolCalls[value.output_index];\n\n              if (toolCall != null) {\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: toolCall.toolCallId,\n                  delta: value.delta,\n                });\n              }\n            } else if (isResponseCodeInterpreterCallCodeDeltaChunk(value)) {\n              const toolCall = ongoingToolCalls[value.output_index];\n\n              if (toolCall != null) {\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: toolCall.toolCallId,\n                  // The delta is code, which is embedding in a JSON string.\n                  // To escape it, we use JSON.stringify and slice to remove the outer quotes.\n                  delta: JSON.stringify(value.delta).slice(1, -1),\n                });\n              }\n            } else if (isResponseCodeInterpreterCallCodeDoneChunk(value)) {\n              const toolCall = ongoingToolCalls[value.output_index];\n\n              if (toolCall != null) {\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: toolCall.toolCallId,\n                  delta: '\"}',\n                });\n\n                controller.enqueue({\n                  type: 'tool-input-end',\n                  id: toolCall.toolCallId,\n                });\n\n                // immediately send the tool call after the input end:\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: toolCall.toolCallId,\n                  toolName: 'code_interpreter',\n                  input: JSON.stringify({\n                    code: value.code,\n                    containerId: toolCall.codeInterpreter!.containerId,\n                  } satisfies InferValidator<\n                    typeof codeInterpreterInputSchema\n                  >),\n                  providerExecuted: true,\n                });\n              }\n            } else if (isResponseCreatedChunk(value)) {\n              responseId = value.response.id;\n              controller.enqueue({\n                type: 'response-metadata',\n                id: value.response.id,\n                timestamp: new Date(value.response.created_at * 1000),\n                modelId: value.response.model,\n              });\n            } else if (isTextDeltaChunk(value)) {\n              controller.enqueue({\n                type: 'text-delta',\n                id: value.item_id,\n                delta: value.delta,\n              });\n\n              if (options.providerOptions?.openai?.logprobs && value.logprobs) {\n                logprobs.push(value.logprobs);\n              }\n            } else if (value.type === 'response.reasoning_summary_part.added') {\n              // the first reasoning start is pushed in isResponseOutputItemAddedReasoningChunk\n              if (value.summary_index > 0) {\n                const activeReasoningPart = activeReasoning[value.item_id]!;\n\n                activeReasoningPart.summaryParts[value.summary_index] =\n                  'active';\n\n                // since there is a new active summary part, we can conclude all can-conclude summary parts\n                for (const summaryIndex of Object.keys(\n                  activeReasoningPart.summaryParts,\n                )) {\n                  if (\n                    activeReasoningPart.summaryParts[summaryIndex] ===\n                    'can-conclude'\n                  ) {\n                    controller.enqueue({\n                      type: 'reasoning-end',\n                      id: `${value.item_id}:${summaryIndex}`,\n                      providerMetadata: {\n                        [providerKey]: { itemId: value.item_id },\n                      },\n                    });\n                    activeReasoningPart.summaryParts[summaryIndex] =\n                      'concluded';\n                  }\n                }\n\n                controller.enqueue({\n                  type: 'reasoning-start',\n                  id: `${value.item_id}:${value.summary_index}`,\n                  providerMetadata: {\n                    [providerKey]: {\n                      itemId: value.item_id,\n                      reasoningEncryptedContent:\n                        activeReasoning[value.item_id]?.encryptedContent ??\n                        null,\n                    },\n                  },\n                });\n              }\n            } else if (value.type === 'response.reasoning_summary_text.delta') {\n              controller.enqueue({\n                type: 'reasoning-delta',\n                id: `${value.item_id}:${value.summary_index}`,\n                delta: value.delta,\n                providerMetadata: {\n                  [providerKey]: {\n                    itemId: value.item_id,\n                  },\n                },\n              });\n            } else if (value.type === 'response.reasoning_summary_part.done') {\n              // when OpenAI stores the message data, we can immediately conclude the reasoning part\n              // since we do not need to send the encrypted content.\n              if (store) {\n                controller.enqueue({\n                  type: 'reasoning-end',\n                  id: `${value.item_id}:${value.summary_index}`,\n                  providerMetadata: {\n                    [providerKey]: { itemId: value.item_id },\n                  },\n                });\n\n                // mark the summary part as concluded\n                activeReasoning[value.item_id]!.summaryParts[\n                  value.summary_index\n                ] = 'concluded';\n              } else {\n                // mark the summary part as can-conclude only\n                // because we need to have a final summary part with the encrypted content\n                activeReasoning[value.item_id]!.summaryParts[\n                  value.summary_index\n                ] = 'can-conclude';\n              }\n            } else if (isResponseFinishedChunk(value)) {\n              finishReason = mapOpenAIResponseFinishReason({\n                finishReason: value.response.incomplete_details?.reason,\n                hasFunctionCall,\n              });\n              usage.inputTokens = value.response.usage.input_tokens;\n              usage.outputTokens = value.response.usage.output_tokens;\n              usage.totalTokens =\n                value.response.usage.input_tokens +\n                value.response.usage.output_tokens;\n              usage.reasoningTokens =\n                value.response.usage.output_tokens_details?.reasoning_tokens ??\n                undefined;\n              usage.cachedInputTokens =\n                value.response.usage.input_tokens_details?.cached_tokens ??\n                undefined;\n              if (typeof value.response.service_tier === 'string') {\n                serviceTier = value.response.service_tier;\n              }\n            } else if (isResponseAnnotationAddedChunk(value)) {\n              ongoingAnnotations.push(value.annotation);\n              if (value.annotation.type === 'url_citation') {\n                controller.enqueue({\n                  type: 'source',\n                  sourceType: 'url',\n                  id: self.config.generateId?.() ?? generateId(),\n                  url: value.annotation.url,\n                  title: value.annotation.title,\n                });\n              } else if (value.annotation.type === 'file_citation') {\n                controller.enqueue({\n                  type: 'source',\n                  sourceType: 'document',\n                  id: self.config.generateId?.() ?? generateId(),\n                  mediaType: 'text/plain',\n                  title:\n                    value.annotation.quote ??\n                    value.annotation.filename ??\n                    'Document',\n                  filename:\n                    value.annotation.filename ?? value.annotation.file_id,\n                  ...(value.annotation.file_id\n                    ? {\n                        providerMetadata: {\n                          [providerKey]: {\n                            fileId: value.annotation.file_id,\n                          },\n                        },\n                      }\n                    : {}),\n                });\n              }\n            } else if (isErrorChunk(value)) {\n              controller.enqueue({ type: 'error', error: value });\n            }\n          },\n\n          flush(controller) {\n            const providerMetadata: SharedV2ProviderMetadata = {\n              [providerKey]: {\n                responseId,\n              },\n            };\n\n            if (logprobs.length > 0) {\n              providerMetadata[providerKey].logprobs = logprobs;\n            }\n\n            if (serviceTier !== undefined) {\n              providerMetadata[providerKey].serviceTier = serviceTier;\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage,\n              providerMetadata,\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n\nfunction isTextDeltaChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & { type: 'response.output_text.delta' } {\n  return chunk.type === 'response.output_text.delta';\n}\n\nfunction isResponseOutputItemDoneChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & { type: 'response.output_item.done' } {\n  return chunk.type === 'response.output_item.done';\n}\n\nfunction isResponseFinishedChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & {\n  type: 'response.completed' | 'response.incomplete';\n} {\n  return (\n    chunk.type === 'response.completed' || chunk.type === 'response.incomplete'\n  );\n}\n\nfunction isResponseCreatedChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & { type: 'response.created' } {\n  return chunk.type === 'response.created';\n}\n\nfunction isResponseFunctionCallArgumentsDeltaChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & {\n  type: 'response.function_call_arguments.delta';\n} {\n  return chunk.type === 'response.function_call_arguments.delta';\n}\n\nfunction isResponseCodeInterpreterCallCodeDeltaChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & {\n  type: 'response.code_interpreter_call_code.delta';\n} {\n  return chunk.type === 'response.code_interpreter_call_code.delta';\n}\n\nfunction isResponseCodeInterpreterCallCodeDoneChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & {\n  type: 'response.code_interpreter_call_code.done';\n} {\n  return chunk.type === 'response.code_interpreter_call_code.done';\n}\n\nfunction isResponseOutputItemAddedChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & { type: 'response.output_item.added' } {\n  return chunk.type === 'response.output_item.added';\n}\n\nfunction isResponseAnnotationAddedChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & {\n  type: 'response.output_text.annotation.added';\n} {\n  return chunk.type === 'response.output_text.annotation.added';\n}\n\nfunction isErrorChunk(\n  chunk: OpenAIResponsesChunk,\n): chunk is OpenAIResponsesChunk & { type: 'error' } {\n  return chunk.type === 'error';\n}\n\nfunction mapWebSearchOutput(\n  action: OpenAIResponsesWebSearchAction | null | undefined,\n): InferValidator<typeof webSearchOutputSchema> {\n  if (action == null) {\n    return {};\n  }\n\n  switch (action.type) {\n    case 'search':\n      return {\n        action: { type: 'search', query: action.query ?? undefined },\n        // include sources when provided by the Responses API (behind include flag)\n        ...(action.sources != null && { sources: action.sources }),\n      };\n    case 'open_page':\n      return { action: { type: 'openPage', url: action.url } };\n    case 'find_in_page':\n      return {\n        action: {\n          type: 'findInPage',\n          url: action.url,\n          pattern: action.pattern,\n        },\n      };\n  }\n}\n","import {\n  codeInterpreter,\n  fileSearch,\n  imageGeneration,\n  webSearchPreview,\n} from '@ai-sdk/openai/internal';\n\nexport const azureOpenaiTools: {\n  codeInterpreter: typeof codeInterpreter;\n  fileSearch: typeof fileSearch;\n  imageGeneration: typeof imageGeneration;\n  webSearchPreview: typeof webSearchPreview;\n} = {\n  codeInterpreter,\n  fileSearch,\n  imageGeneration,\n  webSearchPreview,\n};\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n  typeof __PACKAGE_VERSION__ !== 'undefined'\n    ? __PACKAGE_VERSION__\n    : '0.0.0-test';\n","import {\n  OpenAIChatLanguageModel,\n  OpenAICompletionLanguageModel,\n  OpenAIEmbeddingModel,\n  OpenAIImageModel,\n  OpenAIResponsesLanguageModel,\n  OpenAISpeechModel,\n  OpenAITranscriptionModel,\n} from '@ai-sdk/openai/internal';\nimport type {\n  EmbeddingModelV2,\n  LanguageModelV2,\n  ProviderV2,\n  ImageModelV2,\n  SpeechModelV2,\n  TranscriptionModelV2,\n} from '@ai-sdk/provider';\nimport {\n  type FetchFunction,\n  loadApiKey,\n  loadSetting,\n  withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { azureOpenaiTools } from './azure-openai-tools';\nimport { VERSION } from './version';\n\nexport interface AzureOpenAIProvider extends ProviderV2 {\n  (deploymentId: string): LanguageModelV2;\n\n  /**\nCreates an Azure OpenAI chat model for text generation.\n   */\n  languageModel(deploymentId: string): LanguageModelV2;\n\n  /**\nCreates an Azure OpenAI chat model for text generation.\n   */\n  chat(deploymentId: string): LanguageModelV2;\n\n  /**\nCreates an Azure OpenAI responses API model for text generation.\n   */\n  responses(deploymentId: string): LanguageModelV2;\n\n  /**\nCreates an Azure OpenAI completion model for text generation.\n   */\n  completion(deploymentId: string): LanguageModelV2;\n\n  /**\n@deprecated Use `textEmbedding` instead.\n   */\n  embedding(deploymentId: string): EmbeddingModelV2<string>;\n\n  /**\n   * Creates an Azure OpenAI DALL-E model for image generation.\n   */\n  image(deploymentId: string): ImageModelV2;\n\n  /**\n   * Creates an Azure OpenAI DALL-E model for image generation.\n   */\n  imageModel(deploymentId: string): ImageModelV2;\n\n  textEmbedding(deploymentId: string): EmbeddingModelV2<string>;\n\n  /**\nCreates an Azure OpenAI model for text embeddings.\n   */\n  textEmbeddingModel(deploymentId: string): EmbeddingModelV2<string>;\n\n  /**\n   * Creates an Azure OpenAI model for audio transcription.\n   */\n  transcription(deploymentId: string): TranscriptionModelV2;\n\n  /**\n   * Creates an Azure OpenAI model for speech generation.\n   */\n  speech(deploymentId: string): SpeechModelV2;\n\n  /**\n   * AzureOpenAI-specific tools.\n   */\n  tools: typeof azureOpenaiTools;\n}\n\nexport interface AzureOpenAIProviderSettings {\n  /**\nName of the Azure OpenAI resource. Either this or `baseURL` can be used.\n\nThe resource name is used in the assembled URL: `https://{resourceName}.openai.azure.com/openai/v1{path}`.\n     */\n  resourceName?: string;\n\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers. Either this or `resourceName` can be used.\nWhen a baseURL is provided, the resourceName is ignored.\n\nWith a baseURL, the resolved URL is `{baseURL}/v1{path}`.\n   */\n  baseURL?: string;\n\n  /**\nAPI key for authenticating requests.\n     */\n  apiKey?: string;\n\n  /**\nCustom headers to include in the requests.\n     */\n  headers?: Record<string, string>;\n\n  /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n    */\n  fetch?: FetchFunction;\n\n  /**\nCustom api version to use. Defaults to `preview`.\n    */\n  apiVersion?: string;\n\n  /**\nUse deployment-based URLs for specific model types. Set to true to use legacy deployment format: \n`{baseURL}/deployments/{deploymentId}{path}?api-version={apiVersion}` instead of \n`{baseURL}/v1{path}?api-version={apiVersion}`.\n   */\n  useDeploymentBasedUrls?: boolean;\n}\n\n/**\nCreate an Azure OpenAI provider instance.\n */\nexport function createAzure(\n  options: AzureOpenAIProviderSettings = {},\n): AzureOpenAIProvider {\n  const getHeaders = () => {\n    const baseHeaders = {\n      'api-key': loadApiKey({\n        apiKey: options.apiKey,\n        environmentVariableName: 'AZURE_API_KEY',\n        description: 'Azure OpenAI',\n      }),\n      ...options.headers,\n    };\n    return withUserAgentSuffix(baseHeaders, `ai-sdk/azure/${VERSION}`);\n  };\n\n  const getResourceName = () =>\n    loadSetting({\n      settingValue: options.resourceName,\n      settingName: 'resourceName',\n      environmentVariableName: 'AZURE_RESOURCE_NAME',\n      description: 'Azure OpenAI resource name',\n    });\n\n  const apiVersion = options.apiVersion ?? 'v1';\n\n  const url = ({ path, modelId }: { path: string; modelId: string }) => {\n    const baseUrlPrefix =\n      options.baseURL ?? `https://${getResourceName()}.openai.azure.com/openai`;\n\n    let fullUrl: URL;\n    if (options.useDeploymentBasedUrls) {\n      // Use deployment-based format for compatibility with certain Azure OpenAI models\n      fullUrl = new URL(`${baseUrlPrefix}/deployments/${modelId}${path}`);\n    } else {\n      // Use v1 API format - no deployment ID in URL\n      fullUrl = new URL(`${baseUrlPrefix}/v1${path}`);\n    }\n\n    fullUrl.searchParams.set('api-version', apiVersion);\n    return fullUrl.toString();\n  };\n\n  const createChatModel = (deploymentName: string) =>\n    new OpenAIChatLanguageModel(deploymentName, {\n      provider: 'azure.chat',\n      url,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createCompletionModel = (modelId: string) =>\n    new OpenAICompletionLanguageModel(modelId, {\n      provider: 'azure.completion',\n      url,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createEmbeddingModel = (modelId: string) =>\n    new OpenAIEmbeddingModel(modelId, {\n      provider: 'azure.embeddings',\n      headers: getHeaders,\n      url,\n      fetch: options.fetch,\n    });\n\n  const createResponsesModel = (modelId: string) =>\n    new OpenAIResponsesLanguageModel(modelId, {\n      provider: 'azure.responses',\n      url,\n      headers: getHeaders,\n      fetch: options.fetch,\n      fileIdPrefixes: ['assistant-'],\n    });\n\n  const createImageModel = (modelId: string) =>\n    new OpenAIImageModel(modelId, {\n      provider: 'azure.image',\n      url,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createTranscriptionModel = (modelId: string) =>\n    new OpenAITranscriptionModel(modelId, {\n      provider: 'azure.transcription',\n      url,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createSpeechModel = (modelId: string) =>\n    new OpenAISpeechModel(modelId, {\n      provider: 'azure.speech',\n      url,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const provider = function (deploymentId: string) {\n    if (new.target) {\n      throw new Error(\n        'The Azure OpenAI model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createChatModel(deploymentId);\n  };\n\n  provider.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.completion = createCompletionModel;\n  provider.embedding = createEmbeddingModel;\n  provider.image = createImageModel;\n  provider.imageModel = createImageModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n  provider.responses = createResponsesModel;\n  provider.transcription = createTranscriptionModel;\n  provider.speech = createSpeechModel;\n  provider.tools = azureOpenaiTools;\n  return provider;\n}\n\n/**\nDefault Azure OpenAI provider instance.\n */\nexport const azure = createAzure();\n","import WebSocket from 'ws';\n\nexport interface CreateOpenAIWebSocketFetchOptions {\n  /**\n   * WebSocket endpoint URL.\n   * @default 'wss://api.openai.com/v1/responses'\n   */\n  url?: string;\n  /**\n   * Additional headers sent when establishing the WebSocket connection.\n   * Authorization and OpenAI-Beta are managed internally.\n   */\n  headers?: Record<string, string>;\n  /**\n   * Convert an `api-key` request header into `Authorization: Bearer ...` for\n   * providers whose WebSocket endpoint authenticates API keys as bearer tokens.\n   */\n  apiKeyAsBearer?: boolean;\n  /**\n   * Move an `api-key` request header into the WebSocket URL query string for\n   * providers whose WebSocket endpoint authenticates API keys through a query\n   * parameter. Entra ID and OpenAI bearer-token auth should leave this disabled.\n   */\n  apiKeyQueryParam?: string | false;\n  /**\n   * Optional beta header sent when establishing the WebSocket connection.\n   * @default 'responses_websockets=2026-02-06'\n   */\n  betaHeader?: string | false;\n}\n\nexport type OpenAIWebSocketFetch = ((input: RequestInfo | URL, init?: RequestInit) => Promise<Response>) & {\n  close(): void;\n};\n\nconst TERMINAL_RESPONSE_EVENTS = new Set(['response.completed', 'response.failed', 'response.incomplete', 'error']);\n\n/**\n * Creates a `fetch` function that routes OpenAI Responses API streaming\n * requests through a persistent WebSocket connection instead of HTTP.\n */\nexport function createOpenAIWebSocketFetch(options?: CreateOpenAIWebSocketFetchOptions): OpenAIWebSocketFetch {\n  const wsUrl = options?.url ?? 'wss://api.openai.com/v1/responses';\n  const betaHeader = options?.betaHeader === undefined ? 'responses_websockets=2026-02-06' : options.betaHeader;\n  const apiKeyQueryParam = options?.apiKeyQueryParam ?? false;\n\n  let ws: WebSocket | null = null;\n  let connecting: Promise<WebSocket> | null = null;\n  let connectionKey: string | null = null;\n  let nextQueryCredentialId = 0;\n  const queryCredentialIds = new Map<string, string>();\n  let busy = false;\n\n  function getQueryCredentialCacheId(value?: string): string {\n    if (!value) return '';\n\n    const existing = queryCredentialIds.get(value);\n    if (existing) return existing;\n\n    const id = String(++nextQueryCredentialId);\n    queryCredentialIds.set(value, id);\n    return id;\n  }\n\n  function getConnection(\n    authorization: string,\n    headers: Record<string, string>,\n    signal?: AbortSignal | null,\n  ): Promise<WebSocket> {\n    if (signal?.aborted) {\n      return Promise.reject(getAbortError(signal));\n    }\n\n    const normalizedHeaders = { ...normalizeHeaders(options?.headers), ...headers };\n    const apiKey = normalizedHeaders['api-key'];\n    delete normalizedHeaders['authorization'];\n    delete normalizedHeaders['openai-beta'];\n    if (options?.apiKeyAsBearer || apiKeyQueryParam) {\n      delete normalizedHeaders['api-key'];\n    }\n    const queryCredential = apiKeyQueryParam ? `${apiKeyQueryParam}:${getQueryCredentialCacheId(apiKey)}` : '';\n    const nextConnectionKey = buildConnectionKey(authorization, normalizedHeaders, queryCredential);\n\n    if (ws?.readyState === WebSocket.OPEN && connectionKey === nextConnectionKey) {\n      return Promise.resolve(ws);\n    }\n\n    if (ws?.readyState === WebSocket.OPEN && connectionKey !== nextConnectionKey) {\n      ws.close();\n      ws = null;\n      connectionKey = null;\n    }\n\n    if (connecting && connectionKey === nextConnectionKey) return connecting;\n\n    connectionKey = nextConnectionKey;\n\n    connecting = new Promise<WebSocket>((resolve, reject) => {\n      let settled = false;\n      const socket = new WebSocket(getWebSocketUrl(wsUrl, apiKeyQueryParam, apiKey), {\n        headers: {\n          ...normalizedHeaders,\n          ...(authorization ? { Authorization: authorization } : {}),\n          ...(betaHeader ? { 'OpenAI-Beta': betaHeader } : {}),\n        },\n      });\n\n      function cleanupAbortListener() {\n        signal?.removeEventListener('abort', onAbort);\n      }\n\n      function rejectConnection(err: unknown, closeSocket = true) {\n        if (settled) return;\n        settled = true;\n        connecting = null;\n        connectionKey = null;\n        cleanupAbortListener();\n        if (closeSocket) socket.close();\n        reject(err);\n      }\n\n      function onAbort() {\n        rejectConnection(getAbortError(signal));\n      }\n\n      signal?.addEventListener('abort', onAbort, { once: true });\n\n      socket.on('open', () => {\n        if (signal?.aborted) {\n          rejectConnection(getAbortError(signal));\n          return;\n        }\n        settled = true;\n        ws = socket;\n        connecting = null;\n        cleanupAbortListener();\n        resolve(socket);\n      });\n\n      socket.on('error', err => {\n        rejectConnection(err, false);\n      });\n\n      socket.on('close', () => {\n        if (settled) return;\n        rejectConnection(new Error('WebSocket closed before the connection opened'), false);\n      });\n\n      socket.on('close', () => {\n        if (ws === socket) ws = null;\n        if (ws === null) connectionKey = null;\n      });\n    });\n\n    return connecting;\n  }\n\n  async function websocketFetch(input: RequestInfo | URL, init?: RequestInit): Promise<Response> {\n    const url = input instanceof URL ? input.toString() : typeof input === 'string' ? input : input.url;\n\n    if (init?.method !== 'POST' || !isResponsesUrl(url)) {\n      return globalThis.fetch(input, init);\n    }\n\n    let body: Record<string, unknown>;\n    try {\n      body = JSON.parse(typeof init.body === 'string' ? init.body : '');\n    } catch {\n      return globalThis.fetch(input, init);\n    }\n\n    if (!body.stream) {\n      return globalThis.fetch(input, init);\n    }\n\n    // Prevent concurrent streams from sharing one WebSocket transport instance.\n    // Only fall back to HTTP when the request does not depend on the socket's\n    // connection-local previous_response_id cache.\n    if (busy) {\n      if (body.previous_response_id) {\n        throw new Error(\n          'Cannot start an overlapping WebSocket Responses continuation. Wait for the active stream to finish before sending previous_response_id.',\n        );\n      }\n      return globalThis.fetch(input, init);\n    }\n\n    const headers = normalizeHeaders(init.headers);\n    const authorization =\n      headers['authorization'] ?? (options?.apiKeyAsBearer && headers['api-key'] ? `Bearer ${headers['api-key']}` : '');\n\n    // Acquire the busy lock before awaiting to prevent races\n    busy = true;\n    let connection: WebSocket;\n    try {\n      connection = await getConnection(authorization, headers, init?.signal);\n    } catch (err) {\n      busy = false;\n      throw err;\n    }\n\n    const { stream: _stream, background: _background, ...requestBody } = body;\n    const encoder = new TextEncoder();\n\n    let cleanupActiveStream: ((options?: { closeSocket?: boolean }) => void) | undefined;\n    const responseStream = new ReadableStream<Uint8Array>({\n      start(controller) {\n        let cleanedUp = false;\n        let abortHandler: (() => void) | undefined;\n\n        function cleanup({ closeSocket = false }: { closeSocket?: boolean } = {}) {\n          if (cleanedUp) return;\n          cleanedUp = true;\n          connection.off('message', onMessage);\n          connection.off('error', onError);\n          connection.off('close', onClose);\n          if (abortHandler) {\n            init?.signal?.removeEventListener('abort', abortHandler);\n            abortHandler = undefined;\n          }\n\n          if (closeSocket && ws === connection) {\n            connection.close();\n            ws = null;\n            connectionKey = null;\n          }\n\n          busy = false;\n          cleanupActiveStream = undefined;\n        }\n\n        cleanupActiveStream = cleanup;\n\n        function onMessage(data: WebSocket.RawData) {\n          const text = data.toString();\n          controller.enqueue(encoder.encode(formatSSEData(text)));\n\n          try {\n            const event = JSON.parse(text);\n            if (isTerminalWebSocketEvent(event)) {\n              controller.enqueue(encoder.encode('data: [DONE]\\n\\n'));\n              cleanup({ closeSocket: shouldReconnectAfterEvent(event) });\n              controller.close();\n            }\n          } catch {\n            // non-JSON frame, continue\n          }\n        }\n\n        function onError(err: Error) {\n          cleanup();\n          controller.error(err);\n        }\n\n        function onClose() {\n          cleanup();\n          try {\n            controller.close();\n          } catch {\n            // already closed\n          }\n        }\n\n        connection.on('message', onMessage);\n        connection.on('error', onError);\n        connection.on('close', onClose);\n\n        const signal = init?.signal;\n        if (signal) {\n          if (signal.aborted) {\n            cleanup({ closeSocket: true });\n            controller.error(signal.reason ?? new DOMException('Aborted', 'AbortError'));\n            return;\n          }\n          abortHandler = () => {\n            cleanup({ closeSocket: true });\n            try {\n              controller.error(signal.reason ?? new DOMException('Aborted', 'AbortError'));\n            } catch {\n              // already closed\n            }\n          };\n          signal.addEventListener('abort', abortHandler, { once: true });\n        }\n\n        connection.send(JSON.stringify({ type: 'response.create', ...requestBody }));\n      },\n      cancel() {\n        cleanupActiveStream?.({ closeSocket: true });\n      },\n    });\n\n    return new Response(responseStream, {\n      status: 200,\n      headers: { 'content-type': 'text/event-stream' },\n    });\n  }\n\n  return Object.assign(websocketFetch, {\n    /** Close the underlying WebSocket connection. */\n    close() {\n      if (ws) {\n        ws.close();\n        ws = null;\n      }\n      connectionKey = null;\n      connecting = null;\n    },\n  });\n}\n\nfunction isResponsesUrl(url: string): boolean {\n  try {\n    return new URL(url).pathname.endsWith('/responses');\n  } catch {\n    return url.split('?')[0]?.endsWith('/responses') ?? false;\n  }\n}\n\nfunction getWebSocketUrl(url: string, apiKeyQueryParam: string | false, apiKey?: string): string {\n  if (!apiKeyQueryParam || !apiKey) return url;\n\n  const parsedUrl = new URL(url);\n  parsedUrl.searchParams.set(apiKeyQueryParam, apiKey);\n  return parsedUrl.toString();\n}\n\nfunction formatSSEData(text: string): string {\n  return `${text\n    .split(/\\r?\\n/)\n    .map(line => `data: ${line}`)\n    .join('\\n')}\\n\\n`;\n}\n\nfunction buildConnectionKey(authorization: string, headers: Record<string, string>, queryCredential = ''): string {\n  return JSON.stringify({\n    authorization,\n    queryCredential,\n    headers: Object.entries(headers).sort(([a], [b]) => a.localeCompare(b)),\n  });\n}\n\nfunction isTerminalWebSocketEvent(event: unknown): event is { type: string } {\n  return (\n    typeof event === 'object' &&\n    event !== null &&\n    'type' in event &&\n    typeof event.type === 'string' &&\n    TERMINAL_RESPONSE_EVENTS.has(event.type)\n  );\n}\n\nfunction shouldReconnectAfterEvent(event: unknown): boolean {\n  if (typeof event !== 'object' || event === null || !('error' in event)) return false;\n  const error = event.error;\n  return (\n    typeof error === 'object' &&\n    error !== null &&\n    'code' in error &&\n    error.code === 'websocket_connection_limit_reached'\n  );\n}\n\nfunction getAbortError(signal?: AbortSignal | null): unknown {\n  return signal?.reason ?? new DOMException('Aborted', 'AbortError');\n}\n\nfunction normalizeHeaders(headers: HeadersInit | undefined): Record<string, string> {\n  const result: Record<string, string> = {};\n  if (!headers) return result;\n\n  if (headers instanceof Headers) {\n    headers.forEach((value, key) => {\n      result[key.toLowerCase()] = value;\n    });\n  } else if (Array.isArray(headers)) {\n    for (const [key, value] of headers) {\n      result[key.toLowerCase()] = value;\n    }\n  } else {\n    for (const [key, value] of Object.entries(headers)) {\n      if (value != null) result[key.toLowerCase()] = value;\n    }\n  }\n\n  return result;\n}\n","import { createAzure } from '@ai-sdk/azure';\nimport type { LanguageModelV2 } from '@ai-sdk/provider-v5';\nimport { InMemoryServerCache } from '../../../cache/inmemory.js';\nimport { MastraError } from '../../../error/index.js';\nimport { createOpenAIWebSocketFetch } from '../openai-websocket-fetch.js';\nimport type { OpenAITransport, ResponsesWebSocketOptions } from '../provider-options.js';\nimport { MASTRA_GATEWAY_STREAM_TRANSPORT, MastraModelGateway } from './base.js';\nimport type { ProviderConfig } from './base.js';\nimport { MASTRA_USER_AGENT } from './constants.js';\n\ninterface AzureTokenResponse {\n  token_type: 'Bearer';\n  expires_in: number;\n  access_token: string;\n}\n\ninterface AzureDeployment {\n  name: string;\n  properties: {\n    model: {\n      name: string;\n      version: string;\n      format: string;\n    };\n    provisioningState: string;\n  };\n}\n\ninterface AzureDeploymentsResponse {\n  value: AzureDeployment[];\n  nextLink?: string;\n}\n\ninterface CachedToken {\n  token: string;\n  expiresAt: number;\n}\n\ntype AzureLanguageModelCallOptions = Parameters<LanguageModelV2['doGenerate']>[0];\n\nexport interface AzureAccessToken {\n  token: string;\n  expiresOnTimestamp?: number;\n}\n\nexport interface AzureTokenCredential {\n  getToken(scopes: string | string[], options?: unknown): Promise<AzureAccessToken | null>;\n}\n\nexport interface AzureOpenAIGatewayConfig {\n  resourceName: string;\n  apiKey?: string;\n  apiVersion?: string;\n  useResponsesAPI?: boolean;\n  useDeploymentBasedUrls?: boolean;\n  deployments?: string[];\n  authentication?: {\n    type: 'entraId';\n    credential: AzureTokenCredential;\n    scope?: string;\n  };\n  management?: {\n    tenantId: string;\n    clientId: string;\n    clientSecret: string;\n    subscriptionId: string;\n    resourceGroup: string;\n  };\n}\n\nfunction isRecord(value: unknown): value is Record<string, unknown> {\n  return typeof value === 'object' && value !== null && !Array.isArray(value);\n}\n\nfunction mirrorAzureProviderOptionsForOpenAI<T>(providerOptions: T): T {\n  if (!isRecord(providerOptions) || !isRecord(providerOptions.azure)) {\n    return providerOptions;\n  }\n\n  const openai = isRecord(providerOptions.openai) ? providerOptions.openai : {};\n\n  return {\n    ...providerOptions,\n    openai: {\n      ...openai,\n      ...providerOptions.azure,\n    },\n  } as T;\n}\n\nfunction mirrorAzureResponseProviderOptions(\n  prompt: AzureLanguageModelCallOptions['prompt'],\n): AzureLanguageModelCallOptions['prompt'] {\n  let promptModified = false;\n\n  const mirroredPrompt = prompt.map(message => {\n    const messageWithProviderOptions = message as typeof message & { providerOptions?: unknown };\n    const providerOptions = mirrorAzureProviderOptionsForOpenAI(messageWithProviderOptions.providerOptions);\n    const providerOptionsModified = providerOptions !== messageWithProviderOptions.providerOptions;\n\n    if (!Array.isArray(message.content)) {\n      if (providerOptionsModified) {\n        promptModified = true;\n        return { ...message, providerOptions } as typeof message;\n      }\n\n      return message;\n    }\n\n    let contentModified = false;\n    const content = message.content.map(part => {\n      if (!('providerOptions' in part)) {\n        return part;\n      }\n\n      const providerOptions = mirrorAzureProviderOptionsForOpenAI(part.providerOptions);\n      if (providerOptions === part.providerOptions) {\n        return part;\n      }\n\n      contentModified = true;\n      return { ...part, providerOptions };\n    }) as typeof message.content;\n\n    if (!contentModified) {\n      if (providerOptionsModified) {\n        promptModified = true;\n        return { ...message, providerOptions } as typeof message;\n      }\n\n      return message;\n    }\n\n    promptModified = true;\n    return { ...message, ...(providerOptionsModified ? { providerOptions } : {}), content };\n  });\n\n  return (promptModified ? mirroredPrompt : prompt) as AzureLanguageModelCallOptions['prompt'];\n}\n\nfunction withAzureResponsesInputCompatibility(model: LanguageModelV2): LanguageModelV2 {\n  return new Proxy(model, {\n    get(target, property, receiver) {\n      // Audit this wrapper when AI SDK adds new prompt-taking LanguageModelV2 methods.\n      if (property === 'doGenerate') {\n        return (options: AzureLanguageModelCallOptions) =>\n          target.doGenerate({\n            ...options,\n            providerOptions: mirrorAzureProviderOptionsForOpenAI(options.providerOptions),\n            prompt: mirrorAzureResponseProviderOptions(options.prompt),\n          });\n      }\n\n      if (property === 'doStream') {\n        return (options: Parameters<LanguageModelV2['doStream']>[0]) =>\n          target.doStream({\n            ...options,\n            providerOptions: mirrorAzureProviderOptionsForOpenAI(options.providerOptions),\n            prompt: mirrorAzureResponseProviderOptions(options.prompt),\n          });\n      }\n\n      return Reflect.get(target, property, receiver);\n    },\n  });\n}\n\nexport class AzureOpenAIGateway extends MastraModelGateway {\n  readonly id = 'azure-openai';\n  readonly name = 'azure-openai';\n  private tokenCache = new InMemoryServerCache();\n  private entraIdTokenRequests = new Map<string, Promise<CachedToken>>();\n\n  constructor(private config: AzureOpenAIGatewayConfig) {\n    super();\n    this.validateConfig();\n  }\n\n  private validateConfig(): void {\n    if (!this.config.resourceName) {\n      throw new MastraError({\n        id: 'AZURE_GATEWAY_INVALID_CONFIG',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'resourceName is required for Azure OpenAI gateway',\n      });\n    }\n\n    if (!this.config.apiKey && this.config.authentication?.type !== 'entraId') {\n      throw new MastraError({\n        id: 'AZURE_GATEWAY_INVALID_CONFIG',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'apiKey or Entra ID authentication is required for Azure OpenAI gateway',\n      });\n    }\n\n    if (this.config.authentication?.type === 'entraId' && !this.config.authentication.credential) {\n      throw new MastraError({\n        id: 'AZURE_GATEWAY_INVALID_CONFIG',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'credential is required for Azure OpenAI Entra ID authentication',\n      });\n    }\n\n    if (this.config.apiKey && this.config.authentication?.type === 'entraId') {\n      console.warn(\n        '[AzureOpenAIGateway] Both apiKey and Entra ID authentication provided. Using Entra ID authentication and ignoring apiKey.',\n      );\n    }\n\n    if (this.config.useResponsesAPI && this.config.useDeploymentBasedUrls === true) {\n      throw new MastraError({\n        id: 'AZURE_GATEWAY_INVALID_CONFIG',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'useResponsesAPI: true cannot be combined with useDeploymentBasedUrls: true. Omit useDeploymentBasedUrls or set it to false.',\n      });\n    }\n\n    if (this.config.useResponsesAPI && this.config.apiVersion && !['v1', 'preview'].includes(this.config.apiVersion)) {\n      throw new MastraError({\n        id: 'AZURE_GATEWAY_INVALID_CONFIG',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'useResponsesAPI: true requires apiVersion: \"v1\" or apiVersion: \"preview\". Omit apiVersion to use \"v1\".',\n      });\n    }\n\n    if (\n      this.config.useDeploymentBasedUrls === false &&\n      this.config.apiVersion &&\n      !['v1', 'preview'].includes(this.config.apiVersion)\n    ) {\n      throw new MastraError({\n        id: 'AZURE_GATEWAY_INVALID_CONFIG',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'useDeploymentBasedUrls: false requires apiVersion: \"v1\" or apiVersion: \"preview\". Omit apiVersion to use \"v1\".',\n      });\n    }\n\n    const hasDeployments = this.config.deployments && this.config.deployments.length > 0;\n    const hasManagement = this.config.management !== undefined;\n\n    if (hasDeployments && hasManagement) {\n      console.warn(\n        '[AzureOpenAIGateway] Both deployments and management credentials provided. Using static deployments list and ignoring management API.',\n      );\n    }\n\n    if (hasManagement) {\n      this.getManagementCredentials(this.config.management!);\n    }\n  }\n\n  async fetchProviders(): Promise<Record<string, ProviderConfig>> {\n    if (this.config.deployments && this.config.deployments.length > 0) {\n      return {\n        'azure-openai': {\n          apiKeyEnvVar: [],\n          apiKeyHeader: 'api-key',\n          name: 'Azure OpenAI',\n          models: this.config.deployments,\n          docUrl: 'https://learn.microsoft.com/en-us/azure/ai-services/openai/',\n          gateway: 'azure-openai',\n        },\n      };\n    }\n\n    if (!this.config.management) {\n      return {\n        'azure-openai': {\n          apiKeyEnvVar: [],\n          apiKeyHeader: 'api-key',\n          name: 'Azure OpenAI',\n          models: [],\n          docUrl: 'https://learn.microsoft.com/en-us/azure/ai-services/openai/',\n          gateway: 'azure-openai',\n        },\n      };\n    }\n\n    try {\n      const credentials = this.getManagementCredentials(this.config.management);\n\n      const token = await this.getAzureADToken({\n        tenantId: credentials.tenantId,\n        clientId: credentials.clientId,\n        clientSecret: credentials.clientSecret,\n      });\n\n      const deployments = await this.fetchDeployments(token, {\n        subscriptionId: credentials.subscriptionId,\n        resourceGroup: credentials.resourceGroup,\n        resourceName: this.config.resourceName,\n      });\n\n      return {\n        'azure-openai': {\n          apiKeyEnvVar: [],\n          apiKeyHeader: 'api-key',\n          name: 'Azure OpenAI',\n          models: deployments.map(d => d.name),\n          docUrl: 'https://learn.microsoft.com/en-us/azure/ai-services/openai/',\n          gateway: 'azure-openai',\n        },\n      };\n    } catch (error) {\n      const errorMsg = error instanceof Error ? error.message : String(error);\n      console.warn(\n        `[AzureOpenAIGateway] Deployment discovery failed: ${errorMsg}`,\n        '\\nReturning fallback configuration. Azure OpenAI can still be used by manually specifying deployment names.',\n      );\n\n      return {\n        'azure-openai': {\n          apiKeyEnvVar: [],\n          apiKeyHeader: 'api-key',\n          name: 'Azure OpenAI',\n          models: [],\n          docUrl: 'https://learn.microsoft.com/en-us/azure/ai-services/openai/',\n          gateway: 'azure-openai',\n        },\n      };\n    }\n  }\n\n  private getManagementCredentials(management: NonNullable<AzureOpenAIGatewayConfig['management']>) {\n    const { tenantId, clientId, clientSecret, subscriptionId, resourceGroup } = management;\n\n    const missing = [];\n    if (!tenantId) missing.push('tenantId');\n    if (!clientId) missing.push('clientId');\n    if (!clientSecret) missing.push('clientSecret');\n    if (!subscriptionId) missing.push('subscriptionId');\n    if (!resourceGroup) missing.push('resourceGroup');\n\n    if (missing.length > 0) {\n      throw new MastraError({\n        id: 'AZURE_MANAGEMENT_CREDENTIALS_MISSING',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: `Management credentials incomplete. Missing: ${missing.join(', ')}. Required fields: tenantId, clientId, clientSecret, subscriptionId, resourceGroup.`,\n      });\n    }\n\n    return {\n      tenantId,\n      clientId,\n      clientSecret,\n      subscriptionId,\n      resourceGroup,\n    };\n  }\n\n  private async getAzureADToken(credentials: {\n    tenantId: string;\n    clientId: string;\n    clientSecret: string;\n  }): Promise<string> {\n    const { tenantId, clientId, clientSecret } = credentials;\n\n    const cacheKey = `azure-mgmt-token:${tenantId}:${clientId}`;\n\n    const cached = (await this.tokenCache.get(cacheKey)) as CachedToken | undefined;\n    if (cached && cached.expiresAt > Date.now() / 1000 + 60) {\n      return cached.token;\n    }\n\n    const tokenEndpoint = `https://login.microsoftonline.com/${tenantId}/oauth2/v2.0/token`;\n\n    const body = new URLSearchParams({\n      grant_type: 'client_credentials',\n      client_id: clientId,\n      client_secret: clientSecret,\n      scope: 'https://management.azure.com/.default',\n    });\n\n    const response = await fetch(tokenEndpoint, {\n      method: 'POST',\n      headers: {\n        'Content-Type': 'application/x-www-form-urlencoded',\n      },\n      body: body.toString(),\n    });\n\n    if (!response.ok) {\n      const error = await response.text();\n      throw new MastraError({\n        id: 'AZURE_AD_TOKEN_ERROR',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: `Failed to get Azure AD token: ${response.status} ${error}`,\n      });\n    }\n\n    const tokenResponse = (await response.json()) as AzureTokenResponse;\n\n    const expiresAt = Math.floor(Date.now() / 1000) + tokenResponse.expires_in;\n\n    await this.tokenCache.set(cacheKey, {\n      token: tokenResponse.access_token,\n      expiresAt,\n    });\n\n    return tokenResponse.access_token;\n  }\n\n  private async fetchDeployments(\n    token: string,\n    credentials: {\n      subscriptionId: string;\n      resourceGroup: string;\n      resourceName: string;\n    },\n  ): Promise<AzureDeployment[]> {\n    const { subscriptionId, resourceGroup, resourceName } = credentials;\n\n    let url: string | undefined =\n      `https://management.azure.com/subscriptions/${subscriptionId}/resourceGroups/${resourceGroup}/providers/Microsoft.CognitiveServices/accounts/${resourceName}/deployments?api-version=2024-10-01`;\n\n    const allDeployments: AzureDeployment[] = [];\n\n    while (url) {\n      const response = await fetch(url, {\n        headers: {\n          Authorization: `Bearer ${token}`,\n          'Content-Type': 'application/json',\n        },\n      });\n\n      if (!response.ok) {\n        const error = await response.text();\n        throw new MastraError({\n          id: 'AZURE_DEPLOYMENTS_FETCH_ERROR',\n          domain: 'LLM',\n          category: 'UNKNOWN',\n          text: `Failed to fetch Azure deployments: ${response.status} ${error}`,\n        });\n      }\n\n      const data = (await response.json()) as AzureDeploymentsResponse;\n\n      allDeployments.push(...data.value);\n\n      url = data.nextLink;\n    }\n\n    const successfulDeployments = allDeployments.filter(d => d.properties.provisioningState === 'Succeeded');\n\n    return successfulDeployments;\n  }\n\n  buildUrl(_routerId: string, _envVars?: typeof process.env): undefined {\n    return undefined;\n  }\n\n  async getApiKey(_modelId: string): Promise<string> {\n    return this.config.authentication?.type === 'entraId' ? '' : (this.config.apiKey ?? '');\n  }\n\n  private async getEntraIdToken(): Promise<string> {\n    if (this.config.authentication?.type !== 'entraId') {\n      throw new MastraError({\n        id: 'AZURE_ENTRA_ID_AUTH_NOT_CONFIGURED',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'Entra ID authentication is not configured for Azure OpenAI gateway',\n      });\n    }\n\n    const scope = this.config.authentication.scope ?? 'https://cognitiveservices.azure.com/.default';\n    const cacheKey = `azure-openai-token:${scope}`;\n    const cached = (await this.tokenCache.get(cacheKey)) as CachedToken | undefined;\n    if (cached && cached.expiresAt > Date.now() / 1000 + 60) {\n      return cached.token;\n    }\n\n    let tokenRequest = this.entraIdTokenRequests.get(cacheKey);\n\n    if (!tokenRequest) {\n      tokenRequest = this.fetchEntraIdToken(scope, cacheKey);\n      this.entraIdTokenRequests.set(cacheKey, tokenRequest);\n    }\n\n    try {\n      const token = await tokenRequest;\n      return token.token;\n    } finally {\n      this.entraIdTokenRequests.delete(cacheKey);\n    }\n  }\n\n  private async fetchEntraIdToken(scope: string, cacheKey: string): Promise<CachedToken> {\n    if (this.config.authentication?.type !== 'entraId') {\n      throw new MastraError({\n        id: 'AZURE_ENTRA_ID_AUTH_NOT_CONFIGURED',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'Entra ID authentication is not configured for Azure OpenAI gateway',\n      });\n    }\n\n    const accessToken = await this.config.authentication.credential.getToken(scope);\n    if (!accessToken?.token) {\n      throw new MastraError({\n        id: 'AZURE_ENTRA_ID_TOKEN_ERROR',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'Failed to get Entra ID token for Azure OpenAI gateway',\n      });\n    }\n\n    const token = {\n      token: accessToken.token,\n      expiresAt: accessToken.expiresOnTimestamp\n        ? Math.floor(accessToken.expiresOnTimestamp / 1000)\n        : Math.floor(Date.now() / 1000) + 300,\n    };\n\n    await this.tokenCache.set(cacheKey, token);\n\n    return token;\n  }\n\n  private createEntraIdFetch(innerFetch: typeof globalThis.fetch = fetch): typeof globalThis.fetch {\n    return async (input, init) => {\n      const token = await this.getEntraIdToken();\n      const headers = new Headers(init?.headers);\n      headers.delete('api-key');\n      headers.set('Authorization', `Bearer ${token}`);\n\n      return innerFetch(input, {\n        ...init,\n        headers,\n      });\n    };\n  }\n\n  private createAzureResponsesWebSocketFetch({\n    useEntraId,\n    responsesWebSocket,\n  }: {\n    useEntraId: boolean;\n    responsesWebSocket?: ResponsesWebSocketOptions;\n  }): typeof globalThis.fetch & { close(): void } {\n    const websocketFetch = createOpenAIWebSocketFetch({\n      url: responsesWebSocket?.url ?? `wss://${this.config.resourceName}.openai.azure.com/openai/v1/responses`,\n      headers: responsesWebSocket?.headers,\n      apiKeyQueryParam: useEntraId ? false : 'api-key',\n      betaHeader: false,\n    });\n\n    return useEntraId\n      ? Object.assign(this.createEntraIdFetch(websocketFetch), { close: websocketFetch.close })\n      : websocketFetch;\n  }\n\n  async resolveLanguageModel({\n    modelId,\n    apiKey,\n    headers,\n    transport,\n    responsesWebSocket,\n  }: {\n    modelId: string;\n    providerId: string;\n    apiKey: string;\n    headers?: Record<string, string>;\n    transport?: OpenAITransport;\n    responsesWebSocket?: ResponsesWebSocketOptions;\n  }): Promise<LanguageModelV2> {\n    const useResponsesAPI = this.config.useResponsesAPI ?? false;\n    const apiVersion =\n      this.config.apiVersion ||\n      (useResponsesAPI || this.config.useDeploymentBasedUrls === false ? 'v1' : '2024-04-01-preview');\n    const useDeploymentBasedUrls = this.config.useDeploymentBasedUrls ?? (useResponsesAPI ? false : true);\n    const useEntraId = this.config.authentication?.type === 'entraId';\n    const useWebSocket = useResponsesAPI && transport === 'websocket';\n    const websocketFetch = useWebSocket\n      ? this.createAzureResponsesWebSocketFetch({\n          useEntraId,\n          responsesWebSocket,\n        })\n      : undefined;\n    const azureConfig = {\n      resourceName: this.config.resourceName,\n      apiKey: useEntraId ? '' : apiKey,\n      apiVersion,\n      // Mastra's Azure gateway has historically used deployment-based URLs.\n      // Keep that default for compatibility; set false with apiVersion: 'v1'\n      // to use the newer Azure OpenAI v1 route.\n      useDeploymentBasedUrls,\n      headers: { 'User-Agent': MASTRA_USER_AGENT, ...headers },\n      ...(websocketFetch && !useEntraId ? { fetch: websocketFetch } : {}),\n    };\n\n    const azureProvider = createAzure(\n      useEntraId\n        ? {\n            ...azureConfig,\n            fetch: websocketFetch ?? this.createEntraIdFetch(),\n          }\n        : azureConfig,\n    );\n\n    if (useResponsesAPI) {\n      const model = withAzureResponsesInputCompatibility(azureProvider.responses(modelId));\n      if (websocketFetch) {\n        Object.defineProperty(model, MASTRA_GATEWAY_STREAM_TRANSPORT, {\n          configurable: true,\n          value: {\n            type: 'openai-websocket',\n            close: websocketFetch.close,\n          },\n        });\n      }\n\n      return model;\n    }\n\n    return azureProvider(modelId);\n  }\n}\n","import { createAnthropic } from '@ai-sdk/anthropic-v6';\nimport { createOpenRouter } from '@openrouter/ai-sdk-provider-v5';\nimport { MastraError } from '../../../error/index.js';\nimport { PROVIDER_REGISTRY } from '../provider-registry.js';\nimport { MastraModelGateway } from './base.js';\nimport type { ProviderConfig, GatewayLanguageModel } from './base.js';\nimport { GATEWAY_AUTH_HEADER, MASTRA_USER_AGENT } from './constants.js';\n\nexport interface MastraGatewayConfig {\n  apiKey?: string;\n  baseUrl?: string;\n  customFetch?: typeof globalThis.fetch;\n}\n\nexport class MastraGateway extends MastraModelGateway {\n  readonly id = 'mastra';\n  readonly name = 'Memory Gateway';\n\n  constructor(private config?: MastraGatewayConfig) {\n    super();\n  }\n\n  private getBaseUrl(): string {\n    const raw = this.config?.baseUrl ?? process.env['MASTRA_GATEWAY_URL'] ?? 'https://gateway-api.mastra.ai';\n    return raw.replace(/\\/+$/, '').replace(/\\/v1$/, '');\n  }\n\n  override shouldEnable(): boolean {\n    return !!(this.config?.apiKey ?? process.env['MASTRA_GATEWAY_API_KEY']);\n  }\n\n  async fetchProviders(): Promise<Record<string, ProviderConfig>> {\n    if (!this.shouldEnable()) {\n      return {};\n    }\n\n    const openrouterConfig = PROVIDER_REGISTRY['openrouter'];\n    const models = openrouterConfig?.models ?? [];\n\n    const providers = {\n      mastra: {\n        apiKeyEnvVar: 'MASTRA_GATEWAY_API_KEY',\n        apiKeyHeader: 'Authorization',\n        name: 'Memory Gateway',\n        gateway: 'mastra',\n        models: [...models],\n        docUrl: 'https://mastra.ai/docs/gateway',\n      },\n    };\n\n    return providers;\n  }\n\n  async buildUrl(_modelId: string): Promise<string> {\n    return `${this.getBaseUrl()}/v1`;\n  }\n\n  async getApiKey(): Promise<string> {\n    const apiKey = this.config?.apiKey ?? process.env['MASTRA_GATEWAY_API_KEY'];\n    if (!apiKey) {\n      throw new MastraError({\n        id: 'MASTRA_GATEWAY_NO_API_KEY',\n        domain: 'LLM',\n        category: 'UNKNOWN',\n        text: 'Missing MASTRA_GATEWAY_API_KEY environment variable',\n      });\n    }\n    return apiKey;\n  }\n\n  resolveLanguageModel({\n    modelId,\n    providerId,\n    apiKey,\n    headers,\n  }: {\n    modelId: string;\n    providerId: string;\n    apiKey: string;\n    headers?: Record<string, string>;\n  }): GatewayLanguageModel {\n    const baseURL = `${this.getBaseUrl()}/v1`;\n    const fullModelId = `${providerId}/${modelId}`;\n\n    if (this.config?.customFetch && providerId === 'anthropic') {\n      // Anthropic OAuth path: use native Anthropic SDK (sends /messages, not /chat/completions)\n      return createAnthropic({\n        apiKey: 'oauth-gateway-placeholder',\n        baseURL,\n        headers: {\n          'User-Agent': MASTRA_USER_AGENT,\n          [GATEWAY_AUTH_HEADER]: `Bearer ${apiKey}`,\n          ...headers,\n        },\n        fetch: this.config.customFetch as any,\n      })(modelId) as unknown as GatewayLanguageModel;\n    }\n\n    if (this.config?.customFetch) {\n      // Non-Anthropic OAuth path: gateway key in GATEWAY_AUTH_HEADER, customFetch owns Authorization\n      return createOpenRouter({\n        apiKey: 'oauth-gateway-placeholder',\n        baseURL,\n        headers: {\n          'User-Agent': MASTRA_USER_AGENT,\n          [GATEWAY_AUTH_HEADER]: `Bearer ${apiKey}`,\n          ...headers,\n        },\n        fetch: this.config.customFetch,\n      }).chat(fullModelId) as unknown as GatewayLanguageModel;\n    }\n\n    // API key path: gateway key goes via Authorization (standard flow)\n    return createOpenRouter({\n      apiKey,\n      baseURL,\n      headers: {\n        'User-Agent': MASTRA_USER_AGENT,\n        ...headers,\n      },\n    }).chat(fullModelId) as unknown as GatewayLanguageModel;\n  }\n}\n","import { MastraError } from '../../../error/index.js';\nimport type { MastraModelGatewayInterface } from './base.js';\nexport {\n  MastraModelGateway,\n  type MastraModelGatewayInterface,\n  type ProviderConfig,\n  type GatewayLanguageModel,\n  type GatewayAuthRequest,\n  type GatewayAuthResult,\n  type GatewayAuthSource,\n} from './base.js';\nexport {\n  AzureOpenAIGateway,\n  type AzureAccessToken,\n  type AzureOpenAIGatewayConfig,\n  type AzureTokenCredential,\n} from './azure.js';\nexport { ModelsDevGateway } from './models-dev.js';\nexport { MastraGateway, type MastraGatewayConfig } from './mastra.js';\nexport { NetlifyGateway } from './netlify.js';\n\nexport function getGatewayId(gateway: MastraModelGatewayInterface): string {\n  return gateway.getId?.() ?? gateway.id;\n}\n\nexport function shouldEnableGateway(gateway: MastraModelGatewayInterface): boolean {\n  return gateway.shouldEnable?.() ?? true;\n}\n\nexport function serializeGatewayForSpan(\n  gateway: MastraModelGatewayInterface,\n): { id: string; name: string } & Record<string, unknown> {\n  return gateway.serializeForSpan?.() ?? { id: getGatewayId(gateway), name: gateway.name };\n}\n\n/**\n * Find the gateway that handles a specific model ID based on gateway ID\n * Gateway ID is used as the prefix (e.g., \"netlify\" for netlify gateway)\n * Exception: models.dev is a provider registry and doesn't use a prefix\n */\nexport function findGatewayForModel(\n  gatewayId: string,\n  gateways: MastraModelGatewayInterface[],\n): MastraModelGatewayInterface {\n  // First, check for gateways whose ID matches the prefix (true gateways like netlify, openrouter, vercel)\n  const prefixedGateway = gateways.find(g => {\n    const id = getGatewayId(g);\n    return id !== 'models.dev' && (id === gatewayId || gatewayId.startsWith(`${id}/`));\n  });\n  if (prefixedGateway) {\n    return prefixedGateway;\n  }\n\n  // Then check models.dev (provider registry without prefix)\n  const modelsDevGateway = gateways.find(g => getGatewayId(g) === 'models.dev');\n  if (modelsDevGateway) {\n    return modelsDevGateway;\n  }\n\n  throw new MastraError({\n    id: 'MODEL_ROUTER_NO_GATEWAY_FOUND',\n    category: 'USER',\n    domain: 'MODEL_ROUTER',\n    text: `No Mastra model router gateway found for model id ${gatewayId}`,\n  });\n}\n","{\n  \"providers\": {\n    \"helicone\": {\n      \"url\": \"https://ai-gateway.helicone.ai/v1\",\n      \"apiKeyEnvVar\": \"HELICONE_API_KEY\",\n      \"apiKeyHeader\": \"Authorization\",\n      \"name\": \"Helicone\",\n      \"models\": [\n        \"chatgpt-4o-latest\",\n        \"claude-3-haiku-20240307\",\n        \"claude-3.5-haiku\",\n        \"claude-3.5-sonnet-v2\",\n        \"claude-3.7-sonnet\",\n        \"claude-4.5-haiku\",\n        \"claude-4.5-opus\",\n        \"claude-4.5-sonnet\",\n        \"claude-haiku-4-5-20251001\",\n        \"claude-opus-4\",\n        \"claude-opus-4-1\",\n        \"claude-opus-4-1-20250805\",\n        \"claude-sonnet-4\",\n        \"claude-sonnet-4-5-20250929\",\n        \"deepseek-r1-distill-llama-70b\",\n        \"deepseek-reasoner\",\n        \"deepseek-tng-r1t2-chimera\",\n        \"deepseek-v3\",\n        \"deepseek-v3.1-terminus\",\n        \"deepseek-v3.2\",\n        \"ernie-4.5-21b-a3b-thinking\",\n        \"gemini-2.5-flash\",\n        \"gemini-2.5-flash-lite\",\n        \"gemini-2.5-pro\",\n        \"gemini-3-pro-preview\",\n        \"gemma-3-12b-it\",\n        \"gemma2-9b-it\",\n        \"glm-4.6\",\n        \"gpt-4.1\",\n        \"gpt-4.1-mini\",\n        \"gpt-4.1-mini-2025-04-14\",\n        \"gpt-4.1-nano\",\n        \"gpt-4o\",\n        \"gpt-4o-mini\",\n        \"gpt-5\",\n        \"gpt-5-chat-latest\",\n        \"gpt-5-codex\",\n        \"gpt-5-mini\",\n        \"gpt-5-nano\",\n        \"gpt-5-pro\",\n        \"gpt-5.1\",\n        \"gpt-5.1-chat-latest\",\n        \"gpt-5.1-codex\",\n        \"gpt-5.1-codex-mini\",\n        \"gpt-oss-120b\",\n        \"gpt-oss-20b\",\n        \"grok-3\",\n        \"grok-3-mini\",\n        \"grok-4\",\n        \"grok-4-1-fast-non-reasoning\",\n        \"grok-4-1-fast-reasoning\",\n        \"grok-4-fast-non-reasoning\",\n        \"grok-4-fast-reasoning\",\n        \"grok-code-fast-1\",\n        \"hermes-2-pro-llama-3-8b\",\n        \"kimi-k2-0711\",\n        \"kimi-k2-0905\",\n        \"kimi-k2-thinking\",\n        \"llama-3.1-8b-instant\",\n        \"llama-3.1-8b-instruct\",\n        \"llama-3.1-8b-instruct-turbo\",\n        \"llama-3.3-70b-instruct\",\n        \"llama-3.3-70b-versatile\",\n        \"llama-4-maverick\",\n        \"llama-4-scout\",\n        \"llama-guard-4\",\n        \"llama-prompt-guard-2-22m\",\n        \"llama-prompt-guard-2-86m\",\n        \"mistral-large-2411\",\n        \"mistral-nemo\",\n        \"mistral-small\",\n        \"o1\",\n        \"o1-mini\",\n        \"o3\",\n        \"o3-mini\",\n        \"o3-pro\",\n        \"o4-mini\",\n        \"qwen2.5-coder-7b-fast\",\n        \"qwen3-235b-a22b-thinking\",\n        \"qwen3-30b-a3b\",\n        \"qwen3-32b\",\n        \"qwen3-coder\",\n        \"qwen3-coder-30b-a3b-instruct\",\n        \"qwen3-next-80b-a3b-instruct\",\n        \"qwen3-vl-235b-a22b-instruct\",\n        \"sonar\",\n        \"sonar-deep-research\",\n        \"sonar-pro\",\n        \"sonar-reasoning\",\n        \"sonar-reasoning-pro\"\n      ],\n      \"docUrl\": \"https://helicone.ai/models\",\n      \"gateway\": \"models.dev\"\n    },\n    \"auriko\": {\n      \"url\": \"https://api.auriko.ai/v1\",\n      \"apiKeyEnvVar\": \"AURIKO_API_KEY\",\n      \"apiKeyHeader\": \"Authorization\",\n      \"name\": \"Auriko\",\n      \"models\": [\n        \"claude-opus-4-6\",\n        \"claude-opus-4-7\",\n        \"claude-sonnet-4-6\",\n        \"deepseek-v4-flash\",\n        \"deepseek-v4-pro\",\n        \"gemini-2.5-flash\",\n        \"gemini-2.5-pro\",\n        \"gemini-3.1-pro-preview\",\n        \"glm-5.1\",\n        \"grok-4.3\",\n        \"kimi-k2.5\",\n        \"kimi-k2.6\",\n        \"minimax-m2-7\",\n        \"minimax-m2-7-highspeed\",\n        \"qwen-3.6-plus\"\n      ],\n      \"docUrl\": \"https://docs.auriko.ai\",\n      \"gateway\": \"models.dev\"\n    },\n    \"firepass\": {\n      \"url\": \"https://api.fireworks.ai/inference/v1/\",\n      \"apiKeyEnvVar\": \"FIREPASS_API_KEY\",\n      \"apiKeyHeader\": \"Authorization\",\n      \"name\": \"Fireworks (Firepass)\",\n      \"models\": [\"accounts/fireworks/routers/kimi-k2p6-turbo\"],\n      \"docUrl\": \"https://docs.fireworks.ai/firepass\",\n      \"gateway\": \"models.dev\"\n    },\n    \"nano-gpt\": {\n      \"url\": \"https://nano-gpt.com/api/v1\",\n      \"apiKeyEnvVar\": \"NANO_GPT_API_KEY\",\n      \"apiKeyHeader\": \"Authorization\",\n      \"name\": \"NanoGPT\",\n      \"models\": [\n        \"Alibaba-NLP/Tongyi-DeepResearch-30B-A3B\",\n        \"Baichuan-M2\",\n        \"Baichuan4-Air\",\n        \"Baichuan4-Turbo\",\n        \"CrucibleLab/L3.3-70B-Loki-V2.0\",\n        \"Doctor-Shotgun/MS3.2-24B-Magnum-Diamond\",\n        \"EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0\",\n        \"EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1\",\n        \"EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2\",\n        \"EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2\",\n        \"Envoid/Llama-3.05-NT-Storybreaker-Ministral-70B\",\n        \"Envoid/Llama-3.05-Nemotron-Tenyxchat-Storybreaker-70B\",\n        \"GLM-4.5-Air-Derestricted\",\n        \"GLM-4.5-Air-Derestricted-Iceblink\",\n        \"GLM-4.5-Air-Derestricted-Iceblink-ReExtract\",\n        \"GLM-4.5-Air-Derestricted-Iceblink-v2\",\n        \"GLM-4.5-Air-Derestricted-Iceblink-v2-ReExtract\",\n        \"GLM-4.5-Air-Derestricted-Steam\",\n        \"GLM-4.5-Air-Derestricted-Steam-ReExtract\",\n        \"GLM-4.6-Derestricted-v5\",\n        \"GalrionSoftworks/MN-LooseCannon-12B-v1\",\n        \"Gemma-3-27B-ArliAI-RPMax-v3\",\n        \"Gemma-3-27B-Big-Tiger-v3\",\n        \"Gemma-3-27B-CardProjector-v4\",\n        \"Gemma-3-27B-Glitter\",\n        \"Gemma-3-27B-Nidum-Uncensored\",\n        \"Gemma-3-27B-it\",\n        \"Gemma-3-27B-it-Abliterated\",\n        \"Gryphe/MythoMax-L2-13b\",\n        \"Infermatic/MN-12B-Inferor-v0.0\",\n        \"KAT-Coder-Air-V1\",\n        \"KAT-Coder-Exp-72B-1010\",\n        \"KAT-Coder-Pro-V1\",\n        \"LLM360/K2-Think\",\n        \"LatitudeGames/Wayfarer-Large-70B-Llama-3.3\",\n        \"Llama-3.3+(3.1v3.3)-70B-Hanami-x1\",\n        \"Llama-3.3+(3.1v3.3)-70B-New-Dawn-v1.1\",\n        \"Llama-3.3+(3v3.3)-70B-TenyxChat-DaybreakStorywriter\",\n        \"Llama-3.3-70B-Anthrobomination\",\n        \"Llama-3.3-70B-Argunaut-1-SFT\",\n        \"Llama-3.3-70B-ArliAI-RPMax-v1.4\",\n        \"Llama-3.3-70B-ArliAI-RPMax-v2\",\n        \"Llama-3.3-70B-ArliAI-RPMax-v3\",\n        \"Llama-3.3-70B-Aurora-Borealis\",\n        \"Llama-3.3-70B-Bigger-Body\",\n        \"Llama-3.3-70B-Cirrus-x1\",\n        \"Llama-3.3-70B-Cu-Mai-R1\",\n        \"Llama-3.3-70B-Damascus-R1\",\n        \"Llama-3.3-70B-Dark-Ages-v0.1\",\n        \"Llama-3.3-70B-Electra-R1\",\n        \"Llama-3.3-70B-Electranova-v1.0\",\n        \"Llama-3.3-70B-Fallen-R1-v1\",\n        \"Llama-3.3-70B-Fallen-v1\",\n        \"Llama-3.3-70B-Forgotten-Abomination-v5.0\",\n        \"Llama-3.3-70B-Forgotten-Safeword-3.6\",\n        \"Llama-3.3-70B-GeneticLemonade-Opus\",\n        \"Llama-3.3-70B-GeneticLemonade-Unleashed-v3\",\n        \"Llama-3.3-70B-Ignition-v0.1\",\n        \"Llama-3.3-70B-Incandescent-Malevolence\",\n        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\"Qwen/Qwen2.5-VL-72B-Instruct\",\n      \"Qwen/Qwen3-235B-A22B-Instruct-2507\",\n      \"Qwen/Qwen3-235B-A22B-Thinking-2507-fast\",\n      \"Qwen/Qwen3-30B-A3B-Instruct-2507\",\n      \"Qwen/Qwen3-32B\",\n      \"Qwen/Qwen3-Embedding-8B\",\n      \"Qwen/Qwen3-Next-80B-A3B-Thinking\",\n      \"Qwen/Qwen3-Next-80B-A3B-Thinking-fast\",\n      \"Qwen/Qwen3.5-397B-A17B\",\n      \"Qwen/Qwen3.5-397B-A17B-fast\",\n      \"deepseek-ai/DeepSeek-V3.2\",\n      \"deepseek-ai/DeepSeek-V3.2-fast\",\n      \"deepseek-ai/DeepSeek-V4-Pro\",\n      \"google/gemma-2-2b-it\",\n      \"google/gemma-3-27b-it\",\n      \"meta-llama/Llama-3.3-70B-Instruct\",\n      \"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n      \"moonshotai/Kimi-K2.5\",\n      \"moonshotai/Kimi-K2.5-fast\",\n      \"nvidia/Llama-3_1-Nemotron-Ultra-253B-v1\",\n      \"nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B\",\n      \"nvidia/Nemotron-3-Nano-Omni\",\n      \"nvidia/nemotron-3-super-120b-a12b\",\n      \"openai/gpt-oss-120b\",\n      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\"anthropic/claude-sonnet-4-6\",\n      \"gemini/gemini-2.0-flash\",\n      \"gemini/gemini-2.0-flash-lite\",\n      \"gemini/gemini-2.5-flash\",\n      \"gemini/gemini-2.5-flash-image\",\n      \"gemini/gemini-2.5-flash-lite\",\n      \"gemini/gemini-2.5-pro\",\n      \"gemini/gemini-3-flash-preview\",\n      \"gemini/gemini-3-pro-image-preview\",\n      \"gemini/gemini-3.1-flash-image-preview\",\n      \"gemini/gemini-3.1-flash-lite\",\n      \"gemini/gemini-3.1-flash-lite-preview\",\n      \"gemini/gemini-3.1-pro-preview\",\n      \"gemini/gemini-3.1-pro-preview-customtools\",\n      \"gemini/gemini-3.5-flash\",\n      \"gemini/gemini-flash-latest\",\n      \"gemini/gemini-flash-lite-latest\",\n      \"openai/chat-latest\",\n      \"openai/gpt-4.1\",\n      \"openai/gpt-4.1-mini\",\n      \"openai/gpt-4.1-nano\",\n      \"openai/gpt-4o\",\n      \"openai/gpt-4o-mini\",\n      \"openai/gpt-5\",\n      \"openai/gpt-5-2025-08-07\",\n      \"openai/gpt-5-codex\",\n      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\"openai/o3-mini\",\n      \"openai/o4-mini\"\n    ]\n  },\n  \"version\": \"1.0.0\"\n}\n","/**\n * Runtime provider registry loader\n * Loads provider data from JSON file and exports typed interfaces\n */\n\nimport fs from 'node:fs';\nimport { createRequire } from 'node:module';\nimport os from 'node:os';\nimport path from 'node:path';\nimport type { ProviderConfig, MastraModelGatewayInterface } from './gateways/base.js';\nimport { getGatewayId, shouldEnableGateway } from './gateways/index.js';\nimport { MastraGateway } from './gateways/mastra.js';\nimport { ModelsDevGateway } from './gateways/models-dev.js';\nimport { NetlifyGateway } from './gateways/netlify.js';\nimport staticRegistry from './provider-registry.json';\nimport type { Provider, ModelForProvider, ModelRouterModelId, ProviderModels } from './provider-types.generated.js';\n\n// Re-export types for convenience\nexport type { Provider, ModelForProvider, ModelRouterModelId, ProviderModels };\nexport type { AttachmentCapabilities } from './gateways/base.js';\n\ninterface RegistryData {\n  providers: Record<string, ProviderConfig>;\n  models: Record<string, string[]>;\n  version: string;\n}\n\n/**\n * Check if running in offline/air-gapped mode.\n * When MASTRA_OFFLINE is set to 'true' or '1', all network fetches for provider data are skipped.\n */\nexport function isOfflineMode(): boolean {\n  const value = process.env.MASTRA_OFFLINE;\n  return value === 'true' || value === '1';\n}\n\nfunction getEnabledGatewayIds(gateways: MastraModelGatewayInterface[]): Set<string> {\n  const enabledGatewayIds = new Set<string>();\n\n  for (const gateway of gateways) {\n    const enabled = shouldEnableGateway(gateway);\n    if (enabled) {\n      enabledGatewayIds.add(getGatewayId(gateway));\n    }\n  }\n\n  return enabledGatewayIds;\n}\n\nfunction sanitizeRegistryDataForRuntime(data: RegistryData, enabledGatewayIds: Set<string>): RegistryData {\n  const providers = Object.fromEntries(\n    Object.entries(data.providers).filter(([, config]) => enabledGatewayIds.has(config.gateway)),\n  );\n\n  const models = Object.fromEntries(Object.entries(data.models).filter(([providerId]) => providerId in providers));\n\n  return {\n    ...data,\n    providers,\n    models,\n  };\n}\n\n// In-memory cache for dynamic loading mode\nlet registryData: RegistryData | null = null;\n\n// Cache file helpers (dev mode only)\n// Use functions so we don't call os.homedir() at top level, which\n// causes an error in sandboxed environments when you merely\n// import @mastra/core. In those sandboxes, if you just don't use these\n// functions then you don't hit these errors.\nconst CACHE_DIR = () => path.join(os.homedir(), '.cache', 'mastra');\nconst CACHE_FILE = () => path.join(CACHE_DIR(), 'gateway-refresh-time');\nconst GLOBAL_PROVIDER_REGISTRY_JSON = () => path.join(CACHE_DIR(), 'provider-registry.json');\nconst GLOBAL_PROVIDER_TYPES_DTS = () => path.join(CACHE_DIR(), 'provider-types.generated.d.ts');\nconst GLOBAL_CAPABILITIES_DIR = () => path.join(CACHE_DIR(), 'capabilities');\n\nlet modelRouterCacheFailed = false;\n\n/**\n * Write a file atomically using the write-to-temp-then-rename pattern (synchronous version).\n * This prevents file corruption when multiple processes write to the same file concurrently.\n *\n * @param filePath - The target file path\n * @param content - The content to write\n * @param encoding - The encoding to use (default: 'utf-8')\n */\nfunction atomicWriteFileSync(filePath: string, content: string, encoding: BufferEncoding = 'utf-8'): void {\n  // Use random suffix to avoid collisions between concurrent writes\n  const randomSuffix = Math.random().toString(36).substring(2, 15);\n  const tempPath = `${filePath}.${process.pid}.${Date.now()}.${randomSuffix}.tmp`;\n\n  try {\n    fs.writeFileSync(tempPath, content, encoding);\n    fs.renameSync(tempPath, filePath);\n  } catch (error) {\n    try {\n      fs.unlinkSync(tempPath);\n    } catch {\n      // Ignore cleanup errors\n    }\n    throw error;\n  }\n}\n\n/**\n * Syncs provider files from global cache to local dist/ directory if needed.\n * Compares file contents to determine if copy is necessary.\n * Validates JSON before copying to prevent propagating corrupted files.\n */\nfunction syncGlobalCacheToLocal(): void {\n  try {\n    // Check if global cache files exist\n    const globalJsonExists = fs.existsSync(GLOBAL_PROVIDER_REGISTRY_JSON());\n    const globalDtsExists = fs.existsSync(GLOBAL_PROVIDER_TYPES_DTS());\n\n    if (!globalJsonExists && !globalDtsExists) {\n      // No global cache, nothing to sync\n      return;\n    }\n\n    // Use getPackageRoot() to find the correct location in node_modules or local dev\n    const packageRoot = getPackageRoot();\n    const localJsonPath = path.join(packageRoot, 'dist', 'provider-registry.json');\n    const localDtsPath = path.join(packageRoot, 'dist', 'llm', 'model', 'provider-types.generated.d.ts');\n\n    // Ensure local dist directory exists\n    fs.mkdirSync(path.dirname(localJsonPath), { recursive: true });\n    fs.mkdirSync(path.dirname(localDtsPath), { recursive: true });\n\n    // Sync JSON file if global exists and differs from local\n    if (globalJsonExists) {\n      const globalJsonContent = fs.readFileSync(GLOBAL_PROVIDER_REGISTRY_JSON(), 'utf-8');\n\n      // Validate JSON before copying to prevent propagating corrupted files.\n      // Silently delete on corruption — the next gateway sync will rewrite a\n      // valid file, so logging here just creates noise when an older mastra\n      // version (without the digit-quoting fix) shares the global cache.\n      try {\n        JSON.parse(globalJsonContent);\n      } catch {\n        try {\n          fs.unlinkSync(GLOBAL_PROVIDER_REGISTRY_JSON());\n        } catch {\n          // Ignore deletion errors\n        }\n        return;\n      }\n\n      let shouldCopyJson = true;\n\n      if (fs.existsSync(localJsonPath)) {\n        const localJsonContent = fs.readFileSync(localJsonPath, 'utf-8');\n        shouldCopyJson = globalJsonContent !== localJsonContent;\n      }\n\n      if (shouldCopyJson) {\n        // Use atomic write to prevent corruption from concurrent writes\n        atomicWriteFileSync(localJsonPath, globalJsonContent, 'utf-8');\n      }\n    }\n\n    // Capabilities are loaded lazily per-provider by loadProviderAttachmentModels().\n    // The global cache dir is included in findCapabilitiesDirs() so no bulk sync is needed.\n\n    // Sync .d.ts file if global exists and differs from local\n    if (globalDtsExists) {\n      const globalDtsContent = fs.readFileSync(GLOBAL_PROVIDER_TYPES_DTS(), 'utf-8');\n\n      // Validate .d.ts content: check for unquoted provider names that start with a digit\n      // (e.g. \"readonly 302ai:\" instead of \"readonly '302ai':\"), which produces invalid TypeScript.\n      // This can happen if the global cache was written by an older version without the quoting fix.\n      // Silently delete on corruption — the next gateway sync will rewrite a valid file.\n      if (/readonly\\s+\\d/.test(globalDtsContent)) {\n        try {\n          fs.unlinkSync(GLOBAL_PROVIDER_TYPES_DTS());\n        } catch {\n          // Ignore deletion errors\n        }\n        // Don't sync corrupted .d.ts file; fall through to keep existing local file\n      } else {\n        let shouldCopyDts = true;\n\n        if (fs.existsSync(localDtsPath)) {\n          const localDtsContent = fs.readFileSync(localDtsPath, 'utf-8');\n          shouldCopyDts = globalDtsContent !== localDtsContent;\n        }\n\n        if (shouldCopyDts) {\n          // Use atomic write to prevent corruption from concurrent writes\n          atomicWriteFileSync(localDtsPath, globalDtsContent, 'utf-8');\n        }\n      }\n    }\n  } catch {\n    // Silent fail - fall back to existing files. Sync errors are recoverable\n    // on the next call and don't need to be surfaced to users.\n  }\n}\n\nfunction getLastRefreshTimeFromDisk(): Date | null {\n  try {\n    if (!fs.existsSync(CACHE_FILE())) {\n      return null;\n    }\n    const timestamp = fs.readFileSync(CACHE_FILE(), 'utf-8').trim();\n    return new Date(parseInt(timestamp, 10));\n  } catch (err) {\n    console.warn('[GatewayRegistry] Failed to read cache file:', err);\n    modelRouterCacheFailed = true;\n    return null;\n  }\n}\n\nfunction saveLastRefreshTimeToDisk(date: Date): void {\n  try {\n    if (!fs.existsSync(CACHE_DIR())) {\n      fs.mkdirSync(CACHE_DIR(), { recursive: true });\n    }\n    fs.writeFileSync(CACHE_FILE(), date.getTime().toString(), 'utf-8');\n  } catch (err) {\n    modelRouterCacheFailed = true;\n    console.warn('[GatewayRegistry] Failed to write cache file:', err);\n  }\n}\n\nfunction getPackageRoot(): string {\n  try {\n    // Use require.resolve to find the package root reliably\n    const require = createRequire(import.meta.url || 'file://');\n    const packageJsonPath = require.resolve('@mastra/core/package.json');\n    return path.dirname(packageJsonPath);\n  } catch {\n    // Fallback to cwd if we can't resolve the package\n    return process.cwd();\n  }\n}\n\nfunction loadRegistry(useDynamicLoading: boolean, customGateways: MastraModelGatewayInterface[] = []): RegistryData {\n  const enabledGatewayIds = getEnabledGatewayIds([\n    new ModelsDevGateway({}),\n    new NetlifyGateway(),\n    new MastraGateway(),\n    ...customGateways,\n  ]);\n\n  // Production: use static import (bundled at build time)\n  if (!useDynamicLoading) {\n    return sanitizeRegistryDataForRuntime(staticRegistry, enabledGatewayIds);\n  }\n\n  // Dynamic loading mode: sync global cache to local before loading\n  syncGlobalCacheToLocal();\n\n  // Dynamic loading mode: check in-memory cache first\n  if (registryData) {\n    return registryData;\n  }\n\n  // Dynamic loading mode: load from file system for live updates\n  const packageRoot = getPackageRoot();\n  const possiblePaths: string[] = [\n    // Built: in dist/ relative to package root (first priority - what gets distributed)\n    path.join(packageRoot, 'dist', 'provider-registry.json'),\n    // Development: in src/ relative to package root\n    path.join(packageRoot, 'src', 'llm', 'model', 'provider-registry.json'),\n    // Fallback: relative to cwd (for monorepo setups)\n    path.join(process.cwd(), 'packages/core/src/llm/model/provider-registry.json'),\n    path.join(process.cwd(), 'src/llm/model/provider-registry.json'),\n  ];\n\n  const errors: string[] = [];\n\n  for (const jsonPath of possiblePaths) {\n    try {\n      const content = fs.readFileSync(jsonPath, 'utf-8');\n      const parsed = JSON.parse(content) as RegistryData;\n      registryData = sanitizeRegistryDataForRuntime(parsed, enabledGatewayIds);\n      return registryData!;\n    } catch (err) {\n      const errorMessage = err instanceof Error ? err.message : String(err);\n      errors.push(`${jsonPath}: ${errorMessage}`);\n\n      // If the file exists but has corrupted JSON (not ENOENT), delete it and fall back to static registry\n      // This handles cases where concurrent writes corrupted the file before the atomic write fix\n      const isFileNotFound = err instanceof Error && 'code' in err && (err as NodeJS.ErrnoException).code === 'ENOENT';\n      const isJsonParseError = err instanceof SyntaxError;\n\n      if (!isFileNotFound && isJsonParseError) {\n        console.warn(\n          `[GatewayRegistry] Detected corrupted provider-registry.json at ${jsonPath}. ` +\n            `Deleting corrupted file and falling back to static registry.`,\n        );\n        try {\n          fs.unlinkSync(jsonPath);\n        } catch {\n          // Ignore deletion errors\n        }\n        // Fall back to static registry (bundled at build time)\n        registryData = sanitizeRegistryDataForRuntime(staticRegistry, enabledGatewayIds);\n        return registryData;\n      }\n\n      continue;\n    }\n  }\n\n  // If all paths failed, fall back to static registry instead of throwing\n  // This provides a more graceful degradation\n  console.warn(\n    `[GatewayRegistry] Could not load provider registry from any path. Falling back to static registry.\\n` +\n      `Tried paths:\\n${errors.join('\\n')}`,\n  );\n  registryData = sanitizeRegistryDataForRuntime(staticRegistry, enabledGatewayIds);\n  return registryData;\n}\n\n// Export registry data via Proxy for lazy loading\nexport const PROVIDER_REGISTRY = new Proxy({} as Record<string, ProviderConfig>, {\n  get(_target, prop: string) {\n    const registry = GatewayRegistry.getInstance();\n    const providers = registry.getProviders();\n    return providers[prop];\n  },\n  ownKeys() {\n    const registry = GatewayRegistry.getInstance();\n    const providers = registry.getProviders();\n    return Object.keys(providers);\n  },\n  has(_target, prop: string) {\n    const registry = GatewayRegistry.getInstance();\n    const providers = registry.getProviders();\n    return prop in providers;\n  },\n  getOwnPropertyDescriptor(_target, prop) {\n    const registry = GatewayRegistry.getInstance();\n    const providers = registry.getProviders();\n    if (prop in providers) {\n      return {\n        enumerable: true,\n        configurable: true,\n      };\n    }\n    return undefined;\n  },\n}) as Record<Provider, ProviderConfig>;\n\nexport const PROVIDER_MODELS = new Proxy({} as ProviderModels, {\n  get(_target, prop: string) {\n    const registry = GatewayRegistry.getInstance();\n    const models = registry.getModels();\n    return models[prop];\n  },\n  ownKeys() {\n    const registry = GatewayRegistry.getInstance();\n    const models = registry.getModels();\n    return Object.keys(models);\n  },\n  has(_target, prop: string) {\n    const registry = GatewayRegistry.getInstance();\n    const models = registry.getModels();\n    return prop in models;\n  },\n  getOwnPropertyDescriptor(_target, prop) {\n    const registry = GatewayRegistry.getInstance();\n    const models = registry.getModels();\n    if (prop in models) {\n      return {\n        enumerable: true,\n        configurable: true,\n      };\n    }\n    return undefined;\n  },\n});\n\n/**\n * Parse a model string to extract provider and model ID\n * Examples:\n *   \"openai/gpt-4o\" -> { provider: \"openai\", modelId: \"gpt-4o\" }\n *   \"fireworks/accounts/etc/model\" -> { provider: \"fireworks\", modelId: \"accounts/etc/model\" }\n *   \"gpt-4o\" -> { provider: null, modelId: \"gpt-4o\" }\n */\nexport function parseModelString(modelString: string): { provider: string | null; modelId: string } {\n  const firstSlashIndex = modelString.indexOf('/');\n\n  if (firstSlashIndex !== -1) {\n    // Has at least one slash - extract everything before first slash as provider\n    const provider = modelString.substring(0, firstSlashIndex);\n    const modelId = modelString.substring(firstSlashIndex + 1);\n\n    if (provider && modelId) {\n      return {\n        provider,\n        modelId,\n      };\n    }\n  }\n\n  // No slash or invalid format\n  return {\n    provider: null,\n    modelId: modelString,\n  };\n}\n\n/**\n * Get provider configuration by provider ID\n */\nexport function getProviderConfig(providerId: string): ProviderConfig | undefined {\n  const registry = GatewayRegistry.getInstance();\n  return registry.getProviderConfig(providerId);\n}\n\n/**\n * Check if a provider is registered\n */\nexport function isProviderRegistered(providerId: string): boolean {\n  const registry = GatewayRegistry.getInstance();\n  return registry.isProviderRegistered(providerId);\n}\n\n/**\n * Get all registered provider IDs\n */\nexport function getRegisteredProviders(): string[] {\n  const registry = GatewayRegistry.getInstance();\n  const providers = registry.getProviders();\n  return Object.keys(providers);\n}\n\n// ---------------------------------------------------------------------------\n// Provider capabilities (per-model attachment / modality metadata)\n// ---------------------------------------------------------------------------\n\ninterface ProviderCapabilityFile {\n  attachment: string[];\n}\n\nconst providerCapCache = new Map<string, string[] | null>();\n\nfunction isDirectory(dir: string): boolean {\n  try {\n    return fs.existsSync(dir) && fs.statSync(dir).isDirectory();\n  } catch {\n    return false;\n  }\n}\n\nfunction findCapabilitiesDirs(useDynamicLoading: boolean): string[] {\n  const packageRoot = getPackageRoot();\n  const distCapabilitiesDir = path.join(packageRoot, 'dist', 'capabilities');\n  const sourceCapabilitiesDir = path.join(packageRoot, 'src', 'llm', 'model', 'capabilities');\n  const workspaceSourceCapabilitiesDir = path.join(process.cwd(), 'packages/core/src/llm/model/capabilities');\n\n  const dirs: string[] = [];\n\n  // In dynamic mode, prefer the global cache so fresher gateway-synced data wins.\n  if (useDynamicLoading) {\n    const globalCapDir = GLOBAL_CAPABILITIES_DIR();\n    if (isDirectory(globalCapDir)) dirs.push(globalCapDir);\n  }\n\n  if (isDirectory(distCapabilitiesDir)) dirs.push(distCapabilitiesDir);\n\n  // Published packages only include dist/. Source fallbacks are for local workspace/dev\n  // runs where @mastra/core may resolve through a stale partial dist while checked-in\n  // source capability files are available.\n  if (isDirectory(sourceCapabilitiesDir)) dirs.push(sourceCapabilitiesDir);\n  if (workspaceSourceCapabilitiesDir !== sourceCapabilitiesDir && isDirectory(workspaceSourceCapabilitiesDir)) {\n    dirs.push(workspaceSourceCapabilitiesDir);\n  }\n\n  return dirs;\n}\n\nlet capabilitiesDirCache: string[] | undefined;\n\nfunction loadProviderAttachmentModels(provider: string, useDynamicLoading: boolean): string[] | null {\n  if (providerCapCache.has(provider)) return providerCapCache.get(provider)!;\n\n  if (capabilitiesDirCache === undefined) {\n    capabilitiesDirCache = findCapabilitiesDirs(useDynamicLoading);\n  }\n\n  for (const capabilitiesDir of capabilitiesDirCache) {\n    const filePath = path.join(capabilitiesDir, `${provider}.json`);\n    try {\n      const content = fs.readFileSync(filePath, 'utf-8');\n      const data = JSON.parse(content) as ProviderCapabilityFile;\n      providerCapCache.set(provider, data.attachment);\n      return data.attachment;\n    } catch {\n      continue;\n    }\n  }\n\n  providerCapCache.set(provider, null);\n  return null;\n}\n\n/**\n * Check whether a model supports image/file attachments.\n * Reads only the per-provider capability file for the given model's provider.\n * Returns `true` if the model is listed, `false` if the provider is known but\n * the model isn't listed, or `undefined` when no data exists for the provider.\n */\nfunction getProviderAttachmentSupport(\n  provider: string,\n  modelId: string,\n  useDynamicLoading: boolean,\n): boolean | undefined {\n  const models = loadProviderAttachmentModels(provider, useDynamicLoading);\n  if (!models) return undefined;\n  return models.includes(modelId);\n}\n\nexport function modelSupportsAttachments(modelRouterId: string): boolean | undefined {\n  const { provider, modelId } = parseModelString(modelRouterId);\n  if (!provider) return undefined;\n\n  const registry = GatewayRegistry.getInstance();\n  const useDynamicLoading = registry['useDynamicLoading'];\n  const directSupport = getProviderAttachmentSupport(provider, modelId, useDynamicLoading);\n  if (directSupport !== undefined) return directSupport;\n\n  const nestedProviderDelimiter = modelId.indexOf('/');\n  if (nestedProviderDelimiter !== -1) {\n    const nestedProvider = modelId.substring(0, nestedProviderDelimiter);\n    const nestedModelId = modelId.substring(nestedProviderDelimiter + 1);\n    if (nestedProvider && nestedModelId) {\n      const nestedSupport = getProviderAttachmentSupport(nestedProvider, nestedModelId, useDynamicLoading);\n      if (nestedSupport !== undefined) return nestedSupport;\n    }\n  }\n\n  return directSupport;\n}\n\n/**\n * Type guard to check if a string is a valid OpenAI-compatible model ID\n */\nexport function isValidModelId(modelId: string): modelId is ModelRouterModelId {\n  const { provider } = parseModelString(modelId);\n  return provider !== null && isProviderRegistered(provider);\n}\n\nexport interface GatewayRegistryOptions {\n  /**\n   * Enable dynamic loading from file system instead of using static bundled registry.\n   * Required for syncGateways() and auto-refresh to work.\n   * Defaults to true when MASTRA_DEV=true, false otherwise.\n   */\n  useDynamicLoading?: boolean;\n}\n\n/**\n * GatewayRegistry - Manages dynamic loading and refreshing of provider data from gateways\n * Singleton class that handles runtime updates to the provider registry\n */\nexport class GatewayRegistry {\n  private static instance: GatewayRegistry | null = null;\n  private lastRefreshTime: Date | null = null;\n  private refreshInterval: NodeJS.Timeout | null = null;\n  private isRefreshing = false;\n  private useDynamicLoading: boolean;\n  private customGateways: MastraModelGatewayInterface[] = [];\n\n  private constructor(options: GatewayRegistryOptions = {}) {\n    const isDev = process.env.MASTRA_DEV === 'true' || process.env.MASTRA_DEV === '1';\n    this.useDynamicLoading = options.useDynamicLoading ?? isDev;\n  }\n\n  /**\n   * Get the singleton instance\n   */\n  static getInstance(options?: GatewayRegistryOptions): GatewayRegistry {\n    if (!GatewayRegistry.instance) {\n      GatewayRegistry.instance = new GatewayRegistry(options);\n      return GatewayRegistry.instance;\n    }\n\n    if (options?.useDynamicLoading === true) {\n      GatewayRegistry.instance.useDynamicLoading = true;\n    }\n\n    return GatewayRegistry.instance;\n  }\n\n  /**\n   * Register custom gateways for type generation\n   * @param gateways - Array of custom gateway instances\n   */\n  registerCustomGateways(gateways: MastraModelGatewayInterface[]): void {\n    this.customGateways = gateways;\n  }\n\n  /**\n   * Get all registered custom gateways\n   */\n  getCustomGateways(): MastraModelGatewayInterface[] {\n    return this.customGateways;\n  }\n\n  /**\n   * Sync providers from all gateways\n   * Requires dynamic loading to be enabled (useDynamicLoading=true).\n   * @param forceRefresh - Force refresh even if recently synced\n   * @param writeToSrc - Write to src/ directory in addition to dist/ (useful for manual generation in repo)\n   */\n  async syncGateways(forceRefresh = false, writeToSrc = false): Promise<void> {\n    // Only allow sync when dynamic loading is enabled or when explicitly writing to src (build script)\n    if (!this.useDynamicLoading && !writeToSrc) {\n      // console.debug('[GatewayRegistry] Skipping sync (dynamic loading disabled, registry is static)');\n      return;\n    }\n\n    // Skip all network fetches when running in offline/air-gapped mode\n    if (isOfflineMode()) {\n      return;\n    }\n\n    if (this.isRefreshing && !forceRefresh) {\n      // console.debug('[GatewayRegistry] Sync already in progress, skipping...');\n      return;\n    }\n\n    this.isRefreshing = true;\n\n    try {\n      // console.debug('[GatewayRegistry] Starting gateway sync...');\n\n      // Import gateway classes and generation functions\n      const { ModelsDevGateway } = await import('./gateways/models-dev.js');\n      const { NetlifyGateway } = await import('./gateways/netlify.js');\n      const { MastraGateway } = await import('./gateways/mastra.js');\n      const { fetchProvidersFromGateways, writeRegistryFiles } = await import('./registry-generator.js');\n\n      // Initialize default gateways. Mastra Gateway is dynamic-only and should not be written into checked-in static artifacts.\n      const defaultGateways = [\n        new ModelsDevGateway({}),\n        new NetlifyGateway(),\n        ...(writeToSrc ? [] : [new MastraGateway()]),\n      ];\n\n      // Combine default and custom gateways\n      const gateways = [...defaultGateways, ...this.customGateways];\n\n      // Fetch provider data\n      const { providers, models, attachmentCapabilities } = await fetchProvidersFromGateways(gateways);\n\n      // Get package root for file paths\n      const packageRoot = getPackageRoot();\n\n      // Write to global cache first (so all projects can benefit)\n      try {\n        fs.mkdirSync(CACHE_DIR(), { recursive: true });\n        await writeRegistryFiles(\n          GLOBAL_PROVIDER_REGISTRY_JSON(),\n          GLOBAL_PROVIDER_TYPES_DTS(),\n          providers,\n          models,\n          attachmentCapabilities,\n        );\n        // console.debug(`[GatewayRegistry] ✅ Updated global cache at ${CACHE_DIR()}`);\n      } catch (error) {\n        console.warn('[GatewayRegistry] Failed to write to global cache:', error);\n      }\n\n      // Write to dist/ (the bundled location that gets distributed)\n      const distJsonPath = path.join(packageRoot, 'dist', 'provider-registry.json');\n      const distTypesPath = path.join(packageRoot, 'dist', 'llm', 'model', 'provider-types.generated.d.ts');\n\n      await writeRegistryFiles(distJsonPath, distTypesPath, providers, models, attachmentCapabilities);\n      // console.debug(`[GatewayRegistry] ✅ Updated registry files in dist/`);\n\n      // Copy to src/ only when explicitly requested (e.g., running the generation script)\n      const shouldWriteToSrc = writeToSrc;\n      if (shouldWriteToSrc) {\n        const srcJsonPath = path.join(packageRoot, 'src', 'llm', 'model', 'provider-registry.json');\n        const srcTypesPath = path.join(packageRoot, 'src', 'llm', 'model', 'provider-types.generated.d.ts');\n\n        // Copy the already-generated files\n        await fs.promises.copyFile(distJsonPath, srcJsonPath);\n        await fs.promises.copyFile(distTypesPath, srcTypesPath);\n\n        const distCapDir = path.join(packageRoot, 'dist', 'capabilities');\n        const srcCapDir = path.join(packageRoot, 'src', 'llm', 'model', 'capabilities');\n        if (fs.existsSync(distCapDir)) {\n          await fs.promises.mkdir(srcCapDir, { recursive: true });\n          const capFiles = fs.readdirSync(distCapDir).filter(f => f.endsWith('.json'));\n          for (const file of capFiles) {\n            await fs.promises.copyFile(path.join(distCapDir, file), path.join(srcCapDir, file));\n          }\n        }\n        // console.debug(`[GatewayRegistry] ✅ Copied registry files to src/ (${writeToSrc ? 'manual' : 'dynamic loading'})`);\n      }\n\n      // Clear the in-memory cache to force reload (dynamic loading only)\n      if (this.useDynamicLoading) {\n        registryData = null;\n        providerCapCache.clear();\n        capabilitiesDirCache = undefined;\n      }\n\n      this.lastRefreshTime = new Date();\n      saveLastRefreshTimeToDisk(this.lastRefreshTime);\n      // console.debug(`[GatewayRegistry] ✅ Gateway sync completed at ${this.lastRefreshTime.toISOString()}`);\n    } catch {\n      // Silently ignore — the bundled registry already contains all\n      // model data so a failed sync is non-critical.\n    } finally {\n      this.isRefreshing = false;\n    }\n  }\n\n  /**\n   * Get the last refresh time (from memory or disk cache)\n   */\n  getLastRefreshTime(): Date | null {\n    return this.lastRefreshTime || getLastRefreshTimeFromDisk();\n  }\n\n  /**\n   * Start auto-refresh on an interval\n   * Requires dynamic loading to be enabled (useDynamicLoading=true).\n   * @param intervalMs - Interval in milliseconds (default: 1 hour)\n   */\n  startAutoRefresh(intervalMs = 60 * 60 * 1000): void {\n    // Only allow auto-refresh when dynamic loading is enabled\n    if (!this.useDynamicLoading) {\n      // console.debug('[GatewayRegistry] Skipping auto-refresh (dynamic loading disabled, registry is static)');\n      return;\n    }\n\n    // Skip auto-refresh when running in offline/air-gapped mode\n    if (isOfflineMode()) {\n      return;\n    }\n\n    if (this.refreshInterval) {\n      // console.debug('[GatewayRegistry] Auto-refresh already running');\n      return;\n    }\n\n    // console.debug(`[GatewayRegistry] Starting auto-refresh (interval: ${intervalMs}ms)`);\n\n    // Check if we need to run an immediate sync\n    const lastRefresh = getLastRefreshTimeFromDisk();\n    const now = Date.now();\n    const shouldRefresh = !modelRouterCacheFailed && (!lastRefresh || now - lastRefresh.getTime() > intervalMs);\n\n    if (shouldRefresh) {\n      this.syncGateways().catch(() => {});\n    }\n\n    this.refreshInterval = setInterval(() => {\n      if (modelRouterCacheFailed && this.refreshInterval) {\n        clearInterval(this.refreshInterval);\n        this.refreshInterval = null;\n        return;\n      }\n      this.syncGateways().catch(() => {});\n    }, intervalMs);\n\n    // Prevent the interval from keeping the process alive\n    if (this.refreshInterval.unref) {\n      this.refreshInterval.unref();\n    }\n  }\n\n  /**\n   * Stop auto-refresh\n   */\n  stopAutoRefresh(): void {\n    if (this.refreshInterval) {\n      clearInterval(this.refreshInterval);\n      this.refreshInterval = null;\n      // console.debug('[GatewayRegistry] Auto-refresh stopped');\n    }\n  }\n\n  /**\n   * Get provider configuration by ID\n   */\n  getProviderConfig(providerId: string): ProviderConfig | undefined {\n    const data = loadRegistry(this.useDynamicLoading, this.customGateways);\n    return data.providers[providerId];\n  }\n\n  /**\n   * Check if a provider is registered\n   */\n  isProviderRegistered(providerId: string): boolean {\n    const data = loadRegistry(this.useDynamicLoading, this.customGateways);\n    return providerId in data.providers;\n  }\n\n  /**\n   * Get all registered providers\n   */\n  getProviders(): Record<string, ProviderConfig> {\n    const data = loadRegistry(this.useDynamicLoading, this.customGateways);\n    return data.providers;\n  }\n\n  /**\n   * Get all models\n   */\n  getModels(): Record<string, string[]> {\n    return loadRegistry(this.useDynamicLoading, this.customGateways).models;\n  }\n}\n\n// Auto-start refresh if enabled\n// Defaults to enabled when MASTRA_DEV=true (which enables dynamic loading by default)\n// Disabled entirely when MASTRA_OFFLINE is set (air-gapped/offline environments)\nconst isDev = process.env.MASTRA_DEV === 'true' || process.env.MASTRA_DEV === '1';\nconst autoRefreshEnabled =\n  !isOfflineMode() &&\n  (process.env.MASTRA_AUTO_REFRESH_PROVIDERS === 'true' ||\n    (process.env.MASTRA_AUTO_REFRESH_PROVIDERS !== 'false' && isDev));\n\nif (autoRefreshEnabled) {\n  // console.debug('[GatewayRegistry] Auto-refresh enabled');\n  GatewayRegistry.getInstance({ useDynamicLoading: isDev }).startAutoRefresh();\n}\n"]}