@ai-sdk/anthropic/dist/index.mjs.map

The **[Anthropic provider](https://sdk.vercel.ai/providers/ai-sdk-providers/anthropic)** for the [AI SDK](https://sdk.vercel.ai/docs) contains language model support for the [Anthropic Messages API](https://docs.anthropic.com/claude/reference/messages_pos

{"version":3,"sources":["../src/anthropic-provider.ts","../src/anthropic-messages-language-model.ts","../src/anthropic-error.ts","../src/anthropic-prepare-tools.ts","../src/convert-to-anthropic-messages-prompt.ts","../src/map-anthropic-stop-reason.ts","../src/anthropic-tools.ts"],"sourcesContent":["import {\n  LanguageModelV1,\n  NoSuchModelError,\n  ProviderV1,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  loadApiKey,\n  withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport { AnthropicMessagesLanguageModel } from './anthropic-messages-language-model';\nimport {\n  AnthropicMessagesModelId,\n  AnthropicMessagesSettings,\n} from './anthropic-messages-settings';\nimport { anthropicTools } from './anthropic-tools';\n\nexport interface AnthropicProvider extends ProviderV1 {\n  /**\nCreates a model for text generation.\n*/\n  (\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ): LanguageModelV1;\n\n  /**\nCreates a model for text generation.\n*/\n  languageModel(\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ): LanguageModelV1;\n\n  /**\n@deprecated Use `.languageModel()` instead.\n*/\n  chat(\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ): LanguageModelV1;\n\n  /**\n@deprecated Use `.languageModel()` instead.\n   */\n  messages(\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ): LanguageModelV1;\n\n  /**\nAnthropic-specific computer use tool.\n   */\n  tools: typeof anthropicTools;\n}\n\nexport interface AnthropicProviderSettings {\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n   */\n  baseURL?: string;\n\n  /**\nAPI key that is being send using the `x-api-key` header.\nIt defaults to the `ANTHROPIC_API_KEY` environment variable.\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  generateId?: () => string;\n}\n\n/**\nCreate an Anthropic provider instance.\n */\nexport function createAnthropic(\n  options: AnthropicProviderSettings = {},\n): AnthropicProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ?? 'https://api.anthropic.com/v1';\n\n  const getHeaders = () => ({\n    'anthropic-version': '2023-06-01',\n    'x-api-key': loadApiKey({\n      apiKey: options.apiKey,\n      environmentVariableName: 'ANTHROPIC_API_KEY',\n      description: 'Anthropic',\n    }),\n    ...options.headers,\n  });\n\n  const createChatModel = (\n    modelId: AnthropicMessagesModelId,\n    settings: AnthropicMessagesSettings = {},\n  ) =>\n    new AnthropicMessagesLanguageModel(modelId, settings, {\n      provider: 'anthropic.messages',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n      supportsImageUrls: true,\n    });\n\n  const provider = function (\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ) {\n    if (new.target) {\n      throw new Error(\n        'The Anthropic model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createChatModel(modelId, settings);\n  };\n\n  provider.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.messages = createChatModel;\n  provider.textEmbeddingModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n  };\n\n  provider.tools = anthropicTools;\n\n  return provider;\n}\n\n/**\nDefault Anthropic provider instance.\n */\nexport const anthropic = createAnthropic();\n","import {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1FunctionToolCall,\n  LanguageModelV1ProviderMetadata,\n  LanguageModelV1StreamPart,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  Resolvable,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\nimport { anthropicFailedResponseHandler } from './anthropic-error';\nimport {\n  AnthropicMessagesModelId,\n  AnthropicMessagesSettings,\n} from './anthropic-messages-settings';\nimport { prepareTools } from './anthropic-prepare-tools';\nimport { convertToAnthropicMessagesPrompt } from './convert-to-anthropic-messages-prompt';\nimport { mapAnthropicStopReason } from './map-anthropic-stop-reason';\n\ntype AnthropicMessagesConfig = {\n  provider: string;\n  baseURL: string;\n  headers: Resolvable<Record<string, string | undefined>>;\n  supportsImageUrls: boolean;\n  fetch?: FetchFunction;\n  buildRequestUrl?: (baseURL: string, isStreaming: boolean) => string;\n  transformRequestBody?: (args: Record<string, any>) => Record<string, any>;\n};\n\nexport class AnthropicMessagesLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = 'v1';\n  readonly defaultObjectGenerationMode = 'tool';\n\n  readonly modelId: AnthropicMessagesModelId;\n  readonly settings: AnthropicMessagesSettings;\n\n  private readonly config: AnthropicMessagesConfig;\n\n  constructor(\n    modelId: AnthropicMessagesModelId,\n    settings: AnthropicMessagesSettings,\n    config: AnthropicMessagesConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  supportsUrl(url: URL): boolean {\n    return url.protocol === 'https:';\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get supportsImageUrls(): boolean {\n    return this.config.supportsImageUrls;\n  }\n\n  private async getArgs({\n    mode,\n    prompt,\n    maxTokens = 4096, // 4096: max model output tokens TODO update default in v5\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    providerMetadata: providerOptions,\n  }: Parameters<LanguageModelV1['doGenerate']>[0]) {\n    const type = mode.type;\n\n    const warnings: LanguageModelV1CallWarning[] = [];\n\n    if (frequencyPenalty != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'frequencyPenalty',\n      });\n    }\n\n    if (presencePenalty != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'presencePenalty',\n      });\n    }\n\n    if (seed != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'seed',\n      });\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: messagesPrompt, betas: messagesBetas } =\n      convertToAnthropicMessagesPrompt({\n        prompt,\n        sendReasoning: this.settings.sendReasoning ?? true,\n        warnings,\n      });\n\n    const anthropicOptions = parseProviderOptions({\n      provider: 'anthropic',\n      providerOptions,\n      schema: anthropicProviderOptionsSchema,\n    });\n\n    const isThinking = anthropicOptions?.thinking?.type === 'enabled';\n    const thinkingBudget = anthropicOptions?.thinking?.budgetTokens;\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // standardized settings:\n      max_tokens: maxTokens,\n      temperature,\n      top_k: topK,\n      top_p: topP,\n      stop_sequences: stopSequences,\n\n      // provider specific settings:\n      ...(isThinking && {\n        thinking: { type: 'enabled', budget_tokens: thinkingBudget },\n      }),\n\n      // prompt:\n      system: messagesPrompt.system,\n      messages: messagesPrompt.messages,\n    };\n\n    if (isThinking) {\n      if (thinkingBudget == null) {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'thinking requires a budget',\n        });\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 when thinking is enabled',\n        });\n      }\n\n      if (topK != null) {\n        baseArgs.top_k = undefined;\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'topK',\n          details: 'topK is not supported when thinking is enabled',\n        });\n      }\n\n      if (topP != null) {\n        baseArgs.top_p = undefined;\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'topP',\n          details: 'topP is not supported when thinking is enabled',\n        });\n      }\n\n      // adjust max tokens to account for thinking:\n      baseArgs.max_tokens = maxTokens + thinkingBudget;\n    }\n\n    switch (type) {\n      case 'regular': {\n        const {\n          tools,\n          tool_choice,\n          toolWarnings,\n          betas: toolsBetas,\n        } = prepareTools(mode);\n\n        return {\n          args: { ...baseArgs, tools, tool_choice },\n          warnings: [...warnings, ...toolWarnings],\n          betas: new Set([...messagesBetas, ...toolsBetas]),\n        };\n      }\n\n      case 'object-json': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'json-mode object generation',\n        });\n      }\n\n      case 'object-tool': {\n        const { name, description, parameters } = mode.tool;\n\n        return {\n          args: {\n            ...baseArgs,\n            tools: [{ name, description, input_schema: parameters }],\n            tool_choice: { type: 'tool', name },\n          },\n          warnings,\n          betas: messagesBetas,\n        };\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  private async getHeaders({\n    betas,\n    headers,\n  }: {\n    betas: Set<string>;\n    headers: Record<string, string | undefined> | undefined;\n  }) {\n    return combineHeaders(\n      await resolve(this.config.headers),\n      betas.size > 0 ? { 'anthropic-beta': Array.from(betas).join(',') } : {},\n      headers,\n    );\n  }\n\n  private buildRequestUrl(isStreaming: boolean): string {\n    return (\n      this.config.buildRequestUrl?.(this.config.baseURL, isStreaming) ??\n      `${this.config.baseURL}/messages`\n    );\n  }\n\n  private transformRequestBody(args: Record<string, any>): Record<string, any> {\n    return this.config.transformRequestBody?.(args) ?? args;\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV1['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doGenerate']>>> {\n    const { args, warnings, betas } = await this.getArgs(options);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: this.buildRequestUrl(false),\n      headers: await this.getHeaders({ betas, headers: options.headers }),\n      body: this.transformRequestBody(args),\n      failedResponseHandler: anthropicFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        anthropicMessagesResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n\n    // extract text\n    let text = '';\n    for (const content of response.content) {\n      if (content.type === 'text') {\n        text += content.text;\n      }\n    }\n\n    // extract tool calls\n    let toolCalls: LanguageModelV1FunctionToolCall[] | undefined = undefined;\n    if (response.content.some(content => content.type === 'tool_use')) {\n      toolCalls = [];\n      for (const content of response.content) {\n        if (content.type === 'tool_use') {\n          toolCalls.push({\n            toolCallType: 'function',\n            toolCallId: content.id,\n            toolName: content.name,\n            args: JSON.stringify(content.input),\n          });\n        }\n      }\n    }\n\n    const reasoning = response.content\n      .filter(\n        content =>\n          content.type === 'redacted_thinking' || content.type === 'thinking',\n      )\n      .map(content =>\n        content.type === 'thinking'\n          ? {\n              type: 'text' as const,\n              text: content.thinking,\n              signature: content.signature,\n            }\n          : {\n              type: 'redacted' as const,\n              data: content.data,\n            },\n      );\n\n    return {\n      text,\n      reasoning: reasoning.length > 0 ? reasoning : undefined,\n      toolCalls,\n      finishReason: mapAnthropicStopReason(response.stop_reason),\n      usage: {\n        promptTokens: response.usage.input_tokens,\n        completionTokens: response.usage.output_tokens,\n      },\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: {\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      response: {\n        id: response.id ?? undefined,\n        modelId: response.model ?? undefined,\n      },\n      warnings,\n      providerMetadata: {\n        anthropic: {\n          cacheCreationInputTokens:\n            response.usage.cache_creation_input_tokens ?? null,\n          cacheReadInputTokens: response.usage.cache_read_input_tokens ?? null,\n        },\n      },\n      request: { body: JSON.stringify(args) },\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV1['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doStream']>>> {\n    const { args, warnings, betas } = await this.getArgs(options);\n    const body = { ...args, stream: true };\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: this.buildRequestUrl(true),\n      headers: await this.getHeaders({ betas, headers: options.headers }),\n      body: this.transformRequestBody(body),\n      failedResponseHandler: anthropicFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        anthropicMessagesChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n\n    let finishReason: LanguageModelV1FinishReason = 'unknown';\n    const usage: { promptTokens: number; completionTokens: number } = {\n      promptTokens: Number.NaN,\n      completionTokens: Number.NaN,\n    };\n\n    const toolCallContentBlocks: Record<\n      number,\n      {\n        toolCallId: string;\n        toolName: string;\n        jsonText: string;\n      }\n    > = {};\n\n    let providerMetadata: LanguageModelV1ProviderMetadata | undefined =\n      undefined;\n\n    let blockType:\n      | 'text'\n      | 'thinking'\n      | 'tool_use'\n      | 'redacted_thinking'\n      | undefined = undefined;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof anthropicMessagesChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\n            if (!chunk.success) {\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            switch (value.type) {\n              case 'ping': {\n                return; // ignored\n              }\n\n              case 'content_block_start': {\n                const contentBlockType = value.content_block.type;\n\n                blockType = contentBlockType;\n\n                switch (contentBlockType) {\n                  case 'text':\n                  case 'thinking': {\n                    return; // ignored\n                  }\n\n                  case 'redacted_thinking': {\n                    controller.enqueue({\n                      type: 'redacted-reasoning',\n                      data: value.content_block.data,\n                    });\n                    return;\n                  }\n\n                  case 'tool_use': {\n                    toolCallContentBlocks[value.index] = {\n                      toolCallId: value.content_block.id,\n                      toolName: value.content_block.name,\n                      jsonText: '',\n                    };\n                    return;\n                  }\n\n                  default: {\n                    const _exhaustiveCheck: never = contentBlockType;\n                    throw new Error(\n                      `Unsupported content block type: ${_exhaustiveCheck}`,\n                    );\n                  }\n                }\n              }\n\n              case 'content_block_stop': {\n                // when finishing a tool call block, send the full tool call:\n                if (toolCallContentBlocks[value.index] != null) {\n                  const contentBlock = toolCallContentBlocks[value.index];\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallType: 'function',\n                    toolCallId: contentBlock.toolCallId,\n                    toolName: contentBlock.toolName,\n                    args: contentBlock.jsonText,\n                  });\n\n                  delete toolCallContentBlocks[value.index];\n                }\n\n                blockType = undefined; // reset block type\n\n                return;\n              }\n\n              case 'content_block_delta': {\n                const deltaType = value.delta.type;\n                switch (deltaType) {\n                  case 'text_delta': {\n                    controller.enqueue({\n                      type: 'text-delta',\n                      textDelta: value.delta.text,\n                    });\n\n                    return;\n                  }\n\n                  case 'thinking_delta': {\n                    controller.enqueue({\n                      type: 'reasoning',\n                      textDelta: value.delta.thinking,\n                    });\n\n                    return;\n                  }\n\n                  case 'signature_delta': {\n                    // signature are only supported on thinking blocks:\n                    if (blockType === 'thinking') {\n                      controller.enqueue({\n                        type: 'reasoning-signature',\n                        signature: value.delta.signature,\n                      });\n                    }\n\n                    return;\n                  }\n\n                  case 'input_json_delta': {\n                    const contentBlock = toolCallContentBlocks[value.index];\n\n                    controller.enqueue({\n                      type: 'tool-call-delta',\n                      toolCallType: 'function',\n                      toolCallId: contentBlock.toolCallId,\n                      toolName: contentBlock.toolName,\n                      argsTextDelta: value.delta.partial_json,\n                    });\n\n                    contentBlock.jsonText += value.delta.partial_json;\n\n                    return;\n                  }\n\n                  default: {\n                    const _exhaustiveCheck: never = deltaType;\n                    throw new Error(\n                      `Unsupported delta type: ${_exhaustiveCheck}`,\n                    );\n                  }\n                }\n              }\n\n              case 'message_start': {\n                usage.promptTokens = value.message.usage.input_tokens;\n                usage.completionTokens = value.message.usage.output_tokens;\n\n                providerMetadata = {\n                  anthropic: {\n                    cacheCreationInputTokens:\n                      value.message.usage.cache_creation_input_tokens ?? null,\n                    cacheReadInputTokens:\n                      value.message.usage.cache_read_input_tokens ?? null,\n                  },\n                };\n\n                controller.enqueue({\n                  type: 'response-metadata',\n                  id: value.message.id ?? undefined,\n                  modelId: value.message.model ?? undefined,\n                });\n\n                return;\n              }\n\n              case 'message_delta': {\n                usage.completionTokens = value.usage.output_tokens;\n                finishReason = mapAnthropicStopReason(value.delta.stop_reason);\n                return;\n              }\n\n              case 'message_stop': {\n                controller.enqueue({\n                  type: 'finish',\n                  finishReason,\n                  usage,\n                  providerMetadata,\n                });\n                return;\n              }\n\n              case 'error': {\n                controller.enqueue({ type: 'error', error: value.error });\n                return;\n              }\n\n              default: {\n                const _exhaustiveCheck: never = value;\n                throw new Error(`Unsupported chunk type: ${_exhaustiveCheck}`);\n              }\n            }\n          },\n        }),\n      ),\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      warnings,\n      request: { body: JSON.stringify(body) },\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\nconst anthropicMessagesResponseSchema = z.object({\n  type: z.literal('message'),\n  id: z.string().nullish(),\n  model: z.string().nullish(),\n  content: z.array(\n    z.discriminatedUnion('type', [\n      z.object({\n        type: z.literal('text'),\n        text: z.string(),\n      }),\n      z.object({\n        type: z.literal('thinking'),\n        thinking: z.string(),\n        signature: z.string(),\n      }),\n      z.object({\n        type: z.literal('redacted_thinking'),\n        data: z.string(),\n      }),\n      z.object({\n        type: z.literal('tool_use'),\n        id: z.string(),\n        name: z.string(),\n        input: z.unknown(),\n      }),\n    ]),\n  ),\n  stop_reason: z.string().nullish(),\n  usage: z.object({\n    input_tokens: z.number(),\n    output_tokens: z.number(),\n    cache_creation_input_tokens: z.number().nullish(),\n    cache_read_input_tokens: z.number().nullish(),\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\nconst anthropicMessagesChunkSchema = z.discriminatedUnion('type', [\n  z.object({\n    type: z.literal('message_start'),\n    message: z.object({\n      id: z.string().nullish(),\n      model: z.string().nullish(),\n      usage: z.object({\n        input_tokens: z.number(),\n        output_tokens: z.number(),\n        cache_creation_input_tokens: z.number().nullish(),\n        cache_read_input_tokens: z.number().nullish(),\n      }),\n    }),\n  }),\n  z.object({\n    type: z.literal('content_block_start'),\n    index: z.number(),\n    content_block: z.discriminatedUnion('type', [\n      z.object({\n        type: z.literal('text'),\n        text: z.string(),\n      }),\n      z.object({\n        type: z.literal('thinking'),\n        thinking: z.string(),\n      }),\n      z.object({\n        type: z.literal('tool_use'),\n        id: z.string(),\n        name: z.string(),\n      }),\n      z.object({\n        type: z.literal('redacted_thinking'),\n        data: z.string(),\n      }),\n    ]),\n  }),\n  z.object({\n    type: z.literal('content_block_delta'),\n    index: z.number(),\n    delta: z.discriminatedUnion('type', [\n      z.object({\n        type: z.literal('input_json_delta'),\n        partial_json: z.string(),\n      }),\n      z.object({\n        type: z.literal('text_delta'),\n        text: z.string(),\n      }),\n      z.object({\n        type: z.literal('thinking_delta'),\n        thinking: z.string(),\n      }),\n      z.object({\n        type: z.literal('signature_delta'),\n        signature: z.string(),\n      }),\n    ]),\n  }),\n  z.object({\n    type: z.literal('content_block_stop'),\n    index: z.number(),\n  }),\n  z.object({\n    type: z.literal('error'),\n    error: z.object({\n      type: z.string(),\n      message: z.string(),\n    }),\n  }),\n  z.object({\n    type: z.literal('message_delta'),\n    delta: z.object({ stop_reason: z.string().nullish() }),\n    usage: z.object({ output_tokens: z.number() }),\n  }),\n  z.object({\n    type: z.literal('message_stop'),\n  }),\n  z.object({\n    type: z.literal('ping'),\n  }),\n]);\n\nconst anthropicProviderOptionsSchema = z.object({\n  thinking: z\n    .object({\n      type: z.union([z.literal('enabled'), z.literal('disabled')]),\n      budgetTokens: z.number().optional(),\n    })\n    .optional(),\n});\n\nexport type AnthropicProviderOptions = z.infer<\n  typeof anthropicProviderOptionsSchema\n>;\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\n\nconst anthropicErrorDataSchema = z.object({\n  type: z.literal('error'),\n  error: z.object({\n    type: z.string(),\n    message: z.string(),\n  }),\n});\n\nexport type AnthropicErrorData = z.infer<typeof anthropicErrorDataSchema>;\n\nexport const anthropicFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: anthropicErrorDataSchema,\n  errorToMessage: data => data.error.message,\n});\n","import {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { AnthropicTool, AnthropicToolChoice } from './anthropic-api-types';\n\nexport function prepareTools(\n  mode: Parameters<LanguageModelV1['doGenerate']>[0]['mode'] & {\n    type: 'regular';\n  },\n): {\n  tools: Array<AnthropicTool> | undefined;\n  tool_choice: AnthropicToolChoice | undefined;\n  toolWarnings: LanguageModelV1CallWarning[];\n  betas: Set<string>;\n} {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  const tools = mode.tools?.length ? mode.tools : undefined;\n\n  const toolWarnings: LanguageModelV1CallWarning[] = [];\n  const betas = new Set<string>();\n\n  if (tools == null) {\n    return { tools: undefined, tool_choice: undefined, toolWarnings, betas };\n  }\n\n  const anthropicTools: AnthropicTool[] = [];\n\n  for (const tool of tools) {\n    switch (tool.type) {\n      case 'function':\n        anthropicTools.push({\n          name: tool.name,\n          description: tool.description,\n          input_schema: tool.parameters,\n        });\n        break;\n      case 'provider-defined':\n        switch (tool.id) {\n          case 'anthropic.computer_20250124':\n            betas.add('computer-use-2025-01-24');\n            anthropicTools.push({\n              name: tool.name,\n              type: 'computer_20250124',\n              display_width_px: tool.args.displayWidthPx as number,\n              display_height_px: tool.args.displayHeightPx as number,\n              display_number: tool.args.displayNumber as number,\n            });\n            break;\n          case 'anthropic.computer_20241022':\n            betas.add('computer-use-2024-10-22');\n            anthropicTools.push({\n              name: tool.name,\n              type: 'computer_20241022',\n              display_width_px: tool.args.displayWidthPx as number,\n              display_height_px: tool.args.displayHeightPx as number,\n              display_number: tool.args.displayNumber as number,\n            });\n            break;\n          case 'anthropic.text_editor_20250124':\n            betas.add('computer-use-2025-01-24');\n            anthropicTools.push({\n              name: tool.name,\n              type: 'text_editor_20250124',\n            });\n            break;\n          case 'anthropic.text_editor_20241022':\n            betas.add('computer-use-2024-10-22');\n            anthropicTools.push({\n              name: tool.name,\n              type: 'text_editor_20241022',\n            });\n            break;\n          case 'anthropic.bash_20250124':\n            betas.add('computer-use-2025-01-24');\n            anthropicTools.push({\n              name: tool.name,\n              type: 'bash_20250124',\n            });\n            break;\n          case 'anthropic.bash_20241022':\n            betas.add('computer-use-2024-10-22');\n            anthropicTools.push({\n              name: tool.name,\n              type: 'bash_20241022',\n            });\n            break;\n          default:\n            toolWarnings.push({ type: 'unsupported-tool', tool });\n            break;\n        }\n        break;\n      default:\n        toolWarnings.push({ type: 'unsupported-tool', tool });\n        break;\n    }\n  }\n\n  const toolChoice = mode.toolChoice;\n\n  if (toolChoice == null) {\n    return {\n      tools: anthropicTools,\n      tool_choice: undefined,\n      toolWarnings,\n      betas,\n    };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n      return {\n        tools: anthropicTools,\n        tool_choice: { type: 'auto' },\n        toolWarnings,\n        betas,\n      };\n    case 'required':\n      return {\n        tools: anthropicTools,\n        tool_choice: { type: 'any' },\n        toolWarnings,\n        betas,\n      };\n    case 'none':\n      // Anthropic does not support 'none' tool choice, so we remove the tools:\n      return { tools: undefined, tool_choice: undefined, toolWarnings, betas };\n    case 'tool':\n      return {\n        tools: anthropicTools,\n        tool_choice: { type: 'tool', name: toolChoice.toolName },\n        toolWarnings,\n        betas,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `Unsupported tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  LanguageModelV1CallWarning,\n  LanguageModelV1Message,\n  LanguageModelV1Prompt,\n  LanguageModelV1ProviderMetadata,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { convertUint8ArrayToBase64 } from '@ai-sdk/provider-utils';\nimport {\n  AnthropicAssistantMessage,\n  AnthropicCacheControl,\n  AnthropicMessagesPrompt,\n  AnthropicUserMessage,\n} from './anthropic-api-types';\n\nexport function convertToAnthropicMessagesPrompt({\n  prompt,\n  sendReasoning,\n  warnings,\n}: {\n  prompt: LanguageModelV1Prompt;\n  sendReasoning: boolean;\n  warnings: LanguageModelV1CallWarning[];\n}): {\n  prompt: AnthropicMessagesPrompt;\n  betas: Set<string>;\n} {\n  const betas = new Set<string>();\n  const blocks = groupIntoBlocks(prompt);\n\n  let system: AnthropicMessagesPrompt['system'] = undefined;\n  const messages: AnthropicMessagesPrompt['messages'] = [];\n\n  function getCacheControl(\n    providerMetadata: LanguageModelV1ProviderMetadata | undefined,\n  ): AnthropicCacheControl | undefined {\n    const anthropic = providerMetadata?.anthropic;\n\n    // allow both cacheControl and cache_control:\n    const cacheControlValue =\n      anthropic?.cacheControl ?? anthropic?.cache_control;\n\n    // Pass through value assuming it is of the correct type.\n    // The Anthropic API will validate the value.\n    return cacheControlValue as AnthropicCacheControl | undefined;\n  }\n\n  for (let i = 0; i < blocks.length; i++) {\n    const block = blocks[i];\n    const isLastBlock = i === blocks.length - 1;\n    const type = block.type;\n\n    switch (type) {\n      case 'system': {\n        if (system != null) {\n          throw new UnsupportedFunctionalityError({\n            functionality:\n              'Multiple system messages that are separated by user/assistant messages',\n          });\n        }\n\n        system = block.messages.map(({ content, providerMetadata }) => ({\n          type: 'text',\n          text: content,\n          cache_control: getCacheControl(providerMetadata),\n        }));\n\n        break;\n      }\n\n      case 'user': {\n        // combines all user and tool messages in this block into a single message:\n        const anthropicContent: AnthropicUserMessage['content'] = [];\n\n        for (const message of block.messages) {\n          const { role, content } = message;\n          switch (role) {\n            case 'user': {\n              for (let j = 0; j < content.length; j++) {\n                const part = content[j];\n\n                // cache control: first add cache control from part.\n                // for the last part of a message,\n                // check also if the message has cache control.\n                const isLastPart = j === content.length - 1;\n\n                const cacheControl =\n                  getCacheControl(part.providerMetadata) ??\n                  (isLastPart\n                    ? getCacheControl(message.providerMetadata)\n                    : undefined);\n\n                switch (part.type) {\n                  case 'text': {\n                    anthropicContent.push({\n                      type: 'text',\n                      text: part.text,\n                      cache_control: cacheControl,\n                    });\n                    break;\n                  }\n\n                  case 'image': {\n                    anthropicContent.push({\n                      type: 'image',\n                      source:\n                        part.image instanceof URL\n                          ? {\n                              type: 'url',\n                              url: part.image.toString(),\n                            }\n                          : {\n                              type: 'base64',\n                              media_type: part.mimeType ?? 'image/jpeg',\n                              data: convertUint8ArrayToBase64(part.image),\n                            },\n                      cache_control: cacheControl,\n                    });\n\n                    break;\n                  }\n\n                  case 'file': {\n                    if (part.mimeType !== 'application/pdf') {\n                      throw new UnsupportedFunctionalityError({\n                        functionality: 'Non-PDF files in user messages',\n                      });\n                    }\n\n                    betas.add('pdfs-2024-09-25');\n\n                    anthropicContent.push({\n                      type: 'document',\n                      source:\n                        part.data instanceof URL\n                          ? {\n                              type: 'url',\n                              url: part.data.toString(),\n                            }\n                          : {\n                              type: 'base64',\n                              media_type: 'application/pdf',\n                              data: part.data,\n                            },\n                      cache_control: cacheControl,\n                    });\n\n                    break;\n                  }\n                }\n              }\n\n              break;\n            }\n            case 'tool': {\n              for (let i = 0; i < content.length; i++) {\n                const part = content[i];\n\n                // cache control: first add cache control from part.\n                // for the last part of a message,\n                // check also if the message has cache control.\n                const isLastPart = i === content.length - 1;\n\n                const cacheControl =\n                  getCacheControl(part.providerMetadata) ??\n                  (isLastPart\n                    ? getCacheControl(message.providerMetadata)\n                    : undefined);\n\n                const toolResultContent =\n                  part.content != null\n                    ? part.content.map(part => {\n                        switch (part.type) {\n                          case 'text':\n                            return {\n                              type: 'text' as const,\n                              text: part.text,\n                              cache_control: undefined,\n                            };\n                          case 'image':\n                            return {\n                              type: 'image' as const,\n                              source: {\n                                type: 'base64' as const,\n                                media_type: part.mimeType ?? 'image/jpeg',\n                                data: part.data,\n                              },\n                              cache_control: undefined,\n                            };\n                        }\n                      })\n                    : JSON.stringify(part.result);\n\n                anthropicContent.push({\n                  type: 'tool_result',\n                  tool_use_id: part.toolCallId,\n                  content: toolResultContent,\n                  is_error: part.isError,\n                  cache_control: cacheControl,\n                });\n              }\n\n              break;\n            }\n            default: {\n              const _exhaustiveCheck: never = role;\n              throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n            }\n          }\n        }\n\n        messages.push({ role: 'user', content: anthropicContent });\n\n        break;\n      }\n\n      case 'assistant': {\n        // combines multiple assistant messages in this block into a single message:\n        const anthropicContent: AnthropicAssistantMessage['content'] = [];\n\n        for (let j = 0; j < block.messages.length; j++) {\n          const message = block.messages[j];\n          const isLastMessage = j === block.messages.length - 1;\n          const { content } = message;\n\n          for (let k = 0; k < content.length; k++) {\n            const part = content[k];\n            const isLastContentPart = k === content.length - 1;\n\n            // cache control: first add cache control from part.\n            // for the last part of a message,\n            // check also if the message has cache control.\n            const cacheControl =\n              getCacheControl(part.providerMetadata) ??\n              (isLastContentPart\n                ? getCacheControl(message.providerMetadata)\n                : undefined);\n\n            switch (part.type) {\n              case 'text': {\n                anthropicContent.push({\n                  type: 'text',\n                  text:\n                    // trim the last text part if it's the last message in the block\n                    // because Anthropic does not allow trailing whitespace\n                    // in pre-filled assistant responses\n                    isLastBlock && isLastMessage && isLastContentPart\n                      ? part.text.trim()\n                      : part.text,\n\n                  cache_control: cacheControl,\n                });\n                break;\n              }\n\n              case 'reasoning': {\n                if (sendReasoning) {\n                  anthropicContent.push({\n                    type: 'thinking',\n                    thinking: part.text,\n                    signature: part.signature!,\n                    cache_control: cacheControl,\n                  });\n                } else {\n                  warnings.push({\n                    type: 'other',\n                    message:\n                      'sending reasoning content is disabled for this model',\n                  });\n                }\n                break;\n              }\n\n              case 'redacted-reasoning': {\n                anthropicContent.push({\n                  type: 'redacted_thinking',\n                  data: part.data,\n                  cache_control: cacheControl,\n                });\n                break;\n              }\n\n              case 'tool-call': {\n                anthropicContent.push({\n                  type: 'tool_use',\n                  id: part.toolCallId,\n                  name: part.toolName,\n                  input: part.args,\n                  cache_control: cacheControl,\n                });\n                break;\n              }\n            }\n          }\n        }\n\n        messages.push({ role: 'assistant', content: anthropicContent });\n\n        break;\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return {\n    prompt: { system, messages },\n    betas,\n  };\n}\n\ntype SystemBlock = {\n  type: 'system';\n  messages: Array<LanguageModelV1Message & { role: 'system' }>;\n};\ntype AssistantBlock = {\n  type: 'assistant';\n  messages: Array<LanguageModelV1Message & { role: 'assistant' }>;\n};\ntype UserBlock = {\n  type: 'user';\n  messages: Array<LanguageModelV1Message & { role: 'user' | 'tool' }>;\n};\n\nfunction groupIntoBlocks(\n  prompt: LanguageModelV1Prompt,\n): Array<SystemBlock | AssistantBlock | UserBlock> {\n  const blocks: Array<SystemBlock | AssistantBlock | UserBlock> = [];\n  let currentBlock: SystemBlock | AssistantBlock | UserBlock | undefined =\n    undefined;\n\n  for (const message of prompt) {\n    const { role } = message;\n    switch (role) {\n      case 'system': {\n        if (currentBlock?.type !== 'system') {\n          currentBlock = { type: 'system', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push(message);\n        break;\n      }\n      case 'assistant': {\n        if (currentBlock?.type !== 'assistant') {\n          currentBlock = { type: 'assistant', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push(message);\n        break;\n      }\n      case 'user': {\n        if (currentBlock?.type !== 'user') {\n          currentBlock = { type: 'user', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push(message);\n        break;\n      }\n      case 'tool': {\n        if (currentBlock?.type !== 'user') {\n          currentBlock = { type: 'user', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push(message);\n        break;\n      }\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return blocks;\n}\n","import { LanguageModelV1FinishReason } from '@ai-sdk/provider';\n\nexport function mapAnthropicStopReason(\n  finishReason: string | null | undefined,\n): LanguageModelV1FinishReason {\n  switch (finishReason) {\n    case 'end_turn':\n    case 'stop_sequence':\n      return 'stop';\n    case 'tool_use':\n      return 'tool-calls';\n    case 'max_tokens':\n      return 'length';\n    default:\n      return 'unknown';\n  }\n}\n","import { z } from 'zod';\n\n// Copied from ai package\ntype ExecuteFunction<PARAMETERS, RESULT> =\n  | undefined\n  | ((\n      args: PARAMETERS,\n      options: { abortSignal?: AbortSignal },\n    ) => Promise<RESULT>);\n\n// Copied from ai package\nexport type ToolResultContent = Array<\n  | {\n      type: 'text';\n      text: string;\n    }\n  | {\n      type: 'image';\n      data: string; // base64 encoded png image, e.g. screenshot\n      mimeType?: string; // e.g. 'image/png';\n    }\n>;\n\nconst Bash20241022Parameters = z.object({\n  command: z.string(),\n  restart: z.boolean().optional(),\n});\n\n/**\n * Creates a tool for running a bash command. Must have name \"bash\".\n *\n * Image results are supported.\n *\n * @param execute - The function to execute the tool. Optional.\n */\nfunction bashTool_20241022<RESULT>(\n  options: {\n    execute?: ExecuteFunction<\n      {\n        /**\n         * The bash command to run. Required unless the tool is being restarted.\n         */\n        command: string;\n\n        /**\n         * Specifying true will restart this tool. Otherwise, leave this unspecified.\n         */\n        restart?: boolean;\n      },\n      RESULT\n    >;\n    experimental_toToolResultContent?: (result: RESULT) => ToolResultContent;\n  } = {},\n): {\n  type: 'provider-defined';\n  id: 'anthropic.bash_20241022';\n  args: {};\n  parameters: typeof Bash20241022Parameters;\n  execute: ExecuteFunction<z.infer<typeof Bash20241022Parameters>, RESULT>;\n  experimental_toToolResultContent?: (result: RESULT) => ToolResultContent;\n} {\n  return {\n    type: 'provider-defined',\n    id: 'anthropic.bash_20241022',\n    args: {},\n    parameters: Bash20241022Parameters,\n    execute: options.execute,\n    experimental_toToolResultContent: options.experimental_toToolResultContent,\n  };\n}\n\nconst Bash20250124Parameters = z.object({\n  command: z.string(),\n  restart: z.boolean().optional(),\n});\n\n/**\n * Creates a tool for running a bash command. Must have name \"bash\".\n *\n * Image results are supported.\n *\n * @param execute - The function to execute the tool. Optional.\n */\nfunction bashTool_20250124<RESULT>(\n  options: {\n    execute?: ExecuteFunction<\n      {\n        /**\n         * The bash command to run. Required unless the tool is being restarted.\n         */\n        command: string;\n\n        /**\n         * Specifying true will restart this tool. Otherwise, leave this unspecified.\n         */\n        restart?: boolean;\n      },\n      RESULT\n    >;\n    experimental_toToolResultContent?: (result: RESULT) => ToolResultContent;\n  } = {},\n): {\n  type: 'provider-defined';\n  id: 'anthropic.bash_20250124';\n  args: {};\n  parameters: typeof Bash20250124Parameters;\n  execute: ExecuteFunction<z.infer<typeof Bash20250124Parameters>, RESULT>;\n  experimental_toToolResultContent?: (result: RESULT) => ToolResultContent;\n} {\n  return {\n    type: 'provider-defined',\n    id: 'anthropic.bash_20250124',\n    args: {},\n    parameters: Bash20250124Parameters,\n    execute: options.execute,\n    experimental_toToolResultContent: options.experimental_toToolResultContent,\n  };\n}\n\nconst TextEditor20241022Parameters = z.object({\n  command: z.enum(['view', 'create', 'str_replace', 'insert', 'undo_edit']),\n  path: z.string(),\n  file_text: z.string().optional(),\n  insert_line: z.number().int().optional(),\n  new_str: z.string().optional(),\n  old_str: z.string().optional(),\n  view_range: z.array(z.number().int()).optional(),\n});\n\n/**\n * Creates a tool for editing text. Must have name \"str_replace_editor\".\n *\n * Image results are supported.\n *\n * @param execute - The function to execute the tool. Optional.\n */\nfunction textEditorTool_20241022<RESULT>(\n  options: {\n    execute?: ExecuteFunction<\n      {\n        /**\n         * The commands to run. Allowed options are: `view`, `create`, `str_replace`, `insert`, `undo_edit`.\n         */\n        command: 'view' | 'create' | 'str_replace' | 'insert' | 'undo_edit';\n\n        /**\n         * Absolute path to file or directory, e.g. `/repo/file.py` or `/repo`.\n         */\n        path: string;\n\n        /**\n         * Required parameter of `create` command, with the content of the file to be created.\n         */\n        file_text?: string;\n\n        /**\n         * Required parameter of `insert` command. The `new_str` will be inserted AFTER the line `insert_line` of `path`.\n         */\n        insert_line?: number;\n\n        /**\n         * Optional parameter of `str_replace` command containing the new string (if not given, no string will be added). Required parameter of `insert` command containing the string to insert.\n         */\n        new_str?: string;\n\n        /**\n         * Required parameter of `str_replace` command containing the string in `path` to replace.\n         */\n        old_str?: string;\n\n        /**\n         * Optional parameter of `view` command when `path` points to a file. If none is given, the full file is shown. If provided, the file will be shown in the indicated line number range, e.g. [11, 12] will show lines 11 and 12. Indexing at 1 to start. Setting `[start_line, -1]` shows all lines from `start_line` to the end of the file.\n         */\n        view_range?: number[];\n      },\n      RESULT\n    >;\n    experimental_toToolResultContent?: (result: RESULT) => ToolResultContent;\n  } = {},\n): {\n  type: 'provider-defined';\n  id: 'anthropic.text_editor_20241022';\n  args: {};\n  parameters: typeof TextEditor20241022Parameters;\n  execute: ExecuteFunction<\n    z.infer<typeof TextEditor20241022Parameters>,\n    RESULT\n  >;\n  experimental_toToolResultContent?: (result: RESULT) => ToolResultContent;\n} {\n  return {\n    type: 'provider-defined',\n    id: 'anthropic.text_editor_20241022',\n    args: {},\n    parameters: TextEditor20241022Parameters,\n    execute: options.execute,\n    experimental_toToolResultContent: options.experimental_toToolResultContent,\n  };\n}\n\nconst TextEditor20250124Parameters = z.object({\n  command: z.enum(['view', 'create', 'str_replace', 'insert', 'undo_edit']),\n  path: z.string(),\n  file_text: z.string().optional(),\n  insert_line: z.number().int().optional(),\n  new_str: z.string().optional(),\n  old_str: z.string().optional(),\n  view_range: z.array(z.number().int()).optional(),\n});\n\n/**\n * Creates a tool for editing text. Must have name \"str_replace_editor\".\n *\n * Image results are supported.\n *\n * @param execute - The function to execute the tool. Optional.\n */\nfunction