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

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

{"version":3,"sources":["../src/anthropic-provider.ts","../src/version.ts","../src/anthropic-messages-language-model.ts","../src/anthropic-error.ts","../src/anthropic-messages-api.ts","../src/anthropic-messages-options.ts","../src/anthropic-prepare-tools.ts","../src/get-cache-control.ts","../src/tool/text-editor_20250728.ts","../src/tool/web-search_20260209.ts","../src/tool/web-search_20250305.ts","../src/tool/web-fetch-20260209.ts","../src/tool/web-fetch-20250910.ts","../src/convert-anthropic-messages-usage.ts","../src/convert-to-anthropic-messages-prompt.ts","../src/tool/code-execution_20250522.ts","../src/tool/code-execution_20250825.ts","../src/tool/code-execution_20260120.ts","../src/tool/tool-search-regex_20251119.ts","../src/map-anthropic-stop-reason.ts","../src/tool/bash_20241022.ts","../src/tool/bash_20250124.ts","../src/tool/computer_20241022.ts","../src/tool/computer_20250124.ts","../src/tool/computer_20251124.ts","../src/tool/memory_20250818.ts","../src/tool/text-editor_20241022.ts","../src/tool/text-editor_20250124.ts","../src/tool/text-editor_20250429.ts","../src/tool/tool-search-bm25_20251119.ts","../src/anthropic-tools.ts","../src/forward-anthropic-container-id-from-last-step.ts"],"sourcesContent":["import {\n  InvalidArgumentError,\n  LanguageModelV3,\n  NoSuchModelError,\n  ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  generateId,\n  loadApiKey,\n  loadOptionalSetting,\n  withoutTrailingSlash,\n  withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { AnthropicMessagesLanguageModel } from './anthropic-messages-language-model';\nimport { AnthropicMessagesModelId } from './anthropic-messages-options';\nimport { anthropicTools } from './anthropic-tools';\n\nexport interface AnthropicProvider extends ProviderV3 {\n  /**\n   * Creates a model for text generation.\n   */\n  (modelId: AnthropicMessagesModelId): LanguageModelV3;\n\n  /**\n   * Creates a model for text generation.\n   */\n  languageModel(modelId: AnthropicMessagesModelId): LanguageModelV3;\n\n  chat(modelId: AnthropicMessagesModelId): LanguageModelV3;\n\n  messages(modelId: AnthropicMessagesModelId): LanguageModelV3;\n\n  /**\n   * @deprecated Use `embeddingModel` instead.\n   */\n  textEmbeddingModel(modelId: string): never;\n\n  /**\n   * Anthropic-specific computer use tool.\n   */\n  tools: typeof anthropicTools;\n}\n\nexport interface AnthropicProviderSettings {\n  /**\n   * Use a different URL prefix for API calls, e.g. to use proxy servers.\n   * The default prefix is `https://api.anthropic.com/v1`.\n   */\n  baseURL?: string;\n\n  /**\n   * API key that is being send using the `x-api-key` header.\n   * It defaults to the `ANTHROPIC_API_KEY` environment variable.\n   * Only one of `apiKey` or `authToken` is required.\n   */\n  apiKey?: string;\n\n  /**\n   * Auth token that is being sent using the `Authorization: Bearer` header.\n   * It defaults to the `ANTHROPIC_AUTH_TOKEN` environment variable.\n   * Only one of `apiKey` or `authToken` is required.\n   */\n  authToken?: string;\n\n  /**\n   * Custom headers to include in the requests.\n   */\n  headers?: Record<string, string>;\n\n  /**\n   * Custom fetch implementation. You can use it as a middleware to intercept requests,\n   * or to provide a custom fetch implementation for e.g. testing.\n   */\n  fetch?: FetchFunction;\n\n  generateId?: () => string;\n\n  /**\n   * Custom provider name\n   * Defaults to 'anthropic.messages'.\n   */\n  name?: string;\n}\n\n/**\n * Create an Anthropic provider instance.\n */\nexport function createAnthropic(\n  options: AnthropicProviderSettings = {},\n): AnthropicProvider {\n  const baseURL =\n    withoutTrailingSlash(\n      loadOptionalSetting({\n        settingValue: options.baseURL,\n        environmentVariableName: 'ANTHROPIC_BASE_URL',\n      }),\n    ) ?? 'https://api.anthropic.com/v1';\n\n  const providerName = options.name ?? 'anthropic.messages';\n\n  // Only error if both are explicitly provided in options\n  if (options.apiKey && options.authToken) {\n    throw new InvalidArgumentError({\n      argument: 'apiKey/authToken',\n      message:\n        'Both apiKey and authToken were provided. Please use only one authentication method.',\n    });\n  }\n\n  const getHeaders = () => {\n    const authHeaders: Record<string, string> = options.authToken\n      ? { Authorization: `Bearer ${options.authToken}` }\n      : {\n          'x-api-key': loadApiKey({\n            apiKey: options.apiKey,\n            environmentVariableName: 'ANTHROPIC_API_KEY',\n            description: 'Anthropic',\n          }),\n        };\n\n    return withUserAgentSuffix(\n      {\n        'anthropic-version': '2023-06-01',\n        ...authHeaders,\n        ...options.headers,\n      },\n      `ai-sdk/anthropic/${VERSION}`,\n    );\n  };\n\n  const createChatModel = (modelId: AnthropicMessagesModelId) =>\n    new AnthropicMessagesLanguageModel(modelId, {\n      provider: providerName,\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n      generateId: options.generateId ?? generateId,\n      supportedUrls: () => ({\n        'image/*': [/^https?:\\/\\/.*$/],\n        'application/pdf': [/^https?:\\/\\/.*$/],\n      }),\n    });\n\n  const provider = function (modelId: AnthropicMessagesModelId) {\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);\n  };\n\n  provider.specificationVersion = 'v3' as const;\n  provider.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.messages = createChatModel;\n\n  provider.embeddingModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'embeddingModel' });\n  };\n  provider.textEmbeddingModel = provider.embeddingModel;\n  provider.imageModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n  };\n\n  provider.tools = anthropicTools;\n\n  return provider;\n}\n\n/**\n * Default Anthropic provider instance.\n */\nexport const anthropic = createAnthropic();\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  APICallError,\n  JSONObject,\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3FunctionTool,\n  LanguageModelV3GenerateResult,\n  LanguageModelV3Prompt,\n  LanguageModelV3Source,\n  LanguageModelV3StreamPart,\n  LanguageModelV3StreamResult,\n  LanguageModelV3ToolCall,\n  SharedV3ProviderMetadata,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  createToolNameMapping,\n  FetchFunction,\n  generateId,\n  InferSchema,\n  parseProviderOptions,\n  ParseResult,\n  postJsonToApi,\n  Resolvable,\n  resolve,\n} from '@ai-sdk/provider-utils';\nimport { anthropicFailedResponseHandler } from './anthropic-error';\nimport { AnthropicMessageMetadata } from './anthropic-message-metadata';\nimport {\n  AnthropicContainer,\n  anthropicMessagesChunkSchema,\n  anthropicMessagesResponseSchema,\n  AnthropicReasoningMetadata,\n  AnthropicResponseContextManagement,\n  AnthropicTool,\n  Citation,\n} from './anthropic-messages-api';\nimport {\n  AnthropicMessagesModelId,\n  anthropicLanguageModelOptions,\n} from './anthropic-messages-options';\nimport { prepareTools } from './anthropic-prepare-tools';\nimport {\n  AnthropicMessagesUsage,\n  convertAnthropicMessagesUsage,\n} from './convert-anthropic-messages-usage';\nimport { convertToAnthropicMessagesPrompt } from './convert-to-anthropic-messages-prompt';\nimport { CacheControlValidator } from './get-cache-control';\nimport { mapAnthropicStopReason } from './map-anthropic-stop-reason';\n\nfunction createCitationSource(\n  citation: Citation,\n  citationDocuments: Array<{\n    title: string;\n    filename?: string;\n    mediaType: string;\n  }>,\n  generateId: () => string,\n): LanguageModelV3Source | undefined {\n  if (citation.type === 'web_search_result_location') {\n    return {\n      type: 'source' as const,\n      sourceType: 'url' as const,\n      id: generateId(),\n      url: citation.url,\n      title: citation.title,\n      providerMetadata: {\n        anthropic: {\n          citedText: citation.cited_text,\n          encryptedIndex: citation.encrypted_index,\n        },\n      } satisfies SharedV3ProviderMetadata,\n    };\n  }\n\n  if (citation.type !== 'page_location' && citation.type !== 'char_location') {\n    return;\n  }\n\n  const documentInfo = citationDocuments[citation.document_index];\n\n  if (!documentInfo) {\n    return;\n  }\n\n  return {\n    type: 'source' as const,\n    sourceType: 'document' as const,\n    id: generateId(),\n    mediaType: documentInfo.mediaType,\n    title: citation.document_title ?? documentInfo.title,\n    filename: documentInfo.filename,\n    providerMetadata: {\n      anthropic:\n        citation.type === 'page_location'\n          ? {\n              citedText: citation.cited_text,\n              startPageNumber: citation.start_page_number,\n              endPageNumber: citation.end_page_number,\n            }\n          : {\n              citedText: citation.cited_text,\n              startCharIndex: citation.start_char_index,\n              endCharIndex: citation.end_char_index,\n            },\n    } satisfies SharedV3ProviderMetadata,\n  };\n}\n\ntype AnthropicMessagesConfig = {\n  provider: string;\n  baseURL: string;\n  headers: Resolvable<Record<string, string | undefined>>;\n  fetch?: FetchFunction;\n  buildRequestUrl?: (baseURL: string, isStreaming: boolean) => string;\n  transformRequestBody?: (\n    args: Record<string, any>,\n    betas: Set<string>,\n  ) => Record<string, any>;\n  supportedUrls?: () => LanguageModelV3['supportedUrls'];\n  generateId?: () => string;\n\n  /**\n   * When false, the model will use JSON tool fallback for structured outputs.\n   */\n  supportsNativeStructuredOutput?: boolean;\n};\n\nexport class AnthropicMessagesLanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3';\n\n  readonly modelId: AnthropicMessagesModelId;\n\n  private readonly config: AnthropicMessagesConfig;\n  private readonly generateId: () => string;\n\n  constructor(\n    modelId: AnthropicMessagesModelId,\n    config: AnthropicMessagesConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n    this.generateId = config.generateId ?? generateId;\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  /**\n   * Extracts the dynamic provider name from the config.provider string.\n   * e.g., 'my-custom-anthropic.messages' -> 'my-custom-anthropic'\n   */\n  private get providerOptionsName(): string {\n    const provider = this.config.provider;\n    const dotIndex = provider.indexOf('.');\n    return dotIndex === -1 ? provider : provider.substring(0, dotIndex);\n  }\n\n  get supportedUrls() {\n    return this.config.supportedUrls?.() ?? {};\n  }\n\n  private async getArgs({\n    userSuppliedBetas,\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    stream,\n  }: LanguageModelV3CallOptions & {\n    stream: boolean;\n    userSuppliedBetas: Set<string>;\n  }) {\n    const warnings: SharedV3Warning[] = [];\n\n    if (frequencyPenalty != null) {\n      warnings.push({ type: 'unsupported', feature: 'frequencyPenalty' });\n    }\n\n    if (presencePenalty != null) {\n      warnings.push({ type: 'unsupported', feature: 'presencePenalty' });\n    }\n\n    if (seed != null) {\n      warnings.push({ type: 'unsupported', feature: 'seed' });\n    }\n\n    if (temperature != null && temperature > 1) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'temperature',\n        details: `${temperature} exceeds anthropic maximum of 1.0. clamped to 1.0`,\n      });\n      temperature = 1;\n    } else if (temperature != null && temperature < 0) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'temperature',\n        details: `${temperature} is below anthropic minimum of 0. clamped to 0`,\n      });\n      temperature = 0;\n    }\n\n    if (responseFormat?.type === 'json') {\n      if (responseFormat.schema == null) {\n        warnings.push({\n          type: 'unsupported',\n          feature: 'responseFormat',\n          details:\n            'JSON response format requires a schema. ' +\n            'The response format is ignored.',\n        });\n      }\n    }\n\n    const providerOptionsName = this.providerOptionsName;\n\n    // Parse provider options from both canonical 'anthropic' key and custom key\n    const canonicalOptions = await parseProviderOptions({\n      provider: 'anthropic',\n      providerOptions,\n      schema: anthropicLanguageModelOptions,\n    });\n\n    const customProviderOptions =\n      providerOptionsName !== 'anthropic'\n        ? await parseProviderOptions({\n            provider: providerOptionsName,\n            providerOptions,\n            schema: anthropicLanguageModelOptions,\n          })\n        : null;\n\n    // Track if custom key was explicitly used\n    const usedCustomProviderKey = customProviderOptions != null;\n\n    // Merge options\n    const anthropicOptions = Object.assign(\n      {},\n      canonicalOptions ?? {},\n      customProviderOptions ?? {},\n    );\n\n    const {\n      maxOutputTokens: maxOutputTokensForModel,\n      supportsStructuredOutput: modelSupportsStructuredOutput,\n      isKnownModel,\n    } = getModelCapabilities(this.modelId);\n\n    const supportsStructuredOutput =\n      (this.config.supportsNativeStructuredOutput ?? true) &&\n      modelSupportsStructuredOutput;\n\n    const structureOutputMode =\n      anthropicOptions?.structuredOutputMode ?? 'auto';\n    const useStructuredOutput =\n      structureOutputMode === 'outputFormat' ||\n      (structureOutputMode === 'auto' && supportsStructuredOutput);\n\n    const jsonResponseTool: LanguageModelV3FunctionTool | undefined =\n      responseFormat?.type === 'json' &&\n      responseFormat.schema != null &&\n      !useStructuredOutput\n        ? {\n            type: 'function',\n            name: 'json',\n            description: 'Respond with a JSON object.',\n            inputSchema: responseFormat.schema,\n          }\n        : undefined;\n\n    const contextManagement = anthropicOptions?.contextManagement;\n\n    // Create a shared cache control validator to track breakpoints across tools and messages\n    const cacheControlValidator = new CacheControlValidator();\n\n    const toolNameMapping = createToolNameMapping({\n      tools,\n      providerToolNames: {\n        'anthropic.code_execution_20250522': 'code_execution',\n        'anthropic.code_execution_20250825': 'code_execution',\n        'anthropic.code_execution_20260120': 'code_execution',\n        'anthropic.computer_20241022': 'computer',\n        'anthropic.computer_20250124': 'computer',\n        'anthropic.text_editor_20241022': 'str_replace_editor',\n        'anthropic.text_editor_20250124': 'str_replace_editor',\n        'anthropic.text_editor_20250429': 'str_replace_based_edit_tool',\n        'anthropic.text_editor_20250728': 'str_replace_based_edit_tool',\n        'anthropic.bash_20241022': 'bash',\n        'anthropic.bash_20250124': 'bash',\n        'anthropic.memory_20250818': 'memory',\n        'anthropic.web_search_20250305': 'web_search',\n        'anthropic.web_search_20260209': 'web_search',\n        'anthropic.web_fetch_20250910': 'web_fetch',\n        'anthropic.web_fetch_20260209': 'web_fetch',\n        'anthropic.tool_search_regex_20251119': 'tool_search_tool_regex',\n        'anthropic.tool_search_bm25_20251119': 'tool_search_tool_bm25',\n      },\n    });\n\n    const { prompt: messagesPrompt, betas } =\n      await convertToAnthropicMessagesPrompt({\n        prompt,\n        sendReasoning: anthropicOptions?.sendReasoning ?? true,\n        warnings,\n        cacheControlValidator,\n        toolNameMapping,\n      });\n\n    const thinkingType = anthropicOptions?.thinking?.type;\n    const isThinking =\n      thinkingType === 'enabled' || thinkingType === 'adaptive';\n    let thinkingBudget =\n      thinkingType === 'enabled'\n        ? anthropicOptions?.thinking?.budgetTokens\n        : undefined;\n\n    const maxTokens = maxOutputTokens ?? maxOutputTokensForModel;\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: {\n          type: thinkingType,\n          ...(thinkingBudget != null && { budget_tokens: thinkingBudget }),\n        },\n      }),\n      ...((anthropicOptions?.effort ||\n        (useStructuredOutput &&\n          responseFormat?.type === 'json' &&\n          responseFormat.schema != null)) && {\n        output_config: {\n          ...(anthropicOptions?.effort && {\n            effort: anthropicOptions.effort,\n          }),\n          ...(useStructuredOutput &&\n            responseFormat?.type === 'json' &&\n            responseFormat.schema != null && {\n              format: {\n                type: 'json_schema',\n                schema: responseFormat.schema,\n              },\n            }),\n        },\n      }),\n      ...(anthropicOptions?.speed && {\n        speed: anthropicOptions.speed,\n      }),\n      ...(anthropicOptions?.cacheControl && {\n        cache_control: anthropicOptions.cacheControl,\n      }),\n\n      // mcp servers:\n      ...(anthropicOptions?.mcpServers &&\n        anthropicOptions.mcpServers.length > 0 && {\n          mcp_servers: anthropicOptions.mcpServers.map(server => ({\n            type: server.type,\n            name: server.name,\n            url: server.url,\n            authorization_token: server.authorizationToken,\n            tool_configuration: server.toolConfiguration\n              ? {\n                  allowed_tools: server.toolConfiguration.allowedTools,\n                  enabled: server.toolConfiguration.enabled,\n                }\n              : undefined,\n          })),\n        }),\n\n      // container: For programmatic tool calling (just an ID string) or agent skills (object with id and skills)\n      ...(anthropicOptions?.container && {\n        container:\n          anthropicOptions.container.skills &&\n          anthropicOptions.container.skills.length > 0\n            ? // Object format when skills are provided (agent skills feature)\n              ({\n                id: anthropicOptions.container.id,\n                skills: anthropicOptions.container.skills.map(skill => ({\n                  type: skill.type,\n                  skill_id: skill.skillId,\n                  version: skill.version,\n                })),\n              } satisfies AnthropicContainer)\n            : // String format for container ID only (programmatic tool calling)\n              anthropicOptions.container.id,\n      }),\n\n      // prompt:\n      system: messagesPrompt.system,\n      messages: messagesPrompt.messages,\n\n      ...(contextManagement && {\n        context_management: {\n          edits: contextManagement.edits\n            .map(edit => {\n              const strategy = edit.type;\n              switch (strategy) {\n                case 'clear_tool_uses_20250919':\n                  return {\n                    type: edit.type,\n                    ...(edit.trigger !== undefined && {\n                      trigger: edit.trigger,\n                    }),\n                    ...(edit.keep !== undefined && { keep: edit.keep }),\n                    ...(edit.clearAtLeast !== undefined && {\n                      clear_at_least: edit.clearAtLeast,\n                    }),\n                    ...(edit.clearToolInputs !== undefined && {\n                      clear_tool_inputs: edit.clearToolInputs,\n                    }),\n                    ...(edit.excludeTools !== undefined && {\n                      exclude_tools: edit.excludeTools,\n                    }),\n                  };\n\n                case 'clear_thinking_20251015':\n                  return {\n                    type: edit.type,\n                    ...(edit.keep !== undefined && { keep: edit.keep }),\n                  };\n\n                case 'compact_20260112':\n                  return {\n                    type: edit.type,\n                    ...(edit.trigger !== undefined && {\n                      trigger: edit.trigger,\n                    }),\n                    ...(edit.pauseAfterCompaction !== undefined && {\n                      pause_after_compaction: edit.pauseAfterCompaction,\n                    }),\n                    ...(edit.instructions !== undefined && {\n                      instructions: edit.instructions,\n                    }),\n                  };\n\n                default:\n                  warnings.push({\n                    type: 'other',\n                    message: `Unknown context management strategy: ${strategy}`,\n                  });\n                  return undefined;\n              }\n            })\n            .filter(edit => edit !== undefined),\n        },\n      }),\n    };\n\n    if (isThinking) {\n      if (thinkingType === 'enabled' && thinkingBudget == null) {\n        warnings.push({\n          type: 'compatibility',\n          feature: 'extended thinking',\n          details:\n            'thinking budget is required when thinking is enabled. using default budget of 1024 tokens.',\n        });\n\n        baseArgs.thinking = {\n          type: 'enabled',\n          budget_tokens: 1024,\n        };\n\n        thinkingBudget = 1024;\n      }\n\n      if (baseArgs.temperature != null) {\n        baseArgs.temperature = undefined;\n        warnings.push({\n          type: 'unsupported',\n          feature: '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',\n          feature: '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',\n          feature: '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 ?? 0);\n    } else {\n      // Only check temperature/topP mutual exclusivity when thinking is not enabled\n      if (topP != null && temperature != null) {\n        warnings.push({\n          type: 'unsupported',\n          feature: 'topP',\n          details: `topP is not supported when temperature is set. topP is ignored.`,\n        });\n        baseArgs.top_p = undefined;\n      }\n    }\n\n    // limit to max output tokens for known models to enable model switching without breaking it:\n    if (isKnownModel && baseArgs.max_tokens > maxOutputTokensForModel) {\n      // only warn if max output tokens is provided as input:\n      if (maxOutputTokens != null) {\n        warnings.push({\n          type: 'unsupported',\n          feature: 'maxOutputTokens',\n          details:\n            `${baseArgs.max_tokens} (maxOutputTokens + thinkingBudget) is greater than ${this.modelId} ${maxOutputTokensForModel} max output tokens. ` +\n            `The max output tokens have been limited to ${maxOutputTokensForModel}.`,\n        });\n      }\n      baseArgs.max_tokens = maxOutputTokensForModel;\n    }\n\n    if (\n      anthropicOptions?.mcpServers &&\n      anthropicOptions.mcpServers.length > 0\n    ) {\n      betas.add('mcp-client-2025-04-04');\n    }\n\n    if (contextManagement) {\n      betas.add('context-management-2025-06-27');\n\n      // Add compaction beta if compact edit is present\n      if (contextManagement.edits.some(e => e.type === 'compact_20260112')) {\n        betas.add('compact-2026-01-12');\n      }\n    }\n\n    if (\n      anthropicOptions?.container &&\n      anthropicOptions.container.skills &&\n      anthropicOptions.container.skills.length > 0\n    ) {\n      betas.add('code-execution-2025-08-25');\n      betas.add('skills-2025-10-02');\n      betas.add('files-api-2025-04-14');\n\n      if (\n        !tools?.some(\n          tool =>\n            tool.type === 'provider' &&\n            (tool.id === 'anthropic.code_execution_20250825' ||\n              tool.id === 'anthropic.code_execution_20260120'),\n        )\n      ) {\n        warnings.push({\n          type: 'other',\n          message: 'code execution tool is required when using skills',\n        });\n      }\n    }\n\n    if (anthropicOptions?.effort) {\n      betas.add('effort-2025-11-24');\n    }\n\n    if (anthropicOptions?.speed === 'fast') {\n      betas.add('fast-mode-2026-02-01');\n    }\n\n    // only when streaming: enable fine-grained tool streaming\n    if (stream && (anthropicOptions?.toolStreaming ?? true)) {\n      betas.add('fine-grained-tool-streaming-2025-05-14');\n    }\n\n    const {\n      tools: anthropicTools,\n      toolChoice: anthropicToolChoice,\n      toolWarnings,\n      betas: toolsBetas,\n    } = await prepareTools(\n      jsonResponseTool != null\n        ? {\n            tools: [...(tools ?? []), jsonResponseTool],\n            toolChoice: { type: 'required' },\n            disableParallelToolUse: true,\n            cacheControlValidator,\n            supportsStructuredOutput: false,\n          }\n        : {\n            tools: tools ?? [],\n            toolChoice,\n            disableParallelToolUse: anthropicOptions?.disableParallelToolUse,\n            cacheControlValidator,\n            supportsStructuredOutput,\n          },\n    );\n\n    // Extract cache control warnings once at the end\n    const cacheWarnings = cacheControlValidator.getWarnings();\n\n    return {\n      args: {\n        ...baseArgs,\n        tools: anthropicTools,\n        tool_choice: anthropicToolChoice,\n        stream: stream === true ? true : undefined, // do not send when not streaming\n      },\n      warnings: [...warnings, ...toolWarnings, ...cacheWarnings],\n      betas: new Set([\n        ...betas,\n        ...toolsBetas,\n        ...userSuppliedBetas,\n        ...(anthropicOptions?.anthropicBeta ?? []),\n      ]),\n      usesJsonResponseTool: jsonResponseTool != null,\n      toolNameMapping,\n      providerOptionsName,\n      usedCustomProviderKey,\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      headers,\n      betas.size > 0 ? { 'anthropic-beta': Array.from(betas).join(',') } : {},\n    );\n  }\n\n  private async getBetasFromHeaders(\n    requestHeaders: Record<string, string | undefined> | undefined,\n  ) {\n    const configHeaders = await resolve(this.config.headers);\n\n    const configBetaHeader = configHeaders['anthropic-beta'] ?? '';\n    const requestBetaHeader = requestHeaders?.['anthropic-beta'] ?? '';\n\n    return new Set(\n      [\n        ...configBetaHeader.toLowerCase().split(','),\n        ...requestBetaHeader.toLowerCase().split(','),\n      ]\n        .map(beta => beta.trim())\n        .filter(beta => beta !== ''),\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(\n    args: Record<string, any>,\n    betas: Set<string>,\n  ): Record<string, any> {\n    return this.config.transformRequestBody?.(args, betas) ?? args;\n  }\n\n  private extractCitationDocuments(prompt: LanguageModelV3Prompt): Array<{\n    title: string;\n    filename?: string;\n    mediaType: string;\n  }> {\n    const isCitationPart = (part: {\n      type: string;\n      mediaType?: string;\n      providerOptions?: { anthropic?: { citations?: { enabled?: boolean } } };\n    }) => {\n      if (part.type !== 'file') {\n        return false;\n      }\n\n      if (\n        part.mediaType !== 'application/pdf' &&\n        part.mediaType !== 'text/plain'\n      ) {\n        return false;\n      }\n\n      const anthropic = part.providerOptions?.anthropic;\n      const citationsConfig = anthropic?.citations as\n        | { enabled?: boolean }\n        | undefined;\n      return citationsConfig?.enabled ?? false;\n    };\n\n    return prompt\n      .filter(message => message.role === 'user')\n      .flatMap(message => message.content)\n      .filter(isCitationPart)\n      .map(part => {\n        // TypeScript knows this is a file part due to our filter\n        const filePart = part as Extract<typeof part, { type: 'file' }>;\n        return {\n          title: filePart.filename ?? 'Untitled Document',\n          filename: filePart.filename,\n          mediaType: filePart.mediaType,\n        };\n      });\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3GenerateResult> {\n    const {\n      args,\n      warnings,\n      betas,\n      usesJsonResponseTool,\n      toolNameMapping,\n      providerOptionsName,\n      usedCustomProviderKey,\n    } = await this.getArgs({\n      ...options,\n      stream: false,\n      userSuppliedBetas: await this.getBetasFromHeaders(options.headers),\n    });\n\n    // Extract citation documents for response processing\n    const citationDocuments = [\n      ...this.extractCitationDocuments(options.prompt),\n    ];\n\n    const markCodeExecutionDynamic = hasWebTool20260209WithoutCodeExecution(\n      args.tools,\n    );\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, betas),\n      failedResponseHandler: anthropicFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        anthropicMessagesResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const content: Array<LanguageModelV3Content> = [];\n    const mcpToolCalls: Record<string, LanguageModelV3ToolCall> = {};\n    const serverToolCalls: Record<string, string> = {}; // tool_use_id -> provider tool name\n    let isJsonResponseFromTool = false;\n\n    // map response content to content array\n    for (const part of response.content) {\n      switch (part.type) {\n        case 'text': {\n          if (!usesJsonResponseTool) {\n            content.push({ type: 'text', text: part.text });\n\n            // Process citations if present\n            if (part.citations) {\n              for (const citation of part.citations) {\n                const source = createCitationSource(\n                  citation,\n                  citationDocuments,\n                  this.generateId,\n                );\n\n                if (source) {\n                  content.push(source);\n                }\n              }\n            }\n          }\n          break;\n        }\n        case 'thinking': {\n          content.push({\n            type: 'reasoning',\n            text: part.thinking,\n            providerMetadata: {\n              anthropic: {\n                signature: part.signature,\n              } satisfies AnthropicReasoningMetadata,\n            },\n          });\n          break;\n        }\n        case 'redacted_thinking': {\n          content.push({\n            type: 'reasoning',\n            text: '',\n            providerMetadata: {\n              anthropic: {\n                redactedData: part.data,\n              } satisfies AnthropicReasoningMetadata,\n            },\n          });\n          break;\n        }\n        case 'compaction': {\n          content.push({\n            type: 'text',\n            text: part.content,\n            providerMetadata: {\n              anthropic: {\n                type: 'compaction',\n              },\n            },\n          });\n          break;\n        }\n        case 'tool_use': {\n          const isJsonResponseTool =\n            usesJsonResponseTool && part.name === 'json';\n\n          if (isJsonResponseTool) {\n            isJsonResponseFromTool = true;\n\n            // when a json response tool is used, the tool call becomes the text:\n            content.push({\n              type: 'text',\n              text: JSON.stringify(part.input),\n            });\n          } else {\n            const caller = part.caller;\n            const callerInfo = caller\n              ? {\n                  type: caller.type,\n                  toolId: 'tool_id' in caller ? caller.tool_id : undefined,\n                }\n              : undefined;\n\n            content.push({\n              type: 'tool-call',\n              toolCallId: part.id,\n              toolName: part.name,\n              input: JSON.stringify(part.input),\n              ...(callerInfo && {\n                providerMetadata: {\n                  anthropic: {\n                    caller: callerInfo,\n                  },\n                },\n              }),\n            });\n          }\n\n          break;\n        }\n        case 'server_tool_use': {\n          // code execution 20250825 needs mapping:\n          if (\n            part.name === 'text_editor_code_execution' ||\n            part.name === 'bash_code_execution'\n          ) {\n            content.push({\n              type: 'tool-call',\n              toolCallId: part.id,\n              toolName: toolNameMapping.toCustomToolName('code_execution'),\n              input: JSON.stringify({ type: part.name, ...part.input }),\n              providerExecuted: true,\n            });\n          } else if (\n            part.name === 'web_search' ||\n            part.name === 'code_execution' ||\n            part.name === 'web_fetch'\n          ) {\n            // For code_execution, inject 'programmatic-tool-call' type when input has { code } format\n            const inputToSerialize =\n              part.name === 'code_execution' &&\n              part.input != null &&\n              typeof part.input === 'object' &&\n              'code' in part.input &&\n              !('type' in part.input)\n                ? { type: 'programmatic-tool-call', ...part.input }\n                : part.input;\n\n            content.push({\n              type: 'tool-call',\n              toolCallId: part.id,\n              toolName: toolNameMapping.toCustomToolName(part.name),\n              input: JSON.stringify(inputToSerialize),\n              providerExecuted: true,\n              // We want this 'code_execution' tool call to be allowed even if the tool is not explicitly provided.\n              // Since the validation generally bypasses dynamic tools, we mark this specific tool as dynamic.\n              ...(markCodeExecutionDynamic && part.name === 'code_execution'\n                ? { dynamic: true }\n                : {}),\n            });\n          } else if (\n            part.name === 'tool_search_tool_regex' ||\n            part.name === 'tool_search_tool_bm25'\n          ) {\n            serverToolCalls[part.id] = part.name;\n            content.push({\n              type: 'tool-call',\n              toolCallId: part.id,\n              toolName: toolNameMapping.toCustomToolName(part.name),\n              input: JSON.stringify(part.input),\n              providerExecuted: true,\n            });\n          }\n\n          break;\n        }\n        case 'mcp_tool_use': {\n          mcpToolCalls[part.id] = {\n            type: 'tool-call',\n            toolCallId: part.id,\n            toolName: part.name,\n            input: JSON.stringify(part.input),\n            providerExecuted: true,\n            dynamic: true,\n            providerMetadata: {\n              anthropic: {\n                type: 'mcp-tool-use',\n                serverName: part.server_name,\n              },\n            },\n          };\n          content.push(mcpToolCalls[part.id]);\n          break;\n        }\n        case 'mcp_tool_result': {\n          content.push({\n            type: 'tool-result',\n            toolCallId: part.tool_use_id,\n            toolName: mcpToolCalls[part.tool_use_id].toolName,\n            isError: part.is_error,\n            result: part.content,\n            dynamic: true,\n            providerMetadata: mcpToolCalls[part.tool_use_id].providerMetadata,\n          });\n          break;\n        }\n        case 'web_fetch_tool_result': {\n          if (part.content.type === 'web_fetch_result') {\n            citationDocuments.push({\n              title: part.content.content.title ?? part.content.url,\n              mediaType: part.content.content.source.media_type,\n            });\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName('web_fetch'),\n              result: {\n                type: 'web_fetch_result',\n                url: part.content.url,\n                retrievedAt: part.content.retrieved_at,\n                content: {\n                  type: part.content.content.type,\n                  title: part.content.content.title,\n                  citations: part.content.content.citations,\n                  source: {\n                    type: part.content.content.source.type,\n                    mediaType: part.content.content.source.media_type,\n                    data: part.content.content.source.data,\n                  },\n                },\n              },\n            });\n          } else if (part.content.type === 'web_fetch_tool_result_error') {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName('web_fetch'),\n              isError: true,\n              result: {\n                type: 'web_fetch_tool_result_error',\n                errorCode: part.content.error_code,\n              },\n            });\n          }\n          break;\n        }\n        case 'web_search_tool_result': {\n          if (Array.isArray(part.content)) {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName('web_search'),\n              result: part.content.map(result => ({\n                url: result.url,\n                title: result.title,\n                pageAge: result.page_age ?? null,\n                encryptedContent: result.encrypted_content,\n                type: result.type,\n              })),\n            });\n\n            for (const result of part.content) {\n              content.push({\n                type: 'source',\n                sourceType: 'url',\n                id: this.generateId(),\n                url: result.url,\n                title: result.title,\n                providerMetadata: {\n                  anthropic: {\n                    pageAge: result.page_age ?? null,\n                  },\n                },\n              });\n            }\n          } else {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName('web_search'),\n              isError: true,\n              result: {\n                type: 'web_search_tool_result_error',\n                errorCode: part.content.error_code,\n              },\n            });\n          }\n          break;\n        }\n\n        // code execution 20250522:\n        case 'code_execution_tool_result': {\n          if (part.content.type === 'code_execution_result') {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName('code_execution'),\n              result: {\n                type: part.content.type,\n                stdout: part.content.stdout,\n                stderr: part.content.stderr,\n                return_code: part.content.return_code,\n                content: part.content.content ?? [],\n              },\n            });\n          } else if (part.content.type === 'encrypted_code_execution_result') {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName('code_execution'),\n              result: {\n                type: part.content.type,\n                encrypted_stdout: part.content.encrypted_stdout,\n                stderr: part.content.stderr,\n                return_code: part.content.return_code,\n                content: part.content.content ?? [],\n              },\n            });\n          } else if (part.content.type === 'code_execution_tool_result_error') {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName('code_execution'),\n              isError: true,\n              result: {\n                type: 'code_execution_tool_result_error',\n                errorCode: part.content.error_code,\n              },\n            });\n          }\n          break;\n        }\n\n        // code execution 20250825:\n        case 'bash_code_execution_tool_result':\n        case 'text_editor_code_execution_tool_result': {\n          content.push({\n            type: 'tool-result',\n            toolCallId: part.tool_use_id,\n            toolName: toolNameMapping.toCustomToolName('code_execution'),\n            result: part.content,\n          });\n          break;\n        }\n\n        // tool search tool results:\n        case 'tool_search_tool_result': {\n          let providerToolName = serverToolCalls[part.tool_use_id];\n\n          if (providerToolName == null) {\n            const bm25CustomName = toolNameMapping.toCustomToolName(\n              'tool_search_tool_bm25',\n            );\n            const regexCustomName = toolNameMapping.toCustomToolName(\n              'tool_search_tool_regex',\n            );\n\n            if (bm25CustomName !== 'tool_search_tool_bm25') {\n              providerToolName = 'tool_search_tool_bm25';\n            } else if (regexCustomName !== 'tool_search_tool_regex') {\n              providerToolName = 'tool_search_tool_regex';\n            } else {\n              providerToolName = 'tool_search_tool_regex';\n            }\n          }\n\n          if (part.content.type === 'tool_search_tool_search_result') {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName(providerToolName),\n              result: part.content.tool_references.map(ref => ({\n                type: ref.type,\n                toolName: ref.tool_name,\n              })),\n            });\n          } else {\n            content.push({\n              type: 'tool-result',\n              toolCallId: part.tool_use_id,\n              toolName: toolNameMapping.toCustomToolName(providerToolName),\n              isError: true,\n              result: {\n                type: 'tool_search_tool_result_error',\n                errorCode: part.content.error_code,\n              },\n            });\n          }\n          break;\n        }\n      }\n    }\n\n    return {\n      content,\n      finishReason: {\n        unified: mapAnthropicStopReason({\n          finishReason: response.stop_reason,\n          isJsonResponseFromTool,\n        }),\n        raw: response.stop_reason ?? undefined,\n      },\n      usage: convertAnthropicMessagesUsage({ usage: response.usage }),\n      request: { body: args },\n      response: {\n        id: response.id ?? undefined,\n        modelId: response.model ?? undefined,\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n      providerMetadata: (() => {\n        const anthropicMetadata = {\n          usage: response.usage as JSONObject,\n          cacheCreationInputTokens:\n            response.usage.cache_creation_input_tokens ?? null,\n          stopSequence: response.stop_sequence ?? null,\n\n          iterations: response.usage.iterations\n            ? response.usage.iterations.map(iter => ({\n                type: iter.type,\n                inputTokens: iter.input_tokens,\n                outputTokens: iter.output_tokens,\n              }))\n            : null,\n          container: response.container\n            ? {\n                expiresAt: response.container.expires_at,\n                id: response.container.id,\n                skills:\n                  response.container.skills?.map(skill => ({\n                    type: skill.type,\n                    skillId: skill.skill_id,\n                    version: skill.version,\n                  })) ?? null,\n              }\n            : null,\n          contextManagement:\n            mapAnthropicResponseContextManagement(\n              response.context_management,\n            ) ?? null,\n        } satisfies AnthropicMessageMetadata;\n\n        const providerMetadata: SharedV3ProviderMetadata = {\n          anthropic: anthropicMetadata,\n        };\n\n        if (usedCustomProviderKey && providerOptionsName !== 'anthropic') {\n          providerMetadata[providerOptionsName] = anthropicMetadata;\n        }\n\n        return providerMetadata;\n      })(),\n    };\n  }\n\n  async doStream(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3StreamResult> {\n    const {\n      args: body,\n      warnings,\n      betas,\n      usesJsonResponseTool,\n      toolNameMapping,\n      providerOptionsName,\n      usedCustomProviderKey,\n    } = await this.getArgs({\n      ...options,\n      stream: true,\n      userSuppliedBetas: await this.getBetasFromHeaders(options.headers),\n    });\n\n    // Extract citation documents for response processing\n    const citationDocuments = [\n      ...this.extractCitationDocuments(options.prompt),\n    ];\n\n    const markCodeExecutionDynamic = hasWebTool20260209WithoutCodeExecution(\n      body.tools,\n    );\n\n    const url = this.buildRequestUrl(true);\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url,\n      headers: await this.getHeaders({ betas, headers: options.headers }),\n      body: this.transformRequestBody(body, betas),\n      failedResponseHandler: anthropicFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        anthropicMessagesChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    let finishReason: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    const usage: AnthropicMessagesUsage = {\n      input_tokens: 0,\n      output_tokens: 0,\n      cache_creation_input_tokens: 0,\n      cache_read_input_tokens: 0,\n      iterations: null,\n    };\n\n    const contentBlocks: Record<\n      number,\n      | {\n          type: 'tool-call';\n          toolCallId: string;\n          toolName: string;\n          input: string;\n          providerExecuted?: boolean;\n          firstDelta: boolean;\n          providerToolName?: string;\n          caller?: {\n            type:\n              | 'code_execution_20250825'\n              | 'code_execution_20260120'\n              | 'direct';\n            toolId?: string;\n          };\n        }\n      | { type: 'text' | 'reasoning' }\n    > = {};\n    const mcpToolCalls: Record<string, LanguageModelV3ToolCall> = {};\n    const serverToolCalls: Record<string, string> = {}; // tool_use_id -> provider tool name\n\n    let contextManagement:\n      | AnthropicMessageMetadata['contextManagement']\n      | null = null;\n    let rawUsage: JSONObject | undefined = undefined;\n    let cacheCreationInputTokens: number | null = null;\n    let stopSequence: string | null = null;\n    let container: AnthropicMessageMetadata['container'] | null = null;\n    let isJsonResponseFromTool = false;\n\n    let blockType:\n      | 'text'\n      | 'thinking'\n      | 'tool_use'\n      | 'redacted_thinking'\n      | 'server_tool_use'\n      | 'web_fetch_tool_result'\n      | 'web_search_tool_result'\n      | 'code_execution_tool_result'\n      | 'text_editor_code_execution_tool_result'\n      | 'bash_code_execution_tool_result'\n      | 'tool_search_tool_result'\n      | 'mcp_tool_use'\n      | 'mcp_tool_result'\n      | 'compaction'\n      | undefined = undefined;\n\n    const generateId = this.generateId;\n\n    const transformedStream = response.pipeThrough(\n      new TransformStream<\n        ParseResult<InferSchema<typeof anthropicMessagesChunkSchema>>,\n        LanguageModelV3StreamPart\n      >({\n        start(controller) {\n          controller.enqueue({ type: 'stream-start', warn