--- title: Anthropic description: Learn how to use the Anthropic provider for the AI SDK. --- # Anthropic Provider The [Anthropic](https://www.anthropic.com/) provider contains language model support for the [Anthropic Messages API](https://docs.anthropic.com/claude/reference/messages_post). ## Setup The Anthropic provider is available in the `@ai-sdk/anthropic` module. You can install it with ## Provider Instance You can import the default provider instance `anthropic` from `@ai-sdk/anthropic`: ```ts import { anthropic } from '@ai-sdk/anthropic'; ``` If you need a customized setup, you can import `createAnthropic` from `@ai-sdk/anthropic` and create a provider instance with your settings: ```ts import { createAnthropic } from '@ai-sdk/anthropic'; const anthropic = createAnthropic({ // custom settings }); ``` You can use the following optional settings to customize the Anthropic provider instance: - **baseURL** _string_ Use a different URL prefix for API calls, e.g. to use proxy servers. The default prefix is `https://api.anthropic.com/v1`. - **apiKey** _string_ API key that is being sent using the `x-api-key` header. It defaults to the `ANTHROPIC_API_KEY` environment variable. Only one of `apiKey` or `authToken` is required. - **authToken** _string_ Auth token that is being sent using the `Authorization: Bearer` header. It defaults to the `ANTHROPIC_AUTH_TOKEN` environment variable. Only one of `apiKey` or `authToken` is required. - **headers** _Record<string,string>_ Custom headers to include in the requests. - **fetch** _(input: RequestInfo, init?: RequestInit) => Promise<Response>_ Custom [fetch](https://developer.mozilla.org/en-US/docs/Web/API/fetch) implementation. Defaults to the global `fetch` function. You can use it as a middleware to intercept requests, or to provide a custom fetch implementation for e.g. testing. ## Language Models You can create models that call the [Anthropic Messages API](https://docs.anthropic.com/claude/reference/messages_post) using the provider instance. The first argument is the model id, e.g. `claude-3-haiku-20240307`. Some models have multi-modal capabilities. ```ts const model = anthropic('claude-3-haiku-20240307'); ``` You can also use the following aliases for model creation: - `anthropic.languageModel('claude-3-haiku-20240307')` - Creates a language model - `anthropic.chat('claude-3-haiku-20240307')` - Alias for `languageModel` - `anthropic.messages('claude-3-haiku-20240307')` - Alias for `languageModel` You can use Anthropic language models to generate text with the `generateText` function: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const { text } = await generateText({ model: anthropic('claude-3-haiku-20240307'), prompt: 'Write a vegetarian lasagna recipe for 4 people.', }); ``` Anthropic language models can also be used in the `streamText` function and support structured data generation with [`Output`](/docs/reference/ai-sdk-core/output) (see [AI SDK Core](/docs/ai-sdk-core)). The following optional provider options are available for Anthropic models: - `disableParallelToolUse` _boolean_ Optional. Disables the use of parallel tool calls. Defaults to `false`. When set to `true`, the model will only call one tool at a time instead of potentially calling multiple tools in parallel. - `sendReasoning` _boolean_ Optional. Include reasoning content in requests sent to the model. Defaults to `true`. If you are experiencing issues with the model handling requests involving reasoning content, you can set this to `false` to omit them from the request. - `effort` _"low" | "medium" | "high" | "xhigh" | "max"_ Optional. See [Effort section](#effort) for more details. - `taskBudget` _object_ Optional. See [Task Budgets section](#task-budgets) for more details. - `speed` _"fast" | "standard"_ Optional. See [Fast Mode section](#fast-mode) for more details. - `inferenceGeo` _"us" | "global"_ Optional. See [Data Residency section](#data-residency) for more details. - `thinking` _object_ Optional. See [Reasoning section](#reasoning) for more details. - `toolStreaming` _boolean_ Whether to enable tool streaming (and structured output streaming). Default to `true`. - `structuredOutputMode` _"outputFormat" | "jsonTool" | "auto"_ Determines how structured outputs are generated. Optional. - `"outputFormat"`: Use the `output_format` parameter to specify the structured output format. - `"jsonTool"`: Use a special `"json"` tool to specify the structured output format. - `"auto"`: Use `"outputFormat"` when supported, otherwise fall back to `"jsonTool"` (default). - `metadata` _object_ Optional. Metadata to include with the request. See the [Anthropic API documentation](https://platform.claude.com/docs/en/api/messages/create) for details. - `userId` _string_ - An external identifier for the end-user. Should be a UUID, hash, or other opaque identifier. Must not contain PII. ### Structured Outputs and Tool Input Streaming Tool call streaming is enabled by default. You can opt out by setting the `toolStreaming` provider option to `false`. ```ts import { anthropic } from '@ai-sdk/anthropic'; import { streamText, tool } from 'ai'; import { z } from 'zod'; const result = streamText({ model: anthropic('claude-sonnet-4-20250514'), tools: { writeFile: tool({ description: 'Write content to a file', inputSchema: z.object({ path: z.string(), content: z.string(), }), execute: async ({ path, content }) => { // Implementation return { success: true }; }, }), }, prompt: 'Write a short story to story.txt', }); ``` ### Effort Anthropic introduced an `effort` option with `claude-opus-4-5` that affects thinking, text responses, and function calls. Effort defaults to `high` and you can set it to `medium` or `low` to save tokens and to lower time-to-last-token latency (TTLT). `claude-opus-4-7` additionally supports `xhigh` for maximum reasoning effort. ```ts highlight="8-10" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const { text, usage } = await generateText({ model: anthropic('claude-opus-4-20250514'), prompt: 'How many people will live in the world in 2040?', providerOptions: { anthropic: { effort: 'low', } satisfies AnthropicLanguageModelOptions, }, }); console.log(text); // resulting text console.log(usage); // token usage ``` ### Fast Mode Anthropic supports a [`speed` option](https://code.claude.com/docs/en/fast-mode) for `claude-opus-4-6` that enables faster inference with approximately 2.5x faster output token speeds. ```ts highlight="8-10" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const { text } = await generateText({ model: anthropic('claude-opus-4-6'), prompt: 'Write a short poem about the sea.', providerOptions: { anthropic: { speed: 'fast', } satisfies AnthropicLanguageModelOptions, }, }); ``` The `speed` option accepts `'fast'` or `'standard'` (default behavior). ### Task Budgets `claude-opus-4-7` supports a `taskBudget` option that informs the model of the total token budget available for an agentic turn. The model uses this information to prioritize work, plan ahead, and wind down gracefully as the budget is consumed. Task budgets are advisory — they do not enforce a hard token limit. The model will attempt to stay within budget, but actual usage may vary. ```ts highlight="8-13" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const { text } = await generateText({ model: anthropic('claude-opus-4-8'), prompt: 'Research the pros and cons of Rust vs Go for building CLI tools.', providerOptions: { anthropic: { taskBudget: { type: 'tokens', total: 400000, }, } satisfies AnthropicLanguageModelOptions, }, }); ``` For long-running agents that compact and restart context, you can carry the remaining budget forward using the `remaining` field: ```ts taskBudget: { type: 'tokens', total: 400000, remaining: 215000, // budget left after prior compacted-away contexts } ``` The `taskBudget` object accepts: - `type` _"tokens"_ - Budget type. Currently only `"tokens"` is supported. - `total` _number_ - Total task budget for the agentic turn. Minimum 20,000. - `remaining` _number_ - Budget left after prior compacted-away contexts. Must be between 0 and `total`. Defaults to `total` if omitted. ### Data Residency Anthropic supports an [`inferenceGeo` option](https://platform.claude.com/docs/en/build-with-claude/data-residency) that controls where model inference runs for a request. ```ts highlight="8-10" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const { text } = await generateText({ model: anthropic('claude-opus-4-6'), prompt: 'Summarize the key points of this document.', providerOptions: { anthropic: { inferenceGeo: 'us', } satisfies AnthropicLanguageModelOptions, }, }); ``` The `inferenceGeo` option accepts `'us'` (US-only infrastructure) or `'global'` (default, any available geography). ### Reasoning Anthropic models support extended thinking, where Claude shows its reasoning process before providing a final answer. #### Adaptive Thinking For newer models (`claude-sonnet-4-6`, `claude-opus-4-6`, and later), use adaptive thinking. Claude automatically determines how much reasoning to use based on the complexity of the prompt. ```ts highlight="4,8-10" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const { text, reasoningText, reasoning } = await generateText({ model: anthropic('claude-opus-4-6'), prompt: 'How many people will live in the world in 2040?', providerOptions: { anthropic: { thinking: { type: 'adaptive' }, } satisfies AnthropicLanguageModelOptions, }, }); console.log(reasoningText); // reasoning text console.log(reasoning); // reasoning details including redacted reasoning console.log(text); // text response ``` You can combine adaptive thinking with the `effort` option to control how much reasoning Claude uses: ```ts highlight="6-8" const { text } = await generateText({ model: anthropic('claude-opus-4-6'), prompt: 'Invent a new holiday and describe its traditions.', providerOptions: { anthropic: { thinking: { type: 'adaptive' }, effort: 'max', // 'low' | 'medium' | 'high' | 'max' } satisfies AnthropicLanguageModelOptions, }, }); ``` ##### Thinking Display (Opus 4.7+) Starting with `claude-opus-4-7`, thinking content is omitted from the response by default — thinking blocks are present in the stream but their text is empty. To receive reasoning output, set `display: 'summarized'`: ```ts highlight="5" const { text, reasoningText } = await generateText({ model: anthropic('claude-opus-4-8'), providerOptions: { anthropic: { thinking: { type: 'adaptive', display: 'summarized' }, } satisfies AnthropicLanguageModelOptions, }, prompt: 'How many people will live in the world in 2040?', }); console.log(reasoningText); // reasoning text (empty without display: 'summarized') console.log(text); ``` If you stream reasoning to users with `claude-opus-4-7`, the default `"omitted"` display will cause a long pause before output begins. Set `display: "summarized"` to restore visible progress during thinking. #### Budget-Based Thinking For earlier models (`claude-opus-4-20250514`, `claude-sonnet-4-20250514`, `claude-sonnet-4-5-20250929`), use `type: 'enabled'` with an explicit token budget: ```ts highlight="4,8-10" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const { text, reasoningText, reasoning } = await generateText({ model: anthropic('claude-sonnet-4-5-20250929'), prompt: 'How many people will live in the world in 2040?', providerOptions: { anthropic: { thinking: { type: 'enabled', budgetTokens: 12000 }, } satisfies AnthropicLanguageModelOptions, }, }); console.log(reasoningText); // reasoning text console.log(reasoning); // reasoning details including redacted reasoning console.log(text); // text response ``` See [AI SDK UI: Chatbot](/docs/ai-sdk-ui/chatbot#reasoning) for more details on how to integrate reasoning into your chatbot. ### Context Management Anthropic's Context Management feature allows you to automatically manage conversation context by clearing tool uses or thinking content when certain conditions are met. This helps optimize token usage and manage long conversations more efficiently. You can configure context management using the `contextManagement` provider option: ```ts highlight="7-20" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const result = await generateText({ model: anthropic('claude-sonnet-4-5-20250929'), prompt: 'Continue our conversation...', providerOptions: { anthropic: { contextManagement: { edits: [ { type: 'clear_tool_uses_20250919', trigger: { type: 'input_tokens', value: 10000 }, keep: { type: 'tool_uses', value: 5 }, clearAtLeast: { type: 'input_tokens', value: 1000 }, clearToolInputs: true, excludeTools: ['important_tool'], }, ], }, } satisfies AnthropicLanguageModelOptions, }, }); // Check what was cleared console.log(result.providerMetadata?.anthropic?.contextManagement); ``` #### Context Editing Context editing strategies selectively remove specific content types from earlier in the conversation to reduce token usage without losing the overall conversation flow. ##### Clear Tool Uses The `clear_tool_uses_20250919` edit type removes old tool call/result pairs from the conversation history: - **trigger** - Condition that triggers the clearing (e.g., `{ type: 'input_tokens', value: 10000 }` or `{ type: 'tool_uses', value: 10 }`) - **keep** - How many recent tool uses to preserve (e.g., `{ type: 'tool_uses', value: 5 }`) - **clearAtLeast** - Minimum amount to clear (e.g., `{ type: 'input_tokens', value: 1000 }`) - **clearToolInputs** - Whether to clear tool input parameters (boolean) - **excludeTools** - Array of tool names to never clear ##### Clear Thinking The `clear_thinking_20251015` edit type removes thinking/reasoning blocks from earlier turns, keeping only the most recent ones: - **keep** - How many recent thinking turns to preserve (e.g., `{ type: 'thinking_turns', value: 2 }`) or `'all'` to keep everything ```ts const result = await generateText({ model: anthropic('claude-opus-4-20250514'), prompt: 'Continue reasoning...', providerOptions: { anthropic: { thinking: { type: 'enabled', budgetTokens: 12000 }, contextManagement: { edits: [ { type: 'clear_thinking_20251015', keep: { type: 'thinking_turns', value: 2 }, }, ], }, } satisfies AnthropicLanguageModelOptions, }, }); ``` #### Compaction The `compact_20260112` edit type automatically summarizes earlier conversation context when token limits are reached. This is useful for long-running conversations where you want to preserve the essence of earlier exchanges while staying within token limits. ```ts highlight="7-19" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { streamText } from 'ai'; const result = streamText({ model: anthropic('claude-opus-4-6'), messages: conversationHistory, providerOptions: { anthropic: { contextManagement: { edits: [ { type: 'compact_20260112', trigger: { type: 'input_tokens', value: 50000, // trigger compaction when input exceeds 50k tokens }, instructions: 'Summarize the conversation concisely, preserving key decisions and context.', pauseAfterCompaction: false, }, ], }, } satisfies AnthropicLanguageModelOptions, }, }); ``` **Configuration:** - **trigger** - Condition that triggers compaction (e.g., `{ type: 'input_tokens', value: 50000 }`) - **instructions** - Custom instructions for how the model should summarize the conversation. Use this to guide the compaction summary towards specific aspects of the conversation you want to preserve. - **pauseAfterCompaction** - When `true`, the model will pause after generating the compaction summary, allowing you to inspect or process it before continuing. Defaults to `false`. When compaction occurs, the model generates a summary of the earlier context. This summary appears as a text block with special provider metadata. ##### Detecting Compaction in Streams When using `streamText`, you can detect compaction summaries by checking the `providerMetadata` on `text-start` events: ```ts for await (const part of result.stream) { switch (part.type) { case 'text-start': { const isCompaction = part.providerMetadata?.anthropic?.type === 'compaction'; if (isCompaction) { console.log('[COMPACTION SUMMARY START]'); } break; } case 'text-delta': { process.stdout.write(part.text); break; } } } ``` ##### Compaction in UI Applications When using `useChat` or other UI hooks, compaction summaries appear as regular text parts with `providerMetadata`. You can style them differently in your UI: ```tsx { message.parts.map((part, index) => { if (part.type === 'text') { const isCompaction = (part.providerMetadata?.anthropic as { type?: string } | undefined) ?.type === 'compaction'; if (isCompaction) { return (

[Compaction Summary]

{part.text}

); } return

{part.text}

; } }); } ``` #### Applied Edits Metadata After generation, you can check which edits were applied in the provider metadata: ```ts const metadata = result.providerMetadata?.anthropic?.contextManagement; if (metadata?.appliedEdits) { metadata.appliedEdits.forEach(edit => { if (edit.type === 'clear_tool_uses_20250919') { console.log(`Cleared ${edit.clearedToolUses} tool uses`); console.log(`Freed ${edit.clearedInputTokens} tokens`); } else if (edit.type === 'clear_thinking_20251015') { console.log(`Cleared ${edit.clearedThinkingTurns} thinking turns`); console.log(`Freed ${edit.clearedInputTokens} tokens`); } else if (edit.type === 'compact_20260112') { console.log('Compaction was applied'); } }); } ``` For more details, see [Anthropic's Context Management documentation](https://docs.anthropic.com/en/docs/build-with-claude/context-management). ### Cache Control In the messages and message parts, you can use the `providerOptions` property to set cache control breakpoints. You need to set the `anthropic` property in the `providerOptions` object to `{ cacheControl: { type: 'ephemeral' } }` to set a cache control breakpoint. Cache read and cache write (creation) token counts are returned on the standard `usage` object for both `generateText` and `streamText`. You can access them at `result.usage.inputTokenDetails.cacheReadTokens` and `result.usage.inputTokenDetails.cacheWriteTokens`. ```ts highlight="8,18-20,29-32" import { anthropic } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const errorMessage = '... long error message ...'; const result = await generateText({ model: anthropic('claude-sonnet-4-5'), messages: [ { role: 'user', content: [ { type: 'text', text: 'You are a JavaScript expert.' }, { type: 'text', text: `Error message: ${errorMessage}`, providerOptions: { anthropic: { cacheControl: { type: 'ephemeral' } }, }, }, { type: 'text', text: 'Explain the error message.' }, ], }, ], }); console.log(result.text); console.log( 'Cache read tokens:', result.usage.inputTokenDetails.cacheReadTokens, ); console.log( 'Cache write tokens:', result.usage.inputTokenDetails.cacheWriteTokens, ); ``` You can also use cache control on system messages by providing multiple system messages at the head of your messages array: ```ts highlight="3,7-9" const result = await generateText({ model: anthropic('claude-sonnet-4-5'), messages: [ { role: 'system', content: 'Cached system message part', providerOptions: { anthropic: { cacheControl: { type: 'ephemeral' } }, }, }, { role: 'system', content: 'Uncached system message part', }, { role: 'user', content: 'User prompt', }, ], }); ``` Cache control for tools: ```ts const result = await generateText({ model: anthropic('claude-haiku-4-5'), tools: { cityAttractions: tool({ inputSchema: z.object({ city: z.string() }), providerOptions: { anthropic: { cacheControl: { type: 'ephemeral' }, }, }, }), }, messages: [ { role: 'user', content: 'User prompt', }, ], }); ``` #### Longer cache TTL Anthropic also supports a longer 1-hour cache duration. Here's an example: ```ts const result = await generateText({ model: anthropic('claude-haiku-4-5'), messages: [ { role: 'user', content: [ { type: 'text', text: 'Long cached message', providerOptions: { anthropic: { cacheControl: { type: 'ephemeral', ttl: '1h' }, }, }, }, ], }, ], }); ``` #### Limitations The minimum cacheable prompt length is: - 4096 tokens for Claude Opus 4.5 - 1024 tokens for Claude Opus 4.1, Claude Opus 4, Claude Sonnet 4.5, Claude Sonnet 4, Claude Sonnet 3.7, and Claude Opus 3 - 4096 tokens for Claude Haiku 4.5 - 2048 tokens for Claude Haiku 3.5 and Claude Haiku 3 Shorter prompts cannot be cached, even if marked with `cacheControl`. Any requests to cache fewer than this number of tokens will be processed without caching. For more on prompt caching with Anthropic, see [Anthropic's Cache Control documentation](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching). Because the `UIMessage` type (used by AI SDK UI hooks like `useChat`) does not support the `providerOptions` property, you can use `convertToModelMessages` first before passing the messages to functions like `generateText` or `streamText`. For more details on `providerOptions` usage, see [here](/docs/foundations/prompts#provider-options). ### Bash Tool The Bash Tool allows running bash commands. By default, the tool executes commands through the `experimental_sandbox` that you pass to `generateText` or `streamText`: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText, isStepCount } from 'ai'; const result = await generateText({ model: anthropic('claude-opus-4-8'), tools: { bash: anthropic.tools.bash_20250124(), }, experimental_sandbox: { description: 'A sandboxed shell environment.', run: async ({ command }) => { // Run the command in your sandbox and return the result. return { exitCode: 0, stdout: `Executed: ${command}`, stderr: '', }; }, }, stopWhen: isStepCount(2), prompt: 'List the files in the current directory.', }); ``` Parameters: - `command` (string): The bash command to run. Required unless the tool is being restarted. - `restart` (boolean, optional): Specifying true will restart this tool. Two versions are available: `bash_20250124` (recommended) and `bash_20241022`. Only certain Claude versions are supported. You can also provide a custom `execute` function when you want to handle bash execution directly instead of using the request experimental sandbox. ### Memory Tool The [Memory Tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/memory-tool) allows Claude to use a local memory, e.g. in the filesystem. Here's how to create it: ```ts const memory = anthropic.tools.memory_20250818({ execute: async action => { // Implement your memory command execution logic here // Return the result of the command execution }, }); ``` Only certain Claude versions are supported. ### Text Editor Tool The Text Editor Tool provides functionality for viewing and editing text files. ```ts const tools = { str_replace_based_edit_tool: anthropic.tools.textEditor_20250728({ maxCharacters: 10000, // optional async execute({ command, path, old_str, new_str, insert_text }) { // ... }, }), } satisfies ToolSet; ``` Different models support different versions of the tool: - `textEditor_20250728` - For Claude Sonnet 4, Opus 4, and Opus 4.1 (recommended) - `textEditor_20250124` - For Claude Sonnet 3.7 - `textEditor_20241022` - For Claude Sonnet 3.5 Note: `textEditor_20250429` is deprecated. Use `textEditor_20250728` instead. Parameters: - `command` ('view' | 'create' | 'str_replace' | 'insert' | 'undo_edit'): The command to run. Note: `undo_edit` is only available in Claude 3.5 Sonnet and earlier models. - `path` (string): Absolute path to file or directory, e.g. `/repo/file.py` or `/repo`. - `file_text` (string, optional): Required for `create` command, with the content of the file to be created. - `insert_line` (number, optional): Required for `insert` command. The line number after which to insert the new string. - `new_str` (string, optional): New string for `str_replace` command. - `insert_text` (string, optional): Required for `insert` command, containing the text to insert. - `old_str` (string, optional): Required for `str_replace` command, containing the string to replace. - `view_range` (number[], optional): Optional for `view` command to specify line range to show. ### Computer Tool The Computer Tool enables control of keyboard and mouse actions on a computer: ```ts const computerTool = anthropic.tools.computer_20251124({ displayWidthPx: 1920, displayHeightPx: 1080, displayNumber: 0, // Optional, for X11 environments enableZoom: true, // Optional, enables the zoom action execute: async ({ action, coordinate, text, region }) => { // Implement your computer control logic here // Return the result of the action // Example code: switch (action) { case 'screenshot': { // multipart result: return { type: 'image', data: fs .readFileSync('./data/screenshot-editor.png') .toString('base64'), }; } case 'zoom': { // region is [x1, y1, x2, y2] defining the area to zoom into return { type: 'image', data: fs.readFileSync('./data/zoomed-region.png').toString('base64'), }; } default: { console.log('Action:', action); console.log('Coordinate:', coordinate); console.log('Text:', text); return `executed ${action}`; } } }, // map to tool result content for LLM consumption: toModelOutput({ output }) { return typeof output === 'string' ? [{ type: 'text', text: output }] : [{ type: 'file-data', data: output.data, mediaType: 'image/png' }]; }, }); ``` Use `computer_20251124` for Claude Opus 4.5 which supports the zoom action. Use `computer_20250124` for Claude Sonnet 4.5, Haiku 4.5, Opus 4.1, Sonnet 4, Opus 4, and Sonnet 3.7. Parameters: - `action` ('key' | 'type' | 'mouse_move' | 'left_click' | 'left_click_drag' | 'right_click' | 'middle_click' | 'double_click' | 'screenshot' | 'cursor_position' | 'zoom'): The action to perform. The `zoom` action is only available with `computer_20251124`. - `coordinate` (number[], optional): Required for `mouse_move` and `left_click_drag` actions. Specifies the (x, y) coordinates. - `text` (string, optional): Required for `type` and `key` actions. - `region` (number[], optional): Required for `zoom` action. Specifies `[x1, y1, x2, y2]` coordinates for the area to inspect. - `displayWidthPx` (number): The width of the display in pixels. - `displayHeightPx` (number): The height of the display in pixels. - `displayNumber` (number, optional): The display number for X11 environments. - `enableZoom` (boolean, optional): Enable the zoom action. Only available with `computer_20251124`. Default: `false`. ### Web Search Tool Anthropic provides a provider-defined web search tool that gives Claude direct access to real-time web content, allowing it to answer questions with up-to-date information beyond its knowledge cutoff. You can enable web search using the provider-defined web search tool: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const webSearchTool = anthropic.tools.webSearch_20250305({ maxUses: 5, }); const result = await generateText({ model: anthropic('claude-opus-4-20250514'), prompt: 'What are the latest developments in AI?', tools: { web_search: webSearchTool, }, }); ``` Web search must be enabled in your organization's [Console settings](https://console.anthropic.com/settings/privacy). #### Configuration Options The web search tool supports several configuration options: - **maxUses** _number_ Maximum number of web searches Claude can perform during the conversation. - **allowedDomains** _string[]_ Optional list of domains that Claude is allowed to search. If provided, searches will be restricted to these domains. - **blockedDomains** _string[]_ Optional list of domains that Claude should avoid when searching. - **userLocation** _object_ Optional user location information to provide geographically relevant search results. ```ts const webSearchTool = anthropic.tools.webSearch_20250305({ maxUses: 3, allowedDomains: ['techcrunch.com', 'wired.com'], blockedDomains: ['example-spam-site.com'], userLocation: { type: 'approximate', country: 'US', region: 'California', city: 'San Francisco', timezone: 'America/Los_Angeles', }, }); const result = await generateText({ model: anthropic('claude-opus-4-20250514'), prompt: 'Find local news about technology', tools: { web_search: webSearchTool, }, }); ``` ### Web Fetch Tool Anthropic provides a provider-defined web fetch tool that allows Claude to retrieve content from specific URLs. This is useful when you want Claude to analyze or reference content from a particular webpage or document. You can enable web fetch using the provider-defined web fetch tool: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const result = await generateText({ model: anthropic('claude-sonnet-4-0'), prompt: 'What is this page about? https://en.wikipedia.org/wiki/Maglemosian_culture', tools: { web_fetch: anthropic.tools.webFetch_20250910({ maxUses: 1 }), }, }); ``` ### Advisor Tool Anthropic provides a provider-executed advisor tool that lets an executor model consult a stronger advisor model during generation. This is useful for long-running coding, research, and agentic tasks where a stronger model can provide planning or course correction while the main response is generated by a faster or lower-cost model. You can enable the advisor tool using `anthropic.tools.advisor_20260301`: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const result = await generateText({ model: anthropic('claude-sonnet-4-6'), system: 'You have access to an `advisor` tool backed by a stronger reviewer model.' prompt: 'Build a concurrent worker pool in Go with graceful shutdown. Outline the design first.', tools: { advisor: anthropic.tools.advisor_20260301({ model: 'claude-opus-4-8', maxUses: 3, }), }, }); ``` The AI SDK automatically sends the required `advisor-tool-2026-03-01` beta header when you include this tool. #### Configuration Options The advisor tool supports the following configuration options: - **model** _string_ Required. The advisor model ID, such as `claude-opus-4-8`. The advisor model must be at least as capable as the executor model. - **maxUses** _number_ Optional. Maximum number of advisor calls allowed in a single request. Once the executor reaches this cap, further advisor calls return an `advisor_tool_result_error` with `errorCode: 'max_uses_exceeded'`, and the executor continues without further advice. - **caching** _object_ Optional. Enables prompt caching for the advisor's own transcript across calls within a conversation. Use `{ type: 'ephemeral', ttl: '5m' | '1h' }`. This is most useful for longer agent loops where you expect at least three advisor calls. #### How It Works The advisor tool is provider-executed, so you do not provide an `execute` function. When the executor model calls `advisor`, Anthropic runs a separate server-side inference with the advisor model. The advisor sees the transcript and returns guidance to the executor, which then continues generating within the same request. The advisor tool input is always empty. Anthropic constructs the advisor's view from the full transcript automatically. For multi-turn conversations, pass the full assistant content, including advisor tool results, back on subsequent turns. If you remove the advisor tool from a later request, also remove previous advisor tool results from the message history; otherwise the Anthropic API returns an invalid request error. #### Streaming Advisor sub-inferences do not stream. When using `streamText`, the executor stream pauses while the advisor runs, then the advisor result arrives as a single tool result before executor output resumes. ### Tool Search Anthropic provides provider-defined tool search tools that enable Claude to work with hundreds or thousands of tools by dynamically discovering and loading them on-demand. Instead of loading all tool definitions into the context window upfront, Claude searches your tool catalog and loads only the tools it needs. There are two variants: - **BM25 Search** - Uses natural language queries to find tools - **Regex Search** - Uses regex patterns (Python `re.search()` syntax) to find tools #### Basic Usage ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText, tool } from 'ai'; import { z } from 'zod'; const result = await generateText({ model: anthropic('claude-sonnet-4-5'), prompt: 'What is the weather in San Francisco?', tools: { toolSearch: anthropic.tools.toolSearchBm25_20251119(), get_weather: tool({ description: 'Get the current weather at a specific location', inputSchema: z.object({ location: z.string().describe('The city and state'), }), execute: async ({ location }) => ({ location, temperature: 72, condition: 'Sunny', }), // Defer tool here - Claude discovers these via the tool search tool providerOptions: { anthropic: { deferLoading: true }, }, }), }, }); ``` #### Using Regex Search For more precise tool matching, you can use the regex variant: ```ts const result = await generateText({ model: anthropic('claude-sonnet-4-5'), prompt: 'Get the weather data', tools: { toolSearch: anthropic.tools.toolSearchRegex_20251119(), // ... deferred tools }, }); ``` Claude will construct regex patterns like `weather|temperature|forecast` to find matching tools. #### Custom Tool Search You can implement your own tool search logic (e.g., using embeddings or semantic search) by returning `tool-reference` content blocks via `toModelOutput`: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText, tool } from 'ai'; import { z } from 'zod'; const result = await generateText({ model: anthropic('claude-sonnet-4-5'), prompt: 'What is the weather in San Francisco?', tools: { // Custom search tool searchTools: tool({ description: 'Search for tools by keyword', inputSchema: z.object({ query: z.string() }), execute: async ({ query }) => { // Your custom search logic (embeddings, fuzzy match, etc.) const allTools = ['get_weather', 'get_forecast', 'get_temperature']; return allTools.filter(name => name.includes(query.toLowerCase())); }, toModelOutput: ({ output }) => ({ type: 'content', value: (output as string[]).map(toolName => ({ type: 'custom' as const, providerOptions: { anthropic: { type: 'tool-reference', toolName, }, }, })), }), }), // Deferred tools get_weather: tool({ description: 'Get the current weather', inputSchema: z.object({ location: z.string() }), execute: async ({ location }) => ({ location, temperature: 72 }), providerOptions: { anthropic: { deferLoading: true }, }, }), }, }); ``` This sends `tool_reference` blocks to Anthropic, which loads the corresponding deferred tool schemas into Claude's context. ### MCP Connectors Anthropic supports connecting to [MCP servers](https://docs.claude.com/en/docs/agents-and-tools/mcp-connector) as part of their execution. You can enable this feature with the `mcpServers` provider option: ```ts import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const result = await generateText({ model: anthropic('claude-sonnet-4-5'), prompt: `Call the echo tool with "hello world". what does it respond with back?`, providerOptions: { anthropic: { mcpServers: [ { type: 'url', name: 'echo', url: 'https://echo.mcp.inevitable.fyi/mcp', // optional: authorization token authorizationToken: mcpAuthToken, // optional: tool configuration toolConfiguration: { enabled: true, allowedTools: ['echo'], }, }, ], } satisfies AnthropicLanguageModelOptions, }, }); ``` The tool calls and results are dynamic, i.e. the input and output schemas are not known. #### Configuration Options The web fetch tool supports several configuration options: - **maxUses** _number_ The maxUses parameter limits the number of web fetches performed. - **allowedDomains** _string[]_ Only fetch from these domains. - **blockedDomains** _string[]_ Never fetch from these domains. - **citations** _object_ Unlike web search where citations are always enabled, citations are optional for web fetch. Set `"citations": {"enabled": true}` to enable Claude to cite specific passages from fetched documents. - **maxContentTokens** _number_ The maxContentTokens parameter limits the amount of content that will be included in the context. #### Error Handling Web search errors are handled differently depending on whether you're using streaming or non-streaming: **Non-streaming (`generateText`):** Web search errors throw exceptions that you can catch: ```ts try { const result = await generateText({ model: anthropic('claude-opus-4-20250514'), prompt: 'Search for something', tools: { web_search: webSearchTool, }, }); } catch (error) { if (error.message.includes('Web search failed')) { console.log('Search error:', error.message); // Handle search error appropriately } } ``` **Streaming (`streamText`):** Web search errors are delivered as error parts in the stream: ```ts const result = await streamText({ model: anthropic('claude-opus-4-20250514'), prompt: 'Search for something', tools: { web_search: webSearchTool, }, }); for await (const part of result.textStream) { if (part.type === 'error') { console.log('Search error:', part.error); // Handle search error appropriately } } ``` ## Code Execution Anthropic provides a provider-defined code execution tool that gives Claude direct access to a real Python environment allowing it to execute code to inform its responses. You can enable code execution using the provider-defined code execution tool: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const codeExecutionTool = anthropic.tools.codeExecution_20260120(); const result = await generateText({ model: anthropic('claude-opus-4-20250514'), prompt: 'Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]', tools: { code_execution: codeExecutionTool, }, }); ``` Three versions are available: `codeExecution_20260120` (recommended, does not require a beta header, supports Claude Opus 4.6, Sonnet 4.6, Sonnet 4.5, and Opus 4.5), `codeExecution_20250825` (supports Python and Bash with enhanced file operations), and `codeExecution_20250522` (supports Bash only). #### Uploading Files for Code Execution You can upload files via the [Files API](https://docs.anthropic.com/en/docs/build-with-claude/files) and make them available inside the code execution sandbox. First upload the file, then reference it in your message using `containerUpload: true` in the provider options: ```ts import { anthropic } from '@ai-sdk/anthropic'; import { generateText, uploadFile } from 'ai'; import * as fs from 'fs'; const { providerReference } = await uploadFile({ api: anthropic.files(), data: fs.readFileSync('./data.csv'), filename: 'data.csv', mediaType: 'text/csv', }); const result = await generateText({ model: anthropic('claude-sonnet-4-5'), tools: { code_execution: anthropic.tools.codeExecution_20250825(), }, messages: [ { role: 'user', content: [ { type: 'text', text: 'Analyze this CSV data' }, { type: 'file', mediaType: 'text/csv', data: { type: 'reference', reference: providerReference }, providerOptions: { anthropic: { containerUpload: true }, }, }, ], }, ], }); ``` Supported file types include CSV, Excel, JSON, XML, images (JPEG, PNG, GIF, WebP), and text files. #### Error Handling Code execution errors are handled differently depending on whether you're using streaming or non-streaming: **Non-streaming (`generateText`):** Code execution errors are delivered as tool result parts in the response: ```ts const result = await generateText({ model: anthropic('claude-opus-4-20250514'), prompt: 'Execute some Python script', tools: { code_execution: codeExecutionTool, }, }); const toolErrors = result.content?.filter( content => content.type === 'tool-error', ); toolErrors?.forEach(error => { console.error('Tool execution error:', { toolName: error.toolName, toolCallId: error.toolCallId, error: error.error, }); }); ``` **Streaming (`streamText`):** Code execution errors are delivered as error parts in the stream: ```ts const result = await streamText({ model: anthropic('claude-opus-4-20250514'), prompt: 'Execute some Python script', tools: { code_execution: codeExecutionTool, }, }); for await (const part of result.textStream) { if (part.type === 'error') { console.log('Code execution error:', part.error); // Handle code execution error appropriately } } ``` ### Programmatic Tool Calling [Programmatic Tool Calling](https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/programmatic-tool-calling) allows Claude to write code that calls your tools programmatically within a code execution container, rather than requiring round trips through the model for each tool invocation. This reduces latency for multi-tool workflows and decreases token consumption. To enable programmatic tool calling, use the `allowedCallers` provider option on tools that you want to be callable from within code execution: ```ts highlight="13-17" import { anthropic, forwardAnthropicContainerIdFromLastStep, } from '@ai-sdk/anthropic'; import { generateText, tool, isStepCount } from 'ai'; import { z } from 'zod'; const result = await generateText({ model: anthropic('claude-sonnet-4-5'), stopWhen: isStepCount(10), prompt: 'Get the weather for Tokyo, Sydney, and London, then calculate the average temperature.', tools: { code_execution: anthropic.tools.codeExecution_20260120(), getWeather: tool({ description: 'Get current weather data for a city.', inputSchema: z.object({ city: z.string().describe('Name of the city'), }), execute: async ({ city }) => { // Your weather API implementation return { temp: 22, condition: 'Sunny' }; }, // Enable this tool to be called from within code execution providerOptions: { anthropic: { allowedCallers: ['code_execution_20260120'], }, }, }), }, // Propagate container ID between steps for code execution continuity prepareStep: forwardAnthropicContainerIdFromLastStep, }); ``` In this flow: 1. Claude writes Python code that calls your `getWeather` tool multiple times in parallel 2. The SDK automatically executes your tool and returns results to the code execution container 3. Claude processes the results in code and generates the final response Programmatic tool calling requires `claude-sonnet-4-6`, `claude-sonnet-4-5`, `claude-opus-4-6`, or `claude-opus-4-5` models and uses the `code_execution_20260120` or `code_execution_20250825` tool. #### Container Persistence When using programmatic tool calling across multiple steps, you need to preserve the container ID between steps using `prepareStep`. You can use the `forwardAnthropicContainerIdFromLastStep` helper function to do this automatically. The container ID is available in `providerMetadata.anthropic.container.id` after each step completes. ## Agent Skills [Anthropic Agent Skills](https://docs.claude.com/en/docs/agents-and-tools/agent-skills/overview) enable Claude to perform specialized tasks like document processing (PPTX, DOCX, PDF, XLSX) and data analysis. Skills run in a sandboxed container and require the code execution tool to be enabled. ### Using Built-in Skills Anthropic provides several built-in skills: - **pptx** - Create and edit PowerPoint presentations - **docx** - Create and edit Word documents - **pdf** - Process and analyze PDF files - **xlsx** - Work with Excel spreadsheets To use skills, you need to: 1. Enable the code execution tool 2. Specify the container with skills in `providerOptions` ```ts highlight="4,9-17,19-23" import { anthropic, AnthropicLanguageModelOptions } from '@ai-sdk/anthropic'; import { generateText } from 'ai'; const result = await generateText({ model: anthropic('claude-sonnet-4-5'), tools: { code_execution: anthropic.tools.codeExecution_20260120(), }, prompt: 'Create a presentation about renewable energy with 5 slides', providerOptions: { anthropic: { container: { skills: [ { type: 'anthropic', skillId: 'pptx', version: 'latest', // optional }, ], }, } satisfies AnthropicLanguageModelOptions, }, }); ``` ### Custom Skills You can also use custom skills by specifying `type: 'custom'`: ```ts highlight="9-11" const result = await generateText({ model: anthropic('claude-sonnet-4-5'), tools: { code_execution: anthropic.tools.codeExecution_20260120(), }, prompt: 'Use my custom skill to process this data', providerOptions: { anthropic: { container: { skills: [ { type: 'custom', skillId: 'my-custom-skill-id', version: '1.0', // optional }, ], }, } satisfies AnthropicLanguageModelOptions, }, }); ``` Skills use progressive context loading and execute within a sandboxed container with code execution capabilities. ### PDF support Anthropic Claude models support reading PDF files. You can pass PDF files as part of the message content using the `file` type: Option 1: URL-based PDF document ```ts const result = await generateText({ model: anthropic('claude-sonnet-4-5'), messages: [ { role: 'user', content: [ { type: 'text', text: 'What is an embedding model according to this document?', }, { type: 'file', data: new URL( 'https://github.com/vercel/ai/blob/main/examples/ai-functions/data/ai.pdf?raw=true', ), mimeType: 'application/pdf', }, ], }, ], }); ``` Option 2: Base64-encoded PDF document ```ts const result = await generateText({ model: anthropic('claude-sonnet-4-5'), messages: [ { role: 'user', content: [ { type: 'text', text: 'What is an embedding model according to this document?', }, { type: 'file', data: fs.readFileSync('./data/ai.pdf'), mediaType: 'application/pdf', }, ], }, ], }); ``` The model will have access to the contents of the PDF file and respond to questions about it. The PDF file should be passed using the `data` field, and the `mediaType` should be set to `'application/pdf'`. ### Model Capabilities | Model | Image Input | Object Generation | Tool Usage | Computer Use | Web Search | Tool Search | Compaction | | ------------------- | ------------------- | ------------------- | ------------------- | ------------------- | ------------------- | ------------------- | ------------------- | | `claude-opus-4-8` | | | | | | | | | `claude-opus-4-7` | | | | | | | | | `claude-opus-4-6` | | | | | | | | | `claude-sonnet-4-6` | | | | | | | | | `claude-opus-4-5` | | | | | | | | | `claude-haiku-4-5` | | | | | | | | | `claude-sonnet-4-5` | | | | | | | | | `claude-opus-4-1` | | | | | | | | | `claude-opus-4-0` | | | | | | | | | `claude-sonnet-4-0` | | | | | | | | The table above lists popular models. Please see the [Anthropic docs](https://docs.anthropic.com/en/docs/about-claude/models) for a full list of available models. The table above lists popular models. You can also pass any available provider model ID as a string if needed.