Anthropic Provider

The Anthropic provider contains language model support for the Anthropic Messages API.

Setup

The Anthropic provider is available in the @ai-sdk/anthropic module. You can install it with

pnpm add @ai-sdk/anthropic

Provider Instance

You can import the default provider instance anthropic from @ai-sdk/anthropic:

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:

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.

• headers Record<string,string>

  Custom headers to include in the requests.

• fetch (input: RequestInfo, init?: RequestInit) => Promise<Response>

  Custom 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 using the provider instance. The first argument is the model id, e.g. claude-3-haiku-20240307. Some models have multi-modal capabilities.

const model = anthropic('claude-3-haiku-20240307');

The following optional settings are available for Anthropic models:

• cacheControl boolean

  Enable the Anthropic cache control beta.

  You can then use provider metadata to set cache control breakpoints (example)

You can use Anthropic language models to generate text with the generateText function:

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, generateObject, and streamObject functions (see AI SDK Core and AI SDK RSC).

Cache Control

You can enable the cache control beta by setting the cacheControl option to true when creating the model instance.

In the messages and message parts, you can then use the experimental_providerMetadata property to set cache control breakpoints. You need to set the anthropic property in the experimental_providerMetadata object to { cacheControl: { type: 'ephemeral' } } to set a cache control breakpoint.

The cache creation input tokens are then returned in the experimental_providerMetadata object for generateText and generateObject, again under the anthropic property. When you use streamText or streamObject, the response contains a promise that resolves to the metadata. Alternatively you can receive it in the onFinish callback.

import { anthropic } from '@ai-sdk/anthropic';
import { generateText } from 'ai';

const errorMessage = '... long error message ...';

const result = await generateText({
  model: anthropic('claude-3-5-sonnet-20240620', {
    cacheControl: true,
  }),
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'You are a JavaScript expert.' },
        {
          type: 'text',
          text: `Error message: ${errorMessage}`,
          experimental_providerMetadata: {
            anthropic: { cacheControl: { type: 'ephemeral' } },
          },
        },
        { type: 'text', text: 'Explain the error message.' },
      ],
    },
  ],
});

console.log(result.text);
console.log(result.experimental_providerMetadata?.anthropic);
// e.g. { cacheCreationInputTokens: 2118, cacheReadInputTokens: 0 }

You can also use cache control on system messages by providing multiple system messages at the head of your messages array:

const result = await generateText({
  model: anthropic('claude-3-5-sonnet-20240620', {
    cacheControl: true,
  }),
  messages: [
    {
      role: 'system',
      content: 'Cached system message part',
      experimental_providerMetadata: {
        anthropic: { cacheControl: { type: 'ephemeral' } },
      },
    },
    {
      role: 'system',
      content: 'Uncached system message part',
    },
    {
      role: 'user',
      content: 'User prompt',
    },
  ],
});

Computer Use

Anthropic provides three built-in tools that can be used to interact with external systems:

1. Bash Tool: Allows running bash commands.
2. Text Editor Tool: Provides functionality for viewing and editing text files.
3. Computer Tool: Enables control of keyboard and mouse actions on a computer.

They are available via the tools property of the provider instance.

Bash Tool

The Bash Tool allows running bash commands. Here's how to create and use it:

const bashTool = anthropic.tools.bash_20241022({
  execute: async ({ command, restart }) => {
    // Implement your bash command execution logic here
    // Return the result of the command execution
  },
});

Parameters:

• command (string): The bash command to run. Required unless the tool is being restarted.
• restart (boolean, optional): Specifying true will restart this tool.

Text Editor Tool

The Text Editor Tool provides functionality for viewing and editing text files:

const textEditorTool = anthropic.tools.textEditor_20241022({
  execute: async ({
    command,
    path,
    file_text,
    insert_line,
    new_str,
    old_str,
    view_range,
  }) => {
    // Implement your text editing logic here
    // Return the result of the text editing operation
  },
});

Parameters:

• command ('view' | 'create' | 'str_replace' | 'insert' | 'undo_edit'): The command to run.
• 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 or insert commands.
• 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:

const computerTool = anthropic.tools.computer_20241022({
  displayWidthPx: 1920,
  displayHeightPx: 1080,
  displayNumber: 0, // Optional, for X11 environments

  execute: async ({ action, coordinate, text }) => {
    // 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'),
        };
      }
      default: {
        console.log('Action:', action);
        console.log('Coordinate:', coordinate);
        console.log('Text:', text);
        return `executed ${action}`;
      }
    }
  },

  // map to tool result content for LLM consumption:
  experimental_toToolResultContent(result) {
    return typeof result === 'string'
      ? [{ type: 'text', text: result }]
      : [{ type: 'image', data: result.data, mimeType: 'image/png' }];
  },
});

Parameters:

• action ('key' | 'type' | 'mouse_move' | 'left_click' | 'left_click_drag' | 'right_click' | 'middle_click' | 'double_click' | 'screenshot' | 'cursor_position'): The action to perform.
• 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.

These tools can be used in conjunction with the sonnet-3-5-sonnet-20240620 model to enable more complex interactions and tasks.

PDF support

Anthropic Sonnet claude-3-5-sonnet-20241022 supports reading PDF files. You can pass PDF files as part of the message content using the file type:

const result = await generateText({
  model: anthropic('claude-3-5-sonnet-20241022'),
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'What is an embedding model according to this document?',
        },
        {
          type: 'file',
          data: fs.readFileSync('./data/ai.pdf'),
          mimeType: '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 mimeType should be set to 'application/pdf'.

Model Capabilities