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*   Anthropic Tools

On this page

anthropic\_tools
================

danger

This API is deprecated as Anthropic now officially supports tools. [Click here to read the documentation](/v0.2/docs/integrations/chat/anthropic#tools).

Anthropic Tools
===============

LangChain offers an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

To start, install the `@langchain/anthropic` integration package.

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/anthropic

    yarn add @langchain/anthropic

    pnpm add @langchain/anthropic

Initialize model[​](#initialize-model "Direct link to Initialize model")
------------------------------------------------------------------------

You can initialize this wrapper the same way you'd initialize a standard `ChatAnthropic` instance:

tip

We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys.

    import { ChatAnthropicTools } from "@langchain/anthropic/experimental";const model = new ChatAnthropicTools({  temperature: 0.1,  model: "claude-3-sonnet-20240229",  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ANTHROPIC_API_KEY});

Passing in tools[​](#passing-in-tools "Direct link to Passing in tools")
------------------------------------------------------------------------

You can now pass in tools the same way as OpenAI:

    import { ChatAnthropicTools } from "@langchain/anthropic/experimental";import { HumanMessage } from "@langchain/core/messages";const model = new ChatAnthropicTools({  temperature: 0.1,  model: "claude-3-sonnet-20240229",}).bind({  tools: [    {      type: "function",      function: {        name: "get_current_weather",        description: "Get the current weather in a given location",        parameters: {          type: "object",          properties: {            location: {              type: "string",              description: "The city and state, e.g. San Francisco, CA",            },            unit: { type: "string", enum: ["celsius", "fahrenheit"] },          },          required: ["location"],        },      },    },  ],  // You can set the `function_call` arg to force the model to use a function  tool_choice: {    type: "function",    function: {      name: "get_current_weather",    },  },});const response = await model.invoke([  new HumanMessage({    content: "What's the weather in Boston?",  }),]);console.log(response);/*  AIMessage {    lc_serializable: true,    lc_kwargs: { content: '', additional_kwargs: { tool_calls: [Array] } },    lc_namespace: [ 'langchain_core', 'messages' ],    content: '',    name: undefined,    additional_kwargs: { tool_calls: [ [Object] ] }  }*/console.log(response.additional_kwargs.tool_calls);/*  [    {      id: '0',      type: 'function',      function: {        name: 'get_current_weather',        arguments: '{"location":"Boston, MA","unit":"fahrenheit"}'      }    }  ]*/

#### API Reference:

*   [ChatAnthropicTools](https://v02.api.js.langchain.com/classes/langchain_anthropic_experimental.ChatAnthropicTools.html) from `@langchain/anthropic/experimental`
*   [HumanMessage](https://v02.api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages`

Parallel tool calling[​](#parallel-tool-calling "Direct link to Parallel tool calling")
---------------------------------------------------------------------------------------

The model may choose to call multiple tools. Here is an example using an extraction use-case:

    import { z } from "zod";import { zodToJsonSchema } from "zod-to-json-schema";import { ChatAnthropicTools } from "@langchain/anthropic/experimental";import { PromptTemplate } from "@langchain/core/prompts";import { JsonOutputToolsParser } from "@langchain/core/output_parsers/openai_tools";const EXTRACTION_TEMPLATE = `Extract and save the relevant entities mentioned in the following passage together with their properties.Passage:{input}`;const prompt = PromptTemplate.fromTemplate(EXTRACTION_TEMPLATE);// Use Zod for easier schema declarationconst schema = z.object({  name: z.string().describe("The name of a person"),  height: z.number().describe("The person's height"),  hairColor: z.optional(z.string()).describe("The person's hair color"),});const model = new ChatAnthropicTools({  temperature: 0.1,  model: "claude-3-sonnet-20240229",}).bind({  tools: [    {      type: "function",      function: {        name: "person",        description: "Extracts the relevant people from the passage.",        parameters: zodToJsonSchema(schema),      },    },  ],  // Can also set to "auto" to let the model choose a tool  tool_choice: {    type: "function",    function: {      name: "person",    },  },});// Use a JsonOutputToolsParser to get the parsed JSON response directly.const chain = await prompt.pipe(model).pipe(new JsonOutputToolsParser());const response = await chain.invoke({  input:    "Alex is 5 feet tall. Claudia is 1 foot taller than Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.",});console.log(JSON.stringify(response, null, 2));/*  [    {      "type": "person",      "args": {        "name": "Alex",        "height": 5,        "hairColor": "blonde"      }    },    {      "type": "person",      "args": {        "name": "Claudia",        "height": 6,        "hairColor": "brunette"      }    }  ]*/

#### API Reference:

*   [ChatAnthropicTools](https://v02.api.js.langchain.com/classes/langchain_anthropic_experimental.ChatAnthropicTools.html) from `@langchain/anthropic/experimental`
*   [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [JsonOutputToolsParser](https://v02.api.js.langchain.com/classes/langchain_core_output_parsers_openai_tools.JsonOutputToolsParser.html) from `@langchain/core/output_parsers/openai_tools`

`.withStructuredOutput({ ... })`[​](#withstructuredoutput-- "Direct link to withstructuredoutput--")
----------------------------------------------------------------------------------------------------

info

The `.withStructuredOutput` method is in beta. It is actively being worked on, so the API may change.

Using the `.withStructuredOutput` method, you can make the LLM return structured output, given only a Zod or JSON schema:

    import { z } from "zod";import { ChatAnthropicTools } from "@langchain/anthropic/experimental";import { ChatPromptTemplate } from "@langchain/core/prompts";const calculatorSchema = z.object({  operation: z    .enum(["add", "subtract", "multiply", "divide"])    .describe("The type of operation to execute"),  number1: z.number().describe("The first number to operate on."),  number2: z.number().describe("The second number to operate on."),});const model = new ChatAnthropicTools({  model: "claude-3-sonnet-20240229",  temperature: 0.1,});// Pass the schema and tool name to the withStructuredOutput methodconst modelWithTool = model.withStructuredOutput(calculatorSchema);// You can also set force: false to allow the model scratchpad space.// This may improve reasoning capabilities.// const modelWithTool = model.withStructuredOutput(calculatorSchema, {//   force: false,// });const prompt = ChatPromptTemplate.fromMessages([  [    "system",    "You are a helpful assistant who always needs to use a calculator.",  ],  ["human", "{input}"],]);// Chain your prompt and model togetherconst chain = prompt.pipe(modelWithTool);const response = await chain.invoke({  input: "What is 2 + 2?",});console.log(response);/*  { operation: 'add', number1: 2, number2: 2 }*/

#### API Reference:

*   [ChatAnthropicTools](https://v02.api.js.langchain.com/classes/langchain_anthropic_experimental.ChatAnthropicTools.html) from `@langchain/anthropic/experimental`
*   [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts`

### Using JSON schema:[​](#using-json-schema "Direct link to Using JSON schema:")

    import { ChatAnthropicTools } from "@langchain/anthropic/experimental";import { ChatPromptTemplate } from "@langchain/core/prompts";const calculatorJsonSchema = {  type: "object",  properties: {    operation: {      type: "string",      enum: ["add", "subtract", "multiply", "divide"],      description: "The type of operation to execute.",    },    number1: { type: "number", description: "The first number to operate on." },    number2: {      type: "number",      description: "The second number to operate on.",    },  },  required: ["operation", "number1", "number2"],  description: "A simple calculator tool",};const model = new ChatAnthropicTools({  model: "claude-3-sonnet-20240229",  temperature: 0.1,});// Pass the schema and optionally, the tool name to the withStructuredOutput methodconst modelWithTool = model.withStructuredOutput(calculatorJsonSchema, {  name: "calculator",});const prompt = ChatPromptTemplate.fromMessages([  [    "system",    "You are a helpful assistant who always needs to use a calculator.",  ],  ["human", "{input}"],]);// Chain your prompt and model togetherconst chain = prompt.pipe(modelWithTool);const response = await chain.invoke({  input: "What is 2 + 2?",});console.log(response);/*  { operation: 'add', number1: 2, number2: 2 }*/

#### API Reference:

*   [ChatAnthropicTools](https://v02.api.js.langchain.com/classes/langchain_anthropic_experimental.ChatAnthropicTools.html) from `@langchain/anthropic/experimental`
*   [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts`

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*   [Setup](#setup)
*   [Initialize model](#initialize-model)
*   [Passing in tools](#passing-in-tools)
*   [Parallel tool calling](#parallel-tool-calling)
*   [`.withStructuredOutput({ ... })`](#withstructuredoutput--)
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