ChatAnthropic

Anthropic is an AI safety and research company. They are the creator of Claude.

This will help you getting started with Anthropic chat models. For detailed documentation of all ChatAnthropic features and configurations head to the API reference.

Overview

Integration details

Class	Package	Local	Serializable	PY support	Package downloads	Package latest
ChatAnthropic	`@langchain/anthropic`	❌	✅	✅

Model features

See the links in the table headers below for guides on how to use specific features.

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Token usage	Logprobs
✅	✅	❌	✅	❌	❌	✅	✅	❌

Setup

You’ll need to sign up and obtain an Anthropic API key, and install the @langchain/anthropic integration package.

Credentials

Head to Anthropic’s website to sign up to Anthropic and generate an API key. Once you’ve done this set the ANTHROPIC_API_KEY environment variable:

export ANTHROPIC_API_KEY="your-api-key"

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# export LANGCHAIN_TRACING_V2="true"
# export LANGCHAIN_API_KEY="your-api-key"

Installation

The LangChain ChatAnthropic integration lives in the @langchain/anthropic package:

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/anthropic

yarn add @langchain/anthropic

pnpm add @langchain/anthropic

Instantiation

Now we can instantiate our model object and generate chat completions:

import { ChatAnthropic } from "@langchain/anthropic";

const llm = new ChatAnthropic({
  model: "claude-3-haiku-20240307",
  temperature: 0,
  maxTokens: undefined,
  maxRetries: 2,
  // other params...
});

Invocation

const aiMsg = await llm.invoke([
  [
    "system",
    "You are a helpful assistant that translates English to French. Translate the user sentence.",
  ],
  ["human", "I love programming."],
]);
aiMsg;

AIMessage {
  "id": "msg_013WBXXiggy6gMbAUY6NpsuU",
  "content": "Voici la traduction en français :\n\nJ'adore la programmation.",
  "additional_kwargs": {
    "id": "msg_013WBXXiggy6gMbAUY6NpsuU",
    "type": "message",
    "role": "assistant",
    "model": "claude-3-haiku-20240307",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 29,
      "output_tokens": 20
    }
  },
  "response_metadata": {
    "id": "msg_013WBXXiggy6gMbAUY6NpsuU",
    "model": "claude-3-haiku-20240307",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 29,
      "output_tokens": 20
    },
    "type": "message",
    "role": "assistant"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 29,
    "output_tokens": 20,
    "total_tokens": 49
  }
}

console.log(aiMsg.content);

Voici la traduction en français :

J'adore la programmation.

Chaining

We can chain our model with a prompt template like so:

import { ChatPromptTemplate } from "@langchain/core/prompts";

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant that translates {input_language} to {output_language}.",
  ],
  ["human", "{input}"],
]);

const chain = prompt.pipe(llm);
await chain.invoke({
  input_language: "English",
  output_language: "German",
  input: "I love programming.",
});

AIMessage {
  "id": "msg_01Ca52fpd1mcGRhH4spzAWr4",
  "content": "Ich liebe das Programmieren.",
  "additional_kwargs": {
    "id": "msg_01Ca52fpd1mcGRhH4spzAWr4",
    "type": "message",
    "role": "assistant",
    "model": "claude-3-haiku-20240307",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 23,
      "output_tokens": 11
    }
  },
  "response_metadata": {
    "id": "msg_01Ca52fpd1mcGRhH4spzAWr4",
    "model": "claude-3-haiku-20240307",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 23,
      "output_tokens": 11
    },
    "type": "message",
    "role": "assistant"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 23,
    "output_tokens": 11,
    "total_tokens": 34
  }
}

Content blocks

One key difference to note between Anthropic models and most others is that the contents of a single Anthropic AI message can either be a single string or a list of content blocks. For example when an Anthropic model calls a tool, the tool invocation is part of the message content (as well as being exposed in the standardized AIMessage.tool_calls field):

import { ChatAnthropic } from "@langchain/anthropic";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

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 calculatorTool = {
  name: "calculator",
  description: "A simple calculator tool",
  input_schema: zodToJsonSchema(calculatorSchema),
};

const toolCallingLlm = new ChatAnthropic({
  model: "claude-3-haiku-20240307",
}).bindTools([calculatorTool]);

const toolPrompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant who always needs to use a calculator.",
  ],
  ["human", "{input}"],
]);

// Chain your prompt and model together
const toolCallChain = toolPrompt.pipe(toolCallingLlm);

await toolCallChain.invoke({
  input: "What is 2 + 2?",
});

AIMessage {
  "id": "msg_01DZGs9DyuashaYxJ4WWpWUP",
  "content": [
    {
      "type": "text",
      "text": "Here is the calculation for 2 + 2:"
    },
    {
      "type": "tool_use",
      "id": "toolu_01SQXBamkBr6K6NdHE7GWwF8",
      "name": "calculator",
      "input": {
        "number1": 2,
        "number2": 2,
        "operation": "add"
      }
    }
  ],
  "additional_kwargs": {
    "id": "msg_01DZGs9DyuashaYxJ4WWpWUP",
    "type": "message",
    "role": "assistant",
    "model": "claude-3-haiku-20240307",
    "stop_reason": "tool_use",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 449,
      "output_tokens": 100
    }
  },
  "response_metadata": {
    "id": "msg_01DZGs9DyuashaYxJ4WWpWUP",
    "model": "claude-3-haiku-20240307",
    "stop_reason": "tool_use",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 449,
      "output_tokens": 100
    },
    "type": "message",
    "role": "assistant"
  },
  "tool_calls": [
    {
      "name": "calculator",
      "args": {
        "number1": 2,
        "number2": 2,
        "operation": "add"
      },
      "id": "toolu_01SQXBamkBr6K6NdHE7GWwF8",
      "type": "tool_call"
    }
  ],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 449,
    "output_tokens": 100,
    "total_tokens": 549
  }
}

Custom headers

You can pass custom headers in your requests like this:

import { ChatAnthropic } from "@langchain/anthropic";

const llmWithCustomHeaders = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  maxTokens: 1024,
  clientOptions: {
    defaultHeaders: {
      "X-Api-Key": process.env.ANTHROPIC_API_KEY,
    },
  },
});

await llmWithCustomHeaders.invoke("Why is the sky blue?");

AIMessage {
  "id": "msg_019z4nWpShzsrbSHTWXWQh6z",
  "content": "The sky appears blue due to a phenomenon called Rayleigh scattering. Here's a brief explanation:\n\n1) Sunlight is made up of different wavelengths of visible light, including all the colors of the rainbow.\n\n2) As sunlight passes through the atmosphere, the gases (mostly nitrogen and oxygen) cause the shorter wavelengths of light, such as violet and blue, to be scattered more easily than the longer wavelengths like red and orange.\n\n3) This scattering of the shorter blue wavelengths occurs in all directions by the gas molecules in the atmosphere.\n\n4) Our eyes are more sensitive to the scattered blue light than the scattered violet light, so we perceive the sky as having a blue color.\n\n5) The scattering is more pronounced for light traveling over longer distances through the atmosphere. This is why the sky appears even darker blue when looking towards the horizon.\n\nSo in essence, the selective scattering of the shorter blue wavelengths of sunlight by the gases in the atmosphere is what causes the sky to appear blue to our eyes during the daytime.",
  "additional_kwargs": {
    "id": "msg_019z4nWpShzsrbSHTWXWQh6z",
    "type": "message",
    "role": "assistant",
    "model": "claude-3-sonnet-20240229",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 13,
      "output_tokens": 236
    }
  },
  "response_metadata": {
    "id": "msg_019z4nWpShzsrbSHTWXWQh6z",
    "model": "claude-3-sonnet-20240229",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 13,
      "output_tokens": 236
    },
    "type": "message",
    "role": "assistant"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 13,
    "output_tokens": 236,
    "total_tokens": 249
  }
}

API reference

For detailed documentation of all ChatAnthropic features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_anthropic.ChatAnthropic.html

Chat model conceptual guide
Chat model how-to guides

ChatAnthropic

Overview

Integration details

Model features

Setup

Credentials

Installation

Instantiation

Invocation

Chaining

Content blocks

Custom headers

API reference

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Overview​

Integration details​

Model features​

Setup​

Credentials​

Installation​

Instantiation​

Invocation​

Chaining​

Content blocks​

Custom headers​

API reference​

Related​

Was this page helpful?

You can also leave detailed feedback on GitHub.

Overview

Integration details

Model features

Setup

Credentials

Installation

Instantiation

Invocation

Chaining

Content blocks

Custom headers

API reference

Related