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This notebook provides a quick overview for getting started with Anthropic chat models. For detailed documentation of all ChatAnthropic features and configurations head to the API reference.

Anthropic has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the Anthropic docs.

AWS Bedrock and Google VertexAI

Note that certain Anthropic models can also be accessed via AWS Bedrock and Google VertexAI. See the ChatBedrock and ChatVertexAI integrations to use Anthropic models via these services.


Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatAnthropiclangchain-anthropic❌betaβœ…PyPI - DownloadsPyPI - Version

Model features​

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs


To access Anthropic models you'll need to create an Anthropic account, get an API key, and install the langchain-anthropic integration package.


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

import getpass
import os

os.environ["anthropic_API_KEY"] = getpass.getpass("Enter your Anthropic API key: ")

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

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"


The LangChain Anthropic integration lives in the langchain-anthropic package:

%pip install -qU langchain-anthropic


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

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
# other params...
API Reference:ChatAnthropic


messages = [
"You are a helpful assistant that translates English to French. Translate the user sentence.",
("human", "I love programming."),
ai_msg = llm.invoke(messages)
AIMessage(content="Voici la traduction en français :\n\nJ'aime la programmation.", response_metadata={'id': 'msg_013qztabaFADNnKsHR1rdrju', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 21}}, id='run-a22ab30c-7e09-48f5-bc27-a08a9d8f7fa1-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})
Voici la traduction en français :

J'aime la programmation.


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

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
"You are a helpful assistant that translates {input_language} to {output_language}.",
("human", "{input}"),

chain = prompt | llm
"input_language": "English",
"output_language": "German",
"input": "I love programming.",
API Reference:ChatPromptTemplate
AIMessage(content='Ich liebe Programmieren.', response_metadata={'id': 'msg_01FWrA8w9HbjqYPTQ7VryUnp', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 23, 'output_tokens': 11}}, id='run-b749bf20-b46d-4d62-ac73-f59adab6dd7e-0', 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 invokes a tool, the tool invocation is part of the message content (as well as being exposed in the standardized AIMessage.tool_calls):

from langchain_core.pydantic_v1 import BaseModel, Field

class GetWeather(BaseModel):
"""Get the current weather in a given location"""

location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

llm_with_tools = llm.bind_tools([GetWeather])
ai_msg = llm_with_tools.invoke("Which city is hotter today: LA or NY?")
[{'text': "Okay, let's use the GetWeather tool to check the current temperatures in Los Angeles and New York City.",
'type': 'text'},
{'id': 'toolu_01Tnp5tL7LJZaVyQXKEjbqcC',
'input': {'location': 'Los Angeles, CA'},
'name': 'GetWeather',
'type': 'tool_use'}]
[{'name': 'GetWeather',
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01Tnp5tL7LJZaVyQXKEjbqcC'}]

API reference​

For detailed documentation of all ChatAnthropic features and configurations head to the API reference:

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