Models¶

This page describes how to configure the chat model used by an agent.

Tool calling support¶

To enable tool-calling agents, the underlying LLM must support tool calling.

Compatible models can be found in the LangChain integrations directory.

Specifying a model by name¶

You can configure an agent with a model name string:

from langgraph.prebuilt import create_react_agent

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    # other parameters
)

Using `init_chat_model`¶

The init_chat_model utility simplifies model initialization with configurable parameters:

^{API Reference: init_chat_model}

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "anthropic:claude-3-7-sonnet-latest",
    temperature=0,
    max_tokens=2048
)

Refer to the API reference for advanced options.

Using provider-specific LLMs¶

If a model provider is not available via init_chat_model, you can instantiate the provider's model class directly. The model must implement the BaseChatModel interface and support tool calling:

^{API Reference: ChatAnthropic | create_react_agent}

from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent

model = ChatAnthropic(
    model="claude-3-7-sonnet-latest",
    temperature=0,
    max_tokens=2048
)

agent = create_react_agent(
    model=model,
    # other parameters
)

Illustrative example

The example above uses ChatAnthropic, which is already supported by init_chat_model. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.

Disable streaming¶

To disable streaming of the individual LLM tokens, set disable_streaming=True when initializing the model:

init_chat_modelChatModel

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "anthropic:claude-3-7-sonnet-latest",
    disable_streaming=True
)

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-3-7-sonnet-latest",
    disable_streaming=True
)

Refer to the API reference for more information on disable_streaming

Adding model fallbacks¶

You can add a fallback to a different model or a different LLM provider using model.with_fallbacks([...]):

init_chat_modelChatModel

from langchain.chat_models import init_chat_model

model_with_fallbacks = (
    init_chat_model("anthropic:claude-3-5-haiku-latest")
    .with_fallbacks([
        init_chat_model("openai:gpt-4.1-mini"),
    ])
)

from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI

model_with_fallbacks = (
    ChatAnthropic(model="claude-3-5-haiku-latest")
    .with_fallbacks([
        ChatOpenAI(model="gpt-4.1-mini"),
    ])
)

See this guide for more information on model fallbacks.