Chat models

Reference docs

This page contains reference documentation for chat models. See the docs for conceptual guides, tutorials, and examples on using chat models.

langchain.chat_models

Entrypoint to using chat models in LangChain.

init_chat_model

init_chat_model(
    model: str | None = None,
    *,
    model_provider: str | None = None,
    configurable_fields: Literal["any"] | list[str] | tuple[str, ...] | None = None,
    config_prefix: str | None = None,
    **kwargs: Any,
) -> BaseChatModel | _ConfigurableModel

Initialize a chat model from any supported provider using a unified interface.

Two main use cases:

1. Fixed model – specify the model upfront and get a ready-to-use chat model.
2. Configurable model – choose to specify parameters (including model name) at runtime via config. Makes it easy to switch between models/providers without changing your code

Note

Requires the integration package for the chosen model provider to be installed.

See the model_provider parameter below for specific package names (e.g., pip install langchain-openai).

Refer to the provider integration's API reference for supported model parameters to use as **kwargs.

PARAMETER	DESCRIPTION
model	The name or ID of the model, e.g. 'o3-mini', 'claude-sonnet-4-5-20250929'. You can also specify model and model provider in a single argument using '{model_provider}:{model}' format, e.g. 'openai:o1'. Will attempt to infer model_provider from model if not specified. The following providers will be inferred based on these model prefixes: gpt-... | o1... | o3... -> openai claude... -> anthropic amazon... -> bedrock gemini... -> google_vertexai command... -> cohere accounts/fireworks... -> fireworks mistral... -> mistralai deepseek... -> deepseek grok... -> xai sonar... -> perplexity TYPE: str | None DEFAULT: None
model_provider	The model provider if not specified as part of the model arg (see above). Supported model_provider values and the corresponding integration package are: openai -> langchain-openai anthropic -> langchain-anthropic azure_openai -> langchain-openai azure_ai -> langchain-azure-ai google_vertexai -> langchain-google-vertexai google_genai -> langchain-google-genai bedrock -> langchain-aws bedrock_converse -> langchain-aws cohere -> langchain-cohere fireworks -> langchain-fireworks together -> langchain-together mistralai -> langchain-mistralai huggingface -> langchain-huggingface groq -> langchain-groq ollama -> langchain-ollama google_anthropic_vertex -> langchain-google-vertexai deepseek -> langchain-deepseek ibm -> langchain-ibm nvidia -> langchain-nvidia-ai-endpoints xai -> langchain-xai perplexity -> langchain-perplexity TYPE: str | None DEFAULT: None
configurable_fields	Which model parameters are configurable at runtime: None: No configurable fields (i.e., a fixed model). 'any': All fields are configurable. See security note below. list[str] | Tuple[str, ...]: Specified fields are configurable. Fields are assumed to have config_prefix stripped if a config_prefix is specified. If model is specified, then defaults to None. If model is not specified, then defaults to ("model", "model_provider"). Security note Setting configurable_fields="any" means fields like api_key, base_url, etc., can be altered at runtime, potentially redirecting model requests to a different service/user. Make sure that if you're accepting untrusted configurations that you enumerate the configurable_fields=(...) explicitly. TYPE: Literal['any'] | list[str] | tuple[str, ...] | None DEFAULT: None
config_prefix	Optional prefix for configuration keys. Useful when you have multiple configurable models in the same application. If 'config_prefix' is a non-empty string then model will be configurable at runtime via the config["configurable"]["{config_prefix}_{param}"] keys. See examples below. If 'config_prefix' is an empty string then model will be configurable via config["configurable"]["{param}"]. TYPE: str | None DEFAULT: None
**kwargs	Additional model-specific keyword args to pass to the underlying chat model's __init__ method. Common parameters include: temperature: Model temperature for controlling randomness. max_tokens: Maximum number of output tokens. timeout: Maximum time (in seconds) to wait for a response. max_retries: Maximum number of retry attempts for failed requests. base_url: Custom API endpoint URL. rate_limiter: A BaseRateLimiter instance to control request rate. Refer to the specific model provider's integration reference for all available parameters. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
BaseChatModel | _ConfigurableModel	A BaseChatModel corresponding to the model_name and model_provider specified if configurability is inferred to be False. If configurable, a chat model emulator that initializes the underlying model at runtime once a config is passed in.

RAISES	DESCRIPTION
ValueError	If model_provider cannot be inferred or isn't supported.
ImportError	If the model provider integration package is not installed.

Initialize a non-configurable model

# pip install langchain langchain-openai langchain-anthropic langchain-google-vertexai

from langchain.chat_models import init_chat_model

o3_mini = init_chat_model("openai:o3-mini", temperature=0)
claude_sonnet = init_chat_model("anthropic:claude-sonnet-4-5-20250929", temperature=0)
gemini_2-5_flash = init_chat_model("google_vertexai:gemini-2.5-flash", temperature=0)

o3_mini.invoke("what's your name")
claude_sonnet.invoke("what's your name")
gemini_2-5_flash.invoke("what's your name")

Partially configurable model with no default

# pip install langchain langchain-openai langchain-anthropic

from langchain.chat_models import init_chat_model

# (We don't need to specify configurable=True if a model isn't specified.)
configurable_model = init_chat_model(temperature=0)

configurable_model.invoke("what's your name", config={"configurable": {"model": "gpt-4o"}})
# Use GPT-4o to generate the response

configurable_model.invoke(
    "what's your name",
    config={"configurable": {"model": "claude-sonnet-4-5-20250929"}},
)

Fully configurable model with a default

# pip install langchain langchain-openai langchain-anthropic

from langchain.chat_models import init_chat_model

configurable_model_with_default = init_chat_model(
    "openai:gpt-4o",
    configurable_fields="any",  # This allows us to configure other params like temperature, max_tokens, etc at runtime.
    config_prefix="foo",
    temperature=0,
)

configurable_model_with_default.invoke("what's your name")
# GPT-4o response with temperature 0 (as set in default)

configurable_model_with_default.invoke(
    "what's your name",
    config={
        "configurable": {
            "foo_model": "anthropic:claude-sonnet-4-5-20250929",
            "foo_temperature": 0.6,
        }
    },
)
# Override default to use Sonnet 4.5 with temperature 0.6 to generate response

Bind tools to a configurable model

You can call any chat model declarative methods on a configurable model in the same way that you would with a normal model:

# pip install langchain langchain-openai langchain-anthropic

from langchain.chat_models import init_chat_model
from pydantic 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")


class GetPopulation(BaseModel):
    '''Get the current population in a given location'''

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


configurable_model = init_chat_model(
    "gpt-4o", configurable_fields=("model", "model_provider"), temperature=0
)

configurable_model_with_tools = configurable_model.bind_tools(
    [
        GetWeather,
        GetPopulation,
    ]
)
configurable_model_with_tools.invoke(
    "Which city is hotter today and which is bigger: LA or NY?"
)
# Use GPT-4o

configurable_model_with_tools.invoke(
    "Which city is hotter today and which is bigger: LA or NY?",
    config={"configurable": {"model": "claude-sonnet-4-5-20250929"}},
)
# Use Sonnet 4.5