create_agent() — langchain Function Reference

Source Code

libs/langchain_v1/langchain/agents/factory.py lines 653–1604

def create_agent(
    model: str | BaseChatModel,
    tools: Sequence[BaseTool | Callable[..., Any] | dict[str, Any]] | None = None,
    *,
    system_prompt: str | SystemMessage | None = None,
    middleware: Sequence[AgentMiddleware[StateT_co, ContextT]] = (),
    response_format: ResponseFormat[ResponseT] | type[ResponseT] | dict[str, Any] | None = None,
    state_schema: type[AgentState[ResponseT]] | None = None,
    context_schema: type[ContextT] | None = None,
    checkpointer: Checkpointer | None = None,
    store: BaseStore | None = None,
    interrupt_before: list[str] | None = None,
    interrupt_after: list[str] | None = None,
    debug: bool = False,
    name: str | None = None,
    cache: BaseCache[Any] | None = None,
) -> CompiledStateGraph[
    AgentState[ResponseT], ContextT, _InputAgentState, _OutputAgentState[ResponseT]
]:
    """Creates an agent graph that calls tools in a loop until a stopping condition is met.

    For more details on using `create_agent`,
    visit the [Agents](https://docs.langchain.com/oss/python/langchain/agents) docs.

    Args:
        model: The language model for the agent.

            Can be a string identifier (e.g., `"openai:gpt-4"`) or a direct chat model
            instance (e.g., [`ChatOpenAI`][langchain_openai.ChatOpenAI] or other another
            [LangChain chat model](https://docs.langchain.com/oss/python/integrations/chat)).

            For a full list of supported model strings, see
            [`init_chat_model`][langchain.chat_models.init_chat_model(model_provider)].

            !!! tip ""

                See the [Models](https://docs.langchain.com/oss/python/langchain/models)
                docs for more information.
        tools: A list of tools, `dict`, or `Callable`.

            If `None` or an empty list, the agent will consist of a model node without a
            tool calling loop.


            !!! tip ""

                See the [Tools](https://docs.langchain.com/oss/python/langchain/tools)
                docs for more information.
        system_prompt: An optional system prompt for the LLM.

            Can be a `str` (which will be converted to a `SystemMessage`) or a
            `SystemMessage` instance directly. The system message is added to the
            beginning of the message list when calling the model.
        middleware: A sequence of middleware instances to apply to the agent.

            Middleware can intercept and modify agent behavior at various stages.

            !!! tip ""

                See the [Middleware](https://docs.langchain.com/oss/python/langchain/middleware)
                docs for more information.
        response_format: An optional configuration for structured responses.

            Can be a `ToolStrategy`, `ProviderStrategy`, or a Pydantic model class.

            If provided, the agent will handle structured output during the
            conversation flow.

            Raw schemas will be wrapped in an appropriate strategy based on model
            capabilities.

            !!! tip ""

                See the [Structured output](https://docs.langchain.com/oss/python/langchain/structured-output)
                docs for more information.
        state_schema: An optional `TypedDict` schema that extends `AgentState`.

            When provided, this schema is used instead of `AgentState` as the base
            schema for merging with middleware state schemas. This allows users to
            add custom state fields without needing to create custom middleware.