with_structured_output

Parameters

Name	Type	Description
`schema`*	`dict \| type`	The output schema. Can be passed in as: An Anthropic tool schema, An OpenAI function/tool schema, A JSON Schema, A `TypedDict` class, Or a Pydantic class. If `schema` is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool` for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or `TypedDict` class.
`include_raw`	`bool`	Default:`False` If `False` then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If `True` then both the raw model response (a `BaseMessage`) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a `dict` with keys `'raw'`, `'parsed'`, and `'parsing_error'`.
`method`	`Literal['function_calling', 'json_schema']`	Default:`'function_calling'`
`kwargs`	`Any`	Default:`{}`

Example:

from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel, Field

model = ChatAnthropic(model="claude-sonnet-4-5")

class Movie(BaseModel):
    """A movie with details."""
    title: str = Field(..., description="The title of the movie")
    year: int = Field(..., description="The year the movie was released")
    director: str = Field(..., description="The director of the movie")
    rating: float = Field(..., description="The movie's rating out of 10")

model_with_structure = model.with_structured_output(Movie, method="json_schema")
response = model_with_structure.invoke("Provide details about the movie Inception")
print(response)
# -> Movie(title="Inception", year=2010, director="Christopher Nolan", rating=8.8)

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Parameters