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结构化输出允许代理以特定的、可预测的格式返回数据。您不再需要解析自然语言响应，而是获得JSON对象、Pydantic模型或数据类形式的结构化数据，您的应用程序可以直接使用。 LangChain 的 create_agent 自动处理结构化输出。用户设置所需的结构化输出模式，当模型生成结构化数据时，它会被捕获、验证并以代理状态的 'structured_response' 键返回。
def create_agent(
    ...
    response_format: Union[
        ToolStrategy[StructuredResponseT],
        ProviderStrategy[StructuredResponseT],
        type[StructuredResponseT],
    ]

​
响应格式

控制代理如何返回结构化数据
  • ToolStrategy[StructuredResponseT]：使用工具调用进行结构化输出
  • ProviderStrategy[StructuredResponseT]：使用提供商原生结构化输出
  • type[StructuredResponseT]：架构类型 - 根据模型能力自动选择最佳策略
  • None：无结构化输出
当直接提供架构类型时，LangChain 会自动选择
  • ProviderStrategy 用于支持原生结构化输出的模型（例如 OpenAI、Grok）
  • ToolStrategy 用于所有其他模型
结构化响应在代理最终状态的 structured_response 键中返回。

​
提供商策略

一些模型提供商通过其 API 原生支持结构化输出（目前只有 OpenAI 和 Grok）。这是在可用时最可靠的方法。 要使用此策略，请配置一个 ProviderStrategy：
class ProviderStrategy(Generic[SchemaT]):
    schema: type[SchemaT]
​
架构
必填
定义结构化输出格式的架构。支持
  • Pydantic 模型：带有字段验证的 BaseModel 子类
  • 数据类：带有类型注释的 Python 数据类
  • TypedDict：类型化字典类
  • JSON Schema：带有 JSON 模式规范的字典
当您直接将架构类型传递给 create_agent.response_format 且模型支持原生结构化输出时，LangChain 会自动使用 ProviderStrategy 
from pydantic import BaseModel
from langchain.agents import create_agent


class ContactInfo(BaseModel):
    """Contact information for a person."""
    name: str = Field(description="The name of the person")
    email: str = Field(description="The email address of the person")
    phone: str = Field(description="The phone number of the person")

agent = create_agent(
    model="gpt-5",
    tools=tools,
    response_format=ContactInfo  # Auto-selects ProviderStrategy
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Extract contact info from: John Doe, john@example.com, (555) 123-4567"}]
})

result["structured_response"]
# ContactInfo(name='John Doe', email='john@example.com', phone='(555) 123-4567')
提供商原生结构化输出提供高可靠性和严格验证，因为模型提供商强制执行架构。在可用时使用它。
如果提供商原生支持您选择的模型的结构化输出，那么编写 response_format=ProductReview 而不是 response_format=ToolStrategy(ProductReview) 在功能上是等效的。在任何一种情况下，如果不支持结构化输出，代理将回退到工具调用策略。

​
工具调用策略

对于不支持原生结构化输出的模型，LangChain 使用工具调用来实现相同的结果。这适用于所有支持工具调用的模型，即大多数现代模型。 要使用此策略，请配置一个 ToolStrategy：
class ToolStrategy(Generic[SchemaT]):
    schema: type[SchemaT]
    tool_message_content: str | None
    handle_errors: Union[
        bool,
        str,
        type[Exception],
        tuple[type[Exception], ...],
        Callable[[Exception], str],
    ]
​
架构
必填
定义结构化输出格式的架构。支持
  • Pydantic 模型：带有字段验证的 BaseModel 子类
  • 数据类：带有类型注释的 Python 数据类
  • TypedDict：类型化字典类
  • JSON Schema：带有 JSON 模式规范的字典
  • 联合类型：多个架构选项。模型将根据上下文选择最合适的架构。
​
tool_message_content
生成结构化输出时返回的工具消息的自定义内容。如果未提供，则默认为显示结构化响应数据的消息。
​
handle_errors
结构化输出验证失败的错误处理策略。默认为 True。
  • True：使用默认错误模板捕获所有错误
  • str：使用此自定义消息捕获所有错误
  • type[Exception]：仅使用默认消息捕获此异常类型
  • tuple[type[Exception], ...]：仅使用默认消息捕获这些异常类型
  • Callable[[Exception], str]：返回错误消息的自定义函数
  • False：不重试，让异常传播
from pydantic import BaseModel, Field
from typing import Literal
from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


class ProductReview(BaseModel):
    """Analysis of a product review."""
    rating: int | None = Field(description="The rating of the product", ge=1, le=5)
    sentiment: Literal["positive", "negative"] = Field(description="The sentiment of the review")
    key_points: list[str] = Field(description="The key points of the review. Lowercase, 1-3 words each.")

agent = create_agent(
    model="gpt-5",
    tools=tools,
    response_format=ToolStrategy(ProductReview)
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Analyze this review: 'Great product: 5 out of 5 stars. Fast shipping, but expensive'"}]
})
result["structured_response"]
# ProductReview(rating=5, sentiment='positive', key_points=['fast shipping', 'expensive'])

​
自定义工具消息内容

tool_message_content 参数允许您自定义生成结构化输出时在对话历史中显示的消息 
from pydantic import BaseModel, Field
from typing import Literal
from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


class MeetingAction(BaseModel):
    """Action items extracted from a meeting transcript."""
    task: str = Field(description="The specific task to be completed")
    assignee: str = Field(description="Person responsible for the task")
    priority: Literal["low", "medium", "high"] = Field(description="Priority level")

agent = create_agent(
    model="gpt-5",
    tools=[],
    response_format=ToolStrategy(
        schema=MeetingAction,
        tool_message_content="Action item captured and added to meeting notes!"
    )
)

agent.invoke({
    "messages": [{"role": "user", "content": "From our meeting: Sarah needs to update the project timeline as soon as possible"}]
})

================================ Human Message =================================

From our meeting: Sarah needs to update the project timeline as soon as possible
================================== Ai Message ==================================
Tool Calls:
  MeetingAction (call_1)
 Call ID: call_1
  Args:
    task: Update the project timeline
    assignee: Sarah
    priority: high
================================= Tool Message =================================
Name: MeetingAction

Action item captured and added to meeting notes!
如果没有 tool_message_content，我们的最终 ToolMessage 将是 
================================= Tool Message =================================
Name: MeetingAction

Returning structured response: {'task': 'update the project timeline', 'assignee': 'Sarah', 'priority': 'high'}

​
错误处理

模型在通过工具调用生成结构化输出时可能会出错。LangChain 提供智能重试机制来自动处理这些错误。

​
多个结构化输出错误

当模型错误地调用多个结构化输出工具时，代理在 ToolMessage 中提供错误反馈，并提示模型重试 
from pydantic import BaseModel, Field
from typing import Union
from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


class ContactInfo(BaseModel):
    name: str = Field(description="Person's name")
    email: str = Field(description="Email address")

class EventDetails(BaseModel):
    event_name: str = Field(description="Name of the event")
    date: str = Field(description="Event date")

agent = create_agent(
    model="gpt-5",
    tools=[],
    response_format=ToolStrategy(Union[ContactInfo, EventDetails])  # Default: handle_errors=True
)

agent.invoke({
    "messages": [{"role": "user", "content": "Extract info: John Doe (john@email.com) is organizing Tech Conference on March 15th"}]
})

================================ Human Message =================================

Extract info: John Doe (john@email.com) is organizing Tech Conference on March 15th
None
================================== Ai Message ==================================
Tool Calls:
  ContactInfo (call_1)
 Call ID: call_1
  Args:
    name: John Doe
    email: john@email.com
  EventDetails (call_2)
 Call ID: call_2
  Args:
    event_name: Tech Conference
    date: March 15th
================================= Tool Message =================================
Name: ContactInfo

Error: Model incorrectly returned multiple structured responses (ContactInfo, EventDetails) when only one is expected.
 Please fix your mistakes.
================================= Tool Message =================================
Name: EventDetails

Error: Model incorrectly returned multiple structured responses (ContactInfo, EventDetails) when only one is expected.
 Please fix your mistakes.
================================== Ai Message ==================================
Tool Calls:
  ContactInfo (call_3)
 Call ID: call_3
  Args:
    name: John Doe
    email: john@email.com
================================= Tool Message =================================
Name: ContactInfo

Returning structured response: {'name': 'John Doe', 'email': 'john@email.com'}

​
架构验证错误

当结构化输出与预期架构不匹配时，代理提供特定的错误反馈 
from pydantic import BaseModel, Field
from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


class ProductRating(BaseModel):
    rating: int | None = Field(description="Rating from 1-5", ge=1, le=5)
    comment: str = Field(description="Review comment")

agent = create_agent(
    model="gpt-5",
    tools=[],
    response_format=ToolStrategy(ProductRating),  # Default: handle_errors=True
    system_prompt="You are a helpful assistant that parses product reviews. Do not make any field or value up."
)

agent.invoke({
    "messages": [{"role": "user", "content": "Parse this: Amazing product, 10/10!"}]
})

================================ Human Message =================================

Parse this: Amazing product, 10/10!
================================== Ai Message ==================================
Tool Calls:
  ProductRating (call_1)
 Call ID: call_1
  Args:
    rating: 10
    comment: Amazing product
================================= Tool Message =================================
Name: ProductRating

Error: Failed to parse structured output for tool 'ProductRating': 1 validation error for ProductRating.rating
  Input should be less than or equal to 5 [type=less_than_equal, input_value=10, input_type=int].
 Please fix your mistakes.
================================== Ai Message ==================================
Tool Calls:
  ProductRating (call_2)
 Call ID: call_2
  Args:
    rating: 5
    comment: Amazing product
================================= Tool Message =================================
Name: ProductRating

Returning structured response: {'rating': 5, 'comment': 'Amazing product'}

​
错误处理策略

您可以使用 handle_errors 参数自定义错误处理方式： 自定义错误消息：
ToolStrategy(
    schema=ProductRating,
    handle_errors="Please provide a valid rating between 1-5 and include a comment."
)
如果 handle_errors 是字符串，代理将 始终 提示模型使用固定的工具消息重试 
================================= Tool Message =================================
Name: ProductRating

Please provide a valid rating between 1-5 and include a comment.
仅处理特定异常 
ToolStrategy(
    schema=ProductRating,
    handle_errors=ValueError  # Only retry on ValueError, raise others
)
如果 handle_errors 是一个异常类型，代理将仅在引发的异常是指定类型时重试（使用默认错误消息）。在所有其他情况下，异常将被引发。 处理多个异常类型：
ToolStrategy(
    schema=ProductRating,
    handle_errors=(ValueError, TypeError)  # Retry on ValueError and TypeError
)
如果 handle_errors 是一个异常元组，代理将仅在引发的异常是指定类型之一时重试（使用默认错误消息）。在所有其他情况下，异常将被引发。 自定义错误处理函数：
def custom_error_handler(error: Exception) -> str:
    if isinstance(error, StructuredOutputValidationError):
        return "There was an issue with the format. Try again.
    elif isinstance(error, MultipleStructuredOutputsError):
        return "Multiple structured outputs were returned. Pick the most relevant one."
    else:
        return f"Error: {str(error)}"

ToolStrategy(
    schema=ToolStrategy(Union[ContactInfo, EventDetails]),
    handle_errors=custom_error_handler
)
针对 StructuredOutputValidationError 
================================= Tool Message =================================
Name: ToolStrategy

There was an issue with the format. Try again.
针对 MultipleStructuredOutputsError 
================================= Tool Message =================================
Name: ToolStrategy

Multiple structured outputs were returned. Pick the most relevant one.
针对其他错误 
================================= Tool Message =================================
Name: ToolStrategy

Error: <error message>
无错误处理 
response_format = ToolStrategy(
    schema=ProductRating,
    handle_errors=False  # All errors raised
)
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