结构化输出

结构化输出允许代理以特定、可预测的格式返回数据。无需解析自然语言响应，您将获得结构化的 JSON 对象、Pydantic 模型 或 dataclasses 形式的结构化数据，您的应用程序可以直接使用这些数据。

本页面介绍使用 create_agent 的代理的结构化输出。若要在模型上直接使用结构化输出（代理之外），请参阅 Models - Structured output。

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，并返回在代理状态的 'structured_response' 键中。

def create_agent(
    ...
    response_format: Union[
        ToolStrategy[StructuredResponseT],
        ProviderStrategy[StructuredResponseT],
        type[StructuredResponseT],
        None,
    ]

响应格式

使用 response_format 控制代理如何返回结构化数据：

• ToolStrategy[StructuredResponseT]: 使用工具调用来实现结构化输出
• ProviderStrategy[StructuredResponseT]: 使用提供商原生的结构化输出
• type[StructuredResponseT]: 模式类型 - 根据模型能力自动选择最佳策略
• None: 未明确请求结构化输出

当直接提供模式类型时，LangChain 会自动选择：

• 如果所选模型和提供商支持原生结构化输出（例如 OpenAI、Anthropic (Claude) 或 xAI (Grok)），则使用 ProviderStrategy。
• 对于所有其他模型，使用 ToolStrategy。

如果使用 langchain>=1.1，原生结构化输出功能的支持会从模型的 profile 数据 中动态读取。如果数据不可用，请使用其他条件或手动指定：

custom_profile = {
    "structured_output": True,
    # ...
}
model = init_chat_model("...", profile=custom_profile)

如果指定了工具，模型必须支持同时使用工具和结构化输出。

结构化响应返回在代理最终状态的 structured_response 键中。

提供者策略

某些模型提供商通过其 API 原生支持结构化输出（例如 OpenAI、xAI (Grok)、Gemini、Anthropic (Claude)）。这是可用时的最可靠方法。

要使用此策略，请配置 ProviderStrategy：

class ProviderStrategy(Generic[SchemaT]):
    schema: type[SchemaT]
    strict: bool | None = None

strict 参数需要 langchain>=1.2。

schema · required

定义结构化输出格式的模式。支持：

• Pydantic 模型：具有字段验证的 BaseModel 子类。返回经过验证的 Pydantic 实例。
• Dataclasses：带有类型注解的 Python dataclasses。返回字典。
• TypedDict：Typed 字典类。返回字典。
• JSON Schema：包含 JSON 模式规范的字典。返回字典。

strict

启用严格模式遵循的可选布尔参数。由某些提供商支持（例如，OpenAI 和 xAI）。默认为 None（禁用）。

当您直接将模式类型传递给 create_agent.response_format 且模型支持原生结构化输出时，LangChain 会自动使用 ProviderStrategy：

Pydantic Model

from pydantic import BaseModel, Field
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",
    response_format=ContactInfo  # Auto-selects ProviderStrategy
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Extract contact info from: John Doe, [e-mail], [phone]"}]
})

print(result["structured_response"])
# ContactInfo(name='John Doe', email='[e-mail]', phone='[phone]')

from dataclasses import dataclass
from langchain.agents import create_agent


@dataclass
class ContactInfo:
    """Contact information for a person."""
    name: str # The name of the person
    email: str # The email address of the person
    phone: str # 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, [e-mail], [phone]"}]
})

result["structured_response"]
# {'name': 'John Doe', 'email': '[e-mail]', 'phone': '[phone]'}

from typing_extensions import TypedDict
from langchain.agents import create_agent


class ContactInfo(TypedDict):
    """Contact information for a person."""
    name: str # The name of the person
    email: str # The email address of the person
    phone: str # 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, [e-mail], [phone]"}]
})

result["structured_response"]
# {'name': 'John Doe', 'email': '[e-mail]', 'phone': '[phone]'}

from langchain.agents import create_agent


contact_info_schema = {
    "type": "object",
    "description": "Contact information for a person.",
    "properties": {
        "name": {"type": "string", "description": "The name of the person"},
        "email": {"type": "string", "description": "The email address of the person"},
        "phone": {"type": "string", "description": "The phone number of the person"}
    },
    "required": ["name", "email", "phone"]
}

agent = create_agent(
    model="gpt-5",
    tools=tools,
    response_format=ProviderStrategy(contact_info_schema)
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Extract contact info from: John Doe, [e-mail], [phone]"}]
})

result["structured_response"]
# {'name': 'John Doe', 'email': '[e-mail]', 'phone': '[phone]'}

提供商原生的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模式。在可用时使用它。

如果您的模型选择由提供商原生支持结构化输出，则编写 response_format=ProductReview 与编写 response_format=ProviderStrategy(ProductReview) 在功能上是等效的。

无论哪种情况，如果不受支持结构化输出，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（大多数现代模型）。

要使用此策略，请配置 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],
    ]

schema · required

定义结构化输出格式的模式。支持：

• Pydantic 模型：具有字段验证的 BaseModel 子类。返回经过验证的 Pydantic 实例。
• Dataclasses：带有类型注解的 Python dataclasses。返回字典。
• TypedDict：Typed 字典类。返回字典。
• JSON Schema：包含 JSON 模式规范的字典。返回字典。
• 联合类型：多个模式选项。模型将根据上下文选择最合适的模式。

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，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: 捕获所有错误并使用默认错误模板
• str: 捕获所有错误并使用此自定义消息
• type[Exception]: 仅捕获此异常类型并使用默认消息
• tuple[type[Exception], ...]: 仅捕获这些异常类型并使用默认消息
• Callable[[Exception], str]: 返回错误消息的自定义函数
• False: 不重试，让异常传播

Pydantic Model

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'])

from dataclasses import dataclass
from typing import Literal
from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


@dataclass
class ProductReview:
    """Analysis of a product review."""
    rating: int | None  # The rating of the product (1-5)
    sentiment: Literal["positive", "negative"]  # The sentiment of the review
    key_points: list[str]  # The key points of the review

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"]
# {'rating': 5, 'sentiment': 'positive', 'key_points': ['fast shipping', 'expensive']}

from typing import Literal
from typing_extensions import TypedDict
from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


class ProductReview(TypedDict):
    """Analysis of a product review."""
    rating: int | None  # The rating of the product (1-5)
    sentiment: Literal["positive", "negative"]  # The sentiment of the review
    key_points: list[str]  # The key points of the review

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"]
# {'rating': 5, 'sentiment': 'positive', 'key_points': ['fast shipping', 'expensive']}

from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


product_review_schema = {
    "type": "object",
    "description": "Analysis of a product review.",
    "properties": {
        "rating": {
            "type": ["integer", "null"],
            "description": "The rating of the product (1-5)",
            "minimum": 1,
            "maximum": 5
        },
        "sentiment": {
            "type": "string",
            "enum": ["positive", "negative"],
            "description": "The sentiment of the review"
        },
        "key_points": {
            "type": "array",
            "items": {"type": "string"},
            "description": "The key points of the review"
        }
    },
    "required": ["sentiment", "key_points"]
}

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

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

from pydantic import BaseModel, Field
from typing import Literal, Union
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.")

class CustomerComplaint(BaseModel):
    """A customer complaint about a product or service."""
    issue_type: Literal["product", "service", "shipping", "billing"] = Field(description="The type of issue")
    severity: Literal["low", "medium", "high"] = Field(description="The severity of the complaint")
    description: str = Field(description="Brief description of the complaint")

agent = create_agent(
    model="gpt-5",
    tools=tools,
    response_format=ToolStrategy(Union[ProductReview, CustomerComplaint])
)

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出现在对话历史中的消息：

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!

没有 tool_message_content，我们最终的 ToolMessage 将是：

================================= Tool Message =================================
Name: MeetingAction

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

错误处理

模型在使用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，代理会在 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 ([e-mail]) is organizing Tech Conference on March 15th"}]
})

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

Extract info: John Doe ([e-mail]) is organizing Tech Conference on March 15th
None
================================== Ai Message ==================================
Tool Calls:
  ContactInfo (call_1)
 Call ID: call_1
  Args:
    name: John Doe
    email: [e-mail]
  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: [e-mail]
================================= Tool Message =================================
Name: ContactInfo

Returning structured response: {'name': 'John Doe', 'email': '[e-mail]'}

模式验证错误

当结构化输出不符合预期模式时，代理会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 是异常类型，仅当抛出的异常是指定类型时，代理才会重试（使用默认错误消息）。在所有其他情况下，将抛出异常。

处理多种异常类型：

ToolStrategy(
    schema=ProductRating,
    handle_errors=(ValueError, TypeError)  # Retry on ValueError and TypeError
)

如果 handle_errors 是异常元组，仅当抛出的异常是指定类型之一时，代理才会重试（使用默认错误消息）。在所有其他情况下，将抛出异常。

自定义错误处理函数：

from langchain.agents.structured_output import StructuredOutputValidationError
from langchain.agents.structured_output import MultipleStructuredOutputsError

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)}"


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

result = agent.invoke({
    "messages": [{"role": "user", "content": "Extract info: John Doe ([e-mail]) is organizing Tech Conference on March 15th"}]
})

for msg in result['messages']:
    # If message is actually a ToolMessage object (not a dict), check its class name
    if type(msg).__name__ == "ToolMessage":
        print(msg.content)
    # If message is a dictionary or you want a fallback
    elif isinstance(msg, dict) and msg.get('tool_call_id'):
        print(msg['content'])

在 StructuredOutputValidationError 上：

================================= Tool Message =================================
Name: ToolStrategy

There was an issue with the format. Try again.

在 MultipleStructuredOutputsError 上：

================================= Tool Message =================================
Name: ToolStrategy

Multiple structured outputs were returned. Pick the most relevant one.

在其他错误上：

================================= 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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