结构化输出

结构化输出允许智能体以特定、可预测的格式返回数据。您无需解析自然语言响应，而是可以直接获得类型化的结构化数据。

本页介绍使用 createAgent 的智能体的结构化输出。若要在模型上直接使用结构化输出（在智能体之外），请参阅模型 - 结构化输出。

LangChain 预构建的 ReAct 智能体 createAgent 会自动处理结构化输出。用户设置他们期望的结构化输出模式，当模型生成结构化数据时，它会被捕获、验证，并在智能体状态的 structuredResponse 键中返回。

type ResponseFormat = (
    | ZodSchema<StructuredResponseT> // a Zod schema
    | StandardSchema<StructuredResponseT> // any Standard Schema library
    | Record<string, unknown> // a JSON Schema
)

const agent = createAgent({
    // ...
    responseFormat: ResponseFormat | ResponseFormat[]
})

响应格式

控制智能体如何返回结构化数据。您可以提供一个 Zod 模式、任何 Standard Schema 兼容的模式，或者一个 JSON Schema 对象。默认情况下，智能体使用工具调用策略，即通过额外的工具调用来生成输出。某些模型支持原生结构化输出，此时智能体将改用该策略。您可以通过将 ResponseFormat 包装在 toolStrategy 或 providerStrategy 函数调用中来控制行为：

import { toolStrategy, providerStrategy } from "langchain";

const agent = createAgent({
    // use a provider strategy if supported by the model
    responseFormat: providerStrategy(z.object({ ... }))
    // or enforce a tool strategy
    responseFormat: toolStrategy(z.object({ ... }))
})

结构化响应在智能体的最终状态中的 structuredResponse 键下返回。

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

const customProfile: ModelProfile = {
    structuredOutput: true,
    // ...
}
const model = await initChatModel("...", { profile: customProfile });

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。

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（例如 OpenAI、xAI (Grok)、Gemini、Anthropic (Claude)）。当可用时，这是最可靠的方法。要使用此策略，请配置一个 ProviderStrategy：

function providerStrategy<StructuredResponseT>(
    schema: ZodSchema<StructuredResponseT> | SerializableSchema | JsonSchemaFormat
): ProviderStrategy<StructuredResponseT>

required

定义结构化输出格式的模式。支持：

Zod Schema：一个 Zod 模式
Standard Schema：任何实现了 Standard Schema 规范的模式
JSON Schema：一个 JSON schema 对象

当您将模式类型直接传递给 createAgent.responseFormat 且模型支持原生结构化输出时，LangChain 会自动使用 ProviderStrategy：

import * as z from "zod";
import { createAgent, providerStrategy } from "langchain";

const ContactInfo = z.object({
    name: z.string().describe("The name of the person"),
    email: z.string().describe("The email address of the person"),
    phone: z.string().describe("The phone number of the person"),
});

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: providerStrategy(ContactInfo)
});

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

console.log(result.structuredResponse);
// { name: "John Doe", email: "john@example.com", phone: "(555) 123-4567" }

import * as v from "valibot";
import { toStandardJsonSchema } from "@valibot/to-json-schema";
import { createAgent, providerStrategy } from "langchain";

const ContactInfo = toStandardJsonSchema(
    v.object({
        name: v.pipe(v.string(), v.description("The name of the person")),
        email: v.pipe(v.string(), v.description("The email address of the person")),
        phone: v.pipe(v.string(), v.description("The phone number of the person")),
    })
);

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: providerStrategy(ContactInfo)
});

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

console.log(result.structuredResponse);
// { name: "John Doe", email: "john@example.com", phone: "(555) 123-4567" }

import { createAgent, providerStrategy } from "langchain";

const contactInfoSchema = {
    "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"]
}

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: providerStrategy(contactInfoSchema)
});

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

console.log(result.structuredResponse);
// { name: "John Doe", email: "john@example.com", phone: "(555) 123-4567" }

提供商原生的结构化输出具有高可靠性和严格验证，因为模式是由模型提供商强制执行的。当可用时请使用它。

如果提供商为您的模型选择原生支持结构化输出，那么写 responseFormat: contactInfoSchema 与写 responseFormat: providerStrategy(contactInfoSchema) 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，智能体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，请配置一个 ToolStrategy：

function toolStrategy<StructuredResponseT>(
    responseFormat:
        | JsonSchemaFormat
        | ZodSchema<StructuredResponseT>
        | SerializableSchema
        | (ZodSchema<StructuredResponseT> | SerializableSchema | JsonSchemaFormat)[]
    options?: ToolStrategyOptions
): ToolStrategy<StructuredResponseT>

required

定义结构化输出格式的模式。支持：

Zod Schema：一个 Zod 模式
Standard Schema：任何实现了 Standard Schema 规范的模式
JSON Schema：一个 JSON schema 对象

当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。

包含可选 handleError 参数的选项参数，用于自定义错误处理策略。

true：使用默认错误模板捕获所有错误（默认）
False：不重试，让异常传播
(error: ToolStrategyError) => string | Promise<string>：使用提供的消息重试或抛出错误

import * as z from "zod";
import { createAgent, toolStrategy } from "langchain";

const ProductReview = z.object({
    rating: z.number().min(1).max(5).optional(),
    sentiment: z.enum(["positive", "negative"]),
    keyPoints: z.array(z.string()).describe("The key points of the review. Lowercase, 1-3 words each."),
});

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: toolStrategy(ProductReview)
})

const result = await agent.invoke({
    "messages": [{"role": "user", "content": "Analyze this review: 'Great product: 5 out of 5 stars. Fast shipping, but expensive'"}]
})

console.log(result.structuredResponse);
// { "rating": 5, "sentiment": "positive", "keyPoints": ["fast shipping", "expensive"] }

import * as v from "valibot";
import { toStandardJsonSchema } from "@valibot/to-json-schema";
import { createAgent, toolStrategy } from "langchain";

const ProductReview = toStandardJsonSchema(
    v.object({
        rating: v.optional(v.pipe(v.number(), v.minValue(1), v.maxValue(5))),
        sentiment: v.picklist(["positive", "negative"]),
        keyPoints: v.pipe(v.array(v.string()), v.description("The key points of the review. Lowercase, 1-3 words each.")),
    })
);

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: toolStrategy(ProductReview)
})

const result = await agent.invoke({
    messages: [{"role": "user", "content": "Analyze this review: 'Great product: 5 out of 5 stars. Fast shipping, but expensive'"}]
})

console.log(result.structuredResponse);
// { "rating": 5, "sentiment": "positive", "keyPoints": ["fast shipping", "expensive"] }

import { createAgent, toolStrategy } from "langchain";

const productReviewSchema = {
    "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"]
}

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: toolStrategy(productReviewSchema)
});

const result = await agent.invoke({
    messages: [{"role": "user", "content": "Analyze this review: 'Great product: 5 out of 5 stars. Fast shipping, but expensive'"}]
})

console.log(result.structuredResponse);
// { "rating": 5, "sentiment": "positive", "keyPoints": ["fast shipping", "expensive"] }

import * as z from "zod";
import { createAgent, toolStrategy } from "langchain";

const ProductReview = z.object({
    rating: z.number().min(1).max(5).optional(),
    sentiment: z.enum(["positive", "negative"]),
    keyPoints: z.array(z.string()).describe("The key points of the review. Lowercase, 1-3 words each."),
});

const CustomerComplaint = z.object({
    issueType: z.enum(["product", "service", "shipping", "billing"]),
    severity: z.enum(["low", "medium", "high"]),
    description: z.string().describe("Brief description of the complaint"),
});

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: toolStrategy([ProductReview, CustomerComplaint])
});

const result = await agent.invoke({
    messages: [{"role": "user", "content": "Analyze this review: 'Great product: 5 out of 5 stars. Fast shipping, but expensive'"}]
})

console.log(result.structuredResponse);
// { "rating": 5, "sentiment": "positive", "keyPoints": ["fast shipping", "expensive"] }

自定义工具消息内容

toolMessageContent 参数允许您自定义当生成结构化输出时出现在对话历史记录中的消息：

import * as z from "zod";
import { createAgent, toolStrategy } from "langchain";

const MeetingAction = z.object({
    task: z.string().describe("The specific task to be completed"),
    assignee: z.string().describe("Person responsible for the task"),
    priority: z.enum(["low", "medium", "high"]).describe("Priority level"),
});

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: toolStrategy(MeetingAction, {
        toolMessageContent: "Action item captured and added to meeting notes!"
    })
});

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

console.log(result);
/**
 * {
 *   messages: [
 *     { role: "user", content: "From our meeting: Sarah needs to update the project timeline as soon as possible" },
 *     { role: "assistant", content: "Action item captured and added to meeting notes!", tool_calls: [ { name: "MeetingAction", args: { task: "update the project timeline", assignee: "Sarah", priority: "high" }, id: "call_456" } ] },
 *     { role: "tool", content: "Action item captured and added to meeting notes!", tool_call_id: "call_456", name: "MeetingAction" }
 *   ],
 *   structuredResponse: { task: "update the project timeline", assignee: "Sarah", priority: "high" }
 * }
 */

如果不设置 toolMessageContent，我们将看到：

# console.log(result);
/**
 * {
 *   messages: [
 *     ...
 *     { role: "tool", content: "Returning structured response: {'task': 'update the project timeline', 'assignee': 'Sarah', 'priority': 'high'}", tool_call_id: "call_456", name: "MeetingAction" }
 *   ],
 *   structuredResponse: { 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 提供了智能重试机制来自动处理这些错误。

多个结构化输出错误

当模型错误地调用了多个结构化输出工具时，智能体会在 ToolMessage 中提供错误反馈并提示模型重试：

import * as z from "zod";
import { createAgent, toolStrategy } from "langchain";

const ContactInfo = z.object({
    name: z.string().describe("Person's name"),
    email: z.string().describe("Email address"),
});

const EventDetails = z.object({
    event_name: z.string().describe("Name of the event"),
    date: z.string().describe("Event date"),
});

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: toolStrategy([ContactInfo, EventDetails]),
});

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

console.log(result);

/**
 * {
 *   messages: [
 *     { role: "user", content: "Extract info: John Doe (john@email.com) is organizing Tech Conference on March 15th" },
 *     { role: "assistant", content: "", tool_calls: [ { name: "ContactInfo", args: { name: "John Doe", email: "john@email.com" }, id: "call_1" }, { name: "EventDetails", args: { event_name: "Tech Conference", date: "March 15th" }, id: "call_2" } ] },
 *     { role: "tool", content: "Error: Model incorrectly returned multiple structured responses (ContactInfo, EventDetails) when only one is expected.\n Please fix your mistakes.", tool_call_id: "call_1", name: "ContactInfo" },
 *     { role: "tool", content: "Error: Model incorrectly returned multiple structured responses (ContactInfo, EventDetails) when only one is expected.\n Please fix your mistakes.", tool_call_id: "call_2", name: "EventDetails" },
 *     { role: "assistant", content: "", tool_calls: [ { name: "ContactInfo", args: { name: "John Doe", email: "john@email.com" }, id: "call_3" } ] },
 *     { role: "tool", content: "Returning structured response: {'name': 'John Doe', 'email': 'john@email.com'}", tool_call_id: "call_3", name: "ContactInfo" }
 *   ],
 *   structuredResponse: { name: "John Doe", email: "john@email.com" }
 * }
 */

模式验证错误

当结构化输出与预期模式不匹配时，智能体会提供具体的错误反馈：

import * as z from "zod";
import { createAgent, toolStrategy } from "langchain";

const ProductRating = z.object({
    rating: z.number().min(1).max(5).describe("Rating from 1-5"),
    comment: z.string().describe("Review comment"),
});

const agent = createAgent({
    model: "gpt-5.5",
    tools: [],
    responseFormat: toolStrategy(ProductRating),
});

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

console.log(result);

/**
 * {
 *   messages: [
 *     { role: "user", content: "Parse this: Amazing product, 10/10!" },
 *     { role: "assistant", content: "", tool_calls: [ { name: "ProductRating", args: { rating: 10, comment: "Amazing product" }, id: "call_1" } ] },
 *     { role: "tool", content: "Error: Failed to parse structured output for tool 'ProductRating': 1 validation error for ProductRating\nrating\n  Input should be less than or equal to 5 [type=less_than_equal, input_value=10, input_type=int].\n Please fix your mistakes.", tool_call_id: "call_1", name: "ProductRating" },
 *     { role: "assistant", content: "", tool_calls: [ { name: "ProductRating", args: { rating: 5, comment: "Amazing product" }, id: "call_2" } ] },
 *     { role: "tool", content: "Returning structured response: {'rating': 5, 'comment': 'Amazing product'}", tool_call_id: "call_2", name: "ProductRating" }
 *   ],
 *   structuredResponse: { rating: 5, comment: "Amazing product" }
 * }
 */

错误处理策略

您可以使用 handleErrors 参数自定义错误处理方式： 自定义错误消息：

const responseFormat = toolStrategy(ProductRating, {
    handleError: "Please provide a valid rating between 1-5 and include a comment."
)

// 错误消息变为：
// { role: "tool", content: "Please provide a valid rating between 1-5 and include a comment." }

仅处理特定异常：

import { ToolInputParsingException } from "@langchain/core/tools";

const responseFormat = toolStrategy(ProductRating, {
    handleError: (error: ToolStrategyError) => {
        if (error instanceof ToolInputParsingException) {
        return "Please provide a valid rating between 1-5 and include a comment.";
        }
        return error.message;
    }
)

// 仅验证错误会使用默认消息重试：
// { role: "tool", content: "Error: Failed to parse structured output for tool 'ProductRating': ...\n Please fix your mistakes." }

处理多个异常类型：

const responseFormat = toolStrategy(ProductRating, {
    handleError: (error: ToolStrategyError) => {
        if (error instanceof ToolInputParsingException) {
        return "Please provide a valid rating between 1-5 and include a comment.";
        }
        if (error instanceof CustomUserError) {
        return "This is a custom user error.";
        }
        return error.message;
    }
)

不进行错误处理：

const responseFormat = toolStrategy(ProductRating, {
    handleError: false  // 所有错误都将抛出
)

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响应格式

提供商策略

工具调用策略

自定义工具消息内容

错误处理

多个结构化输出错误

模式验证错误

错误处理策略

​响应格式

​提供商策略

​工具调用策略

​自定义工具消息内容

​错误处理

​多个结构化输出错误

​模式验证错误

​错误处理策略

响应格式

提供商策略

工具调用策略

自定义工具消息内容

错误处理

多个结构化输出错误

模式验证错误

错误处理策略