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，您将获得类型化的结构化数据。

本页面介绍使用 createAgent 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 的预构建 ReAct 代理 createAgent 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 structuredResponse 键中。

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

控制代理如何返回结构化数据。您可以提供 Zod 模式、任何 Standard Schema 兼容的模式，或 JSON Schema 对象。默认情况下，代理使用工具调用策略，其中输出是通过额外的工具调用来创建的。某些模型支持原生结构化输出，在这种情况下，代理将使用该策略。您可以通过将 ResponseFormat 包装在 toolStrategy 或 providerStrategy 函数调用来控制行为：

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({ ... }))
})

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

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：

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：

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

schema

required

定义结构化输出格式的模式。支持：

Zod 模式：Zod 模式
Standard Schema：任何实现 Standard Schema 规范的模式
JSON Schema：JSON 模式对象

当您直接将模式类型传递给 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",
    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" }

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 responseFormat: contactInfoSchema 与编写 responseFormat: providerStrategy(contactInfoSchema) 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：

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

schema

required

定义结构化输出格式的模式。支持：

Zod 模式：Zod 模式
Standard Schema：任何实现 Standard Schema 规范的模式
JSON Schema：JSON 模式对象

options.toolMessageContent

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。

options.handleError

包含可选 handleError 参数的 Options 参数，用于自定义错误处理策略。

true: 捕获所有错误并使用默认错误模板（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",
    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"] }

自定义工具消息内容

toolMessageContent 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：

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",
    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" }
 * }
 */

没有 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：

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",
    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

当结构化输出不符合预期模式时，代理会提供特定的错误反馈：

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",
    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."
)

// Error message becomes:
// { 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;
    }
)

// Only validation errors get retried with default message:
// { role: "tool", content: "Error: Failed to parse structured output for tool 'ProductRating': ...\n Please fix your mistakes." }

处理多种异常类型：

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;
    }
)

无错误处理：

const responseFormat = toolStrategy(ProductRating, {
    handleError: false  // All errors raised
)

Edit this page on GitHub or file an issue.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Get started

Core components

Frontend

Middleware

Advanced usage

Agent development

Deploy with LangSmith

响应格式

提供者策略

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

Get started

Core components

Frontend

Middleware

Advanced usage

Agent development

Deploy with LangSmith

​响应格式

​提供者策略

​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

提供者策略

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