结构化输出允许智能体以特定、可预测的格式返回数据。您可以获得类型化的结构化数据，而不是解析自然语言响应。 LangChain 的预构建 ReAct 智能体 createAgent 自动处理结构化输出。用户设置他们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、验证并在智能体状态的 structuredResponse 键中返回。 
type ResponseFormat = (
    | ZodSchema<StructuredResponseT> // a Zod schema
    | 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 Zod 对象或 JSON schema。默认情况下，智能体使用工具调用策略，其中输出由额外的工具调用创建。某些模型支持原生结构化输出，在这种情况下，智能体将使用该策略。 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最终状态的 structuredResponse 键中返回。

​
提供商策略

某些模型提供商通过其 API 原生支持结构化输出（目前仅 OpenAI 和 Grok）。这是可用时最可靠的方法。 要使用此策略，请配置 ProviderStrategy： 
function providerStrategy<StructuredResponseT>(
    schema: ZodSchema<StructuredResponseT> | JsonSchemaFormat
): ProviderStrategy<StructuredResponseT>
​
schema
required
The schema defining the structured output format. Supports:
  • Zod Schema: A zod schema
  • JSON Schema: A JSON schema object
当您直接将模式类型传递给 createAgent.responseFormat 且模型支持本地结构化输出时，LangChain 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: 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"}]
});

result.structuredResponse;
// { name: "John Doe", email: "john@example.com", phone: "(555) 123-4567" }
提供商本地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。
If the provider natively supports structured output for your model choice, it is functionally equivalent to write responseFormat: contactInfoSchema instead of responseFormat: toolStrategy(contactInfoSchema). In either case, if structured output is not supported, the agent will fall back to a tool calling strategy.

​
工具调用策略

对于不支持本地结构化输出的模型，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，这是大多数现代模型。 要使用此策略，请配置 ToolStrategy： 
function toolStrategy<StructuredResponseT>(
    responseFormat:
        | JsonSchemaFormat
        | ZodSchema<StructuredResponseT>
        | (ZodSchema<StructuredResponseT> | JsonSchemaFormat)[]
    options?: ToolStrategyOptions
): ToolStrategy<StructuredResponseT>
​
schema
required
The schema defining the structured output format. Supports:
  • Zod Schema: A zod schema
  • JSON Schema: A JSON schema object
​
options.toolMessageContent
Custom content for the tool message returned when structured output is generated. If not provided, defaults to a message showing the structured response data.
​
options.handleError
Options parameter containing an optional handleError parameter for customizing the error handling strategy.
  • true: Catch all errors with default error template (default)
  • False: No retry, let exceptions propagate
  • (error: ToolStrategyError) => string | Promise<string>: retry with the provided message or throw the error
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: tools,
    responseFormat: toolStrategy(ProductReview)
})

result = 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"] }

​
Custom tool message content

The toolMessageContent parameter allows you to customize the message that appears in the conversation history when structured output is generated: 
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" }
 * }
 */
Without toolMessageContent, we’d see: 
# 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" }
 * }
 */

​
Error handling

Models can make mistakes when generating structured output via tool calling. LangChain provides intelligent retry mechanisms to handle these errors automatically.

​
Multiple structured outputs error

When a model incorrectly calls multiple structured output tools, the agent provides error feedback in a @[ToolMessage] and prompts the model to retry: 
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" }
 * }
 */

​
Schema validation error

When structured output doesn’t match the expected schema, the agent provides specific error feedback: 
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" }
 * }
 */

​
Error handling strategies

You can customize how errors are handled using the handleErrors parameter: Custom error message: 
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." }
Handle specific exceptions only: 
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." }
Handle multiple exception types: 
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;
    }
)
No error handling: 
const responseFormat = toolStrategy(ProductRating, {
    handleError: false  // All errors raised
)
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