结构化输出允许代理以特定、可预测的格式返回数据。您将获得类型化的结构化数据,而不是解析自然语言响应。 LangChain 预构建的 ReAct 代理 createAgent 会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,并在代理状态的 structuredResponse 键中返回。type ResponseFormat = (
| ZodSchema<StructuredResponseT> // a Zod schema
| Record<string, unknown> // a JSON Schema
)
const agent = createAgent({
// ...
responseFormat: ResponseFormat | ResponseFormat[]
})
控制代理如何返回结构化数据。您可以提供 Zod 对象或 JSON 模式。默认情况下,代理使用工具调用策略,其中输出通过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,代理将改用该策略。 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({ ... }))
})
结构化响应在代理最终状态的 structuredResponse 键中返回。
提供者策略
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>
定义结构化输出格式的模式。支持
- Zod 模式:Zod 模式
- JSON 模式:JSON 模式对象
当您将模式类型直接传递给 createAgent.responseFormat 并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: 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" }
提供者原生结构化输出提供了高可靠性和严格验证,因为模型提供者强制执行模式。可用时请使用它。
如果提供者原生支持您的模型选择的结构化输出,那么编写 responseFormat: contactInfoSchema 而不是 responseFormat: toolStrategy(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,代理将回退到工具调用策略。
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 ToolStrategy:function toolStrategy<StructuredResponseT>(
responseFormat:
| JsonSchemaFormat
| ZodSchema<StructuredResponseT>
| (ZodSchema<StructuredResponseT> | JsonSchemaFormat)[]
options?: ToolStrategyOptions
): ToolStrategy<StructuredResponseT>
定义结构化输出格式的模式。支持
- Zod 模式:Zod 模式
- JSON 模式: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,则默认为显示结构化响应数据的消息。
包含可选的 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",
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"] }
自定义工具消息内容
toolMessageContent 参数允许您在生成结构化输出时自定义会话历史记录中显示的消息
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
当结构化输出与预期模式不匹配时,代理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",
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
)