> ## Documentation Index > Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt > Use this file to discover all available pages before exploring further. # 流式传输 > 从智能体运行中流式获取实时更新 LangChain 实现了一个流式系统，用于展示实时更新。 流式传输对于增强基于大语言模型（LLM）构建的应用程序的响应性至关重要。通过逐步显示输出，甚至在完整响应准备好之前，流式传输显著改善了用户体验（UX），特别是在处理 LLM 的延迟时。 ## 概述 LangChain 的流式系统允许您将智能体运行的实时反馈展示到您的应用程序中。 使用 LangChain 流式传输可以实现： * [**流式传输智能体进度**](#agent-progress)—在每次智能体步骤后获取状态更新。 * [**流式传输 LLM 令牌**](#llm-tokens)—流式传输正在生成的语言模型令牌。 * [**流式传输思考/推理令牌**](#streaming-thinking-/-reasoning-tokens)—展示正在生成的模型推理过程。 * [**流式传输自定义更新**](#custom-updates)—发射用户定义的信号（例如，`"Fetched 10/100 records"`）。 * [**流式传输多种模式**](#stream-multiple-modes)—从 `updates`（智能体进度）、`messages`（LLM 令牌 + 元数据）或 `custom`（任意用户数据）中选择。 请参阅下面的 [常见模式](#common-patterns) 部分以获取更多端到端示例。 ## 支持的流模式 将以下一个或多个流模式作为列表传递给 [`stream`](https://reference.langchain.com/javascript/classes/_langchain_langgraph.index.CompiledStateGraph.html#stream) 方法： | 模式 | 描述 | | ---------- | ------------------------------------------------------- | | `updates` | 在每个智能体步骤后流式传输状态更新。如果同一步骤中有多个更新（例如，运行了多个节点），这些更新将分别流式传输。 | | `messages` | 流式传输来自任何调用 LLM 的图节点的 `(token, metadata)` 元组。 | | `custom` | 使用流写入器从您的图节点内部流式传输自定义数据。 | ## 智能体进度 要流式传输智能体进度，请使用 [`stream`](https://reference.langchain.com/javascript/classes/_langchain_langgraph.index.CompiledStateGraph.html#stream) 方法并设置 `streamMode: "updates"`。这会在每个智能体步骤后发射一个事件。 例如，如果您有一个调用一次工具的代理，您应该看到以下更新： * **LLM 节点**：带有工具调用请求的 [`AIMessage`](https://reference.langchain.com/javascript/langchain-core/messages/AIMessage) * **工具节点**：带有执行结果的 [`ToolMessage`](https://reference.langchain.com/javascript/langchain-core/messages/ToolMessage) * **LLM 节点**：最终 AI 响应 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import z from "zod"; import { createAgent, tool } from "langchain"; const getWeather = tool( async ({ city }) => { return `The weather in ${city} is always sunny!`; }, { name: "get_weather", description: "Get weather for a given city.", schema: z.object({ city: z.string(), }), } ); const agent = createAgent({ model: "gpt-5-nano", tools: [getWeather], }); for await (const chunk of await agent.stream( { messages: [{ role: "user", content: "what is the weather in sf" }] }, { streamMode: "updates" } )) { const [step, content] = Object.entries(chunk)[0]; console.log(`step: ${step}`); console.log(`content: ${JSON.stringify(content, null, 2)}`); } /** * step: model * content: { * "messages": [ * { * "kwargs": { * // ... * "tool_calls": [ * { * "name": "get_weather", * "args": { * "city": "San Francisco" * }, * "type": "tool_call", * "id": "call_0qLS2Jp3MCmaKJ5MAYtr4jJd" * } * ], * // ... * } * } * ] * } * step: tools * content: { * "messages": [ * { * "kwargs": { * "content": "The weather in San Francisco is always sunny!", * "name": "get_weather", * // ... * } * } * ] * } * step: model * content: { * "messages": [ * { * "kwargs": { * "content": "The latest update says: The weather in San Francisco is always sunny!\n\nIf you'd like real-time details (current temperature, humidity, wind, and today's forecast), I can pull the latest data for you. Want me to fetch that?", * // ... * } * } * ] * } */ ``` ## LLM 令牌 要流式传输由 LLM 产生的令牌，请使用 `streamMode: "messages"`： ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import z from "zod"; import { createAgent, tool } from "langchain"; const getWeather = tool( async ({ city }) => { return `The weather in ${city} is always sunny!`; }, { name: "get_weather", description: "Get weather for a given city.", schema: z.object({ city: z.string(), }), } ); const agent = createAgent({ model: "gpt-4.1-mini", tools: [getWeather], }); for await (const [token, metadata] of await agent.stream( { messages: [{ role: "user", content: "what is the weather in sf" }] }, { streamMode: "messages" } )) { console.log(`node: ${metadata.langgraph_node}`); console.log(`content: ${JSON.stringify(token.contentBlocks, null, 2)}`); } ``` ## 自定义更新 要流式传输工具执行时的更新，您可以使用配置中的 `writer` 参数。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import z from "zod"; import { tool, createAgent } from "langchain"; import { LangGraphRunnableConfig } from "@langchain/langgraph"; const getWeather = tool( async (input, config: LangGraphRunnableConfig) => { // Stream any arbitrary data config.writer?.(`Looking up data for city: ${input.city}`); // ... fetch city data config.writer?.(`Acquired data for city: ${input.city}`); return `It's always sunny in ${input.city}!`; }, { name: "get_weather", description: "Get weather for a given city.", schema: z.object({ city: z.string().describe("The city to get weather for."), }), } ); const agent = createAgent({ model: "gpt-4.1-mini", tools: [getWeather], }); for await (const chunk of await agent.stream( { messages: [{ role: "user", content: "what is the weather in sf" }] }, { streamMode: "custom" } )) { console.log(chunk); } ``` ```shell title="Output" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Looking up data for city: San Francisco Acquired data for city: San Francisco ``` 如果您将 `writer` 参数添加到工具中，您将无法在没有提供 writer 函数的情况下在 LangGraph 执行上下文之外调用该工具。 ## 流式传输多种模式 您可以通过传递流模式数组来指定多个流模式：`streamMode: ["updates", "messages", "custom"]`。 流式传输的输出将是 `[mode, chunk]` 元组，其中 `mode` 是流模式的名称，`chunk` 是该模式流式传输的数据。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import z from "zod"; import { tool, createAgent } from "langchain"; import { LangGraphRunnableConfig } from "@langchain/langgraph"; const getWeather = tool( async (input, config: LangGraphRunnableConfig) => { // Stream any arbitrary data config.writer?.(`Looking up data for city: ${input.city}`); // ... fetch city data config.writer?.(`Acquired data for city: ${input.city}`); return `It's always sunny in ${input.city}!`; }, { name: "get_weather", description: "Get weather for a given city.", schema: z.object({ city: z.string().describe("The city to get weather for."), }), } ); const agent = createAgent({ model: "gpt-4.1-mini", tools: [getWeather], }); for await (const [streamMode, chunk] of await agent.stream( { messages: [{ role: "user", content: "what is the weather in sf" }] }, { streamMode: ["updates", "messages", "custom"] } )) { console.log(`${streamMode}: ${JSON.stringify(chunk, null, 2)}`); } ``` ## 常见模式 以下是展示流式传输常见用例的示例。 ### 流式传输思考/推理令牌 某些模型在生成最终答案之前会进行内部推理。您可以通过过滤 [标准内容块](/oss/javascript/langchain/messages#standard-content-blocks) 的 `type` 为 `"reasoning"` 来流式传输这些正在生成的思考/推理令牌。 必须在模型上启用推理输出。 请参阅 [推理部分](/oss/javascript/langchain/models#reasoning) 和您的 [提供商的集成页面](/oss/javascript/integrations/providers/overview) 以获取配置详情。 要快速检查模型的推理支持情况，请查看 [models.dev](https://models.dev)。 要从智能体流式传输思考令牌，请使用 `streamMode: "messages"` 并过滤推理内容块。当模型支持时，使用具有扩展思考功能的模型实例（例如 `ChatAnthropic`）： ```ts theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import z from "zod"; import { createAgent, tool } from "langchain"; import { ChatAnthropic } from "@langchain/anthropic"; const getWeather = tool( async ({ city }) => { return `${city} 总是阳光明媚！`; }, { name: "get_weather", description: "获取指定城市的天气。", schema: z.object({ city: z.string() }), }, ); const agent = createAgent({ model: new ChatAnthropic({ model: "claude-sonnet-4-6", thinking: { type: "enabled", budget_tokens: 5000 }, }), tools: [getWeather], }); for await (const [token, metadata] of await agent.stream( { messages: [{ role: "user", content: "旧金山的天气怎么样？" }] }, { streamMode: "messages" }, // [!code highlight] )) { if (!token.contentBlocks) continue; const reasoning = token.contentBlocks.filter((b) => b.type === "reasoning"); const text = token.contentBlocks.filter((b) => b.type === "text"); if (reasoning.length) { process.stdout.write(`[thinking] ${reasoning[0].reasoning}`); } if (text.length) { process.stdout.write(text[0].text); } } ``` ```shell title="Output" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [thinking] The user is asking about the weather in San Francisco. I have a tool [thinking] available to get this information. Let me call the get_weather tool [thinking] with "San Francisco" as the city parameter. The weather in San Francisco is: It's always sunny in San Francisco! ``` 无论模型提供商如何，其工作原理相同—LangChain 将特定于提供商的格式（Anthropic `thinking` 块、OpenAI `reasoning` 摘要等）通过 [`content_blocks`](/oss/javascript/langchain/messages#standard-content-blocks) 属性标准化为标准的 `"reasoning"` 内容块类型。 要从聊天模型直接流式传输推理令牌（不使用智能体），请参阅 [与聊天模型流式传输](/oss/javascript/langchain/models#reasoning)。 ## 禁用流式传输 在某些应用程序中，您可能需要禁用给定模型的单个令牌的流式传输。这在以下情况下很有用： * 与 [多智能体](/oss/javascript/langchain/multi-agent) 系统一起工作以控制哪些智能体流式传输其输出 * 混合支持流式传输和不支持流式传输的模型 * 部署到 [LangSmith](/langsmith/home) 并希望防止某些模型输出流式传输到客户端 初始化模型时设置 `streaming: false`。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatOpenAI } from "@langchain/openai"; const model = new ChatOpenAI({ model: "gpt-4.1", streaming: false, // [!code highlight] }); ``` 部署到 LangSmith 时，对任何您不希望流式传输到客户端的模型设置 `streaming=False`。这是在部署前在您的图代码中配置的。 并非所有聊天模型集成都支持 `streaming` 参数。如果您的模型不支持它，请使用 `disableStreaming: true`。此参数通过基类在所有聊天模型上可用。 有关更多详细信息，请参阅 [LangGraph 流式指南](/oss/javascript/langgraph/streaming#disable-streaming-for-specific-chat-models)。 ## 相关资源 * [前端流式传输](/oss/javascript/langchain/streaming/frontend)—使用 `useStream` 构建 React UI 以实现实时智能体交互 * [与聊天模型流式传输](/oss/javascript/langchain/models#stream)—直接从聊天模型流式传输令牌，无需使用智能体或图 * [与聊天模型推理](/oss/javascript/langchain/models#reasoning)—配置和访问聊天模型的推理输出 * [标准内容块](/oss/javascript/langchain/messages#standard-content-blocks)—了解用于推理、文本和其他内容类型的标准化内容块格式 * [人机回环流式传输](/oss/javascript/langchain/human-in-the-loop#streaming-with-human-in-the-loop)—在处理人类审查的中断时流式传输智能体进度 * [LangGraph 流式传输](/oss/javascript/langgraph/streaming)—高级流式传输选项，包括 `values`、`debug` 模式和子图流式传输 ***
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