> ## 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 API 智能体需要实时展示其工作过程。Agent Server 提供了可恢复的流式传输功能：如果客户端在流传输过程中断开连接（网络切换、标签页休眠、移动端后台运行），重新连接后可以从断点处继续。多种流式模式让您能够控制粒度，从每个步骤后的完整状态快照，到从提供商实时到达的逐令牌 LLM 输出。 [LangGraph SDK](/langsmith/langgraph-python-sdk) 允许您从 [LangSmith 部署 API](/langsmith/server-api-ref) [流式传输输出](/oss/python/langgraph/streaming/)。 LangGraph SDK 和 Agent Server 是 [LangSmith](/langsmith/home) 的一部分。 ## 基本用法 基本用法示例： ```python {highlight={12}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph_sdk import get_client client = get_client(url=, api_key=) # 使用名为 "agent" 的已部署图 assistant_id = "agent" # 创建一个线程 thread = await client.threads.create() thread_id = thread["thread_id"] # 创建一个流式运行 async for chunk in client.runs.stream( thread_id, assistant_id, input=inputs, stream_mode="updates" ): print(chunk.data) ``` ```javascript {highlight={12}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { Client } from "@langchain/langgraph-sdk"; const client = new Client({ apiUrl: , apiKey: }); // 使用名为 "agent" 的已部署图 const assistantID = "agent"; // 创建一个线程 const thread = await client.threads.create(); const threadID = thread["thread_id"]; // 创建一个流式运行 const streamResponse = client.runs.stream( threadID, assistantID, { input, streamMode: "updates" } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` 创建一个线程： ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads \ --header 'Content-Type: application/json' \ --data '{}' ``` 创建一个流式运行： ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' --data "{ \"assistant_id\": \"agent\", \"input\": , \"stream_mode\": \"updates\" }" ``` 这是一个可以在 Agent Server 中运行的示例图。 更多详情请参阅 [LangSmith 快速入门](/langsmith/deployment-quickstart)。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # graph.py from typing import TypedDict from langgraph.graph import StateGraph, START, END class State(TypedDict): topic: str joke: str def refine_topic(state: State): return {"topic": state["topic"] + " and cats"} def generate_joke(state: State): return {"joke": f"This is a joke about {state['topic']}"} graph = ( StateGraph(State) .add_node(refine_topic) .add_node(generate_joke) .add_edge(START, "refine_topic") .add_edge("refine_topic", "generate_joke") .add_edge("generate_joke", END) .compile() ) ``` 一旦您拥有一个正在运行的 Agent Server，就可以使用 [LangGraph SDK](/langsmith/langgraph-python-sdk) 与其交互。 ```python {highlight={12,16}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph_sdk import get_client client = get_client(url=) # 使用名为 "agent" 的已部署图 assistant_id = "agent" # 创建一个线程 thread = await client.threads.create() thread_id = thread["thread_id"] # 创建一个流式运行 async for chunk in client.runs.stream( # (1)! thread_id, assistant_id, input={"topic": "ice cream"}, stream_mode="updates" # (2)! ): print(chunk.data) ``` 1. `client.runs.stream()` 方法返回一个迭代器，产生流式输出。 2. 设置 `stream_mode="updates"` 以仅流式传输每个节点后图状态的更新。其他流模式也可用。详情请参阅[支持的流模式](#supported-stream-modes)。 ```javascript {highlight={12,17}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { Client } from "@langchain/langgraph-sdk"; const client = new Client({ apiUrl: }); # 使用名为 "agent" 的已部署图 const assistantID = "agent"; # 创建一个线程 const thread = await client.threads.create(); const threadID = thread["thread_id"]; # 创建一个流式运行 const streamResponse = client.runs.stream( // (1)! threadID, assistantID, { input: { topic: "ice cream" }, streamMode: "updates" // (2)! } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` 1. `client.runs.stream()` 方法返回一个迭代器，产生流式输出。 2. 设置 `streamMode: "updates"` 以仅流式传输每个节点后图状态的更新。其他流模式也可用。详情请参阅[支持的流模式](#supported-stream-modes)。 创建一个线程： ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads \ --header 'Content-Type: application/json' \ --data '{}' ``` 创建一个流式运行： ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"topic\": \"ice cream\"}, \"stream_mode\": \"updates\" }" ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} {'run_id': '1f02c2b3-3cef-68de-b720-eec2a4a8e920', 'attempt': 1} {'refine_topic': {'topic': 'ice cream and cats'}} {'generate_joke': {'joke': 'This is a joke about ice cream and cats'}} ``` ### 支持的流模式 | 模式 | 描述 | LangGraph 库方法 | | -------------------------------- | ------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | [`values`](#stream-graph-state) | 在每个[超级步骤](/langsmith/graph-rebuild#define-graphs)后流式传输完整的图状态。 | `.stream()` / `.astream()` 配合 [`stream_mode="values"`](/oss/python/langgraph/streaming#graph-state) | | [`updates`](#stream-graph-state) | 流式传输图每个步骤后状态的更新。如果同一步骤中有多个更新（例如，运行了多个节点），这些更新会分别流式传输。 | `.stream()` / `.astream()` 配合 [`stream_mode="updates"`](/oss/python/langgraph/streaming#graph-state) | | [`messages-tuple`](#messages) | 流式传输调用 LLM 的图节点的令牌和元数据（适用于聊天应用）。 | `.stream()` / `.astream()` 配合 [`stream_mode="messages"`](/oss/python/langgraph/streaming#messages) | | [`debug`](#debug) | 在图执行过程中流式传输尽可能多的信息。 | `.stream()` / `.astream()` 配合 [`stream_mode="debug"`](/oss/python/langgraph/streaming#graph-state) | | [`custom`](#stream-custom-data) | 从图内部流式传输自定义数据 | `.stream()` / `.astream()` 配合 [`stream_mode="custom"`](/oss/python/langgraph/streaming#custom-data) | | [`events`](#stream-events) | 流式传输所有事件（包括图的状态）；主要用于迁移大型 LCEL 应用时。 | `.astream_events()` | ### 流式传输多种模式 您可以将列表作为 `stream_mode` 参数传递，以同时流式传输多种模式。 流式输出将是 `(mode, chunk)` 的元组，其中 `mode` 是流模式的名称，`chunk` 是该模式流式传输的数据。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input=inputs, stream_mode=["updates", "custom"] ): print(chunk) ``` ```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const streamResponse = client.runs.stream( threadID, assistantID, { input, streamMode: ["updates", "custom"] } ); for await (const chunk of streamResponse) { console.log(chunk); } ``` ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": , \"stream_mode\": [ \"updates\" \"custom\" ] }" ``` ## 流式传输图状态 使用流模式 `updates` 和 `values` 来流式传输图执行时的状态。 * `updates` 流式传输图每个步骤后状态的**更新**。 * `values` 流式传输图每个步骤后状态的**完整值**。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from typing import TypedDict from langgraph.graph import StateGraph, START, END class State(TypedDict): topic: str joke: str def refine_topic(state: State): return {"topic": state["topic"] + " and cats"} def generate_joke(state: State): return {"joke": f"This is a joke about {state['topic']}"} graph = ( StateGraph(State) .add_node(refine_topic) .add_node(generate_joke) .add_edge(START, "refine_topic") .add_edge("refine_topic", "generate_joke") .add_edge("generate_joke", END) .compile() ) ``` **有状态运行** 以下示例假设您希望将流式运行的输出持久化到[检查点](/oss/python/langgraph/persistence)数据库，并且已经创建了一个线程。要创建一个线程： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph_sdk import get_client client = get_client(url=) # 使用名为 "agent" 的已部署图 assistant_id = "agent" # 创建一个线程 thread = await client.threads.create() thread_id = thread["thread_id"] ``` ```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { Client } from "@langchain/langgraph-sdk"; const client = new Client({ apiUrl: }); // 使用名为 "agent" 的已部署图 const assistantID = "agent"; // 创建一个线程 const thread = await client.threads.create(); const threadID = thread["thread_id"] ``` ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads \ --header 'Content-Type: application/json' \ --data '{}' ``` 如果您不需要持久化运行的输出，可以在流式传输时传递 `None` 而不是 `thread_id`。 ### 流模式：`updates` 使用此模式仅流式传输每个步骤后节点返回的**状态更新**。流式输出包括节点名称和更新内容。 ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input={"topic": "ice cream"}, stream_mode="updates" ): print(chunk.data) ``` ```javascript {highlight={6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const streamResponse = client.runs.stream( threadID, assistantID, { input: { topic: "ice cream" }, streamMode: "updates" } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"topic\": \"ice cream\"}, \"stream_mode\": \"updates\" }" ``` ### 流模式：`values` 使用此模式流式传输每个步骤后图的**完整状态**。 ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input={"topic": "ice cream"}, stream_mode="values" ): print(chunk.data) ``` ```javascript {highlight={6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const streamResponse = client.runs.stream( threadID, assistantID, { input: { topic: "ice cream" }, streamMode: "values" } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"topic\": \"ice cream\"}, \"stream_mode\": \"values\" }" ``` ## 子图 要在流式输出中包含[子图](/oss/python/langgraph/use-subgraphs)的输出，可以在父图的 `.stream()` 方法中设置 `subgraphs=True`。这将同时流式传输父图和任何子图的输出。 ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input={"foo": "foo"}, stream_subgraphs=True, # (1)! stream_mode="updates", ): print(chunk) ``` 1. 设置 `stream_subgraphs=True` 以流式传输子图的输出。 这是一个可以在 Agent Server 中运行的示例图。 更多详情请参阅 [LangSmith 快速入门](/langsmith/deployment-quickstart)。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # graph.py from langgraph.graph import START, StateGraph from typing import TypedDict # 定义子图 class SubgraphState(TypedDict): foo: str # 注意此键与父图状态共享 bar: str def subgraph_node_1(state: SubgraphState): return {"bar": "bar"} def subgraph_node_2(state: SubgraphState): return {"foo": state["foo"] + state["bar"]} subgraph_builder = StateGraph(SubgraphState) subgraph_builder.add_node(subgraph_node_1) subgraph_builder.add_node(subgraph_node_2) subgraph_builder.add_edge(START, "subgraph_node_1") subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2") subgraph = subgraph_builder.compile() # 定义父图 class ParentState(TypedDict): foo: str def node_1(state: ParentState): return {"foo": "hi! " + state["foo"]} builder = StateGraph(ParentState) builder.add_node("node_1", node_1) builder.add_node("node_2", subgraph) builder.add_edge(START, "node_1") builder.add_edge("node_1", "node_2") graph = builder.compile() ``` 一旦您拥有一个正在运行的 Agent Server，就可以使用 [LangGraph SDK](/langsmith/langgraph-python-sdk) 与其交互。 ```python {highlight={15}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph_sdk import get_client client = get_client(url=) # 使用名为 "agent" 的已部署图 assistant_id = "agent" # 创建一个线程 thread = await client.threads.create() thread_id = thread["thread_id"] async for chunk in client.runs.stream( thread_id, assistant_id, input={"foo": "foo"}, stream_subgraphs=True, # (1)! stream_mode="updates", ): print(chunk) ``` 1. 设置 `stream_subgraphs=True` 以流式传输子图的输出。 ```javascript {highlight={17}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { Client } from "@langchain/langgraph-sdk"; const client = new Client({ apiUrl: }); // 使用名为 "agent" 的已部署图 const assistantID = "agent"; // 创建一个线程 const thread = await client.threads.create(); const threadID = thread["thread_id"]; // 创建一个流式运行 const streamResponse = client.runs.stream( threadID, assistantID, { input: { foo: "foo" }, streamSubgraphs: true, // (1)! streamMode: "updates" } ); for await (const chunk of streamResponse) { console.log(chunk); } ``` 1. 设置 `streamSubgraphs: true` 以流式传输子图的输出。 创建一个线程： ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads \ --header 'Content-Type: application/json' \ --data '{}' ``` 创建一个流式运行： ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"foo\": \"foo\"}, \"stream_subgraphs\": true, \"stream_mode\": [ \"updates\" ] }" ``` **注意**，我们不仅接收节点更新，还接收命名空间信息，告诉我们正在从哪个图（或子图）流式传输。 ## 调试 使用 `debug` 流模式在图执行过程中流式传输尽可能多的信息。流式输出包括节点名称和完整状态。 ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input={"topic": "ice cream"}, stream_mode="debug" ): print(chunk.data) ``` ```javascript {highlight={6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const streamResponse = client.runs.stream( threadID, assistantID, { input: { topic: "ice cream" }, streamMode: "debug" } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"topic\": \"ice cream\"}, \"stream_mode\": \"debug\" }" ``` ## LLM 令牌 使用 `messages-tuple` 流模式从图的任何部分（包括节点、工具、子图或任务）**逐令牌**流式传输大语言模型（LLM）的输出。 [`messages-tuple` 模式](#supported-stream-modes)的流式输出是一个元组 `(message_chunk, metadata)`，其中： * `message_chunk`：来自 LLM 的令牌或消息片段。 * `metadata`：包含图节点和 LLM 调用详细信息的字典。 ```python {highlight={15}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from dataclasses import dataclass from langchain.chat_models import init_chat_model from langgraph.graph import StateGraph, START @dataclass class MyState: topic: str joke: str = "" model = init_chat_model(model="gpt-4.1-mini") def call_model(state: MyState): """调用 LLM 生成关于某个主题的笑话""" model_response = model.invoke( # (1)! [ {"role": "user", "content": f"Generate a joke about {state.topic}"} ] ) return {"joke": model_response.content} graph = ( StateGraph(MyState) .add_node(call_model) .add_edge(START, "call_model") .compile() ) ``` 1. 注意，即使 LLM 使用 `invoke` 而不是 `stream` 运行，消息事件也会被发出。 ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input={"topic": "ice cream"}, stream_mode="messages-tuple", ): if chunk.event != "messages": continue message_chunk, metadata = chunk.data # (1)! if message_chunk["content"]: print(message_chunk["content"], end="|", flush=True) ``` 1. "messages-tuple" 流模式返回一个元组迭代器 `(message_chunk, metadata)`，其中 `message_chunk` 是 LLM 流式传输的令牌，`metadata` 是一个字典，包含有关调用 LLM 的图节点和其他信息。 ```javascript {highlight={6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const streamResponse = client.runs.stream( threadID, assistantID, { input: { topic: "ice cream" }, streamMode: "messages-tuple" } ); for await (const chunk of streamResponse) { if (chunk.event !== "messages") { continue; } console.log(chunk.data[0]["content"]); // (1)! } ``` 1. "messages-tuple" 流模式返回一个元组迭代器 `(message_chunk, metadata)`，其中 `message_chunk` 是 LLM 流式传输的令牌，`metadata` 是一个字典，包含有关调用 LLM 的图节点和其他信息。 ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"topic\": \"ice cream\"}, \"stream_mode\": \"messages-tuple\" }" ``` ### 过滤 LLM 令牌 * 要按 LLM 调用过滤流式令牌，可以[为 LLM 调用关联 `tags`](/oss/python/langgraph/streaming#filter-by-llm-invocation)。 * 要仅从特定节点流式传输令牌，请使用 `stream_mode="messages"` 并[通过流式元数据中的 `langgraph_node` 字段过滤输出](/oss/python/langgraph/streaming#filter-by-node)。 ## 流式传输自定义数据 要发送**自定义用户定义的数据**： ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input={"query": "example"}, stream_mode="custom" ): print(chunk.data) ``` ```javascript {highlight={6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const streamResponse = client.runs.stream( threadID, assistantID, { input: { query: "example" }, streamMode: "custom" } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"query\": \"example\"}, \"stream_mode\": \"custom\" }" ``` ## 流式传输事件 要流式传输所有事件，包括图的状态： ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async for chunk in client.runs.stream( thread_id, assistant_id, input={"topic": "ice cream"}, stream_mode="events" ): print(chunk.data) ``` ```javascript {highlight={6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const streamResponse = client.runs.stream( threadID, assistantID, { input: { topic: "ice cream" }, streamMode: "events" } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /threads//runs/stream \ --header 'Content-Type: application/json' \ --data "{ \"assistant_id\": \"agent\", \"input\": {\"topic\": \"ice cream\"}, \"stream_mode\": \"events\" }" ``` ## 无状态运行 如果您不希望将流式运行的输出持久化到[检查点](/oss/python/langgraph/persistence)数据库，可以在不创建线程的情况下创建无状态运行： ```python {highlight={5}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph_sdk import get_client client = get_client(url=, api_key=) async for chunk in client.runs.stream( None, # (1)! assistant_id, input=inputs, stream_mode="updates" ): print(chunk.data) ``` 1. 我们传递 `None` 而不是 `thread_id` UUID。 ```javascript {highlight={5,6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { Client } from "@langchain/langgraph-sdk"; const client = new Client({ apiUrl: , apiKey: }); // 创建一个流式运行 const streamResponse = client.runs.stream( null, // (1)! assistantID, { input, streamMode: "updates" } ); for await (const chunk of streamResponse) { console.log(chunk.data); } ``` 1. 我们传递 `None` 而不是 `thread_id` UUID。 ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request POST \ --url /runs/stream \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' --data "{ \"assistant_id\": \"agent\", \"input\": , \"stream_mode\": \"updates\" }" ``` ## 加入并流式传输 LangSmith 允许您加入一个活跃的[后台运行](/langsmith/background-run)并从中流式传输输出。为此，您可以使用 [LangGraph SDK 的](/langsmith/langgraph-python-sdk) `client.runs.join_stream` 方法： ```python {highlight={4,6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph_sdk import get_client client = get_client(url=, api_key=) async for chunk in client.runs.join_stream( thread_id, run_id, # (1)! ): print(chunk) ``` 1. 这是您要加入的现有运行的 `run_id`。 ```javascript {highlight={4,6}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { Client } from "@langchain/langgraph-sdk"; const client = new Client({ apiUrl: , apiKey: }); const streamResponse = client.runs.joinStream( threadID, runId // (1)! ); for await (const chunk of streamResponse) { console.log(chunk); } ``` 1. 这是您要加入的现有运行的 `run_id`。 ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} curl --request GET \ --url /threads//runs//stream \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' ``` **输出未缓冲** 当您使用 `.join_stream` 时，输出不会被缓冲，因此在加入之前产生的任何输出都不会被接收。 *** ## API 参考 关于 API 使用和实现，请参阅 [API 参考](https://docs.langchain.com/langsmith/server-api-ref#tag/thread-runs/POST/threads/\{thread_id}/runs/stream)。 ***
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