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—在每次智能体步骤后获取状态更新。
• 流式传输 LLM 令牌—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—展示正在生成的模型推理过程。
• 流式传输自定义更新—发射用户定义的信号（例如，"Fetched 10/100 records"）。
• 流式传输多种模式—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 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 或 astream 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 或 astream 方法并设置 stream_mode="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
• 工具节点：带有执行结果的 ToolMessage
• LLM 节点：最终 AI 响应

Streaming agent progress

from langchain.agents import create_agent


def get_weather(city: str) -> str:
    """Get weather for a given city."""

    return f"It's always sunny in {city}!"

agent = create_agent(
    model="gpt-5-nano",
    tools=[get_weather],
)
for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode="updates",
    version="v2",
):
    if chunk["type"] == "updates":
        for step, data in chunk["data"].items():
            print(f"step: {step}")
            print(f"content: {data['messages'][-1].content_blocks}")

Output

step: model
content: [{'type': 'tool_call', 'name': 'get_weather', 'args': {'city': 'San Francisco'}, 'id': 'call_OW2NYNsNSKhRZpjW0wm2Aszd'}]

step: tools
content: [{'type': 'text', 'text': "It's always sunny in San Francisco!"}]

step: model
content: [{'type': 'text', 'text': 'It's always sunny in San Francisco!'}]

LLM 令牌

要流式传输由 LLM 产生的令牌，请使用 stream_mode="messages"。下面您可以看到代理流式传输工具调用和最终响应的输出。

Streaming LLM tokens

from langchain.agents import create_agent


def get_weather(city: str) -> str:
    """Get weather for a given city."""

    return f"It's always sunny in {city}!"

agent = create_agent(
    model="gpt-5-nano",
    tools=[get_weather],
)
for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode="messages",
    version="v2",
):
    if chunk["type"] == "messages":
        token, metadata = chunk["data"]
        print(f"node: {metadata['langgraph_node']}")
        print(f"content: {token.content_blocks}")
        print("\n")

Output

node: model
content: [{'type': 'tool_call_chunk', 'id': 'call_vbCyBcP8VuneUzyYlSBZZsVa', 'name': 'get_weather', 'args': '', 'index': 0}]


node: model
content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': '{"', 'index': 0}]


node: model
content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': 'city', 'index': 0}]


node: model
content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': '":"', 'index': 0}]


node: model
content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': 'San', 'index': 0}]


node: model
content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': ' Francisco', 'index': 0}]


node: model
content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': '"}', 'index': 0}]


node: model
content: []


node: tools
content: [{'type': 'text', 'text': "It's always sunny in San Francisco!"}]


node: model
content: []


node: model
content: [{'type': 'text', 'text': 'Here'}]


node: model
content: [{'type': 'text', 'text': ''s'}]


node: model
content: [{'type': 'text', 'text': ' what'}]


node: model
content: [{'type': 'text', 'text': ' I'}]


node: model
content: [{'type': 'text', 'text': ' got'}]


node: model
content: [{'type': 'text', 'text': ':'}]


node: model
content: [{'type': 'text', 'text': ' "'}]


node: model
content: [{'type': 'text', 'text': "It's"}]


node: model
content: [{'type': 'text', 'text': ' always'}]


node: model
content: [{'type': 'text', 'text': ' sunny'}]


node: model
content: [{'type': 'text', 'text': ' in'}]


node: model
content: [{'type': 'text', 'text': ' San'}]


node: model
content: [{'type': 'text', 'text': ' Francisco'}]


node: model
content: [{'type': 'text', 'text': '!"\n\n'}]

自定义更新

要流式传输工具执行时的更新，您可以使用 get_stream_writer。

Streaming custom updates

from langchain.agents import create_agent
from langgraph.config import get_stream_writer  


def get_weather(city: str) -> str:
    """Get weather for a given city."""
    writer = get_stream_writer()
    # stream any arbitrary data
    writer(f"Looking up data for city: {city}")
    writer(f"Acquired data for city: {city}")
    return f"It's always sunny in {city}!"

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[get_weather],
)

for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode="custom",
    version="v2",
):
    if chunk["type"] == "custom":
        print(chunk["data"])

Output

Looking up data for city: San Francisco
Acquired data for city: San Francisco

如果您在工具内部添加 get_stream_writer，您将无法在 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列表来指定多个流模式：stream_mode=["updates", "custom"]。

每个流式传输的块都是一个 StreamPart 字典，包含 type、ns 和 data 键。使用 chunk["type"] 确定流模式，并使用 chunk["data"] 访问负载。

Streaming multiple modes

from langchain.agents import create_agent
from langgraph.config import get_stream_writer


def get_weather(city: str) -> str:
    """Get weather for a given city."""
    writer = get_stream_writer()
    writer(f"Looking up data for city: {city}")
    writer(f"Acquired data for city: {city}")
    return f"It's always sunny in {city}!"

agent = create_agent(
    model="gpt-5-nano",
    tools=[get_weather],
)

for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode=["updates", "custom"],
    version="v2",
):
    print(f"stream_mode: {chunk['type']}")
    print(f"content: {chunk['data']}")
    print("\n")

Output

stream_mode: updates
content: {'model': {'messages': [AIMessage(content='', response_metadata={'token_usage': {'completion_tokens': 280, 'prompt_tokens': 132, 'total_tokens': 412, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 256, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-5-nano-2025-08-07', 'system_fingerprint': None, 'id': 'chatcmpl-C9tlgBzGEbedGYxZ0rTCz5F7OXpL7', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='lc_run--480c07cb-e405-4411-aa7f-0520fddeed66-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'San Francisco'}, 'id': 'call_KTNQIftMrl9vgNwEfAJMVu7r', 'type': 'tool_call'}], usage_metadata={'input_tokens': 132, 'output_tokens': 280, 'total_tokens': 412, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 256}})]}}


stream_mode: custom
content: Looking up data for city: San Francisco


stream_mode: custom
content: Acquired data for city: San Francisco


stream_mode: updates
content: {'tools': {'messages': [ToolMessage(content="It's always sunny in San Francisco!", name='get_weather', tool_call_id='call_KTNQIftMrl9vgNwEfAJMVu7r')]}}


stream_mode: updates
content: {'model': {'messages': [AIMessage(content='San Francisco weather: It's always sunny in San Francisco!\n\n', response_metadata={'token_usage': {'completion_tokens': 764, 'prompt_tokens': 168, 'total_tokens': 932, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 704, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-5-nano-2025-08-07', 'system_fingerprint': None, 'id': 'chatcmpl-C9tljDFVki1e1haCyikBptAuXuHYG', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--acbc740a-18fe-4a14-8619-da92a0d0ee90-0', usage_metadata={'input_tokens': 168, 'output_tokens': 764, 'total_tokens': 932, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 704}})]}}

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 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 和您的 提供商的集成页面 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。

要从智能体流式传输思考令牌，请使用 stream_mode="messages" 并过滤推理内容块：

from langchain.agents import create_agent
from langchain.messages import AIMessageChunk
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables import Runnable


def get_weather(city: str) -> str:
    """获取指定城市的天气。"""
    return f"It's always sunny in {city}!"


model = ChatAnthropic(
    model_name="claude-sonnet-4-6",
    timeout=None,
    stop=None,
    thinking={"type": "enabled", "budget_tokens": 5000},
)
agent: Runnable = create_agent(
    model=model,
    tools=[get_weather],
)

for token, metadata in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode="messages",
):
    if not isinstance(token, AIMessageChunk):
        continue
    reasoning = [b for b in token.content_blocks if b["type"] == "reasoning"]
    text = [b for b in token.content_blocks if b["type"] == "text"]
    if reasoning:
        print(f"[thinking] {reasoning[0]['reasoning']}", end="")
    if text:
        print(text[0]["text"], end="")

Output

[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 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。

流式传输工具调用

您可能希望同时流式传输以下内容：

1. 随着 工具调用 生成而生成部分 JSON
2. 已完成的、解析后的执行工具调用

指定 stream_mode="messages" 将流式传输智能体中所有 LLM 调用生成的增量 消息块。要访问带有解析后工具调用的已完成消息：

1. 如果这些消息在 状态 中被跟踪（如 create_agent 的模型节点中），请使用 stream_mode=["messages", "updates"] 通过 状态更新 访问已完成的消息（如下所示演示）。
2. 如果这些消息未在状态中跟踪，请使用 自定义更新 或在流式循环中聚合块（下一节）。

如果您的智能体包含多个 LLM，请参阅下面关于 从子智能体流式传输 的部分。

from typing import Any

from langchain.agents import create_agent
from langchain.messages import AIMessage, AIMessageChunk, AnyMessage, ToolMessage


def get_weather(city: str) -> str:
    """Get weather for a given city."""

    return f"It's always sunny in {city}!"


agent = create_agent("openai:gpt-5.2", tools=[get_weather])


def _render_message_chunk(token: AIMessageChunk) -> None:
    if token.text:
        print(token.text, end="|")
    if token.tool_call_chunks:
        print(token.tool_call_chunks)
    # N.B. all content is available through token.content_blocks


def _render_completed_message(message: AnyMessage) -> None:
    if isinstance(message, AIMessage) and message.tool_calls:
        print(f"Tool calls: {message.tool_calls}")
    if isinstance(message, ToolMessage):
        print(f"Tool response: {message.content_blocks}")


input_message = {"role": "user", "content": "What is the weather in Boston?"}
for chunk in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates"],
    version="v2",
):
    if chunk["type"] == "messages":
        token, metadata = chunk["data"]
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
    elif chunk["type"] == "updates":
        for source, update in chunk["data"].items():
            if source in ("model", "tools"):  # `source` captures node name
                _render_completed_message(update["messages"][-1])

Output

[{'name': 'get_weather', 'args': '', 'id': 'call_D3Orjr89KgsLTZ9hTzYv7Hpf', 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '{"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'city', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '":"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'Boston', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '"}', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
Tool calls: [{'name': 'get_weather', 'args': {'city': 'Boston'}, 'id': 'call_D3Orjr89KgsLTZ9hTzYv7Hpf', 'type': 'tool_call'}]
Tool response: [{'type': 'text', 'text': "It's always sunny in Boston!"}]
The| weather| in| Boston| is| **|sun|ny|**|.|

访问已完成的消息

如果已完成的消息在智能体的 状态 中被跟踪，您可以使用 stream_mode=["messages", "updates"]，如 流式传输工具调用 部分所示，以便在流式传输期间访问已完成的消息。

在某些情况下，已完成的消息不会反映在 状态更新 中。如果您可以访问智能体内部，您可以使用 自定义更新 在流式传输期间访问这些消息。否则，您可以在流式循环中聚合消息块（见下文）。

考虑以下示例，我们将 流写入器 纳入简化的 护栏中间件。此中间件演示了工具调用以生成结构化的“安全/不安全”评估（也可以使用 结构化输出 来完成此操作）：

from typing import Any, Literal

from langchain.agents.middleware import after_agent, AgentState
from langgraph.runtime import Runtime
from langchain.messages import AIMessage
from langchain.chat_models import init_chat_model
from langgraph.config import get_stream_writer  
from pydantic import BaseModel


class ResponseSafety(BaseModel):
    """Evaluate a response as safe or unsafe."""
    evaluation: Literal["safe", "unsafe"]


safety_model = init_chat_model("openai:gpt-5.2")

@after_agent(can_jump_to=["end"])
def safety_guardrail(state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
    """Model-based guardrail: Use an LLM to evaluate response safety."""
    stream_writer = get_stream_writer()
    # Get the model response
    if not state["messages"]:
        return None

    last_message = state["messages"][-1]
    if not isinstance(last_message, AIMessage):
        return None

    # Use another model to evaluate safety
    model_with_tools = safety_model.bind_tools([ResponseSafety], tool_choice="any")
    result = model_with_tools.invoke(
        [
            {
                "role": "system",
                "content": "Evaluate this AI response as generally safe or unsafe."
            },
            {
                "role": "user",
                "content": f"AI response: {last_message.text}"
            }
        ]
    )
    stream_writer(result)

    tool_call = result.tool_calls[0]
    if tool_call["args"]["evaluation"] == "unsafe":
        last_message.content = "I cannot provide that response. Please rephrase your request."

    return None

然后我们可以将此中间件纳入我们的智能体并包含其自定义流事件：

from typing import Any

from langchain.agents import create_agent
from langchain.messages import AIMessageChunk, AIMessage, AnyMessage


def get_weather(city: str) -> str:
    """Get weather for a given city."""

    return f"It's always sunny in {city}!"


agent = create_agent(
    model="openai:gpt-5.2",
    tools=[get_weather],
    middleware=[safety_guardrail],
)

def _render_message_chunk(token: AIMessageChunk) -> None:
    if token.text:
        print(token.text, end="|")
    if token.tool_call_chunks:
        print(token.tool_call_chunks)


def _render_completed_message(message: AnyMessage) -> None:
    if isinstance(message, AIMessage) and message.tool_calls:
        print(f"Tool calls: {message.tool_calls}")
    if isinstance(message, ToolMessage):
        print(f"Tool response: {message.content_blocks}")


input_message = {"role": "user", "content": "What is the weather in Boston?"}
for chunk in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates", "custom"],
    version="v2",
):
    if chunk["type"] == "messages":
        token, metadata = chunk["data"]
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
    elif chunk["type"] == "updates":
        for source, update in chunk["data"].items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])
    elif chunk["type"] == "custom":
        # access completed message in stream
        print(f"Tool calls: {chunk['data'].tool_calls}")

Output

[{'name': 'get_weather', 'args': '', 'id': 'call_je6LWgxYzuZ84mmoDalTYMJC', 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '{"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'city', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '":"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'Boston', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '"}', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
Tool calls: [{'name': 'get_weather', 'args': {'city': 'Boston'}, 'id': 'call_je6LWgxYzuZ84mmoDalTYMJC', 'type': 'tool_call'}]
Tool response: [{'type': 'text', 'text': "It's always sunny in Boston!"}]
The| weather| in| **|Boston|**| is| **|sun|ny|**|.|[{'name': 'ResponseSafety', 'args': '', 'id': 'call_O8VJIbOG4Q9nQF0T8ltVi58O', 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '{"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'evaluation', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '":"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'safe', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '"}', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
Tool calls: [{'name': 'ResponseSafety', 'args': {'evaluation': 'safe'}, 'id': 'call_O8VJIbOG4Q9nQF0T8ltVi58O', 'type': 'tool_call'}]

或者，如果您无法向流添加自定义事件，您可以在流式循环内聚合消息块：

input_message = {"role": "user", "content": "What is the weather in Boston?"}
full_message = None
for chunk in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates"],
    version="v2",
):
    if chunk["type"] == "messages":
        token, metadata = chunk["data"]
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
            full_message = token if full_message is None else full_message + token  
            if token.chunk_position == "last":
                if full_message.tool_calls:
                    print(f"Tool calls: {full_message.tool_calls}")
                full_message = None
    elif chunk["type"] == "updates":
        for source, update in chunk["data"].items():
            if source == "tools":
                _render_completed_message(update["messages"][-1])

人机回环流式传输

为了处理人类参与循环的 中断，我们在 上面的示例 基础上构建：

1. 我们配置智能体使用 人类参与循环中间件和检查点器
2. 我们收集在 "updates" 流模式下生成的中断
3. 我们使用 命令 响应这些中断

from typing import Any

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.messages import AIMessage, AIMessageChunk, AnyMessage, ToolMessage
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import Command, Interrupt


def get_weather(city: str) -> str:
    """Get weather for a given city."""

    return f"It's always sunny in {city}!"


checkpointer = InMemorySaver()

agent = create_agent(
    "openai:gpt-5.2",
    tools=[get_weather],
    middleware=[
        HumanInTheLoopMiddleware(interrupt_on={"get_weather": True}),
    ],
    checkpointer=checkpointer,
)


def _render_message_chunk(token: AIMessageChunk) -> None:
    if token.text:
        print(token.text, end="|")
    if token.tool_call_chunks:
        print(token.tool_call_chunks)


def _render_completed_message(message: AnyMessage) -> None:
    if isinstance(message, AIMessage) and message.tool_calls:
        print(f"Tool calls: {message.tool_calls}")
    if isinstance(message, ToolMessage):
        print(f"Tool response: {message.content_blocks}")


def _render_interrupt(interrupt: Interrupt) -> None:
    interrupts = interrupt.value  
    for request in interrupts["action_requests"]:
        print(request["description"])


input_message = {
    "role": "user",
    "content": (
        "Can you look up the weather in Boston and San Francisco?"
    ),
}
config = {"configurable": {"thread_id": "some_id"}}
interrupts = []
for chunk in agent.stream(
    {"messages": [input_message]},
    config=config,
    stream_mode=["messages", "updates"],
    version="v2",
):
    if chunk["type"] == "messages":
        token, metadata = chunk["data"]
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
    elif chunk["type"] == "updates":
        for source, update in chunk["data"].items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])
            if source == "__interrupt__":
                interrupts.extend(update)
                _render_interrupt(update[0])

Output

[{'name': 'get_weather', 'args': '', 'id': 'call_GOwNaQHeqMixay2qy80padfE', 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '{"ci', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'ty": ', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '"Bosto', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'n"}', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': 'get_weather', 'args': '', 'id': 'call_Ndb4jvWm2uMA0JDQXu37wDH6', 'index': 1, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '{"ci', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'ty": ', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '"San F', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'ranc', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'isco"', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '}', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]
Tool calls: [{'name': 'get_weather', 'args': {'city': 'Boston'}, 'id': 'call_GOwNaQHeqMixay2qy80padfE', 'type': 'tool_call'}, {'name': 'get_weather', 'args': {'city': 'San Francisco'}, 'id': 'call_Ndb4jvWm2uMA0JDQXu37wDH6', 'type': 'tool_call'}]
Tool execution requires approval

Tool: get_weather
Args: {'city': 'Boston'}
Tool execution requires approval

Tool: get_weather
Args: {'city': 'San Francisco'}

接下来，我们为每个中断收集一个 决策。重要的是，决策的顺序必须与我们收集的动作顺序相匹配。

为了说明，我们将编辑一个工具调用并接受另一个：

def _get_interrupt_decisions(interrupt: Interrupt) -> list[dict]:
    return [
        {
            "type": "edit",
            "edited_action": {
                "name": "get_weather",
                "args": {"city": "Boston, U.K."},
            },
        }
        if "boston" in request["description"].lower()
        else {"type": "approve"}
        for request in interrupt.value["action_requests"]
    ]

decisions = {}
for interrupt in interrupts:
    decisions[interrupt.id] = {
        "decisions": _get_interrupt_decisions(interrupt)
    }

decisions

Output

{
    'a96c40474e429d661b5b32a8d86f0f3e': {
        'decisions': [
            {
                'type': 'edit',
                 'edited_action': {
                     'name': 'get_weather',
                     'args': {'city': 'Boston, U.K.'}
                 }
            },
            {'type': 'approve'},
        ]
    }
}

然后，我们可以通过将 命令 传入相同的流式循环来恢复：

interrupts = []
for chunk in agent.stream(
    Command(resume=decisions),
    config=config,
    stream_mode=["messages", "updates"],
    version="v2",
):
    # Streaming loop is unchanged
    if chunk["type"] == "messages":
        token, metadata = chunk["data"]
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
    elif chunk["type"] == "updates":
        for source, update in chunk["data"].items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])
            if source == "__interrupt__":
                interrupts.extend(update)
                _render_interrupt(update[0])

Output

Tool response: [{'type': 'text', 'text': "It's always sunny in Boston, U.K.!"}]
Tool response: [{'type': 'text', 'text': "It's always sunny in San Francisco!"}]
-| **|Boston|**|:| It|'s| always| sunny| in| Boston|,| U|.K|.|
|-| **|San| Francisco|**|:| It|'s| always| sunny| in| San| Francisco|!|

从子智能体流式传输

当智能体中的任何位置有多个 LLM 时，通常有必要区分生成消息的来源。

为此，在创建每个智能体时传递一个 name。然后在 "messages" 模式下流式传输时，该名称可通过 lc_agent_name 键在元数据中获取。

下面，我们更新 流式传输工具调用 示例：

1. 我们用 call_weather_agent 工具替换我们的工具，该工具在内部调用智能体
2. 我们为每个智能体添加一个 name
3. 创建流时指定 subgraphs=True
4. 我们的流处理与之前相同，但我们添加了逻辑以使用 create_agent 的 name 参数跟踪当前活动的智能体

当您为智能体设置 name 时，该名称也会附加到该智能体生成的任何 AIMessage 上。

首先我们构建智能体：

from typing import Any

from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
from langchain.messages import AIMessage, AnyMessage


def get_weather(city: str) -> str:
    """Get weather for a given city."""

    return f"It's always sunny in {city}!"


weather_model = init_chat_model("openai:gpt-5.2")
weather_agent = create_agent(
    model=weather_model,
    tools=[get_weather],
    name="weather_agent",
)


def call_weather_agent(query: str) -> str:
    """Query the weather agent."""
    result = weather_agent.invoke({
        "messages": [{"role": "user", "content": query}]
    })
    return result["messages"][-1].text


supervisor_model = init_chat_model("openai:gpt-5.2")
agent = create_agent(
    model=supervisor_model,
    tools=[call_weather_agent],
    name="supervisor",
)

接下来，我们在流式循环中添加逻辑以报告哪个智能体正在发出令牌：

def _render_message_chunk(token: AIMessageChunk) -> None:
    if token.text:
        print(token.text, end="|")
    if token.tool_call_chunks:
        print(token.tool_call_chunks)


def _render_completed_message(message: AnyMessage) -> None:
    if isinstance(message, AIMessage) and message.tool_calls:
        print(f"Tool calls: {message.tool_calls}")
    if isinstance(message, ToolMessage):
        print(f"Tool response: {message.content_blocks}")


input_message = {"role": "user", "content": "What is the weather in Boston?"}
current_agent = None
for chunk in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates"],
    subgraphs=True,
    version="v2",
):
    if chunk["type"] == "messages":
        token, metadata = chunk["data"]
        if agent_name := metadata.get("lc_agent_name"):
            if agent_name != current_agent:
                print(f"🤖 {agent_name}: ")
                current_agent = agent_name  
        if isinstance(token, AIMessage):
            _render_message_chunk(token)
    elif chunk["type"] == "updates":
        for source, update in chunk["data"].items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])

Output

🤖 supervisor:
[{'name': 'call_weather_agent', 'args': '', 'id': 'call_asorzUf0mB6sb7MiKfgojp7I', 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '{"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'query', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '":"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'Boston', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': ' weather', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': ' right', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': ' now', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': ' and', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': " today's", 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': ' forecast', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '"}', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
Tool calls: [{'name': 'call_weather_agent', 'args': {'query': "Boston weather right now and today's forecast"}, 'id': 'call_asorzUf0mB6sb7MiKfgojp7I', 'type': 'tool_call'}]
🤖 weather_agent:
[{'name': 'get_weather', 'args': '', 'id': 'call_LZ89lT8fW6w8vqck5pZeaDIx', 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '{"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'city', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '":"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': 'Boston', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
[{'name': None, 'args': '"}', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]
Tool calls: [{'name': 'get_weather', 'args': {'city': 'Boston'}, 'id': 'call_LZ89lT8fW6w8vqck5pZeaDIx', 'type': 'tool_call'}]
Tool response: [{'type': 'text', 'text': "It's always sunny in Boston!"}]
Boston| weather| right| now|:| **|Sunny|**|.

|Today|'s| forecast| for| Boston|:| **|Sunny| all| day|**|.|Tool response: [{'type': 'text', 'text': 'Boston weather right now: **Sunny**.\n\nToday's forecast for Boston: **Sunny all day**.'}]
🤖 supervisor:
Boston| weather| right| now|:| **|Sunny|**|.

|Today|'s| forecast| for| Boston|:| **|Sunny| all| day|**|.|

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 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 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。

from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model="gpt-4.1",
    streaming=False
)

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 disable_streaming=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。

v2 流式格式

需要 LangGraph >= 1.1。

向 stream() 或 astream() 传递 version="v2" 以获得统一的输出格式。每个块都是一个 StreamPart 字典，包含 type、ns 和 data 键—无论流模式或模式数量如何，形状都相同：

v2 (new)

# Unified format — no more tuple unpacking
for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode=["updates", "custom"],
    version="v2",
):
    print(chunk["type"])  # "updates" or "custom"
    print(chunk["data"])  # payload

# Must unpack (mode, data) tuples
for mode, chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode=["updates", "custom"],
):
    print(mode)   # "updates" or "custom"
    print(chunk)  # payload

v2 格式还改进了 invoke()—它返回一个带有 .value 和 .interrupts 属性的 GraphOutput 对象，清晰地将状态与中断元数据分离：

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello"}]},
    version="v2",
)
print(result.value)       # state (dict, Pydantic model, or dataclass)
print(result.interrupts)  # tuple of Interrupt objects (empty if none)

有关 v2 格式的更多详细信息，包括类型缩小、Pydantic/dataclass 强制转换和子图流式传输，请参阅 LangGraph 流式文档。

相关资源

• 前端流式传输—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—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—配置和访问聊天模型的推理输出
• 标准内容块—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—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—高级流式传输选项，包括 values、debug 模式和子图流式传输

---

Edit this page on GitHub or file an issue.

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