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流式传输

LangChain 实现了流式传输系统，以提供实时更新。

流式传输对于提升基于大语言模型（LLM）构建的应用程序的响应能力至关重要。通过逐步显示输出，甚至在完整响应准备就绪之前，流式传输能显著改善用户体验（UX），尤其是在处理 LLM 延迟时。

概述 ​

LangChain 的流式传输系统允许您将智能体运行过程中的实时反馈呈现给您的应用程序。

LangChain 流式传输可以实现的功能：

更多端到端示例，请参阅下面的常见模式部分。

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。如果在同一步骤中进行了多次更新（例如，运行了多个节点），这些更新将单独流式传输。
messages从任何调用 LLM 的图节点流式传输 (token, metadata) 元组。
custom使用流写入器从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出一个事件。

例如，如果您有一个调用一次工具的智能体，您应该会看到以下更新：

  • LLM 节点：包含工具调用请求的 AIMessage
  • 工具节点：包含执行结果的 ToolMessage
  • LLM 节点：最终的 AI 响应
python
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",
):
    for step, data in chunk.items():
        print(f"step: {step}")
        print(f"content: {data['messages'][-1].content_blocks}")
shell
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 ​

要在 LLM 生成令牌时流式传输它们，请使用 stream_mode="messages"。下面您可以看到智能体流式传输工具调用和最终响应的输出。

python
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 token, metadata in agent.stream(  
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode="messages",
):
    print(f"node: {metadata['langgraph_node']}")
    print(f"content: {token.content_blocks}")
    print("\n")
shell
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 ​

要在工具执行时流式传输其更新，您可以使用 get_stream_writer。

python
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-4-5-20250929",
    tools=[get_weather],
)

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

您可以通过将流模式作为列表传递来指定多种流模式：stream_mode=["updates", "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。

python
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 stream_mode, chunk in agent.stream(  
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode=["updates", "custom"]
):
    print(f"stream_mode: {stream_mode}")
    print(f"content: {chunk}")
    print("\n")
shell
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。

流式传输工具调用 ​

您可能希望同时流式传输：

  1. 生成工具调用时的部分 JSON
  2. 已执行、解析完成的工具调用

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。要访问包含已解析工具调用的完整消息：

  1. 如果这些消息在状态中被跟踪（如在 create_agent 的模型节点中），请使用 stream_mode=["messages", "updates"] 通过状态更新访问完整消息（如下所示）。
  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传输循环期间聚合块（下一节）。

如果您的智能体包含多个 LLM，请参考下面关于从子智能体流式传输的部分。

python
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 stream_mode, data in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates"],  
):
    if stream_mode == "messages":
        token, metadata = data
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)  
    if stream_mode == "updates":
        for source, update in data.items():
            if source in ("model", "tools"):  # `source` captures node name
                _render_completed_message(update["messages"][-1])
shell
[{'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|**|.|

访问完整消息 ​

如果完整消息在智能体的状态中被跟踪，您可以使用 stream_mode=["messages", "updates"]，如上文所示，在流式传输期间访问完整消息。

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

考虑下面的示例，我们将一个流写入器集成到一个简化的护栏中间件中。该中间件演示了工具调用以生成结构化的“安全/不安全”评估（也可以为此使用结构化输出）：

python
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集成到我们的智能体中，并包含其自定义流事件：

python
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 stream_mode, data in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates", "custom"],  
):
    if stream_mode == "messages":
        token, metadata = data
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
    if stream_mode == "updates":
        for source, update in data.items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])
    if stream_mode == "custom":  
        # access completed message in stream
        print(f"Tool calls: {data.tool_calls}")
shell
[{'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，如果您无法向流中添加自定义事件，可以在流式传输循环内聚合消息块：

python
input_message = {"role": "user", "content": "What is the weather in Boston?"}
full_message = None
for stream_mode, data in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates"],
):
    if stream_mode == "messages":
        token, metadata = 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
    if stream_mode == "updates":
        for source, update in data.items():
            if source == "tools":
                _render_completed_message(update["messages"][-1])

人机交互流式传输 ​

为了处理人机交互中断，我们在上述示例的基础上进行构建：

  1. 我们使用人机交互中间件和检查点配置智能体
  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
python
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 stream_mode, data in agent.stream(
    {"messages": [input_message]},
    config=config,  
    stream_mode=["messages", "updates"],
):
    if stream_mode == "messages":
        token, metadata = data
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
    if stream_mode == "updates":
        for source, update in data.items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])
            if source == "__interrupt__":  
                interrupts.extend(update)  
                _render_interrupt(update[0])
shell
[{'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操作顺序相匹配。

为了说明，我们将编辑一个工具调用并接受另一个：

python
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
shell
{
    '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递到同一个流式传输循环中来恢复执行：

python
interrupts = []
for stream_mode, data in agent.stream(
    Command(resume=decisions),  
    config=config,
    stream_mode=["messages", "updates"],
):
    # Streaming loop is unchanged
    if stream_mode == "messages":
        token, metadata = data
        if isinstance(token, AIMessageChunk):
            _render_message_chunk(token)
    if stream_mode == "updates":
        for source, update in data.items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])
            if source == "__interrupt__":
                interrupts.extend(update)
                _render_interrupt(update[0])
shell
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存在多个 LLM 时，通常需要在消息生成时区分其来源。

为此，您可以使用 tags 初始化任何模型。这些标签在 "messages" 模式下流式传输时，可在元数据中获取。

下面，我们更新流式传输工具调用示例：

  1. 我们将工具替换为内部调用智能体的 call_weather_agent 工具
  2. 我们为此 LLM 和外层"监督器" LLM 添加字符串标签
  3. 我们在创建流时指定 subgraphs=True
  4. 我们的流处理与之前相同，但添加了逻辑来跟踪哪个 LLM 处于活动状态

如果不需要从子智能体流式传输令牌，可以使用 name 初始化子智能体。此名称在流式传输 updates 时，可在子智能体生成的消息上访问。

首先我们构建智能体：

python
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",
    tags=["weather_sub_agent"],
)

weather_agent = create_agent(model=weather_model, tools=[get_weather])

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",
    tags=["supervisor"],
)

agent = create_agent(model=supervisor_model, tools=[call_weather_agent])

接下来，我们向流式传输循环添加逻辑，以报告哪个智能体正在发出令牌：

python
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 _, stream_mode, data in agent.stream(
    {"messages": [input_message]},
    stream_mode=["messages", "updates"],
    subgraphs=True,  
):
    if stream_mode == "messages":
        token, metadata = data
        if tags := metadata.get("tags", []):  
            this_agent = tags[0]  
            if this_agent != current_agent:  
                print(f"🤖 {this_agent}: ")  
                current_agent = this_agent  
        if isinstance(token, AIMessage):
            _render_message_chunk(token)
    if stream_mode == "updates":
        for source, update in data.items():
            if source in ("model", "tools"):
                _render_completed_message(update["messages"][-1])
shell
🤖 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_sub_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，您可能需要为给定模型禁用单个令牌的流式传输。这在以下情况下很有用：

  • 使用多智能体系统来控制哪些智能体流式传输其输出
  • 混合支持流式传输的模型与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被流式传输到客户端

在初始化模型时设置 streaming=False。

python
from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model="gpt-4o",
    streaming=False
)

部署到 LangSmith 时，对于任何不希望其输出流式传输到客户端的模型，请设置 streaming=False。这需要在部署前在您的图代码中进行配置。

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，请改用 disable_streaming=True。此参数可通过基类在所有聊天模型上使用。

更多详细信息，请参阅 LangGraph 流式传输指南。

相关链接 ​

LangChain 中文文档

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