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Overview

Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.

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Stream from an agent

LangChain’s streaming system lets you surface live feedback from agent runs to your application. There are three main categories of data you can stream:
  1. Agent progress — get state updates after each agent step.
  2. LLM tokens — stream language model tokens as they’re generated.
  3. Custom updates — emit user-defined signals (e.g., "Fetched 10/100 records").
You can also stream multiple modes at once.

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Agent progress

To stream agent progress, use the stream() or astream() methods with stream_mode="updates". This emits an event after every agent step. For example, if you have an agent that calls a tool once, you should see the following updates:
  • LLM node: AI message with tool call requests
  • Tool node: Tool message with execution result
  • LLM node: Final AI response
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="openai: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}")
Output
step: agent
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: agent
content: [{'type': 'text', 'text': 'It’s always sunny in San Francisco!'}]

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LLM tokens

To stream tokens as they are produced by the LLM, use stream_mode="messages". Below you can see the output of the agent streaming tool calls and the final response.
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="openai: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")
Output
node: agent
content: [{'type': 'tool_call_chunk', 'id': 'call_vbCyBcP8VuneUzyYlSBZZsVa', 'name': 'get_weather', 'args': '', 'index': 0}]


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


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


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


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


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


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


node: agent
content: []


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


node: agent
content: []


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


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


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


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


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


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


node: agent
content: [{'type': 'text', 'text': ' “'}]


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


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


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


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


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


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


node: agent
content: [{'type': 'text', 'text': '!”\n\n'}]

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Custom updates

To stream updates from tools as they are executed, you can use 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="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
)

for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    stream_mode="custom"
):
    print(chunk)
Output
Looking up data for city: San Francisco
Acquired data for city: San Francisco
If you add get_stream_writer inside your tool, you won’t be able to invoke the tool outside of a LangGraph execution context.

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Stream multiple modes

You can specify multiple streaming modes by passing stream mode as a list: stream_mode=["updates", "custom"]:
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="openai: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")
Output
stream_mode: updates
content: {'agent': {'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: {'agent': {'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}})]}}

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Disable streaming

In some applications you might need to disable streaming of individual tokens for a given model. This is useful in multi-agent systems to control which agents stream their output. See the Models guide to learn how to disable streaming.