Core components

Streaming

LangChain implements a streaming system to surface real-time updates.

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.

Overview

LangChain's streaming system lets you surface live feedback from agent runs to your application.

What's possible with LangChain streaming:

Stream agent progress — get state updates after each agent step.
Stream LLM tokens — stream language model tokens as they're generated.
Stream custom updates — emit user-defined signals (e.g., "Fetched 10/100 records").
Stream multiple modes — choose from updates (agent progress), messages (LLM tokens + metadata), or custom (arbitrary user data).

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: AIMessage with tool call requests
Tool node: ToolMessage with execution result
LLM node: Final AI response

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 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.

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'}]

Custom updates

To stream updates from tools as they are executed, you can use 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

If you add get_stream_writer inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context.

Stream multiple modes

You can specify multiple streaming modes by passing stream mode as a list: stream_mode=["updates", "custom"]:

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}})]}}

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.

Streaming ​

Overview ​

Agent progress ​

LLM tokens ​

Custom updates ​

Stream multiple modes ​

Disable streaming ​

Streaming

Overview

Agent progress

LLM tokens

Custom updates

Stream multiple modes

Disable streaming