Stream outputs

You can stream outputs from a LangGraph agent or workflow.

Supported stream modes

Pass one or more of the following stream modes as a list to the stream() or astream() methods:

Mode	Description
`values`	Streams the full value of the state after each step of the graph.
`updates`	Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately.
`custom`	Streams custom data from inside your graph nodes.
`messages`	Streams 2-tuples (LLM token, metadata) from any graph nodes where an LLM is invoked.
`debug`	Streams as much information as possible throughout the execution of the graph.

Stream from an agent

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

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
)
# highlight-next-line
for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    # highlight-next-line
    stream_mode="updates"
):
    print(chunk)
    print("\n")

LLM tokens

To stream tokens as they are produced by the LLM, use stream_mode="messages":

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
)
# highlight-next-line
for token, metadata in agent.stream(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    # highlight-next-line
    stream_mode="messages"
):
    print("Token", token)
    print("Metadata", metadata)
    print("\n")

Tool updates

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

# highlight-next-line
from langgraph.config import get_stream_writer

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

agent = create_react_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"}]},
    # highlight-next-line
    stream_mode="custom"
):
    print(chunk)
    print("\n")

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", "messages", "custom"]:

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
)

for stream_mode, chunk in agent.stream(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    # highlight-next-line
    stream_mode=["updates", "messages", "custom"]
):
    print(chunk)
    print("\n")

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.

Stream from a workflow

Basic usage example

LangGraph graphs expose the .stream() (sync) and .astream() (async) methods to yield streamed outputs as iterators.

for chunk in graph.stream(inputs, stream_mode="updates"):
    print(chunk)

Extended example: streaming updates

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()
)

# highlight-next-line
for chunk in graph.stream( # (1)!
    {"topic": "ice cream"},
    # highlight-next-line
    stream_mode="updates", # (2)!
):
    print(chunk)

The stream() method returns an iterator that yields streamed outputs.
Set stream_mode="updates" to stream only the updates to the graph state after each node. Other stream modes are also available. See supported stream modes for details.

{'refineTopic': {'topic': 'ice cream and cats'}}
{'generateJoke': {'joke': 'This is a joke about ice cream and cats'}}

Stream multiple modes

You can pass a list as the stream_mode parameter to stream multiple modes at once. The streamed outputs will be tuples of (mode, chunk) where mode is the name of the stream mode and chunk is the data streamed by that mode.

for mode, chunk in graph.stream(inputs, stream_mode=["updates", "custom"]):
    print(chunk)

Stream graph state

Use the stream modes updates and values to stream the state of the graph as it executes.

updates streams the updates to the state after each step of the graph.
values streams the full value of the state after each step of the graph.

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()
)

Use this to stream only the state updates returned by the nodes after each step. The streamed outputs include the name of the node as well as the update.

for chunk in graph.stream(
    {"topic": "ice cream"},
    # highlight-next-line
    stream_mode="updates",
):
    print(chunk)

Stream subgraph outputs

To include outputs from subgraphs in the streamed outputs, you can set subgraphs=True in the .stream() method of the parent graph. This will stream outputs from both the parent graph and any subgraphs. The outputs will be streamed as tuples (namespace, data), where namespace is a tuple with the path to the node where a subgraph is invoked, e.g. ("parent_node:<task_id>", "child_node:<task_id>").

for chunk in graph.stream(
    {"foo": "foo"},
    # highlight-next-line
    subgraphs=True, # (1)!
    stream_mode="updates",
):
    print(chunk)

Set subgraphs=True to stream outputs from subgraphs.

Extended example: streaming from subgraphs

from langgraph.graph import START, StateGraph
from typing import TypedDict

# Define subgraph
class SubgraphState(TypedDict):
    foo: str  # note that this key is shared with the parent graph state
    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()

# Define parent graph
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()

for chunk in graph.stream(
    {"foo": "foo"},
    stream_mode="updates",
    # highlight-next-line
    subgraphs=True, # (1)!
):
    print(chunk)

Set subgraphs=True to stream outputs from subgraphs.

((), {'node_1': {'foo': 'hi! foo'}})
(('node_2:dfddc4ba-c3c5-6887-5012-a243b5b377c2',), {'subgraph_node_1': {'bar': 'bar'}})
(('node_2:dfddc4ba-c3c5-6887-5012-a243b5b377c2',), {'subgraph_node_2': {'foo': 'hi! foobar'}})
((), {'node_2': {'foo': 'hi! foobar'}})

Note that we are receiving not just the node updates, but we also the namespaces which tell us what graph (or subgraph) we are streaming from.

Debugging

Use the debug streaming mode to stream as much information as possible throughout the execution of the graph. The streamed outputs include the name of the node as well as the full state.

for chunk in graph.stream(
    {"topic": "ice cream"},
    # highlight-next-line
    stream_mode="debug",
):
    print(chunk)

LLM tokens

Use the messages streaming mode to stream Large Language Model (LLM) outputs token by token from any part of your graph, including nodes, tools, subgraphs, or tasks. The streamed output from messages mode is a tuple (message_chunk, metadata) where:

message_chunk: the token or message segment from the LLM.
metadata: a dictionary containing details about the graph node and LLM invocation.

If your LLM is not available as a LangChain integration, you can stream its outputs using custom mode instead. See use with any LLM for details.

Manual config required for async in Python < 3.11 When using Python < 3.11 with async code, you must explicitly pass RunnableConfig to ainvoke() to enable proper streaming. See Async with Python < 3.11 for details or upgrade to Python 3.11+.

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 = ""


llm = init_chat_model(model="openai:gpt-4o-mini")

def call_model(state: MyState):
    """Call the LLM to generate a joke about a topic"""
    # highlight-next-line
    llm_response = llm.invoke( # (1)!
        [
            {"role": "user", "content": f"Generate a joke about {state.topic}"}
        ]
    )
    return {"joke": llm_response.content}

graph = (
    StateGraph(MyState)
    .add_node(call_model)
    .add_edge(START, "call_model")
    .compile()
)

for message_chunk, metadata in graph.stream( # (2)!
    {"topic": "ice cream"},
    # highlight-next-line
    stream_mode="messages",
):
    if message_chunk.content:
        print(message_chunk.content, end="|", flush=True)

Note that the message events are emitted even when the LLM is run using .invoke rather than .stream.
The “messages” stream mode returns an iterator of tuples (message_chunk, metadata) where message_chunk is the token streamed by the LLM and metadata is a dictionary with information about the graph node where the LLM was called and other information.

Filter by LLM invocation

You can associate tags with LLM invocations to filter the streamed tokens by LLM invocation.

from langchain.chat_models import init_chat_model

llm_1 = init_chat_model(model="openai:gpt-4o-mini", tags=['joke']) # (1)!
llm_2 = init_chat_model(model="openai:gpt-4o-mini", tags=['poem']) # (2)!

graph = ... # define a graph that uses these LLMs

async for msg, metadata in graph.astream(  # (3)!
    {"topic": "cats"},
    # highlight-next-line
    stream_mode="messages",
):
    if metadata["tags"] == ["joke"]: # (4)!
        print(msg.content, end="|", flush=True)

llm_1 is tagged with “joke”.
llm_2 is tagged with “poem”.
The stream_mode is set to “messages” to stream LLM tokens. The metadata contains information about the LLM invocation, including the tags.
Filter the streamed tokens by the tags field in the metadata to only include the tokens from the LLM invocation with the “joke” tag.

Extended example: filtering by tags

from typing import TypedDict

from langchain.chat_models import init_chat_model
from langgraph.graph import START, StateGraph

joke_model = init_chat_model(model="openai:gpt-4o-mini", tags=["joke"]) # (1)!
poem_model = init_chat_model(model="openai:gpt-4o-mini", tags=["poem"]) # (2)!


class State(TypedDict):
      topic: str
      joke: str
      poem: str


async def call_model(state, config):
      topic = state["topic"]
      print("Writing joke...")
      # Note: Passing the config through explicitly is required for python < 3.11
      # Since context var support wasn't added before then: https://docs.python.org/3/library/asyncio-task.html#creating-tasks
      joke_response = await joke_model.ainvoke(
            [{"role": "user", "content": f"Write a joke about {topic}"}],
            config, # (3)!
      )
      print("\n\nWriting poem...")
      poem_response = await poem_model.ainvoke(
            [{"role": "user", "content": f"Write a short poem about {topic}"}],
            config, # (3)!
      )
      return {"joke": joke_response.content, "poem": poem_response.content}


graph = (
      StateGraph(State)
      .add_node(call_model)
      .add_edge(START, "call_model")
      .compile()
)

async for msg, metadata in graph.astream(
      {"topic": "cats"},
      # highlight-next-line
      stream_mode="messages", # (4)!
):
    if metadata["tags"] == ["joke"]: # (4)!
        print(msg.content, end="|", flush=True)

The joke_model is tagged with “joke”.
The poem_model is tagged with “poem”.
The config is passed through explicitly to ensure the context vars are propagated correctly. This is required for Python < 3.11 when using async code. Please see the async section for more details.
The stream_mode is set to “messages” to stream LLM tokens. The metadata contains information about the LLM invocation, including the tags.

Filter by node

To stream tokens only from specific nodes, use stream_mode="messages" and filter the outputs by the langgraph_node field in the streamed metadata:

for msg, metadata in graph.stream( # (1)!
    inputs,
    # highlight-next-line
    stream_mode="messages",
):
    # highlight-next-line
    if msg.content and metadata["langgraph_node"] == "some_node_name": # (2)!
        ...

The “messages” stream mode returns a tuple of (message_chunk, metadata) where message_chunk is the token streamed by the LLM and metadata is a dictionary with information about the graph node where the LLM was called and other information.
Filter the streamed tokens by the langgraph_node field in the metadata to only include the tokens from the write_poem node.

Extended example: streaming LLM tokens from specific nodes

from typing import TypedDict
from langgraph.graph import START, StateGraph
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini")


class State(TypedDict):
      topic: str
      joke: str
      poem: str


def write_joke(state: State):
      topic = state["topic"]
      joke_response = model.invoke(
            [{"role": "user", "content": f"Write a joke about {topic}"}]
      )
      return {"joke": joke_response.content}


def write_poem(state: State):
      topic = state["topic"]
      poem_response = model.invoke(
            [{"role": "user", "content": f"Write a short poem about {topic}"}]
      )
      return {"poem": poem_response.content}


graph = (
      StateGraph(State)
      .add_node(write_joke)
      .add_node(write_poem)
      # write both the joke and the poem concurrently
      .add_edge(START, "write_joke")
      .add_edge(START, "write_poem")
      .compile()
)

# highlight-next-line
for msg, metadata in graph.stream( # (1)!
    {"topic": "cats"},
    stream_mode="messages",
):
    # highlight-next-line
    if msg.content and metadata["langgraph_node"] == "write_poem": # (2)!
        print(msg.content, end="|", flush=True)

The “messages” stream mode returns a tuple of (message_chunk, metadata) where message_chunk is the token streamed by the LLM and metadata is a dictionary with information about the graph node where the LLM was called and other information.
Filter the streamed tokens by the langgraph_node field in the metadata to only include the tokens from the write_poem node.

Stream custom data

To send custom user-defined data from inside a LangGraph node or tool, follow these steps:

Use get_stream_writer() to access the stream writer and emit custom data.
Set stream_mode="custom" when calling .stream() or .astream() to get the custom data in the stream. You can combine multiple modes (e.g., ["updates", "custom"]), but at least one must be "custom".

No get_stream_writer() in async for Python < 3.11 In async code running on Python < 3.11, get_stream_writer() will not work. Instead, add a writer parameter to your node or tool and pass it manually. See Async with Python < 3.11 for usage examples.

from typing import TypedDict
from langgraph.config import get_stream_writer
from langgraph.graph import StateGraph, START

class State(TypedDict):
    query: str
    answer: str

def node(state: State):
    writer = get_stream_writer()  # (1)!
    writer({"custom_key": "Generating custom data inside node"}) # (2)!
    return {"answer": "some data"}

graph = (
    StateGraph(State)
    .add_node(node)
    .add_edge(START, "node")
    .compile()
)

inputs = {"query": "example"}

# Usage
for chunk in graph.stream(inputs, stream_mode="custom"):  # (3)!
    print(chunk)

Get the stream writer to send custom data.
Emit a custom key-value pair (e.g., progress update).
Set stream_mode="custom" to receive the custom data in the stream.

Use with any LLM

You can use stream_mode="custom" to stream data from any LLM API — even if that API does not implement the LangChain chat model interface. This lets you integrate raw LLM clients or external services that provide their own streaming interfaces, making LangGraph highly flexible for custom setups.

from langgraph.config import get_stream_writer

def call_arbitrary_model(state):
    """Example node that calls an arbitrary model and streams the output"""
    # highlight-next-line
    writer = get_stream_writer() # (1)!
    # Assume you have a streaming client that yields chunks
    for chunk in your_custom_streaming_client(state["topic"]): # (2)!
        # highlight-next-line
        writer({"custom_llm_chunk": chunk}) # (3)!
    return {"result": "completed"}

graph = (
    StateGraph(State)
    .add_node(call_arbitrary_model)
    # Add other nodes and edges as needed
    .compile()
)

for chunk in graph.stream(
    {"topic": "cats"},
    # highlight-next-line
    stream_mode="custom", # (4)!
):
    # The chunk will contain the custom data streamed from the llm
    print(chunk)

Get the stream writer to send custom data.
Generate LLM tokens using your custom streaming client.
Use the writer to send custom data to the stream.
Set stream_mode="custom" to receive the custom data in the stream.

Extended example: streaming arbitrary chat model

import operator
import json

from typing import TypedDict
from typing_extensions import Annotated
from langgraph.graph import StateGraph, START

from openai import AsyncOpenAI

openai_client = AsyncOpenAI()
model_name = "gpt-4o-mini"


async def stream_tokens(model_name: str, messages: list[dict]):
    response = await openai_client.chat.completions.create(
        messages=messages, model=model_name, stream=True
    )
    role = None
    async for chunk in response:
        delta = chunk.choices[0].delta

        if delta.role is not None:
            role = delta.role

        if delta.content:
            yield {"role": role, "content": delta.content}


# this is our tool
async def get_items(place: str) -> str:
    """Use this tool to list items one might find in a place you're asked about."""
    writer = get_stream_writer()
    response = ""
    async for msg_chunk in stream_tokens(
        model_name,
        [
            {
                "role": "user",
                "content": (
                    "Can you tell me what kind of items "
                    f"i might find in the following place: '{place}'. "
                    "List at least 3 such items separating them by a comma. "
                    "And include a brief description of each item."
                ),
            }
        ],
    ):
        response += msg_chunk["content"]
        writer(msg_chunk)

    return response


class State(TypedDict):
    messages: Annotated[list[dict], operator.add]


# this is the tool-calling graph node
async def call_tool(state: State):
    ai_message = state["messages"][-1]
    tool_call = ai_message["tool_calls"][-1]

    function_name = tool_call["function"]["name"]
    if function_name != "get_items":
        raise ValueError(f"Tool {function_name} not supported")

    function_arguments = tool_call["function"]["arguments"]
    arguments = json.loads(function_arguments)

    function_response = await get_items(**arguments)
    tool_message = {
        "tool_call_id": tool_call["id"],
        "role": "tool",
        "name": function_name,
        "content": function_response,
    }
    return {"messages": [tool_message]}


graph = (
    StateGraph(State)
    .add_node(call_tool)
    .add_edge(START, "call_tool")
    .compile()
)

Let’s invoke the graph with an AI message that includes a tool call:

inputs = {
    "messages": [
        {
            "content": None,
            "role": "assistant",
            "tool_calls": [
                {
                    "id": "1",
                    "function": {
                        "arguments": '{"place":"bedroom"}',
                        "name": "get_items",
                    },
                    "type": "function",
                }
            ],
        }
    ]
}

async for chunk in graph.astream(
    inputs,
    stream_mode="custom",
):
    print(chunk["content"], end="|", flush=True)

Disable streaming for specific chat models

If your application mixes models that support streaming with those that do not, you may need to explicitly disable streaming for models that do not support it. Set disable_streaming=True when initializing the model.

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "anthropic:claude-3-7-sonnet-latest",
    # highlight-next-line
    disable_streaming=True # (1)!
)

Set disable_streaming=True to disable streaming for the chat model.

Async with Python < 3.11

In Python versions < 3.11, asyncio tasks do not support the context parameter. This limits LangGraph ability to automatically propagate context, and affects LangGraph’s streaming mechanisms in two key ways:

You must explicitly pass RunnableConfig into async LLM calls (e.g., ainvoke()), as callbacks are not automatically propagated.
You cannot use get_stream_writer() in async nodes or tools — you must pass a writer argument directly.

Extended example: async LLM call with manual config

from typing import TypedDict
from langgraph.graph import START, StateGraph
from langchain.chat_models import init_chat_model

llm = init_chat_model(model="openai:gpt-4o-mini")

class State(TypedDict):
    topic: str
    joke: str

async def call_model(state, config): # (1)!
    topic = state["topic"]
    print("Generating joke...")
    joke_response = await llm.ainvoke(
        [{"role": "user", "content": f"Write a joke about {topic}"}],
        # highlight-next-line
        config, # (2)!
    )
    return {"joke": joke_response.content}

graph = (
    StateGraph(State)
    .add_node(call_model)
    .add_edge(START, "call_model")
    .compile()
)

async for chunk, metadata in graph.astream(
    {"topic": "ice cream"},
    # highlight-next-line
    stream_mode="messages", # (3)!
):
    if chunk.content:
        print(chunk.content, end="|", flush=True)

Accept config as an argument in the async node function.
Pass config to llm.ainvoke() to ensure proper context propagation.
Set stream_mode="messages" to stream LLM tokens.

Extended example: async custom streaming with stream writer

from typing import TypedDict
from langgraph.types import StreamWriter

class State(TypedDict):
      topic: str
      joke: str

# highlight-next-line
async def generate_joke(state: State, writer: StreamWriter): # (1)!
      writer({"custom_key": "Streaming custom data while generating a joke"})
      return {"joke": f"This is a joke about {state['topic']}"}

graph = (
      StateGraph(State)
      .add_node(generate_joke)
      .add_edge(START, "generate_joke")
      .compile()
)

async for chunk in graph.astream(
      {"topic": "ice cream"},
      # highlight-next-line
      stream_mode="custom", # (2)!
):
      print(chunk)

Add writer as an argument in the function signature of the async node or tool. LangGraph will automatically pass the stream writer to the function.
Set stream_mode="custom" to receive the custom data in the stream.

Capabilities

Run and debug

LangGraph APIs

Supported stream modes

Stream from an agent

Agent progress

LLM tokens

Tool updates

Stream multiple modes

Disable streaming

Stream from a workflow

Basic usage example

Stream multiple modes

Stream graph state

Stream subgraph outputs

Debugging

LLM tokens

Filter by LLM invocation

Filter by node

Stream custom data

Use with any LLM

Disable streaming for specific chat models

Async with Python < 3.11

Capabilities

Run and debug

LangGraph APIs

​Supported stream modes

​Stream from an agent

​Agent progress

​LLM tokens

​Tool updates

​Stream multiple modes

​Disable streaming

​Stream from a workflow

​Basic usage example

​Stream multiple modes

​Stream graph state

​Stream subgraph outputs

​Debugging

​LLM tokens

​Filter by LLM invocation

​Filter by node

​Stream custom data

​Use with any LLM

​Disable streaming for specific chat models

​Async with Python < 3.11

Supported stream modes

Stream from an agent

Agent progress

LLM tokens

Tool updates

Stream multiple modes

Disable streaming

Stream from a workflow

Basic usage example

Stream multiple modes

Stream graph state

Stream subgraph outputs

Debugging

LLM tokens

Filter by LLM invocation

Filter by node

Stream custom data

Use with any LLM

Disable streaming for specific chat models

Async with Python < 3.11