Streaming

Tip

For new applications, we recommend event streaming—the typed-projection API introduced in LangGraph v1.2. Event streaming gives you separate iterators per projection (messages, values, subgraphs, output) so you can consume them independently instead of branching on stream_mode chunks.

This page covers LangGraph's stream-mode API. It exposes graph execution through stream modes such as updates, values, messages, custom, checkpoints, tasks, and debug. Use it when you need direct access to graph-runtime events or specific stream-mode output.

Get started

Basic usage

LangGraph graphs expose the stream (sync) and astream (async) methods to yield streamed outputs as iterators. Pass one or more stream modes to control what data you receive.

for chunk in graph.stream(
    {"topic": "ice cream"},
    stream_mode=["updates", "custom"],  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "updates":
        for node_name, state in chunk["data"].items():
            print(f"Node {node_name} updated: {state}")
    elif chunk["type"] == "custom":
        print(f"Status: {chunk['data']['status']}")

Output

Status: thinking of a joke...
Node generate_joke updated: {'joke': 'Why did the ice cream go to school? To get a sundae education!'}

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


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


def generate_joke(state: State):
    writer = get_stream_writer()
    writer({"status": "thinking of a joke..."})
    return {"joke": f"Why did the {state['topic']} go to school? To get a sundae education!"}

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

for chunk in graph.stream(
    {"topic": "ice cream"},
    stream_mode=["updates", "custom"],
    version="v2",
):
    if chunk["type"] == "updates":
        for node_name, state in chunk["data"].items():
            print(f"Node {node_name} updated: {state}")
    elif chunk["type"] == "custom":
        print(f"Status: {chunk['data']['status']}")

Output

Status: thinking of a joke...
Node generate_joke updated: {'joke': 'Why did the ice cream go to school? To get a sundae education!'}

Tip

Debug streaming events, inspect token-by-token LLM output, and monitor latency with LangSmith. Follow the tracing quickstart to get set up.

Stream output format (v2)

Note

Requires LangGraph 1.1 or later. All examples on this page use version="v2".

Pass version="v2" to stream() or astream() to get a unified output format. Every chunk is a StreamPart dict with a consistent shape — regardless of stream mode, number of modes, or subgraph settings:

{
    "type": "values" | "updates" | "messages" | "custom" | "checkpoints" | "tasks" | "debug",
    "ns": (),           # namespace tuple, populated for subgraph events
    "data": ...,        # the actual payload (type varies by stream mode)
}

Each stream mode has a corresponding TypedDict containing ValuesStreamPart, UpdatesStreamPart, MessagesStreamPart, CustomStreamPart, CheckpointStreamPart, TasksStreamPart, DebugStreamPart. You can import these types from langgraph.types. The union type StreamPart is a disjoing union on part["type"], enabling full type narrowing in editors and type checkers.

With v1 (default), the output format changes based on your streaming options (single mode returns raw data, multiple modes return (mode, data) tuples, subgraphs return (namespace, data) tuples). With v2, the format is always the same:

v2 (new)

v1 (current default)

for chunk in graph.stream(inputs, stream_mode="updates", version="v2"):
    print(chunk["type"])  # "updates"
    print(chunk["ns"])    # ()
    print(chunk["data"])  # {"node_name": {"key": "value"}}

for chunk in graph.stream(inputs, stream_mode="updates"):
    print(chunk)  # {"node_name": {"key": "value"}}

The v2 format also enables type narrowing, which means you can filter chunks by chunk["type"] and get the correct payload type. Each branch narrows part["data"] to the specific type for that mode:

for part in graph.stream(
    {"topic": "ice cream"},
    stream_mode=["values", "updates", "messages", "custom"],
    version="v2",
):
    if part["type"] == "values":
        # ValuesStreamPart — full state snapshot after each step
        print(f"State: topic={part['data']['topic']}")
    elif part["type"] == "updates":
        # UpdatesStreamPart — only the changed keys from each node
        for node_name, state in part["data"].items():
            print(f"Node `{node_name}` updated: {state}")
    elif part["type"] == "messages":
        # MessagesStreamPart — (message_chunk, metadata) from LLM calls
        msg, metadata = part["data"]
        print(msg.content, end="", flush=True)
    elif part["type"] == "custom":
        # CustomStreamPart — arbitrary data from get_stream_writer()
        print(f"Progress: {part['data']['progress']}%")

Stream modes

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

Mode	Type	Description
values	`ValuesStreamPart`	Full state after each step.
updates	`UpdatesStreamPart`	State updates after each step. Multiple updates in the same step are streamed separately.
messages	`MessagesStreamPart`	2-tuples of (LLM token, metadata) from LLM calls.
custom	`CustomStreamPart`	Custom data emitted from nodes via `get_stream_writer`.
checkpoints	`CheckpointStreamPart`	Checkpoint events (same format as `get_state()`). Requires a checkpointer.
tasks	`TasksStreamPart`	Task start/finish events with results and errors. Requires a checkpointer.
debug	`DebugStreamPart`	All available info — combines `checkpoints` and `tasks` with extra metadata.

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()
)

updates

values

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"},
    stream_mode="updates",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "updates":
        for node_name, state in chunk["data"].items():
            print(f"Node `{node_name}` updated: {state}")

Output

Node `refine_topic` updated: {'topic': 'ice cream and cats'}
Node `generate_joke` updated: {'joke': 'This is a joke about ice cream and cats'}

Use this to stream the full state of the graph after each step.

for chunk in graph.stream(
    {"topic": "ice cream"},
    stream_mode="values",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "values":
        print(f"topic: {chunk['data']['topic']}, joke: {chunk['data']['joke']}")

Output

topic: ice cream, joke:
topic: ice cream and cats, joke:
topic: ice cream and cats, joke: This is a joke about ice cream and cats

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.

Warning

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 = ""


model = init_chat_model(model="gpt-5.4-mini")

def call_model(state: MyState):
    """Call the LLM to generate a joke about a topic"""
    # Note that message events are emitted even when the LLM is run using .invoke rather than .stream
    model_response = model.invoke(  # [!code highlight]
        [
            {"role": "user", "content": f"Generate a joke about {state.topic}"}
        ]
    )
    return {"joke": model_response.content}

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

# The "messages" stream mode streams LLM tokens with metadata
# Use version="v2" for a unified StreamPart format
for chunk in graph.stream(
    {"topic": "ice cream"},
    stream_mode="messages",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "messages":
        message_chunk, metadata = chunk["data"]
        if message_chunk.content:
            print(message_chunk.content, end="|", flush=True)

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

# model_1 is tagged with "joke"
model_1 = init_chat_model(model="gpt-5.4-mini", tags=['joke'])
# model_2 is tagged with "poem"
model_2 = init_chat_model(model="gpt-5.4-mini", tags=['poem'])

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

# The stream_mode is set to "messages" to stream LLM tokens
# The metadata contains information about the LLM invocation, including the tags
async for chunk in graph.astream(
    {"topic": "cats"},
    stream_mode="messages",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "messages":
        msg, metadata = chunk["data"]
        # Filter the streamed tokens by the tags field in the metadata to only include
        # the tokens from the LLM invocation with the "joke" tag
        if metadata["tags"] == ["joke"]:
            print(msg.content, end="|", flush=True)

from typing import TypedDict

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

# The joke_model is tagged with "joke"
joke_model = init_chat_model(model="gpt-5.4-mini", tags=["joke"])
# The poem_model is tagged with "poem"
poem_model = init_chat_model(model="gpt-5.4-mini", tags=["poem"])


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
      # 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
      joke_response = await joke_model.ainvoke(
            [{"role": "user", "content": f"Write a joke about {topic}"}],
            config,
      )
      print("\n\nWriting poem...")
      poem_response = await poem_model.ainvoke(
            [{"role": "user", "content": f"Write a short poem about {topic}"}],
            config,
      )
      return {"joke": joke_response.content, "poem": poem_response.content}


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

# The stream_mode is set to "messages" to stream LLM tokens
# The metadata contains information about the LLM invocation, including the tags
async for chunk in graph.astream(
      {"topic": "cats"},
      stream_mode="messages",
      version="v2",
):
    if chunk["type"] == "messages":
        msg, metadata = chunk["data"]
        if metadata["tags"] == ["joke"]:
            print(msg.content, end="|", flush=True)

Omit messages from the stream

Use the nostream tag to exclude LLM output from the stream entirely. Invocations tagged with nostream still run and produce output; their tokens are simply not emitted in messages mode.

This is useful when:

You need LLM output for internal processing (for example structured output) but do not want to stream it to the client
You stream the same content through a different channel (for example custom UI messages) and want to avoid duplicate output in the messages stream

from typing import Any, TypedDict

from langchain_anthropic import ChatAnthropic
from langgraph.graph import START, StateGraph

stream_model = ChatAnthropic(model_name="claude-haiku-4-5-20251001")
internal_model = ChatAnthropic(model_name="claude-haiku-4-5-20251001").with_config(
    {"tags": ["nostream"]}
)


class State(TypedDict):
    topic: str
    answer: str
    notes: str


def answer(state: State) -> dict[str, Any]:
    r = stream_model.invoke(
        [{"role": "user", "content": f"Reply briefly about {state['topic']}"}]
    )
    return {"answer": r.content}


def internal_notes(state: State) -> dict[str, Any]:
    # Tokens from this model are omitted from stream_mode="messages" because of nostream
    r = internal_model.invoke(
        [{"role": "user", "content": f"Private notes on {state['topic']}"}]
    )
    return {"notes": r.content}


graph = (
    StateGraph(State)
    .add_node("write_answer", answer)
    .add_node("internal_notes", internal_notes)
    .add_edge(START, "write_answer")
    .add_edge("write_answer", "internal_notes")
    .compile()
)

initial_state: State = {"topic": "AI", "answer": "", "notes": ""}
stream = graph.stream_events(initial_state, version="v3")

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:

# The "messages" stream mode streams LLM tokens with metadata
# Use version="v2" for a unified StreamPart format
for chunk in graph.stream(
    inputs,
    stream_mode="messages",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "messages":
        msg, metadata = chunk["data"]
        # Filter the streamed tokens by the langgraph_node field in the metadata
        # to only include the tokens from the specified node
        if msg.content and metadata["langgraph_node"] == "some_node_name":
            ...

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

model = ChatOpenAI(model="gpt-5.4-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()
)

# The "messages" stream mode streams LLM tokens with metadata
# Use version="v2" for a unified StreamPart format
for chunk in graph.stream(
    {"topic": "cats"},
    stream_mode="messages",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "messages":
        msg, metadata = chunk["data"]
        # Filter the streamed tokens by the langgraph_node field in the metadata
        # to only include the tokens from the write_poem node
        if msg.content and metadata["langgraph_node"] == "write_poem":
            print(msg.content, end="|", flush=True)

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".

Warning

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.

node

tool

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):
    # Get the stream writer to send custom data
    writer = get_stream_writer()
    # Emit a custom key-value pair (e.g., progress update)
    writer({"custom_key": "Generating custom data inside node"})
    return {"answer": "some data"}

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

inputs = {"query": "example"}

# Set stream_mode="custom" to receive the custom data in the stream
for chunk in graph.stream(inputs, stream_mode="custom", version="v2"):
    if chunk["type"] == "custom":
        print(f"Custom event: {chunk['data']['custom_key']}")

from langchain.tools import tool
from langgraph.config import get_stream_writer

@tool
def query_database(query: str) -> str:
    """Query the database."""
    # Access the stream writer to send custom data
    writer = get_stream_writer()  # [!code highlight]
    # Emit a custom key-value pair (e.g., progress update)
    writer({"data": "Retrieved 0/100 records", "type": "progress"})  # [!code highlight]
    # perform query
    # Emit another custom key-value pair
    writer({"data": "Retrieved 100/100 records", "type": "progress"})
    return "some-answer"


graph = ... # define a graph that uses this tool

# Set stream_mode="custom" to receive the custom data in the stream
for chunk in graph.stream(inputs, stream_mode="custom", version="v2"):
    if chunk["type"] == "custom":
        print(f"{chunk['data']['type']}: {chunk['data']['data']}")

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>").

v2 (LangGraph 1.1 or later)

v1 (default)

With version="v2", subgraph events use the same StreamPart format. The ns field identifies the source:

for chunk in graph.stream(
    {"foo": "foo"},
    subgraphs=True,  # [!code highlight]
    stream_mode="updates",
    version="v2", # [!code highlight]
):
    print(chunk["type"])  # "updates"
    print(chunk["ns"])    # () for root, ("node_name:<task_id>",) for subgraph
    print(chunk["data"])  # {"node_name": {"key": "value"}}

for chunk in graph.stream(
    {"foo": "foo"},
    # Set subgraphs=True to stream outputs from subgraphs
    subgraphs=True,  # [!code highlight]
    stream_mode="updates",
):
    print(chunk)

Note

This applies to every stream_mode, including "messages". Agent builders like create_agent return a compiled graph, so adding one as a node turns it into a subgraph. Without subgraphs=True, stream_mode="messages" on the parent graph will not emit token chunks from the inner agent's LLM calls. Invoking agent.stream(...) directly will, which is why this often shows up only after wrapping.

from langchain.agents import create_agent
from langgraph.graph import END, START, StateGraph

graph = (
    StateGraph(State)
    .add_node("agent", create_agent(model, tools, state_schema=State))
    .add_edge(START, "agent")
    .add_edge("agent", END)
    .compile()
)

for chunk in graph.stream(
    {"messages": [{"role": "user", "content": "..."}]},
    stream_mode="messages",
    subgraphs=True,  # [!code highlight]
    version="v2",
):
    print(chunk["type"])  # "messages"
    print(chunk["ns"])    # () for root, ("agent:<task_id>",) for subgraph
    print(chunk["data"])  # (token, metadata)

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",
    # Set subgraphs=True to stream outputs from subgraphs
    subgraphs=True,  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "updates":
        if chunk["ns"]:
            print(f"Subgraph {chunk['ns']}: {chunk['data']}")
        else:
            print(f"Root: {chunk['data']}")

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

Checkpoints

Use the checkpoints streaming mode to receive checkpoint events as the graph executes. Each checkpoint event has the same format as the output of get_state(). Requires a checkpointer.

from langgraph.checkpoint.memory import MemorySaver

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(checkpointer=MemorySaver())
)

config = {"configurable": {"thread_id": "1"}}

for chunk in graph.stream(
    {"topic": "ice cream"},
    config=config,
    stream_mode="checkpoints",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "checkpoints":
        print(chunk["data"])

Tasks

Use the tasks streaming mode to receive task start and finish events as the graph executes. Task events include information about which node is running, its results, and any errors. Requires a checkpointer.

from langgraph.checkpoint.memory import MemorySaver

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(checkpointer=MemorySaver())
)

config = {"configurable": {"thread_id": "1"}}

for chunk in graph.stream(
    {"topic": "ice cream"},
    config=config,
    stream_mode="tasks",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "tasks":
        print(chunk["data"])

Debug

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"},
    stream_mode="debug",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "debug":
        print(chunk["data"])

Note

The debug mode combines checkpoints and tasks events with additional metadata. Use checkpoints or tasks directly if you only need a subset of the debug information.

Multiple modes at once

You can pass a list as the stream_mode parameter to stream multiple modes at once.

With version="v2", every chunk is a StreamPart dict. Use chunk["type"] to distinguish between modes:

for chunk in graph.stream(inputs, stream_mode=["updates", "custom"], version="v2"):
    if chunk["type"] == "updates":
        for node_name, state in chunk["data"].items():
            print(f"Node `{node_name}` updated: {state}")
    elif chunk["type"] == "custom":
        print(f"Custom event: {chunk['data']}")

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

Advanced

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"""
    # Get the stream writer to send custom data
    writer = get_stream_writer()  # [!code highlight]
    # Assume you have a streaming client that yields chunks
    # Generate LLM tokens using your custom streaming client
    for chunk in your_custom_streaming_client(state["topic"]):
        # Use the writer to send custom data to the stream
        writer({"custom_llm_chunk": chunk})  # [!code highlight]
    return {"result": "completed"}

graph = (
    StateGraph(State)
    .add_node(call_arbitrary_model)
    # Add other nodes and edges as needed
    .compile()
)
# Set stream_mode="custom" to receive the custom data in the stream
for chunk in graph.stream(
    {"topic": "cats"},
    stream_mode="custom",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "custom":
        # The chunk data will contain the custom data streamed from the llm
        print(chunk["data"])

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-5.4-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 AIMessage 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",
    version="v2",
):
    if chunk["type"] == "custom":
        print(chunk["data"]["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 streaming=False when initializing the model.

init_chat_model

Chat model interface

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "claude-sonnet-4-6",
    # Set streaming=False to disable streaming for the chat model
    streaming=False  # [!code highlight]
)

from langchain_openai import ChatOpenAI

# Set streaming=False to disable streaming for the chat model
model = ChatOpenAI(model="gpt-5.5", streaming=False)

Note

Not all chat model integrations support the streaming parameter. If your model doesn't support it, use disable_streaming=True instead. This parameter is available on all chat models via the base class.

Migrate to v2

The v2 streaming format (used throughout this page) provides a unified output format. Here's a summary of the key differences and how to migrate:

Scenario	v1 (default)	v2 (`version="v2"`)
Single stream mode	Raw data (dict)	`StreamPart` dict with `type`, `ns`, `data`
Multiple stream modes	`(mode, data)` tuples	Same `StreamPart` dict, filter on `chunk["type"]`
Subgraph streaming	`(namespace, data)` tuples	Same `StreamPart` dict, check `chunk["ns"]`
Multiple modes + subgraphs	`(namespace, mode, data)` triples	Same `StreamPart` dict
`invoke()` return type	Plain dict (state)	`GraphOutput` with `.value` and `.interrupts`
Interrupt location (stream)	`__interrupt__` key in state dict	`interrupts` field on `values` stream parts
Interrupt location (invoke)	`__interrupt__` key in result dict	`.interrupts` attribute on `GraphOutput`
Pydantic/dataclass output	Returns plain dict	Coerces to model/dataclass instance

v2 invoke format

When you pass version="v2" to invoke() or ainvoke(), it returns a GraphOutput object with .value and .interrupts attributes:

from langgraph.types import GraphOutput

result = graph.invoke(inputs, version="v2")

assert isinstance(result, GraphOutput)
result.value       # your output — dict, Pydantic model, or dataclass
result.interrupts  # tuple[Interrupt, ...], empty if none occurred

With any stream mode other than the default "values", invoke(..., stream_mode="updates", version="v2") returns list[StreamPart] instead of list[tuple].

Warning

Dict-style access on GraphOutput (result["key"], "key" in result, result["__interrupt__"]) still works for backwards compatibility but is deprecated and will be removed in a future version. Migrate to result.value and result.interrupts.

This separates state from interrupt metadata. With v1, interrupts are embedded in the returned dict under __interrupt__:

v2 (new)

v1 (current default)

config = {"configurable": {"thread_id": "thread-1"}}
result = graph.invoke(inputs, config=config, version="v2")

if result.interrupts:
    print(result.interrupts[0].value)
    graph.invoke(Command(resume=True), config=config, version="v2")

config = {"configurable": {"thread_id": "thread-1"}}
result = graph.invoke(inputs, config=config)

if "__interrupt__" in result:
    print(result["__interrupt__"][0].value)
    graph.invoke(Command(resume=True), config=config)

Pydantic and dataclass state coercion

When your graph state is a Pydantic model or dataclass, v2 values mode automatically coerces output to the correct type:

from pydantic import BaseModel
from typing import Annotated
import operator

class MyState(BaseModel):
    value: str
    items: Annotated[list[str], operator.add]

# With version="v2", chunk["data"] is a MyState instance
for chunk in graph.stream(
    {"value": "x", "items": []}, stream_mode="values", version="v2"
):
    print(type(chunk["data"]))  # <class 'MyState'>

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.

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

model = init_chat_model(model="gpt-5.4-mini")

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

# Accept config as an argument in the async node function
async def call_model(state, config):
    topic = state["topic"]
    print("Generating joke...")
    # Pass config to model.ainvoke() to ensure proper context propagation
    joke_response = await model.ainvoke(  # [!code highlight]
        [{"role": "user", "content": f"Write a joke about {topic}"}],
        config,
    )
    return {"joke": joke_response.content}

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

# Set stream_mode="messages" to stream LLM tokens
async for chunk in graph.astream(
    {"topic": "ice cream"},
    stream_mode="messages",  # [!code highlight]
    version="v2",  # [!code highlight]
):
    if chunk["type"] == "messages":
        message_chunk, metadata = chunk["data"]
        if message_chunk.content:
            print(message_chunk.content, end="|", flush=True)

from typing import TypedDict
from langgraph.types import StreamWriter

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

# 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
async def generate_joke(state: State, writer: StreamWriter):  # [!code highlight]
      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()
)

# Set stream_mode="custom" to receive the custom data in the stream  # [!code highlight]
async for chunk in graph.astream(
      {"topic": "ice cream"},
      stream_mode="custom",
      version="v2",
):
      if chunk["type"] == "custom":
          print(chunk["data"])

Get started

Basic usage

Full example

Stream output format (v2)

Stream modes

Graph state

LLM tokens

Filter by LLM invocation

Extended example: filtering by tags

Omit messages from the stream

Filter by node

Extended example: streaming LLM tokens from specific nodes

Custom data

Subgraph outputs

Extended example: streaming from subgraphs

Checkpoints

Tasks

Debug

Multiple modes at once

Advanced

Use with any LLM

Extended example: streaming arbitrary chat model

Disable streaming for specific chat models

Migrate to v2

v2 invoke format

Pydantic and dataclass state coercion

Async with Python < 3.11

Extended example: async LLM call with manual config

Extended example: async custom streaming with stream writer