Streaming API

LangGraph SDK allows you to stream outputs from the LangGraph API server.

LangGraph SDK and LangGraph Server are a part of LangGraph Platform.

Basic usage

Basic usage example:

from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)

# Using the graph deployed with the name "agent"
assistant_id = "agent"

# create a thread
thread = await client.threads.create()
thread_id = thread["thread_id"]

# create a streaming run
# highlight-next-line
async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input=inputs,
    stream_mode="updates"
):
    print(chunk.data)

Extended example: streaming updates

This is an example graph you can run in the LangGraph API server. See LangGraph Platform quickstart for more details.

# graph.py
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()
)

Once you have a running LangGraph API server, you can interact with it using LangGraph SDK

from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)

# Using the graph deployed with the name "agent"
assistant_id = "agent"

# create a thread
thread = await client.threads.create()
thread_id = thread["thread_id"]

# create a streaming run
# highlight-next-line
async for chunk in client.runs.stream(  # (1)!
    thread_id,
    assistant_id,
    input={"topic": "ice cream"},
    # highlight-next-line
    stream_mode="updates"  # (2)!
):
    print(chunk.data)

The client.runs.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.

{'run_id': '1f02c2b3-3cef-68de-b720-eec2a4a8e920', 'attempt': 1}
{'refine_topic': {'topic': 'ice cream and cats'}}
{'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}

Supported stream modes

Mode	Description	LangGraph Library Method
`values`	Stream the full graph state after each super-step.	`.stream()` / `.astream()` with `stream_mode="values"`
`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.	`.stream()` / `.astream()` with `stream_mode="updates"`
`messages-tuple`	Streams LLM tokens and metadata for the graph node where the LLM is invoked (useful for chat apps).	`.stream()` / `.astream()` with `stream_mode="messages"`
`debug`	Streams as much information as possible throughout the execution of the graph.	`.stream()` / `.astream()` with `stream_mode="debug"`
`custom`	Streams custom data from inside your graph	`.stream()` / `.astream()` with `stream_mode="custom"`
`events`	Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps.	`.astream_events()`

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.

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input=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.

Example 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()
)

Stateful runs Examples below assume that you want to persist the outputs of a streaming run in the checkpointer DB and have created a thread. To create a thread:

from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)

# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create a thread
thread = await client.threads.create()
thread_id = thread["thread_id"]

If you don’t need to persist the outputs of a run, you can pass None instead of thread_id when streaming.

Stream Mode: `updates`

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.

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"topic": "ice cream"},
    # highlight-next-line
    stream_mode="updates"
):
    print(chunk.data)

Stream Mode: `values`

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

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"topic": "ice cream"},
    # highlight-next-line
    stream_mode="values"
):
    print(chunk.data)

Subgraphs

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.

for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"foo": "foo"},
    # highlight-next-line
    stream_subgraphs=True, # (1)!
    stream_mode="updates",
):
    print(chunk)

Set stream_subgraphs=True to stream outputs from subgraphs.

Extended example: streaming from subgraphs

This is an example graph you can run in the LangGraph API server. See LangGraph Platform quickstart for more details.

# graph.py
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()

Once you have a running LangGraph API server, you can interact with it using LangGraph SDK

from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)

# Using the graph deployed with the name "agent"
assistant_id = "agent"

# create a thread
thread = await client.threads.create()
thread_id = thread["thread_id"]

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"foo": "foo"},
    # highlight-next-line
    stream_subgraphs=True, # (1)!
    stream_mode="updates",
):
    print(chunk)

Set stream_subgraphs=True to stream outputs from subgraphs.

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.

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"topic": "ice cream"},
    # highlight-next-line
    stream_mode="debug"
):
    print(chunk.data)

LLM tokens

Use the messages-tuple 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-tuple 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.

Example graph

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()
)

Note that the message events are emitted even when the LLM is run using .invoke rather than .stream.

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"topic": "ice cream"},
    # highlight-next-line
    stream_mode="messages-tuple",
):
    if chunk.event != "messages":
        continue

    message_chunk, metadata = chunk.data  # (1)!
    if message_chunk["content"]:
        print(message_chunk["content"], end="|", flush=True)

The “messages-tuple” 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 LLM tokens

To filter the streamed tokens by LLM invocation, you can associate tags with LLM invocations.
To stream tokens only from specific nodes, use stream_mode="messages" and filter the outputs by the langgraph_node field in the streamed metadata.

Stream custom data

To send custom user-defined data:

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"query": "example"},
    # highlight-next-line
    stream_mode="custom"
):
    print(chunk.data)

Stream events

To stream all events, including the state of the graph:

async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input={"topic": "ice cream"},
    # highlight-next-line
    stream_mode="events"
):
    print(chunk.data)

Stateless runs

If you don’t want to persist the outputs of a streaming run in the checkpointer DB, you can create a stateless run without creating a thread:

from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)

async for chunk in client.runs.stream(
    # highlight-next-line
    None,  # (1)!
    assistant_id,
    input=inputs,
    stream_mode="updates"
):
    print(chunk.data)

We are passing None instead of a thread_id UUID.

Join and stream

LangGraph Platform allows you to join an active background run and stream outputs from it. To do so, you can use LangGraph SDK’s client.runs.join_stream method:

from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)

# highlight-next-line
async for chunk in client.runs.join_stream(
    thread_id,
    # highlight-next-line
    run_id,  # (1)!
):
    print(chunk)

This is the run_id of an existing run you want to join.

Outputs not buffered When you use .join_stream, output is not buffered, so any output produced before joining will not be received.

API Reference

For API usage and implementation, refer to the API reference.

Build an app with the LangGraph basics

Data models

Core capabilities

LangGraph Studio

Basic usage

Supported stream modes

Stream multiple modes

Stream graph state

Stream Mode: `updates`

Stream Mode: `values`

Subgraphs

Debugging

LLM tokens

Filter LLM tokens

Stream custom data

Stream events

Stateless runs

Join and stream

API Reference

Build an app with the LangGraph basics

Data models

Core capabilities

LangGraph Studio

​Basic usage

​Supported stream modes

​Stream multiple modes

​Stream graph state

​Stream Mode: updates

​Stream Mode: values

​Subgraphs

​Debugging

​LLM tokens

​Filter LLM tokens

​Stream custom data

​Stream events

​Stateless runs

​Join and stream

​API Reference

Basic usage

Supported stream modes

Stream multiple modes

Stream graph state

Stream Mode: `updates`

Stream Mode: `values`

Subgraphs

Debugging

LLM tokens

Filter LLM tokens

Stream custom data

Stream events

Stateless runs

Join and stream

API Reference