# Stream outputs

## Streaming API

LangGraph graphs expose the [`.stream()`][langgraph.pregel.Pregel.stream] (sync) and [`.astream()`][langgraph.pregel.Pregel.astream] (async) methods to yield streamed outputs as iterators.

Basic usage example:

=== "Sync"

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

=== "Async"

    ```python
    async for chunk in graph.astream(inputs, stream_mode="updates"):
        print(chunk)
    ```

??? example "Extended example: streaming updates"

      ```python
      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)
      ```

      1. The `stream()` method returns an iterator that yields streamed outputs.
      2. 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](#supported-stream-modes) for details.

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


### Supported stream modes

| Mode                             | Description                                                                                                                                                                         |
|----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [`values`](#stream-graph-state)  | Streams the full value of the state after each step of the graph.                                                                                                                   |
| [`updates`](#stream-graph-state) | 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`](#stream-custom-data)  | Streams custom data from inside your graph nodes.                                                                                                                                   |
| [`messages`](#messages)          | Streams LLM tokens and metadata for the graph node where the LLM is invoked.                                                                                                        |
| [`debug`](#debug)                | Streams as much information as possible throughout the execution of the graph.                                                                                                      |

### 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.

=== "Sync"

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

=== "Async"

    ```python
    async for mode, chunk in graph.astream(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.

```python
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"

    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.


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

===  "values"

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

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


## Subgraphs

To include outputs from [subgraphs](../concepts/subgraphs.md) 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.

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

1. Set `subgraphs=True` to stream outputs from subgraphs.

??? example "Extended example: streaming from subgraphs"

      ```python
      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)
      ```
      
      1. Set `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 {#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.

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


## LLM tokens {#messages}

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](#supported-stream-modes) 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](#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](#async) for details or upgrade to Python 3.11+.

```python
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)
```

1. Note that the message events are emitted even when the LLM is run using `.invoke` rather than `.stream`.
2. 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.

```python
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)
```

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


??? example "Extended example: filtering by tags"

      ```python
      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)
      ```

      1. The `joke_model` is tagged with "joke".
      2. The `poem_model` is tagged with "poem".
      3. 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](#async) for more details.
      4. 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:

```python
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)!
        ...
```

1. 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.
2. Filter the streamed tokens by the `langgraph_node` field in the metadata to only include the tokens from the `write_poem` node.

??? example "Extended example: streaming LLM tokens from specific nodes"

      ```python
      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)
      ```

      1. 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.
      2. 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:

1. Use `get_stream_writer()` to access the stream writer and emit custom data.
2. 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](#async) for usage examples.


=== "node"

      ```python
      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)
      ```

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

=== "tool"

      ```python
      from langchain_core.tools import tool
      from langgraph.config import get_stream_writer

      @tool
      def query_database(query: str) -> str:
          """Query the database."""
          writer = get_stream_writer() # (1)!
          # highlight-next-line
          writer({"data": "Retrieved 0/100 records", "type": "progress"}) # (2)!
          # perform query
          # highlight-next-line
          writer({"data": "Retrieved 100/100 records", "type": "progress"}) # (3)!
          return "some-answer" 


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

      for chunk in graph.stream(inputs, stream_mode="custom"): # (4)!
          print(chunk)
      ```

      1. Access the stream writer to send custom data.
      2. Emit a custom key-value pair (e.g., progress update).
      3. Emit another custom key-value pair.
      4. 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.

```python
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)
```

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


??? example "Extended example: streaming arbitrary chat model"
      ```python
      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:

      ```python
      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.

=== "init_chat_model"
      
      ```python
      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)!
      )
      ```

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

=== "chat model interface"

      ```python
      from langchain_openai import ChatOpenAI

      llm = ChatOpenAI(model="o1-preview", disable_streaming=True) # (1)!
      ```

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


## Async with Python < 3.11 { #async }

In Python versions < 3.11, [asyncio tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) do not support the `context` parameter.  
This limits LangGraph ability to automatically propagate context, and affects LangGraph’s streaming mechanisms in two key ways:

1. You **must** explicitly pass [`RunnableConfig`](https://python.langchain.com/docs/concepts/runnables/#runnableconfig) into async LLM calls (e.g., `ainvoke()`), as callbacks are not automatically propagated.
2. You **cannot** use `get_stream_writer()` in async nodes or tools — you must pass a `writer` argument directly.

??? example "Extended example: async LLM call with manual config"

      ```python
      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)
      ```

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

??? example "Extended example: async custom streaming with stream writer"

      ```python
      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)
      ```

      1. 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.
      2. Set `stream_mode="custom"` to receive the custom data in the stream.
