langgraph/docs/docs/cloud/how-tos/streaming.md

Stream outputs

Streaming API

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

Basic usage example:

=== "Python"

```python
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)
```

=== "JavaScript"

```js
import { Client } from "@langchain/langgraph-sdk";
const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });

// Using the graph deployed with the name "agent"
const assistantID = "agent";

// create a thread
const thread = await client.threads.create();
const threadID = thread["thread_id"];

// create a streaming run
// highlight-next-line
const streamResponse = client.runs.stream(
  threadID,
  assistantID,
  {
    input,
    streamMode: "updates"
  }
);
for await (const chunk of streamResponse) {
  console.log(chunk.data);
}
```

=== "cURL"

Create a thread:

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads \
--header 'Content-Type: application/json' \
--data '{}'
```

Create a streaming run:

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
--header 'Content-Type: application/json' \
--header 'x-api-key: <API_KEY>'
--data "{
  \"assistant_id\": \"agent\",
  \"input\": <inputs>,
  \"stream_mode\": \"updates\"
}"
```

??? example "Extended example: streaming updates"

This is an example graph you can run in the LangGraph API server.
See [LangGraph Platform quickstart](../quick_start.md) for more details.

```python
# 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](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)

=== "Python"

    ```python
    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)
    ```

    1. The `client.runs.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.

=== "JavaScript"

    ```js
    import { Client } from "@langchain/langgraph-sdk";
    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

    // Using the graph deployed with the name "agent"
    const assistantID = "agent";

    // create a thread
    const thread = await client.threads.create();
    const threadID = thread["thread_id"];

    // create a streaming run
    // highlight-next-line
    const streamResponse = client.runs.stream(  // (1)!
      threadID,
      assistantID,
      {
        input: { topic: "ice cream" },
        // highlight-next-line
        streamMode: "updates"  // (2)!
      }
    );
    for await (const chunk of streamResponse) {
      console.log(chunk.data);
    }
    ```

    1. The `client.runs.stream()` method returns an iterator that yields streamed outputs.
    2. Set `streamMode: "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.

=== "cURL"

    Create a thread:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
    ```

    Create a streaming run:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"input\": {\"topic\": \"ice cream\"},
      \"stream_mode\": \"updates\"
    }"
    ```

```output
{'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.

=== "Python"

```python
async for chunk in client.runs.stream(
    thread_id,
    assistant_id,
    input=inputs,
    stream_mode=["updates", "custom"]
):
    print(chunk)
```

=== "JavaScript"

```js
const streamResponse = client.runs.stream(
  threadID,
  assistantID,
  {
    input,
    streamMode: ["updates", "custom"]
  }
);
for await (const chunk of streamResponse) {
  console.log(chunk);
}
```

=== "cURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": <inputs>,
   \"stream_mode\": [
     \"updates\"
     \"custom\"
   ]
 }"
```

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 "Example 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()
)
```

!!! note "Stateful runs"

Examples below assume that you want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB and have created a thread. To create a thread:

=== "Python"

    ```python
    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"]
    ```

=== "JavaScript"

    ```js
    import { Client } from "@langchain/langgraph-sdk";
    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

    // Using the graph deployed with the name "agent"
    const assistantID = "agent";
    // create a thread
    const thread = await client.threads.create();
    const threadID = thread["thread_id"]
    ```

=== "cURL"

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
    ```

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

=== "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"

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

=== "JavaScript"

    ```js
    const streamResponse = client.runs.stream(
      threadID,
      assistantID,
      {
        input: { topic: "ice cream" },
        // highlight-next-line
        streamMode: "updates"
      }
    );
    for await (const chunk of streamResponse) {
      console.log(chunk.data);
    }
    ```

=== "cURL"

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"input\": {\"topic\": \"ice cream\"},
      \"stream_mode\": \"updates\"
    }"
    ```

=== "values"

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

=== "Python"

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

=== "JavaScript"

    ```js
    const streamResponse = client.runs.stream(
      threadID,
      assistantID,
      {
        input: { topic: "ice cream" },
        // highlight-next-line
        streamMode: "values"
      }
    );
    for await (const chunk of streamResponse) {
      console.log(chunk.data);
    }
    ```

=== "cURL"

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"input\": {\"topic\": \"ice cream\"},
      \"stream_mode\": \"values\"
    }"
    ```

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)

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

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

This is an example graph you can run in the LangGraph API server.
See [LangGraph Platform quickstart](../quick_start.md) for more details.

```python
# 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](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)

=== "Python"

    ```python
    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)
    ```
    
    1. Set `stream_subgraphs=True` to stream outputs from subgraphs.

=== "JavaScript"

    ```js
    import { Client } from "@langchain/langgraph-sdk";
    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

    // Using the graph deployed with the name "agent"
    const assistantID = "agent";

    // create a thread
    const thread = await client.threads.create();
    const threadID = thread["thread_id"];

    // create a streaming run
    const streamResponse = client.runs.stream(
      threadID,
      assistantID,
      {
        input: { foo: "foo" },
        // highlight-next-line
        streamSubgraphs: true,  // (1)!
        streamMode: "updates"
      }
    );
    for await (const chunk of streamResponse) {
      console.log(chunk);
    }
    ```

    1. Set `streamSubgraphs: true` to stream outputs from subgraphs.

=== "cURL"

    Create a thread:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
    ```

    Create a streaming run:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"input\": {\"foo\": \"foo\"},
      \"stream_subgraphs\": true,
      \"stream_mode\": [
        \"updates\"
      ]
    }"
    ```

**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.

=== "Python"

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

=== "JavaScript"

```js
const streamResponse = client.runs.stream(
  threadID,
  assistantID,
  {
    input: { topic: "ice cream" },
    // highlight-next-line
    streamMode: "debug"
  }
);
for await (const chunk of streamResponse) {
  console.log(chunk.data);
}
```

=== "cURL"

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"topic\": \"ice cream\"},
  \"stream_mode\": \"debug\"
}"
```

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 "Example graph"

```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()
)
```

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

=== "Python"

```python
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)
```

1. 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.

=== "JavaScript"

```js
const streamResponse = client.runs.stream(
  threadID,
  assistantID,
  {
    input: { topic: "ice cream" },
    // highlight-next-line
    streamMode: "messages-tuple"
  }
);
for await (const chunk of streamResponse) {
  if (chunk.event !== "messages") {
    continue;
  }
  console.log(chunk.data[0]["content"]);  // (1)!
}
```

1. 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.

=== "cURL"

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"topic\": \"ice cream\"},
  \"stream_mode\": \"messages-tuple\"
}"
```

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:

=== "Python"

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

=== "JavaScript"

```js
const streamResponse = client.runs.stream(
  threadID,
  assistantID,
  {
    input: { query: "example" },
    // highlight-next-line
    streamMode: "custom"
  }
);
for await (const chunk of streamResponse) {
  console.log(chunk.data);
}
```

=== "cURL"

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"query\": \"example\"},
  \"stream_mode\": \"custom\"
}"
```

Stream events

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

=== "Python"

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

=== "JavaScript"

```js
const streamResponse = client.runs.stream(
  threadID,
  assistantID,
  {
    input: { topic: "ice cream" },
    // highlight-next-line
    streamMode: "events"
  }
);
for await (const chunk of streamResponse) {
  console.log(chunk.data);
}
```

=== "cURL"

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"topic\": \"ice cream\"},
  \"stream_mode\": \"events\"
}"
```