> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1778861096-fe7dc74.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Event streaming

> Stream real-time updates from LangChain agent runs

LangChain agents are built on LangGraph, so they support the same streaming stack with agent-focused projections for messages, tool calls, state, and custom updates.

For most application and frontend use cases, use **Event Streaming** through `stream_events(..., version="v3")`. Event Streaming returns a run object with typed projections, so each projection can be consumed independently instead of parsing stream-mode tuples.

```py theme={null}
from langchain.agents import create_agent


def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"It's always sunny in {city}!"


agent = create_agent(
    model="gpt-5-nano",
    tools=[get_weather],
)

stream = agent.stream_events({
    "messages": [{"role": "user", "content": "What is the weather in SF?"}],
}, version="v3")

for message in stream.messages:
    for delta in message.text:
        print(delta, end="", flush=True)

final_state = stream.output
```

## What you can stream

| Projection            | Use                                                                        |
| --------------------- | -------------------------------------------------------------------------- |
| `for event in stream` | Raw protocol events with full envelope and access to every channel.        |
| `stream.messages`     | Model message streams, one per LLM call.                                   |
| `message.text`        | Text deltas and final text for a message.                                  |
| `message.reasoning`   | Reasoning deltas for models that expose reasoning content.                 |
| `message.tool_calls`  | Tool-call argument chunks and finalized tool calls.                        |
| `message.output`      | Final message object after the model call completes.                       |
| `stream.values`       | Agent state snapshots.                                                     |
| `stream.output`       | Final agent state.                                                         |
| `stream.subgraphs`    | Nested graph runs (sub-agents and plain subgraphs).                        |
| `stream.extensions`   | Custom transformer projections.                                            |
| `stream.tool_calls`   | Tool execution lifecycle, inputs, output deltas, final output, and errors. |

`stream.messages` yields `ChatModelStream` objects. Each message stream exposes `.text`, `.reasoning`, `.tool_calls`, and `.output`. Sync projections are iterable for live deltas and drainable for final values: use `str(message.text)` for final text and `message.tool_calls.get()` for finalized tool calls.

## Agent messages

Use `stream.messages` when you want model output from each LLM call.

```py theme={null}
stream = agent.stream_events(input, version="v3")

for message in stream.messages:
    print(f"[{message.node}] ", end="")
    for delta in message.text:
        print(delta, end="", flush=True)

    full_message = message.output
    usage = full_message.usage_metadata
    if usage:
        print(usage)
```

`message.output` gives you the finalized AI message, including provider-specific content blocks. In TypeScript, use `message.usage` when you only need token counts or other usage metadata; in Python, read usage from `message.output.usage_metadata`.

## Reasoning content

Reasoning content uses the same shape as text content, but it is available only when the selected model emits reasoning blocks.

```py theme={null}
stream = agent.stream_events(input, version="v3")

for message in stream.messages:
    for delta in message.reasoning:
        print(f"[thinking] {delta}", end="", flush=True)

    for delta in message.text:
        print(delta, end="", flush=True)
```

See the [reasoning guide](/oss/python/langchain/models#reasoning) and your provider's integration page for model configuration details.

## Tool calls

There are two useful tool-call projections:

* `message.tool_calls` streams tool-call argument chunks while the model is producing the tool call.
* `stream.tool_calls` streams the lifecycle of tool execution after the tool call starts.

```py theme={null}
stream = agent.stream_events(input, version="v3")

for message in stream.messages:
    for chunk in message.tool_calls:
        print(f"tool call chunk: {chunk}")

    finalized = message.tool_calls.get()
    if finalized:
        print(f"finalized tool calls: {finalized}")

for call in stream.tool_calls:
    print(f"{call.tool_name}({call.input})")
    for delta in call.output_deltas:
        print(delta, end="", flush=True)
    print(call.output, call.error)
```

## Streaming sub-agents

When a `create_agent` call invokes another `create_agent` (via a wrapping tool, typically), the inner agent's events flow at a nested namespace and surface as a handle on `stream.subgraphs`. Each handle exposes the inner agent's own `.messages`, `.values`, `.tool_calls`, and `.output` projections. The `name=` you pass to `create_agent` becomes `subagent.graph_name` (Python) / `subagent.name` (JS), which lets you filter and label per agent.

Every nested `CompiledStateGraph` shows up on `stream.subgraphs` — `create_agent` instances are one specific kind. Filter on the name to act only on the ones you care about.

```py theme={null}
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model


def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"


weather_agent = create_agent(
    model=init_chat_model("openai:gpt-5.4"),
    tools=[get_weather],
    name="weather_agent",
)


def call_weather(query: str) -> str:
    """Query the weather agent."""
    result = weather_agent.invoke({"messages": [{"role": "user", "content": query}]})
    return result["messages"][-1].text


supervisor = create_agent(
    model=init_chat_model("openai:gpt-5.4"),
    tools=[call_weather],
    name="supervisor",
)

stream = supervisor.stream_events(
    {"messages": [{"role": "user", "content": "What's the weather in Boston?"}]},
    version="v3",
)

for subagent in stream.subgraphs:
    if subagent.graph_name != "weather_agent":
        continue
    print(f"{subagent.graph_name}: ", end="")
    for message in subagent.messages:
        for token in message.text:
            print(token, end="", flush=True)
    print()
```

The same projection covers plain `StateGraph` subgraphs invoked from a tool — set `name=` on `.compile(name=...)` to get a label in `subagent.graph_name`. There's no separate sub-agent-only projection; the filter is what you write into your loop.

## State and final output

Use `stream.values` for state snapshots and `stream.output` for the final agent state.

```py theme={null}
stream = agent.stream_events(input, version="v3")

for snapshot in stream.values:
    print(snapshot)

final_state = stream.output
```

## Multiple projections

Use `stream.interleave(...)` when you want one sync loop over multiple projections:

```py theme={null}
stream = agent.stream_events(input, version="v3")

for name, item in stream.interleave("messages", "tool_calls", "values"):
    if name == "messages":
        print(item.text)
    elif name == "tool_calls":
        print(item.tool_name, item.input)
    elif name == "values":
        print(item)
```

To access channels that aren't exposed as typed projections, or to inspect the full event envelope, iterate raw protocol events:

```py theme={null}
for event in stream:
    print(event["method"], event["params"]["namespace"], event["params"]["data"])
```

## Custom updates

Use custom stream transformers when your application needs a projection that is not built in, such as retrieval progress, artifacts, or domain-specific events.

```py theme={null}
stream = agent.stream_events(
    input,
    version="v3",
    transformers=[ToolActivityTransformer],
)

for activity in stream.extensions["tool_activity"]:
    print(activity)
```

See [Build your own projection](/oss/python/langgraph/event-streaming#build-your-own-projection) for the transformer contract.

## Related

* [Streaming](/oss/python/langchain/streaming) covers low-level Pregel stream modes.
* [Build your own projection](/oss/python/langgraph/event-streaming#build-your-own-projection) covers writing application-specific projections.
* [Frontend streaming patterns](/oss/python/langchain/frontend/overview) shows UI use cases built on streamed state.

***

<div className="source-links">
  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langchain/event-streaming.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>
</div>