To allow an event to trigger a workflow, Ray Workflows supports pluggable event systems. Using the event framework provides a few properties.

  1. Waits for events efficiently (without requiring a running workflow task while waiting).

  2. Supports exactly-once event delivery semantics while providing fault tolerance.

Like other workflow tasks, events support fault tolerance via checkpointing. When an event occurs, the event is checkpointed, then optionally committed.

Using events#

Workflow events are a special type of workflow task. They “finish” when the event occurs. workflow.wait_for_event(EventListenerType) can be used to create an event task.

import time
import ray
from ray import workflow

# Create an event which finishes after 2 seconds.
event1_task = workflow.wait_for_event(workflow.event_listener.TimerListener, time.time() + 2)

# Create another event which finishes after 1 seconds.
event2_task = workflow.wait_for_event(workflow.event_listener.TimerListener, time.time() + 1)

def gather(*args):
    return args

# Gather will run after 2 seconds when both event1 and event2 are done., event2_task))

HTTP events#

Workflow supports sending external events via HTTP. An HTTP event listener in the workflow is used to connect to an HTTP endpoint. Below is an end-to-end example of using HTTP events in a workflow.

HTTPListener is used to listen for HTTP events in a workflow. Each HTTPListener subscribes to a unique workflow_id and event_key pair. To send an event to the listener, an HTTP request from an external client should specify workflow_id as part of the request URL and the event_key and event_payload keys in the JSON request body (see below).

# File name:
# Create a task waiting for an http event with a JSON message.
# The JSON message is expected to have an event_key field
# and an event_payload field.

event_task = workflow.wait_for_event(HTTPListener, event_key="my_event_key")

obj_ref = workflow.run_async(event_task, workflow_id="workflow_receive_event_by_http")

An HTTP endpoint at http://hostname:port/event/send_event/<workflow_id> can be used to send an event. Locally, the endpoint may be reached at<workflow_id>. Note that the HTTP request must include the same workflow_id. Each request should also include a JSON body with two fields: event_key and event_payload, as shown in the example below. The event_key field should match the argument passed to workflow.wait_for_event() on the listener side. In the workflow, once an HTTP event is received, the event task will return the value of the event_payload field.

In summary, to trigger an HTTP event in the workflow, an external client should have:

  • the HTTP endpoint address (e.g.

  • the workflow_id (e.g. “workflow_receive_event_by_http”)

  • a valid JSON formatted message with the fields event_key and event_payload, where event_key matches the one used in the workflow

The HTTP request will receive a reply once the event has been received by the workflow. The returned status code can be:

  1. 200: event was successfully processed.

  2. 500: event processing failed.

  3. 404: either workflow_id or event_key cannot be found, likely due to event is received before the targeted workflow task is ready.

The code snippet below shows an example of the external client sending an HTTP request.

# File name:
res =
        + "workflow_receive_event_by_http",
        json={"event_key": "my_event_key", "event_payload": "my_event_message"},
if res.status_code == 200:
    print("event processed successfully")
elif res.status_code == 500:
    print("request sent but workflow event processing failed")
elif res.status_code == 404:
    print("request sent but either workflow_id or event_key is not found")

Custom event listeners#

Custom event listeners can be written by subclassing the EventListener interface.

from ray.workflow.common import Event

class EventListener:
    def __init__(self):
        """Optional constructor. Only the constructor with no arguments will be

    async def poll_for_event(self, *args, **kwargs) -> Event:
        """Should return only when the event is received."""
        raise NotImplementedError

    async def event_checkpointed(self, event: Event) -> None:
        """Optional. Called after an event has been checkpointed and a transaction can
          be safely committed."""

The listener.poll_for_events() coroutine should finish when the event is done. Arguments to workflow.wait_for_event are passed to poll_for_events(). For example, an event listener which sleeps until a timestamp can be written as:

class TimerListener(EventListener):
    async def poll_for_event(self, timestamp):
        await asyncio.sleep(timestamp - time.time())

The event_checkpointed routine can be overridden to support systems with exactly-once delivery semantics which typically follows a pattern of:

  1. Wait for an event.

  2. Process the event.

  3. Commit the event.

After the workflow finishes checkpointing the event, the event listener will be invoked and can free the event. For example, to guarantee that events are consumed from a kafkaesque<> queue:

KafkaEventType = ...

class QueueEventListener:
    def __init__(self):
        # Initialize the poll consumer.
        self.consumer = Consumer({'': False})

    async def poll_for_event(self, topic) -> KafkaEventType:

        message = await self.consumer.poll()
        return message

    async def event_checkpointed(self, event: KafkaEventType) -> None:
         self.consumer.commit(event, asynchronous=False)

(Advanced) Event listener semantics#

When writing complex event listeners, there are a few properties the author should be aware of.

  • The event listener definition must be serializable

  • Event listener instances are _not_ serialized.

  • Event listeners should be stateless.