Workflow Run Cancellation
Cancel a running workflow execution from another thread.
Cancel a running workflow execution by starting the run in one thread and cancelling it from another. The example also shows how to handle cancelled responses.
Cancellation is cooperative: a request marks the run for cancellation, and execution stops at its next cancellation check. A fast run can finish first. The final status below determines the outcome; marking a run is not proof that it stopped.
Example
"""
Example demonstrating how to cancel a running workflow execution.
This example shows how to:
1. Start a workflow run in a separate thread
2. Cancel the run from another thread
3. Handle the cancelled response
"""
import threading
import time
from uuid import uuid4
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.run.agent import RunEvent
from agno.run.base import RunStatus
from agno.run.workflow import WorkflowRunEvent
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow
def long_running_task(workflow: Workflow, run_id_container: dict):
"""Consume the parent run's stream and retain its terminal outcome."""
run_id = run_id_container["run_id"]
status = "unknown"
content_pieces = []
try:
for chunk in workflow.run(
"Write a detailed story about a dragon who learns to code.",
run_id=run_id,
stream=True,
stream_events=True,
):
# Member/step runs have their own IDs and do not determine parent status.
if chunk.run_id != run_id:
continue
if chunk.event == WorkflowRunEvent.workflow_cancelled:
status = "cancelled"
elif chunk.event == WorkflowRunEvent.workflow_error:
status = "error"
elif chunk.event == WorkflowRunEvent.workflow_paused:
status = "paused"
elif chunk.event == WorkflowRunEvent.workflow_completed and status == "unknown":
status = "completed"
content = getattr(chunk, "content", None)
if isinstance(content, str):
content_pieces.append(content)
run_id_container["result"] = {
"status": status,
"run_id": run_id,
"cancelled": status == "cancelled",
"content": "".join(content_pieces)[:200],
}
except Exception as exc:
run_id_container["result"] = {
"status": "error", "run_id": run_id, "cancelled": False,
"error": str(exc), "content": "Run raised an exception",
}
def cancel_after_delay(
workflow: Workflow, run_id_container: dict, delay_seconds: int = 3
):
"""
Cancel the workflow run after a specified delay.
Args:
workflow: The workflow whose run should be cancelled
run_id_container: Dictionary containing the run_id to cancel
delay_seconds: How long to wait before cancelling
"""
print(f"Will cancel workflow run in {delay_seconds} seconds...")
time.sleep(delay_seconds)
run_id = run_id_container.get("run_id")
if run_id:
print(f"Cancelling workflow run: {run_id}")
success = workflow.cancel_run(run_id)
if success:
print(f"Workflow run {run_id} marked for cancellation")
else:
print(
f"Failed to cancel workflow run {run_id} (may not exist or already completed)"
)
else:
print("No run_id found to cancel")
def main():
"""Main function demonstrating workflow run cancellation."""
# Create workflow agents
researcher = Agent(
name="Research Agent",
model=OpenAIResponses(id="gpt-5.2"),
tools=[HackerNewsTools()],
instructions="Research the given topic and provide key facts and insights.",
)
writer = Agent(
name="Writing Agent",
model=OpenAIResponses(id="gpt-5.2"),
instructions="Write a comprehensive article based on the research provided. Make it engaging and well-structured.",
)
research_step = Step(
name="research",
agent=researcher,
description="Research the topic and gather information",
)
writing_step = Step(
name="writing",
agent=writer,
description="Write an article based on the research",
)
# Create a Steps sequence that chains these above steps together
article_workflow = Workflow(
description="Automated article creation from research to writing",
steps=[research_step, writing_step],
debug_mode=True,
)
print("Starting workflow run cancellation example...")
print("=" * 50)
# Container to share run_id between threads
run_id_container = {"run_id": str(uuid4())}
# Start the workflow run in a separate thread
workflow_thread = threading.Thread(
target=lambda: long_running_task(article_workflow, run_id_container),
name="WorkflowRunThread",
)
# Start the cancellation thread
cancel_thread = threading.Thread(
target=cancel_after_delay,
args=(article_workflow, run_id_container, 8), # Cancel after 8 seconds
name="CancelThread",
)
# Start both threads
print("Starting workflow run thread...")
workflow_thread.start()
print("Starting cancellation thread...")
cancel_thread.start()
# Wait for both threads to complete
print("Waiting for threads to complete...")
workflow_thread.join()
cancel_thread.join()
# Print the results
print("\n" + "=" * 50)
print("RESULTS:")
print("=" * 50)
result = run_id_container.get("result")
if result:
print(f"Status: {result['status']}")
print(f"Run ID: {result['run_id']}")
print(f"Was Cancelled: {result['cancelled']}")
if result.get("error"):
print(f"Error: {result['error']}")
else:
print(f"Content Preview: {result['content']}")
if result["cancelled"]:
print("\nSUCCESS: Workflow run was successfully cancelled!")
elif result["status"] == "completed":
print("Run completed before cancellation")
else:
print(f"Run ended with status: {result['status']}")
else:
print("No result obtained - check if cancellation happened during streaming")
print("\nWorkflow cancellation example completed!")
if __name__ == "__main__":
# Run the main example
main()Usage
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activateInstall dependencies
uv pip install -U agno openaiExport your OpenAI API key
Set OpenAI Key
Set your OPENAI_API_KEY as an environment variable. You can get one from OpenAI.
export OPENAI_API_KEY=sk-***Run example
python workflow_cancel_run.pyAPI Endpoint
Workflow runs can be cancelled via the AgentOS API:
POST /workflows/{workflow_id}/runs/{run_id}/cancelStart a separate AgentOS server with the entity registered before using this endpoint. The thread example above does not start an HTTP server. Replace the entity and run IDs below with IDs returned by that server and include its authentication headers when configured.
Example:
curl --location 'http://localhost:7777/workflows/analysis-workflow/runs/789/cancel' \
--request POSTReference: Cancel Workflow Run API