Background Workflow Execution

Pass background=True to Workflow.arun() to start a detached asynchronous task. Configure a workflow database when you need to poll the persisted run. Combine background=True with stream=True for buffered SSE events that can be resumed after a client disconnects.

See Background Execution for the full guide covering polling, resumable streaming, and the /resume endpoint.

Start background execution with .arun(). The polling pattern below requires a workflow database. Call workflow.get_run(run_id) and check .has_completed() on the returned run.

Prerequisites

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno openai sqlalchemy yfinance

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Example

import asyncio

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.yfinance import YFinanceTools
from agno.utils.pprint import pprint_run_response
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow

# Define agents
hackernews_agent = Agent(
    name="HackerNews Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from HackerNews posts",
)
finance_agent = Agent(
    name="Finance Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools()],
    role="Get stock prices and financial data",
)

# Define research team for complex analysis
research_team = Team(
    name="Research Team",
    members=[hackernews_agent, finance_agent],
    instructions="Research tech topics and related stocks",
)

content_planner = Agent(
    name="Content Planner",
    model=OpenAIResponses(id="gpt-5.2"),
    instructions=[
        "Plan a content schedule over 4 weeks for the provided topic and research content",
        "Ensure that I have posts for 3 posts per week",
    ],
)

# Define steps
research_step = Step(
    name="Research Step",
    team=research_team,
)

content_planning_step = Step(
    name="Content Planning Step",
    agent=content_planner,
)

content_creation_workflow = Workflow(
    name="Content Creation Workflow",
    description="Automated content creation from blog posts to social media",
    db=SqliteDb(
        session_table="workflow_session",
        db_file="tmp/workflow.db",
    ),
    steps=[research_step, content_planning_step],
)


async def main():
    print("Starting Async Background Workflow Test")

    # Start background execution (async)
    bg_response = await content_creation_workflow.arun(
        input="AI trends in 2024", background=True
    )
    print(f"Initial Response: {bg_response.status} - {bg_response.content}")
    print(f"Run ID: {bg_response.run_id}")

    # Poll every 5 seconds until completion
    poll_count = 0

    while True:
        poll_count += 1
        print(f"\nPoll #{poll_count} (every 5s)")

        result = content_creation_workflow.get_run(bg_response.run_id)

        if result is None:
            print("Workflow not found yet, still waiting...")
            if poll_count > 50:
                print(f"Timeout after {poll_count} attempts")
                break
            await asyncio.sleep(5)
            continue

        if result.has_completed() or result.is_cancelled:
            break

        if poll_count > 200:
            print(f"Timeout after {poll_count} attempts")
            break

        await asyncio.sleep(5)

    final_result = content_creation_workflow.get_run(bg_response.run_id)

    print("\nFinal Result:")
    print("=" * 50)
    if final_result is not None:
        pprint_run_response(final_result, markdown=True)
    else:
        print("No persisted result was found.")


if __name__ == "__main__":
    asyncio.run(main())

You can also use WebSocket for background workflows. See the Workflow WebSocket examples.