function_workflow.py
"""
Function Workflow
=================
Demonstrates using a single execution function in place of explicit step lists across sync and async run modes.
"""
import asyncio
from typing import AsyncIterator, Iterator
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from agno.workflow.types import WorkflowExecutionInput
from agno.workflow.workflow import Workflow
# ---------------------------------------------------------------------------
# Create Agents
# ---------------------------------------------------------------------------
hackernews_agent = Agent(
name="Hackernews Agent",
model=OpenAIChat(id="gpt-4o-mini"),
tools=[HackerNewsTools()],
role="Extract key insights and content from Hackernews posts",
)
web_agent = Agent(
name="Web Agent",
model=OpenAIChat(id="gpt-4o-mini"),
tools=[WebSearchTools()],
role="Search the web for the latest news and trends",
)
streaming_hackernews_agent = Agent(
name="Hackernews Agent",
model=OpenAIChat(id="gpt-5.2"),
tools=[HackerNewsTools()],
role="Research key insights and content from Hackernews posts",
)
content_planner = Agent(
name="Content Planner",
model=OpenAIChat(id="gpt-4o"),
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",
],
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
research_team = Team(
name="Research Team",
members=[hackernews_agent, web_agent],
instructions="Research tech topics from Hackernews and the web",
)
# ---------------------------------------------------------------------------
# Define Execution Functions
# ---------------------------------------------------------------------------
def custom_execution_function(
workflow: Workflow,
execution_input: WorkflowExecutionInput,
) -> str:
print(f"Executing workflow: {workflow.name}")
run_response = research_team.run(execution_input.input)
research_content = run_response.content
planning_prompt = f"""
STRATEGIC CONTENT PLANNING REQUEST:
Core Topic: {execution_input.input}
Research Results: {research_content[:500]}
Planning Requirements:
1. Create a comprehensive content strategy based on the research
2. Leverage the research findings effectively
3. Identify content formats and channels
4. Provide timeline and priority recommendations
5. Include engagement and distribution strategies
Please create a detailed, actionable content plan.
"""
content_plan = content_planner.run(planning_prompt)
return content_plan.content
def custom_execution_function_stream(
workflow: Workflow,
execution_input: WorkflowExecutionInput,
) -> Iterator:
print(f"Executing workflow: {workflow.name}")
research_content = ""
for response in streaming_hackernews_agent.run(
execution_input.input,
stream=True,
stream_events=True,
):
if hasattr(response, "content") and response.content:
research_content += str(response.content)
planning_prompt = f"""
STRATEGIC CONTENT PLANNING REQUEST:
Core Topic: {execution_input.input}
Research Results: {research_content[:500]}
Planning Requirements:
1. Create a comprehensive content strategy based on the research
2. Leverage the research findings effectively
3. Identify content formats and channels
4. Provide timeline and priority recommendations
5. Include engagement and distribution strategies
Please create a detailed, actionable content plan.
"""
yield from content_planner.run(
planning_prompt,
stream=True,
stream_events=True,
)
async def custom_execution_function_async(
workflow: Workflow,
execution_input: WorkflowExecutionInput,
) -> str:
print(f"Executing workflow: {workflow.name}")
run_response = research_team.run(execution_input.input)
research_content = run_response.content
planning_prompt = f"""
STRATEGIC CONTENT PLANNING REQUEST:
Core Topic: {execution_input.input}
Research Results: {research_content[:500]}
Planning Requirements:
1. Create a comprehensive content strategy based on the research
2. Leverage the research findings effectively
3. Identify content formats and channels
4. Provide timeline and priority recommendations
5. Include engagement and distribution strategies
Please create a detailed, actionable content plan.
"""
content_plan = await content_planner.arun(planning_prompt)
return content_plan.content
async def custom_execution_function_async_stream(
workflow: Workflow,
execution_input: WorkflowExecutionInput,
) -> AsyncIterator:
print(f"Executing workflow: {workflow.name}")
research_content = ""
async for response in streaming_hackernews_agent.arun(
execution_input.input,
stream=True,
stream_events=True,
):
if hasattr(response, "content") and response.content:
research_content += str(response.content)
planning_prompt = f"""
STRATEGIC CONTENT PLANNING REQUEST:
Core Topic: {execution_input.input}
Research Results: {research_content[:500]}
Planning Requirements:
1. Create a comprehensive content strategy based on the research
2. Leverage the research findings effectively
3. Identify content formats and channels
4. Provide timeline and priority recommendations
5. Include engagement and distribution strategies
Please create a detailed, actionable content plan.
"""
async for response in content_planner.arun(
planning_prompt,
stream=True,
stream_events=True,
):
yield response
# ---------------------------------------------------------------------------
# Create Workflows
# ---------------------------------------------------------------------------
sync_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=custom_execution_function,
)
sync_stream_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=custom_execution_function_stream,
)
async_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=custom_execution_function_async,
)
async_stream_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=custom_execution_function_async_stream,
)
# ---------------------------------------------------------------------------
# Run Workflow
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# Sync
sync_workflow.print_response(
input="AI trends in 2024",
)
# Sync Streaming
sync_stream_workflow.print_response(
input="AI trends in 2024",
stream=True,
)
# Async
asyncio.run(
async_workflow.aprint_response(
input="AI trends in 2024",
)
)
# Async Streaming
asyncio.run(
async_stream_workflow.aprint_response(
input="AI trends in 2024",
stream=True,
)
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno ddgs fastapi openai sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
function_workflow.py, then run:python function_workflow.py