valkey_for_workflow.py
"""
Valkey Storage for Workflow
===========================
Demonstrates using ValkeyDb as the session storage backend for a workflow.
"""
from agno.agent import Agent
from agno.db.valkey import ValkeyDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow
# ---------------------------------------------------------------------------
# Create Workflow
# ---------------------------------------------------------------------------
hackernews_agent = Agent(
name="Hackernews Agent",
model=OpenAIResponses(id="gpt-5.5"),
tools=[HackerNewsTools()],
role="Extract key insights and content from Hackernews posts",
)
web_agent = Agent(
name="Web Agent",
model=OpenAIResponses(id="gpt-5.5"),
tools=[WebSearchTools()],
role="Search the web for the latest news and trends",
)
# Define research team for complex analysis
research_team = Team(
name="Research Team",
members=[hackernews_agent, web_agent],
instructions="Research tech topics from Hackernews and the web",
)
content_planner = Agent(
name="Content Planner",
model=OpenAIResponses(id="gpt-5.5"),
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,
)
# ---------------------------------------------------------------------------
# Run Workflow
# ---------------------------------------------------------------------------
if __name__ == "__main__":
content_creation_workflow = Workflow(
name="Content Creation Workflow",
description="Automated content creation from blog posts to social media",
db=ValkeyDb(
session_table="workflow_session",
),
steps=[research_step, content_planning_step],
)
content_creation_workflow.print_response(
input="AI trends in 2024",
markdown=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 valkey-glide-sync
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 Valkey
docker run -d --name my-valkey -p 6379:6379 valkey/valkey-bundle
5
Run the example
Save the code above as
valkey_for_workflow.py, then run:python valkey_for_workflow.py