This example demonstrates Workflows 2.0 parallel execution for independent tasks that can run simultaneously. Shows how to optimize workflow performance by executing non-dependent steps in parallel, significantly reducing total execution time. When to use: When you have independent tasks that don’t depend on each other’s output but can contribute to the same final goal. Ideal for research from multiple sources, parallel data processing, or any scenario where tasks can run simultaneously.
parallel_steps_workflow.py
from agno.agent import Agent
from agno.tools.googlesearch import GoogleSearchTools
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.v2 import Step, Workflow
from agno.workflow.v2.parallel import Parallel

# Create agents
researcher = Agent(name="Researcher", tools=[HackerNewsTools(), GoogleSearchTools()])
writer = Agent(name="Writer")
reviewer = Agent(name="Reviewer")

# Create individual steps
research_hn_step = Step(name="Research HackerNews", agent=researcher)
research_web_step = Step(name="Research Web", agent=researcher)
write_step = Step(name="Write Article", agent=writer)
review_step = Step(name="Review Article", agent=reviewer)

# Create workflow with direct execution
workflow = Workflow(
    name="Content Creation Pipeline",
    steps=[
        Parallel(research_hn_step, research_web_step, name="Research Phase"),
        write_step,
        review_step,
    ],
)

workflow.print_response("Write about the latest AI developments")
This was a synchronous non-streaming example of this pattern. To checkout async and streaming versions, see the cookbooks-