> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-redirect-agent-platform-overview.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Conditional Workflow

> Execute steps based on content analysis or business logic.

This example demonstrates **Workflows 2.0** conditional execution pattern. Shows how to conditionally execute steps based on content analysis,
providing intelligent selection of steps based on the actual data being processed.

**When to use**: When you need intelligent selection of steps based on content analysis rather than
simple input parameters or some other business logic. Ideal for quality gates, content-specific processing, or
adaptive workflows that respond to intermediate results.

```python condition_steps_workflow_stream.py theme={null}
from agno.agent import Agent
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.condition import Condition
from agno.workflow.step import Step
from agno.workflow.types import StepInput
from agno.workflow.workflow import Workflow

# === BASIC AGENTS ===
researcher = Agent(
    name="Researcher",
    instructions="Research the given topic and provide detailed findings.",
    tools=[HackerNewsTools()],
)

summarizer = Agent(
    name="Summarizer",
    instructions="Create a clear summary of the research findings.",
)

fact_checker = Agent(
    name="Fact Checker",
    instructions="Verify facts and check for accuracy in the research.",
    tools=[HackerNewsTools()],
)

writer = Agent(
    name="Writer",
    instructions="Write a comprehensive article based on all available research and verification.",
)

# === CONDITION EVALUATOR ===


def needs_fact_checking(step_input: StepInput) -> bool:
    """Determine if the research contains claims that need fact-checking"""
    summary = step_input.previous_step_content or ""

    # Look for keywords that suggest factual claims
    fact_indicators = [
        "study shows",
        "research indicates",
        "according to",
        "statistics",
        "data shows",
        "survey",
        "report",
        "million",
        "billion",
        "percent",
        "%",
        "increase",
        "decrease",
    ]

    return any(indicator in summary.lower() for indicator in fact_indicators)


# === WORKFLOW STEPS ===
research_step = Step(
    name="research",
    description="Research the topic",
    agent=researcher,
)

summarize_step = Step(
    name="summarize",
    description="Summarize research findings",
    agent=summarizer,
)

# Conditional fact-checking step
fact_check_step = Step(
    name="fact_check",
    description="Verify facts and claims",
    agent=fact_checker,
)

write_article = Step(
    name="write_article",
    description="Write final article",
    agent=writer,
)

# === BASIC LINEAR WORKFLOW ===
basic_workflow = Workflow(
    name="Basic Linear Workflow",
    description="Research -> Summarize -> Condition(Fact Check) -> Write Article",
    steps=[
        research_step,
        summarize_step,
        Condition(
            name="fact_check_condition",
            description="Check if fact-checking is needed",
            evaluator=needs_fact_checking,
            steps=[fact_check_step],
        ),
        write_article,
    ],
)

if __name__ == "__main__":
    print("Running Basic Linear Workflow Example")
    print("=" * 50)

    try:
        basic_workflow.print_response(
            input="Recent breakthroughs in quantum computing",
            stream=True,
        )
    except Exception as e:
        print(f"Error: {e}")
        import traceback

        traceback.print_exc()
```