Pydantic Model as Input

Demonstrates Workflows 2.0 using Pydantic models as structured input to workflows.

This example demonstrates how to use Pydantic models as structured input to workflows.

This is particularly useful when you need to:

• Validate input parameters before workflow execution
• Ensure consistent data structure across different workflow runs and have type safety
• Provide clear documentation of expected input format
• Handle complex input requirements with multiple fields
• Create reusable input models for different workflow scenarios

pydantic_model_as_input.py

from typing import List, Optional

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.storage.sqlite import SqliteStorage
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.v2.step import Step
from agno.workflow.v2.workflow import Workflow
from pydantic import BaseModel, Field


class ResearchTopic(BaseModel):
    """Structured research topic with specific requirements"""

    topic: str
    focus_areas: List[str] = Field(description="Specific areas to focus on")
    target_audience: str = Field(description="Who this research is for")
    sources_required: int = Field(description="Number of sources needed", default=5)


# Define 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=[DuckDuckGoTools()],
    role="Search the web for the latest news and trends",
)

# Define research team for complex analysis
research_team = Team(
    name="Research Team",
    mode="coordinate",
    members=[hackernews_agent, web_agent],
    instructions="Research tech topics from Hackernews and the web",
)

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",
    ],
)

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

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

# Create and use workflow
if __name__ == "__main__":
    content_creation_workflow = Workflow(
        name="Content Creation Workflow",
        description="Automated content creation from blog posts to social media",
        storage=SqliteStorage(
            table_name="workflow_v2",
            db_file="tmp/workflow_v2.db",
            mode="workflow_v2",
        ),
        steps=[research_step, content_planning_step],
    )

    print("=== Example: Research with Structured Topic ===")
    research_topic = ResearchTopic(
        topic="AI trends in 2024",
        focus_areas=[
            "Machine Learning",
            "Natural Language Processing",
            "Computer Vision",
            "AI Ethics",
        ],
        target_audience="Tech professionals and business leaders",
        sources_required=8,
    )
    content_creation_workflow.print_response(
        message=research_topic,
        markdown=True,
    )