Pydantic Models as Team Input

This example demonstrates how to use Pydantic models as input to teams, showing how structured data can be passed as messages for more precise and validated input handling.

Code

cookbook/examples/teams/structured_input_output/01_pydantic_model_as_input.py

"""
This example demonstrates how to use Pydantic models as input to teams.

Shows how structured data can be passed as messages to teams for more
precise and validated input handling.
"""

from typing import List

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from pydantic import BaseModel, Field


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

    topic: str = Field(description="The main research topic")
    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)


# Create specialized Hacker News research agent
hackernews_agent = Agent(
    name="Hackernews Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from Hackernews posts",
    instructions=[
        "Search Hacker News for relevant articles and discussions",
        "Extract key insights and summarize findings",
        "Focus on high-quality, well-discussed posts",
    ],
)

# Create collaborative research team
team = Team(
    name="Hackernews Research Team",
    model=OpenAIChat(id="gpt-5-mini"),
    members=[hackernews_agent],
    determine_input_for_members=False,
    instructions=[
        "Conduct thorough research based on the structured input",
        "Address all focus areas mentioned in the research topic",
        "Tailor the research to the specified target audience",
        "Provide the requested number of sources",
    ],
    show_members_responses=True,
)

# Use Pydantic model as structured input
research_request = ResearchTopic(
    topic="AI Agent Frameworks",
    focus_areas=["AI Agents", "Framework Design", "Developer Tools", "Open Source"],
    target_audience="Software Developers and AI Engineers",
    sources_required=7,
)

# Execute research with structured input
team.print_response(input=research_request)

# Alternative example with different topic
alternative_research = ResearchTopic(
    topic="Distributed Systems",
    focus_areas=["Microservices", "Event-Driven Architecture", "Scalability"],
    target_audience="Backend Engineers",
    sources_required=5,
)

team.print_response(input=alternative_research)

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install required libraries

   pip install agno pydantic
   
3. Set environment variables

   export OPENAI_API_KEY=****
   
4. Run the agent

   python cookbook/examples/teams/structured_input_output/01_pydantic_model_as_input.py