Pydantic Models as Team Output

This example demonstrates how to use Pydantic models as output from teams, showing how structured data can be returned as responses for more precise and validated output handling.

Code

cookbook/examples/teams/structured_input_output/00_pydantic_model_output.py

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.utils.pprint import pprint_run_response
from pydantic import BaseModel


class StockAnalysis(BaseModel):
    symbol: str
    company_name: str
    analysis: str


class CompanyAnalysis(BaseModel):
    company_name: str
    analysis: str


stock_searcher = Agent(
    name="Stock Searcher",
    model=OpenAIChat("gpt-5-mini"),
    output_schema=StockAnalysis,
    role="Searches for information on stocks and provides price analysis.",
    tools=[DuckDuckGoTools()],
)

company_info_agent = Agent(
    name="Company Info Searcher",
    model=OpenAIChat("gpt-5-mini"),
    role="Searches for information about companies and recent news.",
    output_schema=CompanyAnalysis,
    tools=[DuckDuckGoTools()],
)


class StockReport(BaseModel):
    symbol: str
    company_name: str
    analysis: str


team = Team(
    name="Stock Research Team",
    model=OpenAIChat("gpt-5-mini"),
    members=[stock_searcher, company_info_agent],
    output_schema=StockReport,
    markdown=True,
)

# This should route to the stock_searcher
response = team.run("What is the current stock price of NVDA?")
assert isinstance(response.content, StockReport)
pprint_run_response(response)

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 openai ddgs
   
3. Set environment variables

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

   python cookbook/examples/teams/structured_input_output/00_pydantic_model_output.py