output_model.py
"""
Output Model
============
Demonstrates setting a dedicated model for final team response generation.
"""
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.websearch import WebSearchTools
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
itinerary_planner = Agent(
name="Itinerary Planner",
model=OpenAIResponses(id="gpt-5.2"),
description="You help people plan amazing vacations. Use the tools at your disposal to find latest information about the destination.",
tools=[WebSearchTools()],
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
travel_expert = Team(
model=OpenAIResponses(id="gpt-5-mini"),
members=[itinerary_planner],
output_model=OpenAIResponses(id="gpt-5-mini"),
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
travel_expert.print_response("Plan a summer vacation in Paris", stream=True)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno ddgs openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
output_model.py, then run:python output_model.py