Team with Parser Model

This example demonstrates using a parser model with teams to generate structured output, creating detailed national park adventure guides with validated Pydantic schemas.

Code

cookbook/examples/teams/structured_input_output/02_team_with_parser_model.py

import random
from typing import Iterator, List  # noqa

from agno.agent import Agent, RunOutput, RunOutputEvent  # noqa
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.team import Team
from pydantic import BaseModel, Field
from rich.pretty import pprint


class NationalParkAdventure(BaseModel):
    park_name: str = Field(..., description="Name of the national park")
    best_season: str = Field(
        ...,
        description="Optimal time of year to visit this park (e.g., 'Late spring to early fall')",
    )
    signature_attractions: List[str] = Field(
        ...,
        description="Must-see landmarks, viewpoints, or natural features in the park",
    )
    recommended_trails: List[str] = Field(
        ...,
        description="Top hiking trails with difficulty levels (e.g., 'Angel's Landing - Strenuous')",
    )
    wildlife_encounters: List[str] = Field(
        ..., description="Animals visitors are likely to spot, with viewing tips"
    )
    photography_spots: List[str] = Field(
        ...,
        description="Best locations for capturing stunning photos, including sunrise/sunset spots",
    )
    camping_options: List[str] = Field(
        ..., description="Available camping areas, from primitive to RV-friendly sites"
    )
    safety_warnings: List[str] = Field(
        ..., description="Important safety considerations specific to this park"
    )
    hidden_gems: List[str] = Field(
        ..., description="Lesser-known spots or experiences that most visitors miss"
    )
    difficulty_rating: int = Field(
        ...,
        ge=1,
        le=5,
        description="Overall park difficulty for average visitor (1=easy, 5=very challenging)",
    )
    estimated_days: int = Field(
        ...,
        ge=1,
        le=14,
        description="Recommended number of days to properly explore the park",
    )
    special_permits_needed: List[str] = Field(
        default=[],
        description="Any special permits or reservations required for certain activities",
    )


itinerary_planner = Agent(
    name="Itinerary Planner",
    model=Claude(id="claude-sonnet-4-20250514"),
    description="You help people plan amazing national park adventures and provide detailed park guides.",
)

weather_expert = Agent(
    name="Weather Expert",
    model=Claude(id="claude-sonnet-4-20250514"),
    description="You are a weather expert and can provide detailed weather information for a given location.",
)

national_park_expert = Team(
    model=OpenAIChat(id="gpt-5-mini"),
    members=[itinerary_planner, weather_expert],
    output_schema=NationalParkAdventure,
    parser_model=OpenAIChat(id="gpt-5-mini"),
)

# Get the response in a variable
national_parks = [
    "Yellowstone National Park",
    "Yosemite National Park",
    "Grand Canyon National Park",
    "Zion National Park",
    "Grand Teton National Park",
    "Rocky Mountain National Park",
    "Acadia National Park",
    "Mount Rainier National Park",
    "Great Smoky Mountains National Park",
    "Rocky National Park",
]
# Get the response in a variable
run: RunOutput = national_park_expert.run(
    f"What is the best season to visit {national_parks[random.randint(0, len(national_parks) - 1)]}? Please provide a detailed one week itinerary for a visit to the park."
)
pprint(run.content)

# Stream the response
# run_events: Iterator[RunOutputEvent] = national_park_expert.run(f"What is the best season to visit {national_parks[random.randint(0, len(national_parks) - 1)]}? Please provide a detailed one week itinerary for a visit to the park.", stream=True)
# for event in run_events:
#     pprint(event)

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 rich
   
3. Set environment variables

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

   python cookbook/examples/teams/structured_input_output/02_team_with_parser_model.py