Streaming Agent with Parser Model

This example demonstrates how to use a parser model with streaming output, combining Claude for parsing and OpenAI for generation.

Code

parser_model_stream.py

import random
from typing import Iterator, List

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


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",
    )


agent = Agent(
    parser_model=Claude(id="claude-sonnet-4-20250514"),
    description="You help people plan amazing national park adventures and provide detailed park guides.",
    output_schema=NationalParkAdventure,
    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_events: Iterator[RunOutputEvent] = agent.run(
    national_parks[random.randint(0, len(national_parks) - 1)], 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 libraries

   pip install -U agno pydantic rich
   
3. Export your ANTHROPIC API key

   Mac/Linux

     export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
   
4. Create a Python file

   Create a Python file and add the above code.

   touch parser_model_stream.py
   
5. Run Agent

   Mac

   python parser_model_stream.py
   
6. Find All Cookbooks

   Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

   Agno Cookbooks on GitHub