Code
structured_output.py
from typing import List
from agno.agent import Agent, RunOutput # noqa
from agno.models.langdb import LangDB
from pydantic import BaseModel, Field
from rich.pretty import pprint # noqa
class MovieScript(BaseModel):
setting: str = Field(
..., description="Provide a nice setting for a blockbuster movie."
)
ending: str = Field(
...,
description="Ending of the movie. If not available, provide a happy ending.",
)
genre: str = Field(
...,
description="Genre of the movie. If not available, select action, thriller or romantic comedy.",
)
name: str = Field(..., description="Give a name to this movie")
characters: List[str] = Field(..., description="Name of characters for this movie.")
storyline: str = Field(
..., description="3 sentence storyline for the movie. Make it exciting!"
)
# Agent that uses JSON mode
json_mode_agent = Agent(
model=LangDB(id="llama3-1-70b-instruct-v1.0"),
description="You write movie scripts.",
output_schema=MovieScript,
use_json_mode=True,
)
# Agent that uses structured outputs
structured_output_agent = Agent(
model=LangDB(id="llama3-1-70b-instruct-v1.0"),
description="You write movie scripts.",
output_schema=MovieScript,
)
# Get the response in a variable
# json_mode_response: RunOutput = json_mode_agent.run("New York")
# pprint(json_mode_response.content)
# structured_output_response: RunOutput = structured_output_agent.run("New York")
# pprint(structured_output_response.content)
json_mode_agent.print_response("New York")
structured_output_agent.print_response("New York")
Usage
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Export environment variables
export LANGDB_API_KEY="your_langdb_api_key"
export LANGDB_PROJECT_ID="your_langdb_project_id"
export LANGDB_API_BASE_URL="https://api.langdb.ai"
3
Install dependencies
uv pip install -U openai agno
4
Run Agent
Save the code above as
structured_output.py, then run:python structured_output.py