Llama Structured Output

Generate a MovieScript Pydantic object from Llama 4 Maverick with a JSON schema output.

This model configuration sends a JSON Schema request. Agno then parses the returned content against the Pydantic schema; check its type before reading model fields.

structured_output.py
"""
Meta Structured Output
======================

Cookbook example for `meta/llama/structured_output.py`.
"""

from typing import List

from agno.agent import Agent, RunOutput  # noqa
from agno.models.meta import Llama
from pydantic import BaseModel, Field
from rich.pretty import pprint  # noqa

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------


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 a JSON schema output
json_schema_output_agent = Agent(
    model=Llama(id="Llama-4-Maverick-17B-128E-Instruct-FP8", temperature=0.1),
    output_schema=MovieScript,
)

json_schema_output_agent.print_response("New York")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

output_schema describes the expected type. If parsing or validation fails, result.content can remain a string. Before accessing schema fields in a run result, use isinstance(result.content, YourSchema), replacing YourSchema with the class you passed as output_schema.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno llama-api-client

Export your Meta Llama API key

export LLAMA_API_KEY="your_llama_api_key_here"

Run the example

Save the code above as structured_output.py, then run:

python structured_output.py

Full source: cookbook/90_models/meta/llama/structured_output.py