> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Llama Cpp Structured Output

> Generate a MovieScript Pydantic object from a local LlamaCpp model with output_schema.

```python structured_output.py theme={null}
"""
Llama Cpp Structured Output
===========================

Cookbook example for `llama_cpp/structured_output.py`.
"""

from typing import List

from agno.agent import Agent
from agno.models.llama_cpp import LlamaCpp
from agno.run.agent import RunOutput
from pydantic import BaseModel, Field
from rich.pretty import pprint  # noqa

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


class MovieScript(BaseModel):
    name: str = Field(..., description="Give a name to this movie")
    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.",
    )
    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 returns a structured output
structured_output_agent = Agent(
    model=LlamaCpp(id="ggml-org/gpt-oss-20b-GGUF"),
    description="You write movie scripts.",
    output_schema=MovieScript,
)

# Run the agent synchronously
structured_output_response: RunOutput = structured_output_agent.run("New York")
pprint(structured_output_response.content)

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

if __name__ == "__main__":
    pass
```

## Run the Example

<Snippet file="create-venv-step.mdx" />

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno openai
```

**2. Install llama.cpp**

Install the `llama-server` binary. This command supports macOS and Linux with Homebrew; see the [llama.cpp installation guide](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) for other platforms:

```bash theme={null}
brew install llama.cpp
```

**3. Start llama.cpp**

Serve `ggml-org/gpt-oss-20b-GGUF` at `http://127.0.0.1:8080/v1`:

```bash theme={null}
llama-server -hf ggml-org/gpt-oss-20b-GGUF --ctx-size 0 --jinja -ub 2048 -b 2048
```

**4. Run the example**

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

```bash theme={null}
python structured_output.py
```

Full source: [cookbook/90\_models/llama\_cpp/structured\_output.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/90_models/llama_cpp/structured_output.py)


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