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

# Expected Output

> Guide agent responses using the expected_output parameter.

```python expected_output.py theme={null}
"""
Expected Output
=============================

Guide agent responses using the expected_output parameter.
"""

from agno.agent import Agent
from agno.models.openai import OpenAIResponses

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    # expected_output gives the agent a clear target for what the response should look like
    expected_output="A numbered list of exactly 5 items, each with a title and one-sentence description.",
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    agent.print_response(
        "What are the most important principles of clean code?",
        stream=True,
    )
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `expected_output.py`, then run:

    ```bash theme={null}
    python expected_output.py
    ```
  </Step>
</Steps>

Full source: [cookbook/02\_agents/02\_input\_output/expected\_output.py](https://github.com/agno-agi/agno/blob/main/cookbook/02_agents/02_input_output/expected_output.py)