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

# Input Schema

> Validate agent input against a Pydantic input_schema, passing either a dict or a model instance.

```python input_schema.py theme={null}
"""
Input Schema
=============================

Input Schema.
"""

from typing import List

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.hackernews import HackerNewsTools
from pydantic import BaseModel, Field


class ResearchTopic(BaseModel):
    """Structured research topic with specific requirements"""

    topic: str
    focus_areas: List[str] = Field(description="Specific areas to focus on")
    target_audience: str = Field(description="Who this research is for")
    sources_required: int = Field(description="Number of sources needed", default=5)


# Define agents
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
hackernews_agent = Agent(
    name="Hackernews Agent",
    model=OpenAIResponses(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from Hackernews posts",
    input_schema=ResearchTopic,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    # Pass a dict that matches the input schema
    hackernews_agent.print_response(
        input={
            "topic": "AI",
            "focus_areas": ["AI", "Machine Learning"],
            "target_audience": "Developers",
            "sources_required": "5",
        }
    )

    # Pass a pydantic model that matches the input schema
    hackernews_agent.print_response(
        input=ResearchTopic(
            topic="AI",
            focus_areas=["AI", "Machine Learning"],
            target_audience="Developers",
            sources_required=5,
        )
    )
```

## 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 `input_schema.py`, then run:

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

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