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This example demonstrates how to define an input schema for an agent using Pydantic models, ensuring structured input validation.

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Code

input_schema_on_agent.py
from typing import List

from agno.agent import Agent
from agno.models.openai import OpenAIChat
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
hackernews_agent = Agent(
    name="Hackernews Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from Hackernews posts",
    input_schema=ResearchTopic,
)

# 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,
#     )
# )

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U agno pydantic
3

Export your OpenAI API key

  export OPENAI_API_KEY="your_openai_api_key_here"
4

Create a Python file

Create a Python file and add the above code.
touch input_schema_on_agent.py
5

Run Agent

python input_schema_on_agent.py
6

Find All Cookbooks

Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:Agno Cookbooks on GitHub