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This example demonstrates how to capture and work with agent responses as variables, enabling programmatic access to response data and metadata.

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Code

response_as_variable.py
from typing import Iterator  # noqa
from rich.pretty import pprint
from agno.agent import Agent, RunOutput
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools


agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[
        DuckDuckGoTools(
            stock_price=True,
            analyst_recommendations=True,
            company_info=True,
            company_news=True,
        )
    ],
    instructions=["Use tables where possible"],
    markdown=True,
)

run_response: RunOutput = agent.run("What is the stock price of NVDA")
pprint(run_response)

# run_response_strem: Iterator[RunOutputEvent] = agent.run("What is the stock price of NVDA", stream=True)
# for response in run_response_strem:
#     pprint(response)

​
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 openai ddgs rich
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 response_as_variable.py
5

Run Agent

python response_as_variable.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
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