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

# Response As Variable

> Demonstrates capturing typed team responses as variables for downstream logic.

```python response_as_variable.py theme={null}
"""
Response As Variable
====================

Demonstrates capturing typed team responses as variables for downstream logic.
"""

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team import Team, TeamMode
from agno.tools.yfinance import YFinanceTools
from agno.utils.pprint import pprint_run_response
from pydantic import BaseModel


class StockAnalysis(BaseModel):
    """Stock analysis data structure."""

    symbol: str
    company_name: str
    analysis: str


class CompanyAnalysis(BaseModel):
    """Company analysis data structure."""

    company_name: str
    analysis: str


# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
stock_searcher = Agent(
    name="Stock Searcher",
    model=OpenAIResponses(id="gpt-5-mini"),
    output_schema=StockAnalysis,
    role="Searches for stock price and analyst information",
    tools=[
        YFinanceTools(
            enable_stock_price=True,
            enable_analyst_recommendations=True,
        )
    ],
    instructions=[
        "Provide detailed stock analysis with price information",
        "Include analyst recommendations when available",
    ],
)

company_info_agent = Agent(
    name="Company Info Searcher",
    model=OpenAIResponses(id="gpt-5-mini"),
    role="Searches for company news and information",
    output_schema=CompanyAnalysis,
    tools=[
        YFinanceTools(
            enable_stock_price=False,
            enable_company_info=True,
            enable_company_news=True,
        )
    ],
    instructions=[
        "Focus on company news and business information",
        "Provide comprehensive analysis of company developments",
    ],
)

# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
team = Team(
    name="Stock Research Team",
    model=OpenAIResponses(id="gpt-5-mini"),
    mode=TeamMode.route,
    members=[stock_searcher, company_info_agent],
    markdown=True,
    show_members_responses=True,
    instructions=[
        "Route stock price questions to the Stock Searcher",
        "Route company news and info questions to the Company Info Searcher",
    ],
)

# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    print("=" * 50)
    print("STOCK PRICE ANALYSIS")
    print("=" * 50)

    stock_response = team.run("What is the current stock price of NVDA?")
    assert isinstance(stock_response.content, StockAnalysis)
    print(f"Response type: {type(stock_response.content)}")
    print(f"Symbol: {stock_response.content.symbol}")
    print(f"Company: {stock_response.content.company_name}")
    print(f"Analysis: {stock_response.content.analysis}")
    pprint_run_response(stock_response)

    print("\n" + "=" * 50)
    print("COMPANY NEWS ANALYSIS")
    print("=" * 50)

    news_response = team.run("What is in the news about NVDA?")
    assert isinstance(news_response.content, CompanyAnalysis)
    print(f"Response type: {type(news_response.content)}")
    print(f"Company: {news_response.content.company_name}")
    print(f"Analysis: {news_response.content.analysis}")
    pprint_run_response(news_response)

    print("\n" + "=" * 50)
    print("BATCH PROCESSING")
    print("=" * 50)

    companies = ["AAPL", "GOOGL", "MSFT"]
    responses = []

    for company in companies:
        response = team.run(f"Analyze {company} stock")
        responses.append(response)
        print(f"Processed {company}: {type(response.content).__name__}")

    print(f"Total responses processed: {len(responses)}")
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai yfinance
    ```
  </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 `response_as_variable.py`, then run:

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

Full source: [cookbook/03\_teams/04\_structured\_input\_output/response\_as\_variable.py](https://github.com/agno-agi/agno/blob/main/cookbook/03_teams/04_structured_input_output/response_as_variable.py)