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

# Async MySQL for Team

> Store a HackerNews research team's structured article output in MySQL asynchronously.

```python async_mysql_for_team.py theme={null}
"""Use Async MySQL as the database for a team.
Run `uv pip install openai duckduckgo-search newspaper4k lxml_html_clean agno sqlalchemy asyncmy` to install the dependencies
"""

import asyncio
import uuid
from typing import List

from agno.agent import Agent
from agno.db.base import SessionType
from agno.db.mysql import AsyncMySQLDb
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from pydantic import BaseModel

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
db_url = "mysql+asyncmy://ai:ai@localhost:3306/ai"
db = AsyncMySQLDb(db_url=db_url)


# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
class Article(BaseModel):
    title: str
    summary: str
    reference_links: List[str]


hn_researcher = Agent(
    name="HackerNews Researcher",
    role="Gets top stories from hackernews.",
    tools=[HackerNewsTools()],
)

web_searcher = Agent(
    name="Web Searcher",
    role="Searches the web for information on a topic",
    tools=[WebSearchTools()],
    add_datetime_to_context=True,
)


hn_team = Team(
    name="HackerNews Team",
    members=[hn_researcher, web_searcher],
    db=db,
    instructions=[
        "First, search hackernews for what the user is asking about.",
        "Then, ask the web searcher to search for each story to get more information.",
        "Finally, provide a thoughtful and engaging summary.",
    ],
    output_schema=Article,
    markdown=True,
    show_members_responses=True,
)


# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
async def main():
    """Run the agent queries in the same event loop"""
    session_id = str(uuid.uuid4())
    await hn_team.aprint_response(
        "Write an article about the top 2 stories on hackernews", session_id=session_id
    )
    session_data = await db.get_session(
        session_id=session_id, session_type=SessionType.TEAM
    )
    print("\n=== SESSION DATA ===")
    print(session_data.to_dict())


if __name__ == "__main__":
    asyncio.run(main())
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno asyncmy ddgs openai sqlalchemy
    ```
  </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 MySQL">
    ```bash theme={null}
    docker run -d --name mysql -e MYSQL_ROOT_PASSWORD=ai -e MYSQL_DATABASE=ai -e MYSQL_USER=ai -e MYSQL_PASSWORD=ai -p 3306:3306 mysql:8
    ```
  </Step>

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

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

Full source: [cookbook/06\_storage/mysql/async\_mysql/async\_mysql\_for\_team.py](https://github.com/agno-agi/agno/blob/main/cookbook/06_storage/mysql/async_mysql/async_mysql_for_team.py)