> ## 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.

# Example showing how to use Valkey as the database for a team.

> Use Valkey as the storage backend for a team.

Run `uv pip install ddgs openai valkey-glide-sync` to install dependencies.

```python theme={null}
"""
Example showing how to use Valkey as the database for a team.

Run: `uv pip install ddgs valkey-glide-sync` to install the dependencies

We can start Valkey locally using docker:
1. Start Valkey container
`docker run --name my-valkey -p 6379:6379 -d valkey/valkey-bundle`

2. Verify container is running
`docker ps`

3. Run the file
`python cookbook/06_storage/valkey/valkey_for_team.py`
"""

from typing import List

from agno.agent import Agent
from agno.db.valkey import ValkeyDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from pydantic import BaseModel

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
db = ValkeyDb()


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


hn_researcher = Agent(
    name="HackerNews Researcher",
    model=OpenAIResponses(id="gpt-5.5"),
    role="Gets top stories from hackernews.",
    tools=[HackerNewsTools()],
)

web_searcher = Agent(
    name="Web Searcher",
    model=OpenAIResponses(id="gpt-5.5"),
    role="Searches the web for information on a topic",
    tools=[WebSearchTools()],
    add_datetime_to_context=True,
)


hn_team = Team(
    name="HackerNews Team",
    model=OpenAIResponses(id="gpt-5.5"),
    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
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    hn_team.print_response("Write an article about the top 2 stories on hackernews")
```

## Run the Example

<Steps>
  <Step title="Clone Agno">
    Clone the repository and run the remaining commands from its root:

    ```bash theme={null}
    git clone https://github.com/agno-agi/agno.git
    cd agno
    ```
  </Step>

  <Step title="Set up the demo environment">
    ```bash theme={null}
    ./scripts/demo_setup.sh
    source .venvs/demo/bin/activate
    uv pip install -U ddgs openai valkey-glide-sync
    ```
  </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 Valkey">
    ```bash theme={null}
    docker run -d --name my-valkey -p 6379:6379 valkey/valkey-bundle
    ```
  </Step>

  <Step title="Run the example">
    ```bash theme={null}
    python cookbook/06_storage/valkey/valkey_for_team.py
    ```
  </Step>
</Steps>