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

# Valkey for Team

Agno supports using Valkey as a storage backend for Teams using the `ValkeyDb` class.

## Usage

### Run Valkey

Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) and run **Valkey** on port **6379** using:

```bash theme={null}
docker run -d \
  --name my-valkey \
  -p 6379:6379 \
  valkey/valkey
```

```python valkey_for_team.py theme={null}
"""
Run: `uv pip install openai agno valkey-glide-sync` to install the dependencies
"""

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 pydantic import BaseModel

db = ValkeyDb(
    host="localhost",
    port=6379,
)

class Article(BaseModel):
    title: str
    summary: str
    reference_links: List[str]

hn_researcher = Agent(
    name="HackerNews Researcher",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Gets top stories from hackernews.",
    tools=[HackerNewsTools()],
)

web_searcher = Agent(
    name="Web Searcher",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Searches the web for information on a topic",
    tools=[HackerNewsTools()],
    add_datetime_to_context=True,
)

hn_team = Team(
    name="HackerNews Team",
    model=OpenAIResponses(id="gpt-5.2"),
    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,
)

hn_team.print_response("Write an article about the top 2 stories on hackernews")

```

## Params

<Snippet file="db-valkey-params.mdx" />