> ## 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.

# Redis for Team

> Store team sessions in Redis with RedisDb.

Agno supports using Redis as a storage backend for Teams using the `RedisDb` class.

## Usage

Install dependencies:

```shell theme={null}
uv pip install agno redis openai ddgs
```

Export your OpenAI API key:

<CodeGroup>
  ```bash Mac/Linux theme={null}
  export OPENAI_API_KEY="your_openai_api_key_here"
  ```

  ```powershell Windows theme={null}
  $Env:OPENAI_API_KEY="your_openai_api_key_here"
  ```
</CodeGroup>

### Run Redis

Install [Docker Desktop](https://docs.docker.com/get-started/get-docker/), then start Redis on port `6379`:

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

```python redis_for_team.py theme={null}
"""
Run: `uv pip install agno redis openai ddgs` to install the dependencies
"""

from typing import List

from agno.agent import Agent
from agno.db.redis import RedisDb
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

db = RedisDb(db_url="redis://localhost: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=[WebSearchTools()],
    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-redis-params.mdx" />