> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Playground

> **Agno provides an intuitive interface for testing and interacting with your AI agents.**

<Frame caption="Agno Platform - Playground">
  <img height="200" src="https://mintcdn.com/agno/QZOB15dhrj4yAmBd/images/playground.png?fit=max&auto=format&n=QZOB15dhrj4yAmBd&q=85&s=b494f982d20d7abea547ab6d47d413f5" style={{ borderRadius: '8px' }} data-path="images/playground.png" />
</Frame>

The Playground gives a robust interface to test your agentic systems with extensive features.

* **Streaming Support**: Real-time response streaming and intermediate states back to the user.

* **Session History**: Visualize conversation history right in the playground.

* **User Memory**: Visualize user details and preferences across conversations.

* **Configuration**: Comprehensive configuration interface allowing you to see agent parameters, model settings, tool configurations.

* **Reasoning Support**: Built-in support for detailed reasoning traces displayed in the playground interface.

* **Human in Loop Support**: Enable manual intervention in agent workflows with specialized human oversight and approval.

* **Multimodal Support**: Support for processing and generating text, images, audio, and other media types.

* **Multi-Agent Systems**: Support for multi-agent teams and workflows.

## Interact with your agents Locally

<Steps>
  <Step title="Create a file with sample code">
    ```python playground.py theme={null}
    from agno.agent import Agent
    from agno.models.openai import OpenAIChat
    from agno.playground import Playground
    from agno.storage.sqlite import SqliteStorage
    from agno.tools.duckduckgo import DuckDuckGoTools

    agent_storage: str = "tmp/agents.db"

    web_agent = Agent(
        name="Web Agent",
        model=OpenAIChat(id="gpt-4o"),
        tools=[DuckDuckGoTools()],
        instructions=["Always include sources"],
        # Store the agent sessions in a sqlite database
        storage=SqliteStorage(table_name="web_agent", db_file=agent_storage),
        # Adds the current date and time to the instructions
        add_datetime_to_instructions=True,
        # Adds the history of the conversation to the messages
        add_history_to_messages=True,
        # Number of history responses to add to the messages
        num_history_responses=5,
        # Adds markdown formatting to the messages
        markdown=True,
    )

    news_agent = Agent(
        name="News Agent",
        model=OpenAIChat(id="gpt-4o"),
        tools=[DuckDuckGoTools(search=True, news=True)],
        instructions=["Always use tables to display data"],
        storage=SqliteStorage(table_name="news_agent", db_file=agent_storage),
        add_datetime_to_instructions=True,
        add_history_to_messages=True,
        num_history_responses=5,
        markdown=True,
    )

    playground_app = Playground(agents=[web_agent, news_agent])
    app = playground_app.get_app()

    if __name__ == "__main__":
        playground_app.serve("playground:app", reload=True)
    ```

    Remember to export your `OPENAI_API_KEY` before running the playground application.

    <Tip>Make sure the `serve()` points to the file that contains your `Playground` app.</Tip>
  </Step>

  <Step title="Authenticate with Agno">
    Authenticate with [agno.com](https://app.agno.com) so your local application can let agno know which port you are running the playground on.

    Check out [Authentication guide](/how-to/authentication) for instructions on how to Authenticate with Agno.

    <Note>
      No data is sent to agno.com, all agent data is stored locally in your sqlite database.
    </Note>
  </Step>

  <Step title="Run the Playground Server">
    Install dependencies and run your playground server:

    ```shell theme={null}
    pip install openai duckduckgo-search sqlalchemy 'fastapi[standard]' agno

    python playground.py
    ```
  </Step>

  <Step title="View the Playground">
    * Open the link provided or navigate to `http://app.agno.com/playground` (login required).
    * Add/Select the `localhost:7777/v1` (v1 is default prefix) endpoint and start chatting with your agents!

    <video autoPlay muted controls className="w-full aspect-video" src="https://mintcdn.com/agno/QZOB15dhrj4yAmBd/videos/playground.mp4?fit=max&auto=format&n=QZOB15dhrj4yAmBd&q=85&s=675000c02a8a49854b62db7ef96dda19" data-path="videos/playground.mp4" />
  </Step>
</Steps>

<Accordion title="Looking for a self-hosted alternative?">
  Looking for a self-hosted alternative? Check out our [Open Source Agent UI](https://github.com/agno-agi/agent-ui) - A modern Agent interface built with Next.js and TypeScript that works exactly like the Agent Playground.

  <img src="https://mintcdn.com/agno/0omqNXZ9CSqcNU7_/images/agent-ui.png?fit=max&auto=format&n=0omqNXZ9CSqcNU7_&q=85&s=fab0c5289a0251cbeece2ad0fff261ac" style={{ borderRadius: '10px', width: '100%', maxWidth: '800px' }} alt="agent-ui" width="1788" height="936" data-path="images/agent-ui.png" />

  ### Get Started with Agent UI

  ```bash theme={null}
  # Create a new Agent UI project
  npx create-agent-ui@latest

  # Or clone and run manually
  git clone https://github.com/agno-agi/agent-ui.git
  cd agent-ui && pnpm install && pnpm dev
  ```

  The UI will connect to `localhost:7777` by default, matching the Playground setup above. Visit [GitHub](https://github.com/agno-agi/agent-ui) for more details.
</Accordion>

<Info>Facing connection issues? Check out our [troubleshooting guide](/faq/playground-connection)</Info>