Agno Platform - Playground

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.

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Interact with your agents Locally

1

Create a file with sample code

playground.py
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
from agno.tools.yfinance import YFinanceTools

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,
)

finance_agent = Agent(
    name="Finance Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
    instructions=["Always use tables to display data"],
    storage=SqliteStorage(table_name="finance_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, finance_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.
Make sure the serve() points to the file that contains your Playground app.
2

Authenticate with Agno

Authenticate with agno.com so your local application can let agno know which port you are running the playground on.Check out Authentication guide for instructions on how to Authenticate with Agno.
No data is sent to agno.com, all agent data is stored locally in your sqlite database.
3

Run the Playground Server

Install dependencies and run your playground server:
pip install openai duckduckgo-search yfinance sqlalchemy 'fastapi[standard]' agno

python playground.py
4

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!
Facing connection issues? Check out our troubleshooting guide