Function demonstrate_basic_agents

examples/agno/agno_basic_agents.py:43–101

Demonstrate basic agent creation and team coordination. This function shows how to: 1. Create specialized agents with specific roles 2. Organize agents into a team 3. Use the team to solve tasks that require multiple perspectives

()

def demonstrate_basic_agents():
    """
    Demonstrate basic agent creation and team coordination.

    This function shows how to:
    1. Create specialized agents with specific roles
    2. Organize agents into a team
    3. Use the team to solve tasks that require multiple perspectives
    """
    tracer = agentops.start_trace(trace_name="Agno Basic Agents and Teams Demonstration")

    try:
        # Create individual agents with specific roles
        # Each agent has a name and a role that defines its expertise

        # News Agent: Specializes in gathering and analyzing news information
        news_agent = Agent(
            name="News Agent", role="Get the latest news and provide news analysis", model=OpenAIChat(id="gpt-4o-mini")
        )

        # Weather Agent: Specializes in weather forecasting and analysis
        weather_agent = Agent(
            name="Weather Agent",
            role="Get weather forecasts and provide weather analysis",
            model=OpenAIChat(id="gpt-4o-mini"),
        )

        # Create a team with coordination mode
        # The "coordinate" mode allows agents to work together and share information
        team = Team(
            name="News and Weather Team",
            mode="coordinate",  # Agents will coordinate their responses
            members=[news_agent, weather_agent],
        )

        # Run a task that requires team coordination
        # The team will automatically determine which agent(s) should respond
        response = team.run("What is the weather in Tokyo?")

        print("\nTeam Response:")
        print("-" * 60)
        print(f"{response.content}")
        print("-" * 60)

        agentops.end_trace(tracer, end_state="Success")

    except Exception as e:
        print(f"An error occurred: {e}")
        agentops.end_trace(tracer, end_state="Error")

    # Let's check programmatically that spans were recorded in AgentOps
    print("\n" + "=" * 50)
    print("Now let's verify that our LLM calls were tracked properly...")
    try:
        agentops.validate_trace_spans(trace_context=tracer)
        print("\n✅ Success! All LLM spans were properly recorded in AgentOps.")
    except agentops.ValidationError as e:
        print(f"\n❌ Error validating spans: {e}")