Agno (tracing)

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from respan import Respan
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from respan_instrumentation_agno import AgnoInstrumentor
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respan = Respan(
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    app_name="agno-support-api",
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    instrumentations=[AgnoInstrumentor()],
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    customer_identifier="user_123",
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    metadata={"service": "agno-support-api", "version": "1.0.0"},
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)

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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from respan import Respan, workflow
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from respan_instrumentation_agno import AgnoInstrumentor
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respan = Respan(instrumentations=[AgnoInstrumentor()])
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agent = Agent(name="Support Agent", model=OpenAIChat(id="gpt-4o-mini"))
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@workflow(name="agno_support_request")
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def answer_question(question: str) -> str:
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    result = agent.run(question)
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    return str(result.content)
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def handle_request(user_id: str, question: str):
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    with respan.propagate_attributes(
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        customer_identifier=user_id,
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        thread_identifier="conv_abc_123",
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        trace_group_identifier="support-request",
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        metadata={"plan": "pro"},
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    ):
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        print(answer_question(question))

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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from respan import Respan, task, workflow
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from respan_instrumentation_agno import AgnoInstrumentor
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respan = Respan(instrumentations=[AgnoInstrumentor()])
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agent = Agent(
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    name="Checklist Agent",
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    model=OpenAIChat(id="gpt-4o-mini"),
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    instructions="Return exactly two bullets.",
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)
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@task(name="draft_prompt")
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def draft_prompt(topic: str) -> str:
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    return f"Write a two-bullet operational checklist for {topic}."
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@workflow(name="agno_checklist_workflow")
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def run_checklist(topic: str) -> str:
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    result = agent.run(draft_prompt(topic))
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    return str(result.content)
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print(run_checklist("reviewing an SDK integration"))
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respan.flush()

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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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def lookup_shipping_status(order_id: str) -> str:
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    """Look up the shipping status for an order id."""
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    return f"Order {order_id} is packed and ready for pickup."
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agent = Agent(
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    name="Support Agent",
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    model=OpenAIChat(id="gpt-4o-mini"),
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    instructions="Use tools when order status is requested.",
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    tools=[lookup_shipping_status],
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)
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result = agent.run("What is the shipping status for order A-100?")
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print(result.content)

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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.team import Team
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researcher = Agent(
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    name="Researcher",
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    model=OpenAIChat(id="gpt-4o-mini"),
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    instructions="Find the most relevant operational facts.",
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)
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writer = Agent(
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    name="Writer",
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    model=OpenAIChat(id="gpt-4o-mini"),
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    instructions="Turn operational facts into concise prose.",
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)
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team = Team(
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    name="Tracing Team",
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    model=OpenAIChat(id="gpt-4o-mini"),
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    members=[researcher, writer],
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    instructions="Coordinate a concise final answer.",
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)
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result = team.run("Explain why tool spans are useful in one paragraph.")
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print(result.content)

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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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agent = Agent(
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    name="Streaming Agent",
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    model=OpenAIChat(id="gpt-4o-mini"),
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    instructions="Answer in one short paragraph.",
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)
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for event in agent.run("Explain streaming agent output.", stream=True):
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    if getattr(event, "content", None):
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        print(event.content, end="")