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Overview

The @agent decorator creates a span representing an autonomous agent. Agent spans set the workflow name context, so all nested tasks and tools appear grouped under this agent in the trace tree. 
from respan_tracing import agent

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Parameters

ParameterTypeDefaultDescription
namestr | NoneFunction nameDisplay name for the agent span.
versionint | NoneNoneVersion number for the agent.
method_namestr | NoneNoneRequired when decorating a class. Specifies which method to use as the entry point.
processorsstr | List[str] | NoneNoneRoute this span to specific named processors. See add_processor.

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Function usage

from respan_tracing import RespanTelemetry, agent, task, tool
from openai import OpenAI

telemetry = RespanTelemetry(api_key="your-api-key")
client = OpenAI()

@tool(name="search")
def search(query: str):
    return f"Results for: {query}"

@task(name="think")
def think(context: str):
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": context}],
    )
    return response.choices[0].message.content

@agent(name="research_agent")
def research_agent(topic: str):
    results = search(topic)
    return think(results)

print(research_agent("OpenTelemetry tracing"))

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Class usage

from respan_tracing import RespanTelemetry, agent, task

telemetry = RespanTelemetry(api_key="your-api-key")

@agent(name="assistant", method_name="run")
class Assistant:
    @task(name="respond")
    def respond(self, input_text: str):
        return f"Response: {input_text}"

    def run(self):
        return self.respond("Hello")

print(Assistant().run())

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Notes

  • Agent spans set the workflow name context — nested spans inherit the agent name for grouping
  • Use @agent for top-level autonomous entities and @task for individual operations within them