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The Respan tracing SDK provides decorators and wrappers to instrument your code. This page covers the detailed features.

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Decorators & span types

Use decorators (Python) or wrapper methods (JS/TS) to create spans. Each decorator creates a different span type in the trace tree.
DecoratorSpan typeUse for
@workflow / withWorkflowWorkflowTop-level parent span
@task / withTaskTaskIndividual steps within a workflow
@agent / withAgentAgentAgent-level operations
@tool / withToolToolTool/function calls
from openai import OpenAI
from respan_tracing.decorators import workflow, task
from respan_tracing.main import RespanTelemetry

k_tl = RespanTelemetry()
client = OpenAI()

@task(name="joke_creation")
def create_joke():
    completion = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Tell me a joke about opentelemetry"}],
        temperature=0.5,
        max_tokens=100,
    )
    return completion.choices[0].message.content

@task(name="pirate_joke_translation")
def translate_joke_to_pirate(joke: str):
    completion = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "translate to pirate language:\n\n" + joke}],
    )
    return completion.choices[0].message.content

@workflow(name="joke_workflow")
def joke_workflow():
    joke = create_joke()
    pirate_joke = translate_joke_to_pirate(joke)
    return pirate_joke

result = joke_workflow()
print(result)
For the full SDK API reference, see the Python SDK or TypeScript SDK.

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Class-based workflows

Apply @workflow to a class with method_name to designate the entry point. Use @task on individual methods. 
from openai import OpenAI
from respan_tracing import RespanTelemetry
from respan_tracing.decorators import workflow, task

k_tl = RespanTelemetry()
client = OpenAI()

@workflow(name="joke_agent", method_name="run")
class JokeAgent:
    @task(name="joke_creation")
    def create_joke(self):
        completion = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": "Tell me a joke"}],
        )
        return completion.choices[0].message.content

    def run(self):
        return self.create_joke()

agent = JokeAgent()
result = agent.run()

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Override span input & output

Control what appears as a span’s input/output in the trace UI using OpenTelemetry attributes:
Python
import json
from opentelemetry.semconv_ai import SpanAttributes
from respan_tracing.main import RespanTelemetry
from respan_tracing.decorators import workflow

k_tl = RespanTelemetry()
client = k_tl.get_client()

@workflow(name="update_attributes_test")
def update_attributes_test(input: str):
    force_set_attributes = {
        SpanAttributes.TRACELOOP_ENTITY_INPUT: json.dumps({
            "args": [],
            "kwargs": {"text": "custom input"}
        }),
    }

    client.update_current_span(
        attributes=force_set_attributes,
        name="update_attributes_test",
        respan_params={"metadata": {"test": "test"}},
    )

    return "Some desired output"

if __name__ == "__main__":
    update_attributes_test("Some input")
Use update_current_span(attributes=...) for overriding displayed input/output. Values must be JSON-serializable strings via json.dumps(...).