Semantic Kernel Tracing

import openlit
from openinference.instrumentation.openlit import OpenInferenceSpanProcessor
from phoenix.otel import BatchSpanProcessor, HTTPSpanExporter, register

tracer_provider = register(project_name="semantic-kernel")

# Convert OpenLIT's spans to OpenInference. This replaces register()'s default
# exporter, so add the Phoenix exporter back after it.
tracer_provider.add_span_processor(OpenInferenceSpanProcessor())
tracer_provider.add_span_processor(BatchSpanProcessor(HTTPSpanExporter()))

# openlit.init() picks up the global tracer provider that register() set.
# disable_metrics stops OpenLIT printing metrics to the console.
openlit.init(disable_metrics=True)

# Import instrumentation first so tracing is set up before Semantic Kernel runs.
from instrumentation import tracer_provider

import asyncio

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.contents import ChatHistory


async def main() -> None:
    # OpenAIChatCompletion reads OPENAI_API_KEY from the environment.
    kernel = Kernel()
    chat = OpenAIChatCompletion(ai_model_id="gpt-5.5")
    kernel.add_service(chat)

    history = ChatHistory()
    history.add_user_message("Why is the ocean salty? Answer in two sentences.")
    response = await chat.get_chat_message_content(
        chat_history=history,
        settings=chat.get_prompt_execution_settings_class()(),
    )
    print(str(response))


asyncio.run(main())
tracer_provider.force_flush()