> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Semantic Kernel
> Trace Semantic Kernel applications with OpenLIT + OpenInference and send spans to Arize AX for LLM observability.
[Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/) is Microsoft's open-source SDK for blending LLMs with traditional code — kernel functions, planners, and prompt templates. Semantic Kernel emits OpenTelemetry spans natively when [OpenLIT](https://github.com/openlit/openlit-python) is initialized; the [`openinference-instrumentation-openlit`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-openlit) span processor reshapes them into the OpenInference format Arize AX expects.
This guide covers the Python implementation of Semantic Kernel. The same OpenTelemetry principles apply to Semantic Kernel for [C#](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/?pivots=programming-language-csharp) and [Java](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/?pivots=programming-language-java).
## Prerequisites
* Python 3.10+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* An `OPENAI_API_KEY` from the [OpenAI Platform](https://platform.openai.com/api-keys)
## Launch Arize
1. Sign in to your [Arize AX account](https://app.arize.com/).
2. From **Space Settings**, copy your **Space ID** and **API Key**. You will set them as `ARIZE_SPACE_ID` and `ARIZE_API_KEY` below.
## Install
```bash theme={null}
pip install arize-otel \
openinference-instrumentation-openlit \
openlit semantic-kernel openai
```
## Configure credentials
```bash theme={null}
export ARIZE_SPACE_ID=""
export ARIZE_API_KEY=""
export ARIZE_PROJECT_NAME="semantic-kernel-tracing-example"
export OPENAI_API_KEY=""
```
## Setup tracing
```python theme={null}
# instrumentation.py
import os
import openlit
from arize.otel import BatchSpanProcessor, PROJECT_NAME, Resource
from openinference.instrumentation.openlit import OpenInferenceSpanProcessor
from opentelemetry import trace as otel_trace
from opentelemetry.sdk.trace import TracerProvider
resource = Resource.create({PROJECT_NAME: os.environ["ARIZE_PROJECT_NAME"]})
tracer_provider = TracerProvider(resource=resource)
# Export spans to Arize AX.
tracer_provider.add_span_processor(
BatchSpanProcessor(
space_id=os.environ["ARIZE_SPACE_ID"],
api_key=os.environ["ARIZE_API_KEY"],
)
)
# Reshape raw OpenLIT spans into the OpenInference format Arize AX expects.
tracer_provider.add_span_processor(OpenInferenceSpanProcessor())
otel_trace.set_tracer_provider(tracer_provider)
# openlit.init() auto-detects the global TracerProvider set above.
openlit.init()
print("Arize AX tracing initialized for Semantic Kernel.")
```
## Run Semantic Kernel
```python theme={null}
# example.py
# Importing instrumentation first ensures tracing is set up
# before `semantic_kernel` is imported.
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")
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())
```
### Expected output
```text wrap theme={null}
Arize AX tracing initialized for Semantic Kernel.
The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity.
```
## Verify in Arize
1. Open your Arize AX space and select project **`semantic-kernel-tracing-example`**.
2. You should see a new trace within \~30 seconds containing a `chat gpt-5` span (the span name reflects whichever model you called) emitted by OpenLIT and reshaped by the OpenInference processor, with the prompt, response, and token usage attached.
3. If no traces appear, see [Troubleshooting](#troubleshooting).
## Troubleshooting
* **No traces in Arize.** Confirm `ARIZE_SPACE_ID` and `ARIZE_API_KEY` are set in the same shell that runs `example.py`. Enable OpenTelemetry debug logs with `export OTEL_LOG_LEVEL=debug` and re-run.
* **Code ran but no spans appear.** OpenLIT must be initialized after the global tracer provider is set. Confirm `otel_trace.set_tracer_provider(tracer_provider)` and `openlit.init()` both run before any Semantic Kernel call.
* **`401` from OpenAI.** Verify `OPENAI_API_KEY` is set and has access to `gpt-5`. Swap for a model your key can call.
* **Other LLM providers.** Semantic Kernel supports many AI services — Azure OpenAI, Anthropic, Google, and others via the matching `connectors.ai.` modules. The same OpenLIT + OpenInference setup covers them.
## Resources