comet-ml--opik/apps/opik-documentation/documentation/fern/docs-v2/integrations/semantic-kernel.mdx

---
description: Start here to integrate Opik into your Semantic Kernel-based genai application
  for end-to-end LLM observability, unit testing, and optimization.
headline: Semantic Kernel | Opik Documentation
og:description: Leverage Semantic Kernel to integrate LLMs with languages like C#
  and Python, enabling rapid development of enterprise-grade AI solutions.
og:site_name: Opik Documentation
og:title: Build AI Applications with Semantic Kernel - Opik
title: Observability for Semantic Kernel (Python) with Opik
---

[Semantic Kernel](https://github.com/microsoft/semantic-kernel) is a powerful open-source SDK from Microsoft. It facilitates the combination of LLMs with popular programming languages like C#, Python, and Java. Semantic Kernel empowers developers to build sophisticated AI applications by seamlessly integrating AI services, data sources, and custom logic, accelerating the delivery of enterprise-grade AI solutions.

Learn more about Semantic Kernel in the [official documentation](https://learn.microsoft.com/en-us/semantic-kernel/overview/).

![Semantic Kernel Integration](/img/tracing/semantic_kernel_integration.png)

## Getting started

To use the Semantic Kernel integration with Opik, you will need to have Semantic Kernel and the required OpenTelemetry packages installed:

```bash
pip install semantic-kernel opentelemetry-exporter-otlp-proto-http
```

## Environment configuration

Configure your environment variables based on your Opik deployment:

<Tabs>
    <Tab value="Opik Cloud" title="Opik Cloud">
        If you are using Opik Cloud, you will need to set the following
        environment variables:
            
        ```bash wordWrap
        export OTEL_EXPORTER_OTLP_ENDPOINT=https://www.comet.com/opik/api/v1/private/otel
        export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default'
        ```

        <Tip>
            To log the traces to a specific project, you can add the
            `projectName` parameter to the `OTEL_EXPORTER_OTLP_HEADERS`
            environment variable:

            ```bash wordWrap
            export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default,projectName=<your-project-name>'
            ```

            You can also update the `Comet-Workspace` parameter to a different
            value if you would like to log the data to a different workspace.
        </Tip>
    </Tab>
    <Tab value="Enterprise deployment" title="Enterprise deployment">
        If you are using an Enterprise deployment of Opik, you will need to set the following
        environment variables:

        ```bash wordWrap
        export OTEL_EXPORTER_OTLP_ENDPOINT=https://<comet-deployment-url>/opik/api/v1/private/otel
        export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default'
        ```

        <Tip>
            To log the traces to a specific project, you can add the
            `projectName` parameter to the `OTEL_EXPORTER_OTLP_HEADERS`
            environment variable:

            ```bash wordWrap
            export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default,projectName=<your-project-name>'
            ```

            You can also update the `Comet-Workspace` parameter to a different
            value if you would like to log the data to a different workspace.
        </Tip>
    </Tab>
    <Tab value="Self-hosted instance" title="Self-hosted instance">

    If you are self-hosting Opik, you will need to set the following environment
    variables:

    ```bash
    export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:5173/api/v1/private/otel
    ```

    <Tip>
        To log the traces to a specific project, you can add the `projectName`
        parameter to the `OTEL_EXPORTER_OTLP_HEADERS` environment variable:

        ```bash
        export OTEL_EXPORTER_OTLP_HEADERS='projectName=<your-project-name>'
        ```

    </Tip>
    </Tab>

</Tabs>

## Using Opik with Semantic Kernel

<Warning>
**Important:** By default, Semantic Kernel does not emit spans for AI connectors because they contain experimental `gen_ai` attributes. You **must** set one of these environment variables to enable telemetry:

- `SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true` - Includes **sensitive data** (prompts and completions)
- `SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS=true` - **Non-sensitive data only** (model names, operation names, token usage)

Without one of these variables set, no AI connector spans will be emitted.

For more details, see [Microsoft's Semantic Kernel Environment Variables documentation](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/telemetry-with-console?tabs=Powershell-CreateFile%2CEnvironmentFile&pivots=programming-language-python#environment-variables).
</Warning>

Semantic Kernel has built-in OpenTelemetry support. Enable telemetry and configure the OTLP exporter:

```python
import asyncio
import os

# REQUIRED: Enable Semantic Kernel diagnostics
# Option 1: Include sensitive data (prompts and completions)
os.environ["SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE"] = (
    "true"
)

# Option 2: Hide sensitive data (prompts and completions)
# os.environ["SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS"] = "true"

from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.semconv.resource import ResourceAttributes
from opentelemetry.trace import set_tracer_provider
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.function_choice_behavior import (
    FunctionChoiceBehavior,
)
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.connectors.ai.prompt_execution_settings import (
    PromptExecutionSettings,
)
from semantic_kernel.functions.kernel_arguments import KernelArguments
from semantic_kernel.functions.kernel_function_decorator import kernel_function


class BookingPlugin:
    @kernel_function(
        name="find_available_rooms",
        description="Find available conference rooms for today.",
    )
    def find_available_rooms(
        self,
    ) -> list[str]:
        return ["Room 101", "Room 201", "Room 301"]

    @kernel_function(
        name="book_room",
        description="Book a conference room.",
    )
    def book_room(self, room: str) -> str:
        return f"Room {room} booked."


def set_up_tracing():
    # Create a resource to represent the service/sample
    resource = Resource.create(
        {ResourceAttributes.SERVICE_NAME: "semantic-kernel-app"}
    )

    exporter = OTLPSpanExporter()

    # Initialize a trace provider for the application. This is a factory for creating tracers.
    tracer_provider = TracerProvider(resource=resource)
    # Span processors are initialized with an exporter which is responsible
    # for sending the telemetry data to a particular backend.
    tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
    # Sets the global default tracer provider
    set_tracer_provider(tracer_provider)


# This must be done before any other telemetry calls
set_up_tracing()


async def main():
    # Create a kernel and add a service
    kernel = Kernel()
    kernel.add_service(OpenAIChatCompletion(ai_model_id="gpt-4.1"))
    kernel.add_plugin(BookingPlugin(), "BookingPlugin")

    answer = await kernel.invoke_prompt(
        "Reserve a conference room for me today.",
        arguments=KernelArguments(
            settings=PromptExecutionSettings(
                function_choice_behavior=FunctionChoiceBehavior.Auto(),
            ),
        ),
    )
    print(answer)


if __name__ == "__main__":
    asyncio.run(main())
```

<Tip>
**Choosing between the environment variables:**

- Use `SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true` if you want complete visibility into your LLM interactions, including the actual prompts and responses. This is useful for debugging and development.

- Use `SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS=true` for production environments where you want to avoid logging sensitive data while still capturing important metrics like token usage, model names, and operation performance.
</Tip>

## Further improvements

If you have any questions or suggestions for improving the Semantic Kernel integration, please [open an issue](https://github.com/comet-ml/opik/issues/new/choose) on our GitHub repository.