> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Trace Semantic Kernel applications

LangSmith can capture traces generated by [Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/) using its built-in OpenTelemetry support. This guide shows you how to automatically capture traces from your Semantic Kernel applications and send them to LangSmith for monitoring and analysis.

## Installation

Install the required packages using your preferred package manager:

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langsmith semantic-kernel opentelemetry-instrumentation-openai
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langsmith semantic-kernel opentelemetry-instrumentation-openai
  ```
</CodeGroup>

## Setup

### 1. Configure environment variables

Set your [API keys](/langsmith/create-account-api-key) and project name:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export LANGSMITH_API_KEY=<your_langsmith_api_key>
export LANGSMITH_PROJECT=<your_project_name>
export OPENAI_API_KEY=<your_openai_api_key>
```

### 2. Configure OpenTelemetry integration

In your Semantic Kernel application, configure the LangSmith OpenTelemetry integration along with the OpenAI instrumentor:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langsmith.integrations.otel import configure
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

# Configure LangSmith tracing
configure(project_name="semantic-kernel-demo")

# Instrument OpenAI calls
OpenAIInstrumentor().instrument()
```

<Note>
  You do not need to set any OpenTelemetry environment variables or configure exporters manually—`configure()` handles everything automatically.
</Note>

### 3. Create and run your Semantic Kernel application

Once configured, your Semantic Kernel application will automatically send traces to LangSmith:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import asyncio
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig
from langsmith.integrations.otel import configure
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

# Configure LangSmith tracing
configure(project_name="semantic-kernel-assistant")

# Instrument OpenAI calls
OpenAIInstrumentor().instrument()

# Configure Semantic Kernel
kernel = Kernel()
kernel.add_service(OpenAIChatCompletion())

# Create a prompt template
code_analysis_prompt = """
Analyze the following code and provide insights:

Code: {{$code}}

Please provide:
1. A brief summary of what the code does
2. Any potential improvements
3. Code quality assessment
"""

prompt_template_config = PromptTemplateConfig(
    template=code_analysis_prompt,
    name="code_analyzer",
    template_format="semantic-kernel",
    input_variables=[
        InputVariable(name="code", description="The code to analyze", is_required=True),
    ],
)

# Add the function to the kernel
code_analyzer = kernel.add_function(
    function_name="analyzeCode",
    plugin_name="codeAnalysisPlugin",
    prompt_template_config=prompt_template_config,
)

async def main():
    sample_code = """
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
    """

    result = await kernel.invoke(code_analyzer, code=sample_code)
    print("Code Analysis:")
    print(result)

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

## Advanced usage

### Custom metadata and tags

You can add custom metadata to your traces by setting span attributes:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from opentelemetry import trace

tracer = trace.get_tracer(__name__)

async def analyze_with_metadata(code: str):
    with tracer.start_as_current_span("semantic_kernel_workflow") as span:
        span.set_attribute("langsmith.metadata.workflow_type", "code_analysis")
        span.set_attribute("langsmith.metadata.user_id", "developer_123")
        span.set_attribute("langsmith.span.tags", "semantic-kernel,code-analysis")

        result = await kernel.invoke(code_analyzer, code=code)
        return result
```

### Combining with other instrumentors

You can combine Semantic Kernel tracing with other OpenTelemetry instrumentors:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from opentelemetry.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

# Initialize multiple instrumentors
OpenAIInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()
```

## Resources

* [Semantic Kernel documentation](https://learn.microsoft.com/en-us/semantic-kernel/overview/)
* [Semantic Kernel observability guide](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/)
* [LangSmith OpenTelemetry guide](/langsmith/trace-with-opentelemetry)

***

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