> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-eugene-1763577327-bfe51c2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Trace with Semantic Kernel

LangSmith can capture traces generated by [Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/) using OpenInference's OpenAI instrumentation. 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={null}
  pip install langsmith semantic-kernel openinference-instrumentation-openai
  ```

  ```bash uv theme={null}
  uv add langsmith semantic-kernel openinference-instrumentation-openai
  ```
</CodeGroup>

<Info>
  Requires LangSmith Python SDK version `langsmith>=0.4.26` for optimal OpenTelemetry support.
</Info>

## Setup

### 1. Configure environment variables

Set your API keys and project name:

<CodeGroup>
  ```bash Shell theme={null}
  export LANGSMITH_API_KEY=<your_langsmith_api_key>
  export LANGSMITH_PROJECT=<your_project_name>
  export OPENAI_API_KEY=<your_openai_api_key>
  ```
</CodeGroup>

### 2. Configure OpenTelemetry integration

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

```python theme={null}
from langsmith.integrations.otel import configure
from openinference.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:

This example includes a minimal app that configures the kernel, defines prompt-based functions, and invokes them to generate traced activity.

```python theme={null}
import os
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 openinference.instrumentation.openai import OpenAIInstrumentor
from langsmith.integrations.otel import configure
import dotenv

# Load environment variables
dotenv.load_dotenv(".env.local")

# 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 code analysis 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,
)

# Create a documentation generator
doc_prompt = """
Generate comprehensive documentation for the following function:

{{$function_code}}

Include:
- Purpose and functionality
- Parameters and return values
- Usage examples
- Any important notes
"""

doc_template_config = PromptTemplateConfig(
    template=doc_prompt,
    name="doc_generator",
    template_format="semantic-kernel",
    input_variables=[
        InputVariable(name="function_code", description="The function code to document", is_required=True),
    ],
)

doc_generator = kernel.add_function(
    function_name="generateDocs",
    plugin_name="documentationPlugin",
    prompt_template_config=doc_template_config,
)

async def main():
    # Example code to analyze
    sample_code = """
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
    """

    # Analyze the code
    analysis_result = await kernel.invoke(code_analyzer, code=sample_code)
    print("Code Analysis:")
    print(analysis_result)
    print("\n" + "="*50 + "\n")

    # Generate documentation
    doc_result = await kernel.invoke(doc_generator, function_code=sample_code)
    print("Generated Documentation:")
    print(doc_result)

    return {"analysis": str(analysis_result), "documentation": str(doc_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={null}
from opentelemetry import trace

# Get the current tracer
tracer = trace.get_tracer(__name__)

async def main():
    with tracer.start_as_current_span("semantic_kernel_workflow") as span:
        # Add custom metadata
        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")

        # Your Semantic Kernel code here
        result = await kernel.invoke(code_analyzer, code=sample_code)
        return result
```

### Combining with other instrumentors

You can combine Semantic Kernel instrumentation with other instrumentors (e.g., DSPy, AutoGen) by adding them and initializing them as instrumentors:

```python theme={null}
from langsmith.integrations.otel import configure
from openinference.instrumentation.openai import OpenAIInstrumentor
from openinference.instrumentation.dspy import DSPyInstrumentor

# Configure LangSmith tracing
configure(project_name="multi-framework-app")

# Initialize multiple instrumentors
OpenAIInstrumentor().instrument()
DSPyInstrumentor().instrument()

# Your application code using multiple frameworks
```

***

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