Trace Semantic Kernel applications ↗

Original Documentation

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LangSmith can capture traces generated by Semantic Kernel 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:

pip install langsmith semantic-kernel opentelemetry-instrumentation-openai

uv add langsmith semantic-kernel opentelemetry-instrumentation-openai

Setup#

1. Configure environment variables#

Set your API keys and project name:

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:

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()

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

3. Create and run your Semantic Kernel application#

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

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:

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:

from opentelemetry.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

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

Resources#

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Link last verified June 7, 2026. View original ↗

Source: LangChain Docs

Link last verified: 2026-03-04