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Semantic Kernel Observability - Monitor AI Orchestration

Semantic Kernel now supports observability via OpenTelemetry, enabling monitoring of AI orchestration with logs, traces, and metrics exported to SigNoz. The integration provides real-time visibility into latency, error rates, and usage trends for AI workflows. Developers can set up automatic instrumentation with environment variables to capture sensitive or non-sensitive telemetry data.

read8 min views1 publishedJun 29, 2026

Overview

This guide walks you through setting up observability and monitoring for Semantic Kernel using OpenTelemetry and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe various models performance, capture request/response details, and track system-level metrics in SigNoz, giving you real-time visibility into latency, error rates, and usage trends for your Semantic Kernel applications.

Instrumenting Semantic Kernel in your AI applications with telemetry ensures full observability across your AI workflows, making it easier to debug issues, optimize performance, and understand user interactions. By leveraging SigNoz, you can analyze correlated traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to continuously improve reliability, responsiveness, and user experience.

Prerequisites

  • A SigNoz Cloud accountwith an active ingestion key - Internet access to send telemetry data to SigNoz Cloud
  • Python 3.10+ with Semantic Kernel installed
  • For Python: pip

installed for managing Python packages and*(optional but recommended)*a Python virtual environment to isolate dependencies

Monitoring Semantic Kernel

For more detailed info on tracing your Semantic Kernel applications click here. For more detailed info on Semantic Kernel telemetry data click here.

No-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries.

Step 1: Install the necessary packages in your Python environment.

pip install \
  opentelemetry-distro \
  opentelemetry-exporter-otlp \
  httpx \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  semantic-kernel

Step 2: Add Automatic Instrumentation

opentelemetry-bootstrap --action=install

Step 3: Instrument your Semantic Kernel application

By default, the kernel doesn't emit spans for the AI connectors, because these spans carry gen_ai attributes that are considered experimental. To enable the feature, you can set one of the following environment variables:

SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS=true

  • Emits non-sensitive data (model name, operation name, token usage)SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true

  • Emits all telemetry including sensitive data (prompts and completions)

In this guide, we use the sensitive variant for comprehensive observability:

export SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true

Step 4: Run an example

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig

import asyncio
import logging

import os

async def main():

    kernel = Kernel()
    
    kernel.add_service(
        OpenAIChatCompletion(
            ai_model_id="gpt-4.1",
            api_key=os.getenv("OPENAI_API_KEY")
        )
    )

    prompt = """{{$input}}
    Answer the question above.
    """
    
    prompt_template_config = PromptTemplateConfig(
        template=prompt,
        name="summarize",
        template_format="semantic-kernel",
        input_variables=[
            InputVariable(name="input", description="The user input", is_required=True),
        ]
    )
    
    summarize = kernel.add_function(
        function_name="summarizeFunc",
        plugin_name="summarizePlugin",
        prompt_template_config=prompt_template_config,
    )

    input_text = "What is SigNoz?"
    
    summary = await kernel.invoke(summarize, input=input_text)
    
    print(summary)

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

📌 Note: Before running this code, ensure that you have set the environment variable

OPENAI_API_KEY

with your generated API key.

Step 5: Run your application with auto-instrumentation

OTEL_RESOURCE_ATTRIBUTES="service.name=<service_name>" \
OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest.<region>.signoz.cloud:443" \
OTEL_EXPORTER_OTLP_HEADERS="signoz-ingestion-key=<your-ingestion-key>" \
OTEL_EXPORTER_OTLP_PROTOCOL=grpc \
OTEL_TRACES_EXPORTER=otlp \
OTEL_METRICS_EXPORTER=otlp \
OTEL_LOGS_EXPORTER=otlp \
OTEL_PYTHON_LOG_CORRELATION=true \
OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \
OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai \
opentelemetry-instrument <your_run_command>

📌 Note: We're using

OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai

in the run command to disable the OpenAI instrumentor for tracing. This avoids duplicate OpenAI spans with Semantic Kernel's native telemetry/instrumentation, ensuring that telemetry is captured exclusively through Semantic Kernel's built-in instrumentation.

is the name of your service<service_name>

<region>

: YourSigNoz Cloud region<your-ingestion-key>

: Your SigNozingestion key- Replace <your_run_command>

with the actual command you would use to run your application. For example:python main.py

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted.

Code-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure.

Step 1: Install the necessary packages in your Python environment.

pip install \
  opentelemetry-api \
  opentelemetry-sdk \
  opentelemetry-exporter-otlp \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  semantic-kernel

Step 2: Import the necessary modules in your Python application

Traces:

from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

Logs:

from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
from opentelemetry._logs import set_logger_provider
import logging

Metrics:

from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud

from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry import trace
import os

resource = Resource.create({"service.name": "<service_name>"})
provider = TracerProvider(resource=resource)
span_exporter = OTLPSpanExporter(
    endpoint= os.getenv("OTEL_EXPORTER_TRACES_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
processor = BatchSpanProcessor(span_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

is the name of your service<service_name>

→ SigNoz Cloud trace endpoint with appropriateOTEL_EXPORTER_TRACES_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/traces

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted.

Step 4: Setup Logs

import logging
from opentelemetry.sdk.resources import Resource
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
import os

resource = Resource.create({"service.name": "<service_name>"})
logger_provider = LoggerProvider(resource=resource)
set_logger_provider(logger_provider)

otlp_log_exporter = OTLPLogExporter(
    endpoint= os.getenv("OTEL_EXPORTER_LOGS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(otlp_log_exporter)
)
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.basicConfig(level=logging.INFO, handlers=[handler])

logger = logging.getLogger(__name__)

is the name of your service<service_name>

→ SigNoz Cloud endpoint with appropriateOTEL_EXPORTER_LOGS_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/logs

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted.

Step 5: Setup Metrics

from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor
import os

resource = Resource.create({"service.name": "<service-name>"})
metric_exporter = OTLPMetricExporter(
    endpoint= os.getenv("OTEL_EXPORTER_METRICS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
reader = PeriodicExportingMetricReader(metric_exporter)
metric_provider = MeterProvider(metric_readers=[reader], resource=resource)
metrics.set_meter_provider(metric_provider)

meter = metrics.get_meter(__name__)

SystemMetricsInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()

is the name of your service<service_name>

→ SigNoz Cloud endpoint with appropriateOTEL_EXPORTER_METRICS_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/metrics

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted.

📌 Note: SystemMetricsInstrumentor provides system metrics (CPU, memory, etc.), and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. If you want to add custom metrics to your Semantic Kernel application, see

[Python Custom Metrics].

Step 6: Instrument your Semantic Kernel application

By default, the kernel doesn't emit spans for the AI connectors, because these spans carry gen_ai attributes that are considered experimental. To enable the feature, you can set one of the following environment variables:

SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS=true

  • Emits non-sensitive data (model name, operation name, token usage)SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true

  • Emits all telemetry including sensitive data (prompts and completions)

In this guide, we use the sensitive variant for comprehensive observability:

export SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true

Step 7: Run an example

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig

import asyncio
import logging

import os

async def main():

    kernel = Kernel()
    
    kernel.add_service(
        OpenAIChatCompletion(
            ai_model_id="gpt-4.1",
            api_key=os.getenv("OPENAI_API_KEY")
        )
    )

    prompt = """{{$input}}
    Answer the question above.
    """
    
    prompt_template_config = PromptTemplateConfig(
        template=prompt,
        name="summarize",
        template_format="semantic-kernel",
        input_variables=[
            InputVariable(name="input", description="The user input", is_required=True),
        ]
    )
    
    summarize = kernel.add_function(
        function_name="summarizeFunc",
        plugin_name="summarizePlugin",
        prompt_template_config=prompt_template_config,
    )

    input_text = "What is SigNoz?"
    
    summary = await kernel.invoke(summarize, input=input_text)
    
    print(summary)

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

📌 Note: Before running this code, ensure that you have set the environment variable

OPENAI_API_KEY

with your generated API key.

View Traces, Logs, and Metrics in SigNoz

Your Semantic Kernel commands should now automatically emit traces, logs, and metrics.

You should be able to view traces in Signoz Cloud under the traces tab:

When you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes.

You should be able to view logs in Signoz Cloud under the logs tab. You can also view logs by clicking on the “Related Logs” button in the trace view to see correlated logs:

When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:

You should be able to see Semantic Kernel related metrics in Signoz Cloud under the metrics tab:

When you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:

Troubleshooting

If you don't see your telemetry data:

Verify network connectivity- Ensure your application can reach SigNoz Cloud endpoints** Check ingestion key**- Verify your SigNoz ingestion key is correct** Wait for data**- OpenTelemetry batches data before sending, so wait 10-30 seconds after making API calls** Try a console exporter**— Enable a console exporter locally to confirm that your application is generating telemetry data before it’s sent to SigNoz

Next Steps

You can also check out our custom Semantic Kernel dashboard here which provides specialized visualizations for monitoring your Semantic-kernel usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.

Additional resources:

source & further reading
signoz.io — original article Pydantic AI Observability & Monitoring with OpenTelemetry Essential Guide to Server Health Monitoring - Tools and Best Practices Tomcat Performance Monitoring with OpenTelemetry - Key Metrics and Setup Guide
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