{"slug": "semantic-kernel-observability-monitor-ai-orchestration", "title": "Semantic Kernel Observability - Monitor AI Orchestration", "summary": "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.", "body_md": "Overview\n\nThis guide walks you through setting up observability and monitoring for Semantic Kernel using [OpenTelemetry](https://opentelemetry.io/) 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.\n\nInstrumenting 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.\n\nPrerequisites\n\n- A\n[SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key - Internet access to send telemetry data to SigNoz Cloud\n- Python 3.10+ with Semantic Kernel installed\n- For Python:\n`pip`\n\ninstalled for managing Python packages and*(optional but recommended)*a Python virtual environment to isolate dependencies\n\nMonitoring Semantic Kernel\n\nFor more detailed info on tracing your Semantic Kernel applications click [here](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/telemetry-with-console?tabs=Bash-CreatFile%2CBash-EnvironmentVariable&pivots=programming-language-python).\nFor more detailed info on Semantic Kernel telemetry data click [here](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/?pivots=programming-language-python).\n\nNo-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.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-distro \\\n  opentelemetry-exporter-otlp \\\n  httpx \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  semantic-kernel\n```\n\n**Step 2:** Add Automatic Instrumentation\n\n```\nopentelemetry-bootstrap --action=install\n```\n\n**Step 3:** Instrument your Semantic Kernel application\n\nBy 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:\n\n`SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS=true`\n\n- Emits non-sensitive data (model name, operation name, token usage)`SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true`\n\n- Emits all telemetry including sensitive data (prompts and completions)\n\nIn this guide, we use the sensitive variant for comprehensive observability:\n\n```\nexport SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true\n```\n\n**Step 4:** Run an example\n\n``` python\nfrom semantic_kernel import Kernel\nfrom semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion\nfrom semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig\n\nimport asyncio\nimport logging\n\nimport os\n\nasync def main():\n\n    kernel = Kernel()\n    \n    kernel.add_service(\n        OpenAIChatCompletion(\n            ai_model_id=\"gpt-4.1\",\n            api_key=os.getenv(\"OPENAI_API_KEY\")\n        )\n    )\n\n    prompt = \"\"\"{{$input}}\n    Answer the question above.\n    \"\"\"\n    \n    prompt_template_config = PromptTemplateConfig(\n        template=prompt,\n        name=\"summarize\",\n        template_format=\"semantic-kernel\",\n        input_variables=[\n            InputVariable(name=\"input\", description=\"The user input\", is_required=True),\n        ]\n    )\n    \n    summarize = kernel.add_function(\n        function_name=\"summarizeFunc\",\n        plugin_name=\"summarizePlugin\",\n        prompt_template_config=prompt_template_config,\n    )\n\n    input_text = \"What is SigNoz?\"\n    \n    summary = await kernel.invoke(summarize, input=input_text)\n    \n    print(summary)\n\nif __name__ == \"__main__\":\n    asyncio.run(main())\n```\n\n📌 Note: Before running this code, ensure that you have set the environment variable\n\n`OPENAI_API_KEY`\n\nwith your generated API key.\n\n**Step 5:** Run your application with auto-instrumentation\n\n```\nOTEL_RESOURCE_ATTRIBUTES=\"service.name=<service_name>\" \\\nOTEL_EXPORTER_OTLP_ENDPOINT=\"https://ingest.<region>.signoz.cloud:443\" \\\nOTEL_EXPORTER_OTLP_HEADERS=\"signoz-ingestion-key=<your-ingestion-key>\" \\\nOTEL_EXPORTER_OTLP_PROTOCOL=grpc \\\nOTEL_TRACES_EXPORTER=otlp \\\nOTEL_METRICS_EXPORTER=otlp \\\nOTEL_LOGS_EXPORTER=otlp \\\nOTEL_PYTHON_LOG_CORRELATION=true \\\nOTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \\\nOTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai \\\nopentelemetry-instrument <your_run_command>\n```\n\n📌 Note: We're using\n\n`OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai`\n\nin 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.\n\nis the name of your service`<service_name>`\n\n`<region>`\n\n: Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`<your-ingestion-key>`\n\n: Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace\n`<your_run_command>`\n\nwith the actual command you would use to run your application. For example:`python main.py`\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and\nremove the ingestion key header as shown in\n[Cloud → Self-Hosted](/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\nCode-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.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-api \\\n  opentelemetry-sdk \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  semantic-kernel\n```\n\n**Step 2:** Import the necessary modules in your Python application\n\n**Traces:**\n\n``` python\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n```\n\n**Logs:**\n\n``` python\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nfrom opentelemetry._logs import set_logger_provider\nimport logging\n```\n\n**Metrics:**\n\n``` python\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nfrom opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor\n```\n\n**Step 3:** Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\nfrom opentelemetry import trace\nimport os\n\nresource = Resource.create({\"service.name\": \"<service_name>\"})\nprovider = TracerProvider(resource=resource)\nspan_exporter = OTLPSpanExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_TRACES_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nprocessor = BatchSpanProcessor(span_exporter)\nprovider.add_span_processor(processor)\ntrace.set_tracer_provider(provider)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/traces`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and\nremove the ingestion key header as shown in\n[Cloud → Self-Hosted](/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 4**: Setup Logs\n\n``` python\nimport logging\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry._logs import set_logger_provider\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nimport os\n\nresource = Resource.create({\"service.name\": \"<service_name>\"})\nlogger_provider = LoggerProvider(resource=resource)\nset_logger_provider(logger_provider)\n\notlp_log_exporter = OTLPLogExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_LOGS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nlogger_provider.add_log_record_processor(\n    BatchLogRecordProcessor(otlp_log_exporter)\n)\n# Attach OTel logging handler to root logger\nhandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)\nlogging.basicConfig(level=logging.INFO, handlers=[handler])\n\nlogger = logging.getLogger(__name__)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/logs`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and\nremove the ingestion key header as shown in\n[Cloud → Self-Hosted](/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 5**: Setup Metrics\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nfrom opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor\nimport os\n\nresource = Resource.create({\"service.name\": \"<service-name>\"})\nmetric_exporter = OTLPMetricExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_METRICS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nreader = PeriodicExportingMetricReader(metric_exporter)\nmetric_provider = MeterProvider(metric_readers=[reader], resource=resource)\nmetrics.set_meter_provider(metric_provider)\n\nmeter = metrics.get_meter(__name__)\n\n# turn on out-of-the-box metrics\nSystemMetricsInstrumentor().instrument()\nHTTPXClientInstrumentor().instrument()\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/metrics`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and\nremove the ingestion key header as shown in\n[Cloud → Self-Hosted](/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n📌 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\n\n[Python Custom Metrics].\n\n**Step 6:** Instrument your Semantic Kernel application\n\nBy 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:\n\n`SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS=true`\n\n- Emits non-sensitive data (model name, operation name, token usage)`SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true`\n\n- Emits all telemetry including sensitive data (prompts and completions)\n\nIn this guide, we use the sensitive variant for comprehensive observability:\n\n```\nexport SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE=true\n```\n\n**Step 7:** Run an example\n\n``` python\nfrom semantic_kernel import Kernel\nfrom semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion\nfrom semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig\n\nimport asyncio\nimport logging\n\nimport os\n\nasync def main():\n\n    kernel = Kernel()\n    \n    kernel.add_service(\n        OpenAIChatCompletion(\n            ai_model_id=\"gpt-4.1\",\n            api_key=os.getenv(\"OPENAI_API_KEY\")\n        )\n    )\n\n    prompt = \"\"\"{{$input}}\n    Answer the question above.\n    \"\"\"\n    \n    prompt_template_config = PromptTemplateConfig(\n        template=prompt,\n        name=\"summarize\",\n        template_format=\"semantic-kernel\",\n        input_variables=[\n            InputVariable(name=\"input\", description=\"The user input\", is_required=True),\n        ]\n    )\n    \n    summarize = kernel.add_function(\n        function_name=\"summarizeFunc\",\n        plugin_name=\"summarizePlugin\",\n        prompt_template_config=prompt_template_config,\n    )\n\n    input_text = \"What is SigNoz?\"\n    \n    summary = await kernel.invoke(summarize, input=input_text)\n    \n    print(summary)\n\nif __name__ == \"__main__\":\n    asyncio.run(main())\n```\n\n📌 Note: Before running this code, ensure that you have set the environment variable\n\n`OPENAI_API_KEY`\n\nwith your generated API key.\n\nView Traces, Logs, and Metrics in SigNoz\n\nYour Semantic Kernel commands should now automatically emit traces, logs, and metrics.\n\nYou should be able to view traces in Signoz Cloud under the traces tab:\n\nWhen 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.\n\nYou 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:\n\nWhen you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:\n\nYou should be able to see Semantic Kernel related metrics in Signoz Cloud under the metrics tab:\n\nWhen you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:\n\nTroubleshooting\n\nIf you don't see your telemetry data:\n\n**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\n\nNext Steps\n\nYou can also check out our custom Semantic Kernel dashboard [here](https://signoz.io/docs/dashboards/dashboard-templates/semantic-kernel-dashboard/) 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.\n\nAdditional resources:\n\n- Set up\n[alerts](https://signoz.io/docs/alerts/)for high latency or error rates - Learn more about\n[querying traces](https://signoz.io/docs/userguide/traces/) - Explore\n[log correlation](https://signoz.io/docs/userguide/logs_query_builder/)", "url": "https://wpnews.pro/news/semantic-kernel-observability-monitor-ai-orchestration", "canonical_source": "https://signoz.io/docs/semantic-kernel-observability", "published_at": "2026-06-29 00:00:00+00:00", "updated_at": "2026-06-30 01:20:10.093096+00:00", "lang": "en", "topics": ["ai-tools", "developer-tools", "artificial-intelligence", "large-language-models"], "entities": ["Semantic Kernel", "OpenTelemetry", "SigNoz", "OpenAI", "Microsoft"], "alternates": {"html": "https://wpnews.pro/news/semantic-kernel-observability-monitor-ai-orchestration", "markdown": "https://wpnews.pro/news/semantic-kernel-observability-monitor-ai-orchestration.md", "text": "https://wpnews.pro/news/semantic-kernel-observability-monitor-ai-orchestration.txt", "jsonld": "https://wpnews.pro/news/semantic-kernel-observability-monitor-ai-orchestration.jsonld"}}