{"slug": "agno-monitoring-and-observability-with-opentelemetry", "title": "Agno Monitoring and Observability with OpenTelemetry", "summary": "SigNoz has released a guide for setting up monitoring and observability for Agno AI agents using OpenTelemetry, enabling developers to export logs, traces, and metrics to the SigNoz platform. The integration provides real-time visibility into latency, error rates, and usage trends across agent workflows, with support for both auto-instrumentation for quick setup and manual instrumentation for fine-grained control.", "body_md": "Overview\n\nThis guide walks you through setting up monitoring and observability for Agno using [OpenTelemetry](https://opentelemetry.io/) and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe the performance of various models, 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 Agno applications.\n\nInstrumenting Agno in your AI applications with telemetry ensures full observability across your agent 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 or[Self Hosted SigNoz instance](https://signoz.io/docs/install/self-host/) - Internet access to send telemetry data to SigNoz Cloud\n- Python 3.10+ with Agno installed\n- For Python:\n`pip`\n\ninstalled for managing Python packages\n\nMonitoring Agno\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\nStep 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  agno \\\n  openinference-instrumentation-agno\n```\n\nStep 2: Add Automatic Instrumentation\n\n```\nopentelemetry-bootstrap --action=install\n```\n\nStep 3: Create an example Agno agent workflow\n\n``` python\nimport os\n\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom agno.tools.duckduckgo import DuckDuckGoTools\n\n# Create and configure the agent\nagent = Agent(\n    name=\"Stock Market Agent\",\n    model=OpenAIChat(id=\"gpt-4o-mini\"),\n    tools=[DuckDuckGoTools()],\n    markdown=True,\n    debug_mode=True,\n)\n\n# Use the agent\nagent.print_response(\"What is news on the stock market?\")\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\nStep 4: Run your application with auto-instrumentation\n\nRun your application with the following environment variables set. This configures OpenTelemetry to export traces, logs, and metrics to SigNoz Cloud and enables automatic log correlation:\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 \\\nopentelemetry-instrument <your_run_command>\n```\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. In this case we would use:`python main.py`\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\nCode-based manual 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\nStep 1: Install additional OpenTelemetry dependencies\n\n```\npip install \\\n  opentelemetry-api \\\n  opentelemetry-sdk \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  agno \\\n  openinference-instrumentation-agno\n```\n\nStep 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\nStep 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\nfrom openinference.instrumentation.agno import AgnoInstrumentor\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# Start instrumenting agno\nAgnoInstrumentor().instrument()\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 remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\nStep 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 remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\nStep 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\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 remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/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 Agno application, see\n\n[Python Custom Metrics].\n\nStep 6: Run an example Agno agent workflow\n\n📌 Note: Ensure you have completed the steps above (traces, logs, and metrics configuration) before running this code. All OpenTelemetry instrumentation must be initialized first.\n\n``` python\nimport os\n\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom agno.tools.duckduckgo import DuckDuckGoTools\n\n# Create and configure the agent\nagent = Agent(\n    name=\"Stock Market Agent\",\n    model=OpenAIChat(id=\"gpt-4o-mini\"),\n    tools=[DuckDuckGoTools()],\n    markdown=True,\n    debug_mode=True,\n)\n\n# Use the agent\nagent.print_response(\"What is news on the stock market?\")\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 Agno agent usage 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 Agno 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\n[Troubleshooting](#troubleshooting)\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 Agno dashboard [here](https://signoz.io/docs/dashboards/dashboard-templates/agno-dashboard/) which provides specialized visualizations for monitoring your Agno 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/agno-monitoring-and-observability-with-opentelemetry", "canonical_source": "https://signoz.io/docs/agno-monitoring", "published_at": "2026-06-09 00:00:00+00:00", "updated_at": "2026-06-11 17:59:39.542313+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-tools", "ai-infrastructure", "mlops"], "entities": ["Agno", "OpenTelemetry", "SigNoz", "Python"], "alternates": {"html": "https://wpnews.pro/news/agno-monitoring-and-observability-with-opentelemetry", "markdown": "https://wpnews.pro/news/agno-monitoring-and-observability-with-opentelemetry.md", "text": "https://wpnews.pro/news/agno-monitoring-and-observability-with-opentelemetry.txt", "jsonld": "https://wpnews.pro/news/agno-monitoring-and-observability-with-opentelemetry.jsonld"}}