{"slug": "pydantic-ai-observability-monitoring-with-opentelemetry", "title": "Pydantic AI Observability & Monitoring with OpenTelemetry", "summary": "Pydantic AI has introduced observability and monitoring for its AI agents using OpenTelemetry, enabling users to export logs, traces, and metrics to SigNoz for real-time visibility into latency, error rates, and usage trends. The integration allows developers to instrument Pydantic AI applications with minimal code changes, leveraging SigNoz dashboards and alerts to debug issues and optimize performance.", "body_md": "Overview\n\nThis guide walks you through setting up observability and monitoring for Pydantic AI API using [OpenTelemetry](https://opentelemetry.io/) and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe model and agent 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 Pydantic AI applications.\n\nInstrumenting Pydantic AI 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[Pydantic AI](https://ai.pydantic.dev/)integrated into your Python application.- For Python:\n`pip`\n\ninstalled for managing Python packages and*(optional but recommended)*a Python virtual environment to isolate dependencies\n\nMonitoring Pydantic AI\n\nFor more detailed info on instrumenting your Pydantic AI applications click [here](https://ai.pydantic.dev/logfire/#otel-without-logfire).\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  pydantic-ai\n```\n\n**Step 2:** Add Automatic Instrumentation\n\n```\nopentelemetry-bootstrap --action=install\n```\n\n**Step 3:** Instrument your Pydantic AI application\n\nAfter setting up the OpenTelemetry configurations for traces, logs, and metrics, initialize Pydantic AI instrumentation by calling `Agent.instrument_all()`\n\n:\n\n``` python\nfrom pydantic_ai.agent import Agent\n\n# Initialize Pydantic AI instrumentation\nAgent.instrument_all()\n```\n\nThis call enables automatic tracing, logs, and metrics collection for all Pydantic AI agents in your application.\n\n📌 Note: Ensure this is called before any Pydantic AI related calls to properly configure instrumentation of your application\n\n**Step 4:** Run an example\n\n``` python\nfrom pydantic_ai import Agent, RunContext\nimport asyncio\n\nAgent.instrument_all()\n\nroulette_agent = Agent(\n    'openai:gpt-4o',\n    deps_type=int,\n    system_prompt=(\n        'Use the `roulette_wheel` function to see if the '\n        'customer has won based on the number they provide.'\n    ),\n    instrument=True\n)\n\n@roulette_agent.tool\nasync def roulette_wheel(ctx: RunContext[int], square: int) -> str:\n    \"\"\"check if the square is a winner\"\"\"\n    return 'winner' if square == ctx.deps else 'loser'\n\nasync def main():\n    success_number = 18\n    result = await roulette_agent.run('Put my money on square eighteen', deps=success_number)\n    print(result.output)\n\nif __name__ == '__main__':\n    asyncio.run(main())\n```\n\n📌 Note: Pydantic AI supports a\n\n[variety of model providers]for LLMs. In this example, we're using OpenAI. Before running this code, ensure that you have set the environment variable`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 \\\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. For example:`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 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  pydantic-ai\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 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**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 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**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 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. Pydantic AI additionally exposes LLM specific metrics as part of their SDK. If you want to add custom metrics to your Pydantic AI application, see\n\n[Python Custom Metrics].\n\n**Step 6:** Instrument your Pydantic AI application\n\nAfter setting up the OpenTelemetry configurations for traces, logs, and metrics, initialize Pydantic AI instrumentation by calling `Agent.instrument_all()`\n\n:\n\n``` python\nfrom pydantic_ai.agent import Agent\n\n# Initialize Pydantic AI instrumentation\nAgent.instrument_all()\n```\n\nThis call enables automatic tracing, logs, and metrics collection for all Pydantic AI agents in your application.\n\n📌 Note: Ensure this is called before any Pydantic AI related calls to properly configure instrumentation of your application\n\n**Step 7:** Run an example\n\n``` python\nfrom pydantic_ai import Agent, RunContext\nimport asyncio\n\nroulette_agent = Agent(\n    'openai:gpt-4o',\n    deps_type=int,\n    system_prompt=(\n        'Use the `roulette_wheel` function to see if the '\n        'customer has won based on the number they provide.'\n    ),\n    instrument=True\n)\n\n@roulette_agent.tool\nasync def roulette_wheel(ctx: RunContext[int], square: int) -> str:\n    \"\"\"check if the square is a winner\"\"\"\n    return 'winner' if square == ctx.deps else 'loser'\n\nasync def main():\n    success_number = 18\n    result = await roulette_agent.run('Put my money on square eighteen', deps=success_number)\n    print(result.output)\n\nif __name__ == '__main__':\n    asyncio.run(main())\n```\n\n📌 Note: Pydantic AI supports a\n\n[variety of model providers]for LLMs. In this example, we're using OpenAI. Before running this code, ensure that you have set the environment variable`OPENAI_API_KEY`\n\nwith your generated API key.\n\nView Traces, Logs, and Metrics in SigNoz\n\nYour Pydantic AI 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 Pydantic 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\nDashboard\n\nYou can also check out our custom Pydantic AI dashboard [here](https://signoz.io/docs/dashboards/dashboard-templates/pydantic-ai-dashboard/) which provides specialized visualizations for monitoring your Pydantic AI usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.", "url": "https://wpnews.pro/news/pydantic-ai-observability-monitoring-with-opentelemetry", "canonical_source": "https://signoz.io/docs/pydantic-ai-observability", "published_at": "2026-06-29 00:00:00+00:00", "updated_at": "2026-06-30 01:20:17.595588+00:00", "lang": "en", "topics": ["ai-tools", "developer-tools", "ai-infrastructure", "ai-agents"], "entities": ["Pydantic AI", "OpenTelemetry", "SigNoz", "OpenAI"], "alternates": {"html": "https://wpnews.pro/news/pydantic-ai-observability-monitoring-with-opentelemetry", "markdown": "https://wpnews.pro/news/pydantic-ai-observability-monitoring-with-opentelemetry.md", "text": "https://wpnews.pro/news/pydantic-ai-observability-monitoring-with-opentelemetry.txt", "jsonld": "https://wpnews.pro/news/pydantic-ai-observability-monitoring-with-opentelemetry.jsonld"}}