Temporal Observability & Monitoring with OpenTelemetry

SigNoz has released a guide for instrumenting Temporal workflow executions and AI agent patterns with OpenTelemetry, enabling real-time visibility through traces, logs, and metrics. The integration allows developers to monitor workflow activity, debug agent execution, and set alerts for failures or high latency from a single SigNoz dashboard. The setup requires Python 3.10+, a SigNoz account or self-hosted instance, and installation of OpenTelemetry and Temporal packages for automatic instrumentation.

What is Temporal Observability? Temporal observability gives you real-time visibility into your workflow executions and AI agent patterns by collecting traces, logs, and metrics using OpenTelemetry https://opentelemetry.io/ . This guide shows you how to instrument your Temporal-based applications and send telemetry to SigNoz, so you can monitor workflow activity, debug agent execution, and optimize performance end-to-end. With full Temporal observability in SigNoz, you can correlate traces, logs, and metrics in a single dashboard, set up alerts for workflow failures or high latency, and analyze agent execution patterns over time to continuously improve reliability and efficiency. Prerequisites - A 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 - Python 3.10+ with temporalio installed - For Python: pip installed for managing Python packages Monitor Temporal Workflows with OpenTelemetry 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. For more information on getting started with Temporal in your Python environment, refer to the Temporal Python Setup Guide https://docs.temporal.io/develop/python/set-up-your-local-python 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 \ temporalio \ openinference-instrumentation-openai-agents \ openai \ openai-agents Step 2: Add Automatic Instrumentation opentelemetry-bootstrap --action=install Step 3: Set up environment variables Create a .env file in your project root and add the following environment variables based on your Temporal deployment: OPENAI API KEY=<your-openai-api-key TEMPORAL ADDRESS=<local-temporal-server is the location where your local Temporal server is hosted default: <local-temporal-server localhost:7233 OPENAI API KEY=<your-openai-api-key TEMPORAL ADDRESS=<your-temporal-cloud-address TEMPORAL NAMESPACE=<your-temporal-namespace-name TEMPORAL API KEY=<your-temporal-api-key TEMPORAL TLS=true Step 4: Create an example Temporal agent workflow python from future import annotations from dotenv import load dotenv import asyncio import os from temporalio import workflow from temporalio.client import Client from temporalio.worker import Worker from temporalio.contrib.openai agents import OpenAIAgentsPlugin from temporalio.worker import UnsandboxedWorkflowRunner from agents import Agent, Runner load dotenv @workflow.defn class HelloWorldAgent: @workflow.run async def run self, prompt: str - str: agent = Agent name="Assistant", model="gpt-5", instructions="You only respond in haikus.", result = await Runner.run agent, input=prompt return result.final output async def main : tls = os.environ.get "TEMPORAL TLS", "" .lower in "1", "true", "yes" api key = os.environ.get "TEMPORAL API KEY" plugin = OpenAIAgentsPlugin client = await Client.connect target host=os.environ.get "TEMPORAL ADDRESS", "localhost:7233" , namespace=os.environ.get "TEMPORAL NAMESPACE", "default" , api key=api key or None, tls=tls, plugins= plugin worker = Worker client, task queue=os.environ.get "TEMPORAL TASK QUEUE", "openai-agents-task-queue" , workflows= HelloWorldAgent , workflow runner=UnsandboxedWorkflowRunner async with worker: handle = await client.start workflow HelloWorldAgent.run, id="hello-world-workflow-01", task queue=os.environ.get "TEMPORAL TASK QUEUE", "openai-agents-task-queue" , args= "Tell me about SigNoz" , result = await handle.result print "\nWorkflow result:\n", result asyncio.run main Step 5: Run your application with auto-instrumentation Run 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: 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 \ opentelemetry-instrument <your run command is the name of your service <service name <region : Your SigNoz Cloud region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint <your-ingestion-key : Your SigNoz ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ - Replace <your run command with the actual command you would use to run your application. In this case we would use: 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 https://signoz.io/docs/ingestion/cloud-vs-self-hosted cloud-to-self-hosted . Code-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. Step 1: Install additional OpenTelemetry dependencies pip install \ opentelemetry-api \ opentelemetry-sdk \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ temporalio \ openinference-instrumentation-openai-agents Step 2: Import the necessary modules in your Python application Traces: python 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: python 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: python 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 python 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 from openinference.instrumentation.openai agents import OpenAIAgentsInstrumentor 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 Start instrumenting Temporal Agents OpenAIAgentsInstrumentor .instrument is the name of your service <service name → SigNoz Cloud trace endpoint with appropriate OTEL EXPORTER TRACES ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/traces → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ 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 https://signoz.io/docs/ingestion/cloud-vs-self-hosted cloud-to-self-hosted . Step 4: Setup Logs python 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 Attach OTel logging handler to root logger 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 appropriate OTEL EXPORTER LOGS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/logs → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ 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 https://signoz.io/docs/ingestion/cloud-vs-self-hosted cloud-to-self-hosted . Step 5: Setup Metrics python 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 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 turn on out-of-the-box metrics SystemMetricsInstrumentor .instrument HTTPXClientInstrumentor .instrument is the name of your service <service name → SigNoz Cloud endpoint with appropriate OTEL EXPORTER METRICS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/metrics → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ 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 https://signoz.io/docs/ingestion/cloud-vs-self-hosted cloud-to-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 Temporal application, see Python Custom Metrics . Step 6: Set up environment variables Create a .env file in your project root and add the following environment variables based on your Temporal deployment: OPENAI API KEY=<your-openai-api-key TEMPORAL ADDRESS=<local-temporal-server is the location where your local Temporal server is hosted default: <local-temporal-server localhost:7233 OPENAI API KEY=<your-openai-api-key TEMPORAL ADDRESS=<your-temporal-cloud-address TEMPORAL NAMESPACE=<your-temporal-namespace-name TEMPORAL API KEY=<your-temporal-api-key TEMPORAL TLS=true Step 7: Run an example Temporal agent workflow Ensure you have completed the steps above traces, logs, and metrics configuration before running this code. All OpenTelemetry instrumentation must be initialized first. python from future import annotations from dotenv import load dotenv import asyncio import os from temporalio import workflow from temporalio.client import Client from temporalio.worker import Worker from temporalio.contrib.openai agents import OpenAIAgentsPlugin from temporalio.worker import UnsandboxedWorkflowRunner from agents import Agent, Runner load dotenv @workflow.defn class HelloWorldAgent: @workflow.run async def run self, prompt: str - str: agent = Agent name="Assistant", model="gpt-5", instructions="You only respond in haikus.", result = await Runner.run agent, input=prompt return result.final output async def main : tls = os.environ.get "TEMPORAL TLS", "" .lower in "1", "true", "yes" api key = os.environ.get "TEMPORAL API KEY" plugin = OpenAIAgentsPlugin client = await Client.connect target host=os.environ.get "TEMPORAL ADDRESS", "localhost:7233" , namespace=os.environ.get "TEMPORAL NAMESPACE", "default" , api key=api key or None, tls=tls, plugins= plugin worker = Worker client, task queue=os.environ.get "TEMPORAL TASK QUEUE", "openai-agents-task-queue" , workflows= HelloWorldAgent , workflow runner=UnsandboxedWorkflowRunner async with worker: handle = await client.start workflow HelloWorldAgent.run, id="hello-world-workflow-01", task queue=os.environ.get "TEMPORAL TASK QUEUE", "openai-agents-task-queue" , args= "Tell me about SigNoz" , result = await handle.result print "\nWorkflow result:\n", result asyncio.run main Before running this code, ensure that you have set the environment variable OPENAI API KEY with your generated API key. View Temporal Traces, Logs, and Metrics in SigNoz Once configured, your Temporal application automatically emits traces, logs, and metrics. Temporal traces are available in SigNoz 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. Temporal logs are available in SigNoz under the Logs tab. Click 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: Temporal metrics are available in SigNoz 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: If you're using Temporal Cloud, you can also monitor cloud-specific metrics. For detailed information on setting up Temporal Cloud metrics monitoring, see the Temporal Cloud Metrics integration guide https://signoz.io/docs/integrations/temporal-cloud-metrics/ . Temporal Cloud metrics are available in SigNoz under the Metrics tab: When you click on any of these Temporal Cloud metrics in SigNoz, you'll see a detailed view of the metric, including attributes: Troubleshooting Temporal Observability Troubleshooting Temporal Observability troubleshooting-temporal-observability 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 Temporal Observability Dashboard The Temporal dashboard template https://signoz.io/docs/dashboards/dashboard-templates/temporal-dashboard/ provides pre-built visualizations for monitoring Temporal workflow performance, agent execution patterns, and resource usage in SigNoz. Import it directly to get started without manual chart configuration. Additional resources: - Set up alerts https://signoz.io/docs/product-features/alert-management for high latency or error rates - Learn more about querying traces https://signoz.io/docs/product-features/trace-explorer - Explore log correlation https://signoz.io/docs/product-features/logs-explorer