# Temporal Observability & Monitoring with OpenTelemetry > Source: > Published: 2026-05-27 00:00:00+00:00 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= TEMPORAL_ADDRESS= ``` is the location where your local Temporal server is hosted(default:`` `localhost:7233` ) ``` OPENAI_API_KEY= TEMPORAL_ADDRESS= TEMPORAL_NAMESPACE= 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=" \ OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest..signoz.cloud:443" \ OTEL_EXPORTER_OTLP_HEADERS="signoz-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 ``` is the name of your service`` `` : Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`` : Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace `` 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": ""}) 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`` → SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT` [region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest..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": ""}) 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`` → SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT` [region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest..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": ""}) 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`` → SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT` [region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest..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= TEMPORAL_ADDRESS= ``` is the location where your local Temporal server is hosted(default:`` `localhost:7233` ) ``` OPENAI_API_KEY= TEMPORAL_ADDRESS= TEMPORAL_NAMESPACE= 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)