Temporal Monitoring with OpenTelemetry and SigNoz

Overview

This guide demonstrates how to set up observability and monitoring for OpenAI Agent SDK applications running on Temporal using OpenTelemetry to export traces, logs, and metrics to SigNoz. This integration allows for monitoring of your Temporal workflows and agent execution patterns.

Instrumenting your Temporal-based AI applications with OpenTelemetry 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.

Prerequisites

A SigNoz Cloud account with an active ingestion key or Self Hosted SigNoz instance
Internet access to send telemetry data to SigNoz Cloud
Python 3.10+ with temporalio installed
For Python: pip installed for managing Python packages

Monitoring Temporal

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

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:

.env

OPENAI_API_KEY=<your-openai-api-key>
TEMPORAL_ADDRESS=<local-temporal-server>

<local-temporal-server> is the location where your local Temporal server is hosted(default: localhost:7233)

Step 4: Create an example Temporal agent workflow

main.py

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>

<service_name> is the name of your service
Set the <region> to match your SigNoz Cloud region
Replace <your_ingestion_key> with your SigNoz ingestion key
Replace <your_run_command> with the actual command you would use to run your application. In this case we would use: python main.py

✅ Info

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.

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:

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:

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:

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

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()

<service_name> is the name of your service
OTEL_EXPORTER_TRACES_ENDPOINT → SigNoz Cloud trace endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/traces
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

✅ Info

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.

Step 4: Setup Logs

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__)

<service_name> is the name of your service
OTEL_EXPORTER_LOGS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/logs
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

✅ Info

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.

Step 5: Setup Metrics

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()

<service_name> is the name of your service
OTEL_EXPORTER_METRICS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/metrics
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

✅ Info

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.

📌 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:

.env

OPENAI_API_KEY=<your-openai-api-key>
TEMPORAL_ADDRESS=<local-temporal-server>

<local-temporal-server> is the location where your local Temporal server is hosted(default: localhost:7233)

Step 7: Run an example Temporal agent workflow

⚠️ Warning

Ensure you have completed the steps above (traces, logs, and metrics configuration) before running this code. All OpenTelemetry instrumentation must be initialized first.

main.py

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())

⚠️ Warning

Before running this code, ensure that you have set the environment variable OPENAI_API_KEY with your generated API key.

View Traces, Logs, and Metrics in SigNoz

Your Temporal agent usage should now automatically emit traces, logs, and metrics.

You should be able to view traces in Signoz Cloud 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.

You 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:

When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:

Temporal Detailed Log View — *Temporal Detailed Logs View*

You should be able to see Temporal related metrics in Signoz Cloud 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.

You should be able to see Temporal Cloud metrics in SigNoz Cloud 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

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

Next Steps

You can also check out our custom Temporal dashboard which provides specialized visualizations for monitoring your Temporal usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.

Additional resources:

Set up alerts for high latency or error rates
Learn more about querying traces
Explore log correlation

Temporal Monitoring with OpenTelemetry and SigNoz

Overview

Prerequisites

Monitoring Temporal

Step 1: Install the necessary packages in your Python environment.

Step 2: Add Automatic Instrumentation

Step 3: Set up environment variables

Step 4: Create an example Temporal agent workflow

Step 5: Run your application with auto-instrumentation

Step 1: Install additional OpenTelemetry dependencies

Step 2: Import the necessary modules in your Python application

Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud

Step 4: Setup Logs

Step 5: Setup Metrics

Step 6: Set up environment variables

Step 7: Run an example Temporal agent workflow

View Traces, Logs, and Metrics in SigNoz

Troubleshooting

Next Steps

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