AutoGen Observability & Monitoring with OpenTelemetry

Microsoft and SigNoz have released a guide for setting up observability and monitoring for AutoGen AI applications using OpenTelemetry, enabling developers to export logs, traces, and metrics to SigNoz for real-time visibility into latency, error rates, and usage trends. The integration allows users to instrument AutoGen workflows with no-code auto-instrumentation or manual configuration, capturing request/response details and system-level metrics to debug issues, optimize performance, and improve reliability.

Overview This guide walks you through setting up observability and monitoring for AutoGen using OpenTelemetry https://opentelemetry.io/ and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe various models 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 AutoGen applications. Instrumenting AutoGen 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. Prerequisites - A SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key - Internet access to send telemetry data to SigNoz Cloud - Python 3.10+ with AutoGen installed pip install autogen-agentchat autogen-ext autogen-core - For Python: pip installed for managing Python packages and optional but recommended a Python virtual environment to isolate dependencies Monitoring AutoGen For more detailed info on tracing your AutoGen applications click here https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/framework/telemetry.html . For logging, click here https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/framework/logging.html . 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. Step 1: Install the necessary packages in your Python environment. pip install \ opentelemetry-api \ opentelemetry-distro \ opentelemetry-exporter-otlp \ httpx \ "autogen-agentchat" \ "autogen-ext openai " \ "autogen-core" Step 2: Add Automatic Instrumentation opentelemetry-bootstrap --action=install Step 3: Instrument your AutoGen application For Traces: Configure AutoGen to use OpenTelemetry tracing by passing the tracer provider to the runtime: python from opentelemetry import trace from autogen core import SingleThreadedAgentRuntime tracer provider = trace.get tracer provider single threaded runtime = SingleThreadedAgentRuntime tracer provider=tracer provider For Logs: Configure AutoGen logging to capture trace logs: python import logging from autogen core import TRACE LOGGER NAME logging.basicConfig level=logging.WARNING logger = logging.getLogger TRACE LOGGER NAME logger.addHandler logging.StreamHandler logger.setLevel logging.DEBUG 📌 Note: Ensure this is configured before initializing your AutoGen agents to properly instrument your application Step 4: Run an example python from autogen agentchat.agents import AssistantAgent from autogen agentchat.ui import Console from autogen ext.models.openai import OpenAIChatCompletionClient from opentelemetry import trace from autogen core import SingleThreadedAgentRuntime, TRACE LOGGER NAME import logging import asyncio logging.basicConfig level=logging.WARNING logger = logging.getLogger TRACE LOGGER NAME logger.addHandler logging.StreamHandler logger.setLevel logging.DEBUG Get the tracer provider from your application tracer provider = trace.get tracer provider for single threaded runtime single threaded runtime = SingleThreadedAgentRuntime tracer provider=tracer provider Define a model client. model client = OpenAIChatCompletionClient model="gpt-4o-mini" Define a simple function tool that the agent can use. For this example, we use a fake weather tool for demonstration purposes. async def get weather city: str - str: """Get the weather for a given city.""" return f"The weather in {city} is 73 degrees and Sunny." Define an AssistantAgent with the model, tool, system message, and reflection enabled. The system message instructs the agent via natural language. agent = AssistantAgent name="weather agent", model client=model client, tools= get weather , system message="You are a helpful assistant.", reflect on tool use=True, model client stream=True, Enable streaming tokens from the model client. Run the agent and stream the messages to the console. async def main - None: await Console agent.run stream task="What is the weather in New York?" Close the connection to the model client. await model client.close asyncio.run main 📌 Note: Before running this code, ensure that you have set the environment variable OPENAI API KEY with your generated API key. Step 5: Run your application with auto-instrumentation 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. For example: 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 /docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Code-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. Step 1: Install the necessary packages in your Python environment. pip install \ opentelemetry-api \ opentelemetry-sdk \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ "autogen-agentchat" \ "autogen-ext openai " \ "autogen-core" 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 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 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 /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 /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 /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 AutoGen application, see Python Custom Metrics . Step 6: Instrument your AutoGen application For Traces: Configure AutoGen to use OpenTelemetry tracing by passing the tracer provider to the runtime: python from opentelemetry import trace from autogen core import SingleThreadedAgentRuntime tracer provider = trace.get tracer provider single threaded runtime = SingleThreadedAgentRuntime tracer provider=tracer provider For Logs: Configure AutoGen logging to capture trace logs: python import logging from autogen core import TRACE LOGGER NAME logging.basicConfig level=logging.WARNING logger = logging.getLogger TRACE LOGGER NAME logger.addHandler logging.StreamHandler logger.setLevel logging.DEBUG 📌 Note: Ensure this is configured before initializing your AutoGen agents to properly instrument your application Step 7: Run an example python from autogen agentchat.agents import AssistantAgent from autogen agentchat.ui import Console from autogen ext.models.openai import OpenAIChatCompletionClient import asyncio Define a model client. model client = OpenAIChatCompletionClient model="gpt-4o-mini" Define a simple function tool that the agent can use. For this example, we use a fake weather tool for demonstration purposes. async def get weather city: str - str: """Get the weather for a given city.""" return f"The weather in {city} is 73 degrees and Sunny." Define an AssistantAgent with the model, tool, system message, and reflection enabled. The system message instructs the agent via natural language. agent = AssistantAgent name="weather agent", model client=model client, tools= get weather , system message="You are a helpful assistant.", reflect on tool use=True, model client stream=True, Enable streaming tokens from the model client. Run the agent and stream the messages to the console. async def main - None: await Console agent.run stream task="What is the weather in New York?" Close the connection to the model client. await model client.close asyncio.run main 📌 Note: 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 AutoGen commands 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: You should be able to see AutoGen 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: 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 AutoGen dashboard here https://signoz.io/docs/dashboards/dashboard-templates/autogen-dashboard/ which provides specialized visualizations for monitoring your AutoGen 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 https://signoz.io/docs/alerts/ for high latency or error rates - Learn more about querying traces https://signoz.io/docs/userguide/traces/ - Explore log correlation https://signoz.io/docs/userguide/logs query builder/