LiteLLM Observability & Monitoring with OpenTelemetry

SigNoz has released a guide for integrating LiteLLM observability with OpenTelemetry, enabling developers to capture traces, logs, and metrics from LiteLLM SDK and Proxy Server deployments. The integration provides real-time visibility into model performance, request/response details, latency, error rates, and usage trends through unified dashboards and alerts.

What is LiteLLM Observability? LiteLLM observability with OpenTelemetry https://opentelemetry.io/ gives you full visibility into your LiteLLM SDK and Proxy Server by capturing traces, logs, and metrics. This guide covers how to instrument LiteLLM and export telemetry data to SigNoz, enabling real-time visibility into model performance, request/response details, latency, error rates, and usage trends. With full LiteLLM observability in SigNoz, you can correlate traces, logs, and metrics in unified dashboards and configure alerts for LiteLLM monitoring. This makes it easier to debug issues, optimize performance, and improve reliability across your AI workflows. Prerequisites - A SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key - Internet access to send telemetry data to SigNoz Cloud LiteLLM https://www.litellm.ai/ SDK or Proxy integration- For Python: pip installed for managing Python packages and optional but recommended a Python virtual environment to isolate dependencies Instrumenting LiteLLM with OpenTelemetry LiteLLM can be monitored in two ways: using the LiteLLM SDK directly embedded in your Python application code for programmatic LLM calls or the LiteLLM Proxy Server a standalone server that acts as a centralized gateway for managing and routing LLM requests across your infrastructure . For more detailed info on instrumenting your LiteLLM SDK applications, see the LiteLLM OpenTelemetry integration docs https://docs.litellm.ai/docs/observability/opentelemetry integration . 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 \ opentelemetry-instrumentation-httpx \ litellm Step 2: Add Automatic Instrumentation opentelemetry-bootstrap --action=install Step 3: Instrument your LiteLLM SDK application Initialize LiteLLM SDK instrumentation by calling litellm.callbacks = "otel" : python from litellm import litellm litellm.callbacks = "otel" This call enables automatic tracing, logs, and metrics collection for all LiteLLM SDK calls in your application. 📌 Note: Ensure this is called before any LiteLLM related calls to properly configure instrumentation of your application Step 4: Run an example python from litellm import completion, litellm litellm.callbacks = "otel" response = completion model="openai/gpt-4o", messages= { "content": "What is SigNoz","role": "user"} print response 📌 Note: LiteLLM supports a 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 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 \ OTEL PYTHON DISABLED INSTRUMENTATIONS=openai \ opentelemetry-instrument <your run command 📌 Note: We're using OTEL PYTHON DISABLED INSTRUMENTATIONS=openai in the run command to disable the OpenAI instrumentor for tracing. This avoids conflicts with LiteLLM's native telemetry/instrumentation, ensuring that telemetry is captured exclusively through LiteLLM's built-in instrumentation. 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 https://signoz.io/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 \ litellm 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 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 LiteLLM application, see Python Custom Metrics . Step 6: Instrument your LiteLLM application Initialize LiteLLM SDK instrumentation by calling litellm.callbacks = "otel" : python from litellm import litellm litellm.callbacks = "otel" This call enables automatic tracing, logs, and metrics collection for all LiteLLM SDK calls in your application. 📌 Note: Ensure this is called before any LiteLLM related calls to properly configure instrumentation of your application Step 7: Run an example python from litellm import completion, litellm litellm.callbacks = "otel" response = completion model="openai/gpt-4o", messages= { "content": "What is SigNoz","role": "user"} print response 📌 Note: LiteLLM supports a 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 with your generated API key. View LiteLLM Traces, Logs, and Metrics in SigNoz Once configured, your LiteLLM application automatically emits traces, logs, and metrics. LiteLLM 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. LiteLLM logs are available in SigNoz under the logs tab. You can also view correlated logs by clicking the “Related Logs” button in the trace view: When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes: LiteLLM 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: LiteLLM SDK Observability Dashboard The LiteLLM SDK Dashboard https://signoz.io/docs/dashboards/dashboard-templates/litellm-sdk-dashboard/ provides specialized visualizations for LiteLLM monitoring, including pre-built charts for LLM usage and import instructions to get started quickly. Step 1: Install the necessary packages in your Python environment. pip install opentelemetry-api \ opentelemetry-sdk \ opentelemetry-exporter-otlp \ 'litellm proxy ' Step 2: Configure otel for the LiteLLM Proxy Server Add the following to config.yaml : litellm settings: callbacks: 'otel' Step 3: Set the following environment variables: export OTEL EXPORTER OTLP ENDPOINT="https://ingest.<region .signoz.cloud:443" export OTEL EXPORTER OTLP HEADERS="signoz-ingestion-key=<your-ingestion-key " export OTEL EXPORTER OTLP PROTOCOL="grpc" export OTEL TRACES EXPORTER="otlp" export OTEL METRICS EXPORTER="otlp" export OTEL LOGS EXPORTER="otlp" <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/ 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: Run the proxy server using the config file: litellm --config config.yaml Now any calls made through your LiteLLM proxy server will be traced and sent to SigNoz. LiteLLM Proxy 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. LiteLLM Proxy Dashboard The LiteLLM Proxy Dashboard https://signoz.io/docs/dashboards/dashboard-templates/litellm-proxy-dashboard/ provides specialized visualizations for LiteLLM Proxy monitoring, including pre-built charts for LLM usage and import instructions to get started quickly.