LiveKit Observability & Monitoring with OpenTelemetry

LiveKit has released a new integration with OpenTelemetry and SigNoz that enables developers to monitor and observe voice agent applications in real time. The setup allows teams to track latency, error rates, and usage trends across AI models by exporting logs, traces, and metrics to SigNoz dashboards. This observability framework helps developers debug issues, optimize performance, and analyze user interactions in LiveKit-powered voice workflows.

Overview This guide walks you through setting up observability and monitoring for LiveKit using OpenTelemetry https://opentelemetry.io/ and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe the performance of various models, 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 LiveKit applications. Instrumenting LiveKit in your AI applications with telemetry ensures full observability across your voice 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 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 LiveKit installed - For Python: uv installed for managing Python packages LiveKit Cloud Account https://cloud.livekit.io/ - Installed LiveKit CLI https://docs.livekit.io/home/cli/ Monitoring LiveKit For more detailed info on instrumenting your LiveKit applications with OpenTelemetry click here https://docs.livekit.io/agents/observability/data/ . Get started with a sample LiveKit starter project by following the LiveKit Getting Started Docs https://docs.livekit.io/agents/start/voice-ai/ 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: Clone the sample voice agent project and setup dependencies git clone https://github.com/livekit-examples/agent-starter-python cd agent-starter-python uv sync Step 2: Setup Credentials Copy .env.example to .env.local and filling in the required keys: LIVEKIT URL LIVEKIT API KEY LIVEKIT API SECRET Load the LiveKit environment automatically using the LiveKit CLI: lk cloud auth lk app env -w -d .env.local Step 3: Add Automatic Instrumentation uv pip install opentelemetry-distro opentelemetry-exporter-otlp uv run opentelemetry-bootstrap -a requirements | uv pip install --requirement - Step 4: Instrument your LiveKit application Metrics: python from livekit.agents import metrics from livekit.agents.voice import MetricsCollectedEvent @session.on "metrics collected" def on metrics collected ev: MetricsCollectedEvent : metrics.log metrics ev.metrics Traces: python from livekit.agents.telemetry import set tracer provider from opentelemetry import trace set tracer provider trace.get tracer provider See this example repo https://github.com/livekit/agents/blob/main/examples/voice agents/langfuse trace.py for more details on how to configure instrumentation. Step 5: Your agent.py should look something like this: python import logging from dotenv import load dotenv from livekit import rtc from livekit.agents import Agent, AgentServer, AgentSession, JobContext, JobProcess, cli, inference, room io, metrics, from livekit.plugins import noise cancellation, silero from livekit.plugins.turn detector.multilingual import MultilingualModel from livekit.agents.telemetry import set tracer provider from opentelemetry import trace from livekit.agents.voice import MetricsCollectedEvent logger = logging.getLogger "agent" load dotenv ".env.local" class Assistant Agent : def init self - None: super . init instructions="""You are a helpful voice AI assistant. The user is interacting with you via voice, even if you perceive the conversation as text. You eagerly assist users with their questions by providing information from your extensive knowledge. Your responses are concise, to the point, and without any complex formatting or punctuation including emojis, asterisks, or other symbols. You are curious, friendly, and have a sense of humor.""", server = AgentServer def prewarm proc: JobProcess : proc.userdata "vad" = silero.VAD.load server.setup fnc = prewarm @server.rtc session async def my agent ctx: JobContext : set tracer provider trace.get tracer provider Logging setup Add any other context you want in all log entries here ctx.log context fields = { "room": ctx.room.name, } Set up a voice AI pipeline using OpenAI, Cartesia, AssemblyAI, and the LiveKit turn detector session = AgentSession Speech-to-text STT is your agent's ears, turning the user's speech into text that the LLM can understand See all available models at https://docs.livekit.io/agents/models/stt/ stt=inference.STT model="assemblyai/universal-streaming", language="en" , A Large Language Model LLM is your agent's brain, processing user input and generating a response See all available models at https://docs.livekit.io/agents/models/llm/ llm=inference.LLM model="openai/gpt-4.1-mini" , Text-to-speech TTS is your agent's voice, turning the LLM's text into speech that the user can hear See all available models as well as voice selections at https://docs.livekit.io/agents/models/tts/ tts=inference.TTS model="cartesia/sonic-3", voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc" , VAD and turn detection are used to determine when the user is speaking and when the agent should respond See more at https://docs.livekit.io/agents/build/turns turn detection=MultilingualModel , vad=ctx.proc.userdata "vad" , allow the LLM to generate a response while waiting for the end of turn See more at https://docs.livekit.io/agents/build/audio/ preemptive-generation preemptive generation=True, @session.on "metrics collected" def on metrics collected ev: MetricsCollectedEvent : metrics.log metrics ev.metrics Start the session, which initializes the voice pipeline and warms up the models await session.start agent=Assistant , room=ctx.room, room options=room io.RoomOptions audio input=room io.AudioInputOptions noise cancellation=lambda params: noise cancellation.BVCTelephony if params.participant.kind == rtc.ParticipantKind.PARTICIPANT KIND SIP else noise cancellation.BVC , , , Join the room and connect to the user await ctx.connect if name == " main ": cli.run app server Step 6: Run your application with auto-instrumentation Before your first run, you must download certain models such as Silero VAD and the LiveKit turn detector: uv run python src/agent.py download-files Next, run this command to speak to your agent directly in your terminal: 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 \ <your run command with opentelemetry-instrument 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: uv run opentelemetry-instrument python src/agent.py console 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: Clone the sample voice agent project and setup dependencies git clone https://github.com/livekit-examples/agent-starter-python cd agent-starter-python uv sync Step 2: Setup Credentials Copy .env.example to .env.local and filling in the required keys: LIVEKIT URL LIVEKIT API KEY LIVEKIT API SECRET Load the LiveKit environment automatically using the LiveKit CLI: lk cloud auth lk app env -w -d .env.local Step 3: Install additional OpenTelemetry dependencies uv pip install \ opentelemetry-api \ opentelemetry-sdk \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics Step 4: 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 5: 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 6: 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 7: 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 LiveKit application, see Python Custom Metrics . Step 8: Instrument your LiveKit application Metrics: python from livekit.agents import metrics from livekit.agents.voice import MetricsCollectedEvent @session.on "metrics collected" def on metrics collected ev: MetricsCollectedEvent : metrics.log metrics ev.metrics Traces: python from livekit.agents.telemetry import set tracer provider from opentelemetry import trace set tracer provider trace.get tracer provider See this example repo https://github.com/livekit/agents/blob/main/examples/voice agents/langfuse trace.py for more details on how to configure instrumentation. Step 9: Run your example agent.py Before your first run, you must download certain models such as Silero VAD and the LiveKit turn detector: uv run python src/agent.py download-files Next, run this command to speak to your agent directly in your terminal: uv run python src/agent.py console View Traces, Logs, and Metrics in SigNoz Your LiveKit voice 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: You should be able to see LiveKit 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 LiveKit dashboard here https://signoz.io/docs/dashboards/dashboard-templates/livekit-dashboard/ which provides specialized visualizations for monitoring your LiveKit 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/