LiveKit Observability with SigNoz

Overview

This guide walks you through setting up observability and monitoring for LiveKit using OpenTelemetry 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 with an active ingestion key or Self Hosted SigNoz instance
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
Installed LiveKit CLI

Monitoring LiveKit

For more detailed info on instrumenting your LiveKit applications with OpenTelemetry click here.

Get started with a sample LiveKit starter project by following the LiveKit Getting Started Docs

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:

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:

from livekit.agents.telemetry import set_tracer_provider
from opentelemetry import trace

set_tracer_provider(trace.get_tracer_provider())

See this example repo for more details on how to configure instrumentation.

Step 5: Your `agent.py` should look something like this:

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>

<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: uv run opentelemetry-instrument python src/agent.py console

✅ 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 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:

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 5: 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


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)

<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 6: 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 7: 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 LiveKit application, see Python Custom Metrics.

Step 8: Instrument your LiveKit application

Metrics:

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:

from livekit.agents.telemetry import set_tracer_provider
from opentelemetry import trace

set_tracer_provider(trace.get_tracer_provider())

See this example repo 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:

LiveKit Detailed Log View — *LiveKit Detailed Logs View*

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

LiveKit Observability with SigNoz

Overview

Prerequisites

Monitoring LiveKit

Step 1: Clone the sample voice agent project and setup dependencies

Step 2: Setup Credentials

Step 3: Add Automatic Instrumentation

Step 4: Instrument your LiveKit application

Step 5: Your agent.py should look something like this:

Step 6: Run your application with auto-instrumentation

Step 1: Clone the sample voice agent project and setup dependencies

Step 2: Setup Credentials

Step 3: Install additional OpenTelemetry dependencies

Step 4: Import the necessary modules in your Python application

Step 5: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud

Step 6: Setup Logs

Step 7: Setup Metrics

Step 8: Instrument your LiveKit application

Step 9: Run your example agent.py

View Traces, Logs, and Metrics in SigNoz

Troubleshooting

Next Steps

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Step 5: Your `agent.py` should look something like this:

Step 9: Run your example `agent.py`