LiveKit Agents

LiveKit Agents includes built-in OpenTelemetry tracing for real-time voice and multimodal agents. Configure the LiveKit tracer provider with an OTLP exporter to send agent session, turn, speaking, and model spans to Arize AX.

This page uses LiveKit’s native OpenTelemetry pipeline. It does not require a LiveKit-specific OpenInference instrumentor.

Prerequisites

Python 3.9+
A LiveKit Agents application
An Arize AX account (sign up)
Your Arize AX Space ID and API Key

Launch Arize AX

Sign in to your Arize AX account.
From Space Settings, copy your Space ID and API Key.

Install

pip install livekit-agents opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

Configure credentials

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="livekit-agents-tracing-example"

Setup tracing

Create a shared tracer provider, attach an Arize OTLP exporter, and pass the provider to LiveKit before the session starts. LiveKit also lets you attach metadata to every span; use that for stable session or room attributes.

# instrumentation.py
import os

from livekit.agents.telemetry import set_tracer_provider
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.util.types import AttributeValue

project_name = os.environ.get(
    "ARIZE_PROJECT_NAME",
    "livekit-agents-tracing-example",
)

tracer_provider = TracerProvider(
    resource=Resource.create(
        {
            "service.name": "livekit-agent",
            "openinference.project.name": project_name,
        }
    )
)

tracer_provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://otlp.arize.com/v1/traces",
            headers={
                "space_id": os.environ["ARIZE_SPACE_ID"],
                "api_key": os.environ["ARIZE_API_KEY"],
            },
        )
    )
)

def configure_livekit_tracing(
    metadata: dict[str, AttributeValue] | None = None,
) -> TracerProvider:
    set_tracer_provider(tracer_provider, metadata=metadata)
    return tracer_provider

For EU spaces, change the exporter endpoint to:

https://otlp.eu-west-1a.arize.com/v1/traces

Use the tracer provider in your agent

Configure tracing before the agent session starts, then flush spans during shutdown:

# agent.py
from instrumentation import configure_livekit_tracing

from livekit.agents import Agent, AgentSession, JobContext


class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant.")


async def entrypoint(ctx: JobContext):
    tracer_provider = configure_livekit_tracing(
        metadata={
            "session.id": ctx.room.name,
            "livekit.room.name": ctx.room.name,
        }
    )

    async def flush_trace():
        tracer_provider.force_flush()

    ctx.add_shutdown_callback(flush_trace)

    session = AgentSession()
    await session.start(agent=Assistant(), room=ctx.room)

Verify in Arize

Run a LiveKit agent session.
Open your Arize AX space and select project livekit-agents-tracing-example.
You should see spans for the agent session and turn activity within ~30 seconds.

Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

Arize skill (agent)
AX CLI
SDK

Install the Arize Skills plugin and let your coding agent check for you:

npx skills add Arize-ai/arize-skills

Then prompt your agent:

Use the arize-trace skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.

Export recent spans for your project — any rows mean traces are landing:

ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
  --limit 5 --stdout | jq 'length'

A non-zero count confirms spans reached Arize AX. Run ax auth login first if you have not authenticated. See the ax spans reference.

Query the project’s spans and check that at least one came back.

import os
from arize import ArizeClient

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
resp = client.spans.list(
    project=os.environ["ARIZE_PROJECT_NAME"],
    space=os.environ["ARIZE_SPACE_ID"],
    limit=5,
)
count = len(resp.spans)
print(
    f"{count} span(s) found" if count else "No spans yet — recheck setup"
)

// Reads ARIZE_API_KEY from the environment.
import { listSpans } from "@arizeai/ax-client";

const { data: spans } = await listSpans({
  project: process.env.ARIZE_PROJECT_NAME!,
  space: process.env.ARIZE_SPACE_ID!,
  limit: 5,
});
const count = spans.length;
console.log(
  count ? `${count} span(s) found` : "No spans yet — recheck setup",
);

client, err := arize.NewClient(
    arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
)
if err != nil {
    log.Fatal(err)
}
resp, err := client.Spans.List(ctx, spans.ListRequest{
    Project: os.Getenv("ARIZE_PROJECT_NAME"),
    Space:   os.Getenv("ARIZE_SPACE_ID"),
    Limit:   5,
})
if err != nil {
    log.Fatal(err)
}
fmt.Printf("%d span(s) found\n", len(resp.Spans))

SDK span references: Python · TypeScript · Go.

What Arize captures

LiveKit emits OpenTelemetry spans from its own agent runtime. Depending on your LiveKit version and plugins, those spans can include:

agent session and turn spans
user and agent speaking spans
lifecycle spans for agent activity
model or realtime-model spans and metrics
custom metadata you attach through LiveKit telemetry setup

Troubleshooting

No spans in Arize. Confirm tracing is set before AgentSession.start(...) and that force_flush() runs before the worker exits.
Spans are generic. LiveKit spans are native OpenTelemetry spans. Use OpenInference instrumentors for the LLM SDKs inside your agent if you also need rich LLM prompt, response, token, and tool metadata.
Project name is missing. Make sure the tracer provider resource includes openinference.project.name.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Use the tracer provider in your agent

Verify in Arize

Check from the skill, CLI, or SDK

What Arize captures

Troubleshooting

Resources

LiveKit export traces

Arize AX OTLP exporter

OpenInference resource attributes

​Prerequisites

​Launch Arize AX

​Install

​Configure credentials

​Setup tracing

​Use the tracer provider in your agent

​Verify in Arize

​Check from the skill, CLI, or SDK

​What Arize captures

​Troubleshooting

​Resources

LiveKit export traces

Arize AX OTLP exporter

OpenInference resource attributes

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Use the tracer provider in your agent

Verify in Arize

Check from the skill, CLI, or SDK

What Arize captures

Troubleshooting

Resources