LiveKit Agents integration

AI Gateway

Route your LLM calls through the AI Gateway with a single base URL change. Zero vendor lock-in: always run on the best model at the lowest cost for your use case.

Observability

Instrument your code with OpenTelemetry to capture traces, logs, and metrics for every LLM call, agent step, and tool use.

AI Gateway

Overview

LiveKit Agents is a framework for building real-time voice and multimodal AI agents that communicate over WebRTC. By connecting LiveKit Agents to Orq.ai’s AI Gateway, you get production-ready voice AI with enterprise-grade LLM access without vendor lock-in.

Key Benefits

Orq.ai’s AI Gateway enhances your LiveKit Agents with:

Complete Observability

Track every LLM call, tool use, and agent interaction with detailed traces

Built-in Reliability

Automatic fallbacks, retries, and load balancing for production resilience

Cost Optimization

Real-time cost tracking and spend management across all your AI operations

Multi-Provider Access

Access 300+ LLMs and 20+ providers through a single, unified integration

Prerequisites

Before integrating LiveKit Agents with Orq.ai, ensure you have:

An Orq.ai account and API Key
Python 3.9 or higher
A LiveKit account with URL, API key, and API secret

To setup your API key, see API keys & Endpoints.

Installation

Install LiveKit Agents with the OpenAI plugin:

pip install "livekit-agents[openai]~=1.0"

Configuration

Configure LiveKit Agents to use Orq.ai’s AI Gateway via the OpenAI plugin’s base_url parameter:

Python

from livekit.plugins import openai
import os

# Configure OpenAI-compatible LLM with Orq.ai AI Gateway
llm = openai.LLM(
    model="gpt-4o",
    base_url="https://api.orq.ai/v3/router",
    api_key=os.getenv("ORQ_API_KEY"),
)

base_url: https://api.orq.ai/v3/router

Environment Variables

Set up your LiveKit and Orq.ai credentials:

export LIVEKIT_URL="wss://your-project.livekit.cloud"
export LIVEKIT_API_KEY="your-livekit-api-key"
export LIVEKIT_API_SECRET="your-livekit-api-secret"
export ORQ_API_KEY="your-orq-api-key"

Basic Voice Agent

Here’s a complete example of a voice agent using Orq.ai’s AI Gateway:

Python

import os
from livekit.agents import Agent, AgentSession, WorkerOptions, cli
from livekit.plugins import openai

class Assistant(Agent):
    def __init__(self):
        super().__init__(
            instructions="You are a helpful voice assistant.",
        )

async def entrypoint(ctx):
    session = AgentSession(
        llm=openai.LLM(
            model="gpt-4o",
            base_url="https://api.orq.ai/v3/router",
            api_key=os.getenv("ORQ_API_KEY"),
        ),
    )
    await session.start(ctx.room, agent=Assistant())

if __name__ == "__main__":
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

Agent with Function Tools

Add tools to your voice agent for dynamic responses:

Python

import os
from livekit.agents import Agent, AgentSession, WorkerOptions, cli, function_tool
from livekit.plugins import openai

@function_tool
async def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is sunny and 72°F"

@function_tool
async def get_time(timezone: str) -> str:
    """Get the current time in a timezone."""
    return f"The current time in {timezone} is 14:30"

class Assistant(Agent):
    def __init__(self):
        super().__init__(
            instructions="You are a helpful voice assistant with access to weather and time tools.",
            tools=[get_weather, get_time],
        )

async def entrypoint(ctx):
    session = AgentSession(
        llm=openai.LLM(
            model="gpt-4o",
            base_url="https://api.orq.ai/v3/router",
            api_key=os.getenv("ORQ_API_KEY"),
        ),
    )
    await session.start(ctx.room, agent=Assistant())

if __name__ == "__main__":
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

Observability

Installation

pip install opentelemetry-api \
    opentelemetry-sdk \
    "opentelemetry-exporter-otlp-proto-http" \
    "livekit-agents[openai]~=1.0"

LiveKit Agents has built-in OTEL support via livekit.agents.telemetry. No additional instrumentation package is required.

Configuring Orq.ai Observability

Use set_tracer_provider from livekit.agents.telemetry to register the exporter. Call it before your agent entrypoint starts:

import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from livekit.agents.telemetry import set_tracer_provider

exporter = OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
)
provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(exporter))
set_tracer_provider(provider)

LiveKit uses livekit.agents.telemetry.set_tracer_provider, not the standard opentelemetry.trace.set_tracer_provider. BatchSpanProcessor is preferred over SimpleSpanProcessor for production voice workloads.

Basic Example

import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from livekit.agents.telemetry import set_tracer_provider
from livekit.agents import AutoSubscribe, JobContext, WorkerOptions, cli
from livekit.plugins import openai


def setup_tracing():
    exporter = OTLPSpanExporter(
        endpoint="https://api.orq.ai/v2/otel/v1/traces",
        headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
    )
    provider = TracerProvider()
    provider.add_span_processor(BatchSpanProcessor(exporter))
    set_tracer_provider(provider)


async def entrypoint(ctx: JobContext):
    await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)

    assistant = openai.realtime.RealtimeModel(voice="alloy")
    agent = openai.realtime.RealtimeAgent(model=assistant)
    agent.start(ctx.room)


if __name__ == "__main__":
    setup_tracing()
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

Evaluations & Experiments

Once your agents are running, use Evaluatorq to score outputs across a dataset and Experiments to compare configurations side-by-side.

Run Evaluations with Evaluatorq

Run parallel evaluations across your agents and compare results.

Run Experiments via the API

Compare agent configurations and view results in the AI Studio.

AI Providers

Coding Assistants

Frameworks

Automations

AI Gateway

Observability

AI Gateway

Overview

Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

Prerequisites

Installation

Configuration

Environment Variables

Basic Voice Agent

Agent with Function Tools

Observability

Installation

Configuring Orq.ai Observability

Basic Example

Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Observability

​AI Gateway

​Overview

​Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

​Prerequisites

​Installation

​Configuration

​Environment Variables

​Basic Voice Agent

​Agent with Function Tools

​Observability

​Installation

​Configuring Orq.ai Observability

​Basic Example

​Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Overview

Key Benefits

Prerequisites

Installation

Configuration

Environment Variables

Basic Voice Agent

Agent with Function Tools

Observability

Installation

Configuring Orq.ai Observability

Basic Example

Evaluations & Experiments