> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orq.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LiveKit Agents integration

> Connect LiveKit Agents to Orq.ai's AI Router for complete observability, built-in reliability, and access to 300+ LLMs across 20+ providers.

<CardGroup cols={2}>
  <Card title="AI Router" icon="arrow-right-arrow-left" href="#ai-router">
    Route your LLM calls through the AI Router with a single base URL change. Zero vendor lock-in: always run on the best model at the lowest cost for your use case.
  </Card>

  <Card title="Observability" icon="chart-line" href="#observability">
    Instrument your code with OpenTelemetry to capture traces, logs, and metrics for every LLM call, agent step, and tool use.
  </Card>
</CardGroup>

## AI Router

### 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 Router, you get production-ready voice AI with enterprise-grade LLM access without vendor lock-in.

### Key Benefits

Orq.ai's AI Router enhances your LiveKit Agents with:

<CardGroup cols={2}>
  <Card title="Complete Observability" icon="chart-line">
    Track every LLM call, tool use, and agent interaction with detailed traces
  </Card>

  <Card title="Built-in Reliability" icon="shield-check">
    Automatic fallbacks, retries, and load balancing for production resilience
  </Card>

  <Card title="Cost Optimization" icon="chart-pie">
    Real-time cost tracking and spend management across all your AI operations
  </Card>

  <Card title="Multi-Provider Access" icon="cubes">
    Access 300+ LLMs and 20+ providers through a single, unified integration
  </Card>
</CardGroup>

### Prerequisites

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

* An Orq.ai account and [API Key](/docs/administer/api-keys)
* Python 3.9 or higher
* A LiveKit account with URL, API key, and API secret

<Info>
  To setup your API key, see [API keys & Endpoints](/docs/administer/api-keys).
</Info>

### Installation

Install LiveKit Agents with the OpenAI plugin:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install "livekit-agents[openai]~=1.0"
```

### Configuration

Configure LiveKit Agents to use Orq.ai's AI Router via the OpenAI plugin's `base_url` parameter:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from livekit.plugins import openai
import os

# Configure OpenAI-compatible LLM with Orq.ai AI Router
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:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
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 Router:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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 Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install opentelemetry-api \
    opentelemetry-sdk \
    "opentelemetry-exporter-otlp-proto-http" \
    "livekit-agents[openai]~=1.0"
```

<Info>
  LiveKit Agents has built-in OTEL support via `livekit.agents.telemetry`. No additional instrumentation package is required.
</Info>

### Configuring Orq.ai Observability

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

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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)
```

<Info>
  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.
</Info>

### Basic Example

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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.

<CardGroup cols={2}>
  <Card title="Run Evaluations with Evaluatorq" icon="flask" href="/docs/evaluators/build#evaluatorq">
    Run parallel evaluations across your agents and compare results.
  </Card>

  <Card title="Run Experiments via the API" icon="flask-vial" href="/docs/experiments/api">
    Compare agent configurations and view results in the AI Studio.
  </Card>
</CardGroup>