Realtime API support is coming soon! Join our Discord community to be the first to know when LiveKit’s realtime model integration with Portkey is available.

LiveKit is a powerful platform for building real-time voice and video applications. When combined with Portkey, you get enterprise-grade features that make your LiveKit voice agents production-ready:

Unified AI Gateway - Single interface for 250+ LLMs with API key management
Centralized AI observability: Real-time usage tracking for 40+ key metrics and logs for every request
Governance - Real-time spend tracking, set budget limits and RBAC in your LiveKit agents
Security Guardrails - PII detection, content filtering, and compliance controls

This guide will walk you through integrating Portkey with LiveKit’s STT-LLM-TTS pipeline to build enterprise-ready voice AI agents.

If you are an enterprise looking to deploy LiveKit agents in production, check out this section.

1. Setting up Portkey

Portkey allows you to use 250+ LLMs with your LiveKit agents, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.

Connect your LLM`

To create a new integration:

Go to Integrations in the Portkey App
Click “Add Integration” and select OpenAI
Provision workspaces and budget/rate limits if required

When you create a new integration, Portkey creates a unique provider slug for the integration that you can then use in your requests.

Create Default Config

Configs in Portkey define how your requests are routed and can enable features like fallbacks, caching, and more.To create your config:

Go to Configs in Portkey dashboard

Create new config with:

{
    "provider":"@YOUR_PROVIDER_FROM_STEP1"
}

Save and note the Config ID for the next step

This basic config connects to your integration. You can add advanced features like caching, fallbacks, and guardrails later.

Configure Portkey API Key

Now create a Portkey API key and attach the config you created:

Go to API Keys in Portkey
Create new API key
Select your config from Step 2
Generate and save your API key

Save your API key securely - you’ll need it for LiveKit integration.

2. Integrate Portkey with LiveKit

Now that you have your Portkey components set up, let’s integrate them with LiveKit agents.

Installation

Install the required packages:

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

Configuration

llm=openai.LLM(model="gpt-4o", # your preferred model
               api_key="YOUR_PORTKEY_API_KEY", # you can also set OPENAI_API_KEY=<Your Portkey API key> in .env
               base_url="https://api.portkey.ai/v1", # Portkey Base Url
               ),

Make sure your Portkey integration has sufficient budget and rate limits for your expected usage.

End-to-End Example using Portkey and LiveKit

Build a simple voice assistant with Python in less than 10 minutes.

Setup

  pip install \
    "livekit-agents[deepgram,openai,cartesia,silero,turn-detector]~=1.0" \
    "livekit-plugins-noise-cancellation~=0.2" \
    "python-dotenv"

Add your .env file

DEEPGRAM_API_KEY=<Your Deepgram API Key>
OPENAI_API_KEY=<Your PORTKEY API Key>
CARTESIA_API_KEY=<Your Cartesia API Key>
LIVEKIT_API_KEY=<your API Key>
LIVEKIT_API_SECRET=<your API Secret>
LIVEKIT_URL=<your LiveKit server URL>

Full STT-to-TTS agent code

from dotenv import load_dotenv

from livekit import agents
from livekit.agents import AgentSession, Agent, RoomInputOptions
from livekit.plugins import (
    openai,
    cartesia,
    deepgram,
    noise_cancellation,
    silero,
)
from livekit.plugins.turn_detector.multilingual import MultilingualModel

load_dotenv()


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


async def entrypoint(ctx: agents.JobContext):
    session = AgentSession(
        stt=deepgram.STT(model="nova-3", language="multi"),
        llm=openai.LLM(model="gpt-4o",
                    api_key="YOUR_PORTKEY_API_KEY",
                    base_url="https://api.portkey.ai/v1",
                    ),
        tts=cartesia.TTS(),
        vad=silero.VAD.load(),
        turn_detection=MultilingualModel(),
    )

    await session.start(
        room=ctx.room,
        agent=Assistant(),
        room_input_options=RoomInputOptions(
            # LiveKit Cloud enhanced noise cancellation
            # - If self-hosting, omit this parameter
            # - For telephony applications, use `BVCTelephony` for best results
            noise_cancellation=noise_cancellation.BVC(),
        ),
    )

    await ctx.connect()

    await session.generate_reply(
        instructions="Greet the user and offer your assistance."
    )


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

3. Set Up Enterprise Governance for Livekit

Why Enterprise Governance? If you are using Livekit inside your orgnaization, you need to consider several governance aspects:

Cost Management: Controlling and tracking AI spending across teams
Access Control: Managing which teams can use specific models
Usage Analytics: Understanding how AI is being used across the organization
Security & Compliance: Maintaining enterprise security standards
Reliability: Ensuring consistent service across all users

Portkey adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step. Enterprise Implementation Guide

Step 1: Implement Budget Controls & Rate Limits

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. Portkey Configs provide this control layer with features like:

Access Control Features:

Model Restrictions: Limit access to specific models
Data Protection: Implement guardrails for sensitive data
Reliability Controls: Add fallbacks and retry logic

Example Configuration:

Here’s a basic configuration to route requests to OpenAI, specifically using GPT-4o:

{
	"strategy": {
		"mode": "single"
	},
	"targets": [
		{
			"provider":"@YOUR_OPENAI_PROVIDER",
			"override_params": {
				"model": "gpt-4o"
			}
		}
	]
}

Create your config on the Configs page in your Portkey dashboard. You’ll need the config ID for connecting to Livekit’s setup.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

Track usage per user/team with the help of metadata
Apply appropriate configs to route requests
Collect relevant metadata to filter logs
Enforce access permissions

Create API keys through:

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="engineering-team",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {
            "environment": "production",
            "department": "engineering"
        }
    },
    scopes=["logs.view", "configs.read"]
)

For detailed key management instructions, see our API Keys documentation.

Step 4: Deploy & Monitor

Enterprise Features Now Available

Livekit now has:

Departmental budget controls
Model access governance
Usage tracking & attribution
Security guardrails
Reliability features

Portkey Features

Now that you have enterprise-grade Livekit setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.

1. Comprehensive Metrics

Using Portkey you can track 40+ key metrics including cost, token usage, response time, and performance across all your LLM providers in real time. You can also filter these metrics based on custom metadata that you can set in your configs. Learn more about metadata here.

2. Advanced Logs

Portkey’s logging dashboard provides detailed logs for every request made to your LLMs. These logs include:

Complete request and response tracking
Metadata tags for filtering
Cost attribution and much more…

3. Unified Access to 1600+ LLMs

You can easily switch between 1600+ LLMs. Call various LLMs such as Anthropic, Gemini, Mistral, Azure OpenAI, Google Vertex AI, AWS Bedrock, and many more by simply changing the provider in your default config object.

4. Advanced Metadata Tracking

Using Portkey, you can add custom metadata to your LLM requests for detailed tracking and analytics. Use metadata tags to filter logs, track usage, and attribute costs across departments and teams.

Custom Metata

5. Enterprise Access Management

Budget Controls

Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.

Single Sign-On (SSO)

Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.

Organization Management

Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.

Access Rules & Audit Logs

Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.

6. Reliability Features

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Load Balancing

Distribute requests across multiple targets based on defined weights.

Caching

Enable caching of responses to improve performance and reduce costs.

Smart Retries

Automatic retry handling with exponential backoff for failed requests

Budget Limits

Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.

7. Advanced Guardrails

Protect your Project’s data and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:

Prevent sensitive data leaks
Enforce compliance with organizational policies
PII detection and masking
Content filtering
Custom security rules
Data compliance checks

Guardrails

Implement real-time protection for your LLM interactions with automatic detection and filtering of sensitive content, PII, and custom security rules. Enable comprehensive data protection while maintaining compliance with organizational policies.

FAQs

Can I use models other than OpenAI with LiveKit through Portkey?

How do I track costs for different voice agents?

What happens if my primary LLM provider goes down?

Can I implement custom business logic for my agents?

How do I migrate existing LiveKit agents to use Portkey?

Next Steps

Ready to build production voice AI?

Join our Discord for support and updates
Explore more integrations
Read about advanced configs
Learn about guardrails

For enterprise support and custom features for your LiveKit deployment, contact our enterprise team.

Ecosystem

LLM Integrations

Cloud Platforms

Guardrails

Plugins

Vector Databases

Agents

AI Apps

Libraries

Tracing Providers

​1. Setting up Portkey

​2. Integrate Portkey with LiveKit

​Installation

​Configuration

​End-to-End Example using Portkey and LiveKit

​3. Set Up Enterprise Governance for Livekit

​Enterprise Features Now Available

​Portkey Features

​1. Comprehensive Metrics

​2. Advanced Logs

​3. Unified Access to 1600+ LLMs

​4. Advanced Metadata Tracking

Custom Metata

​5. Enterprise Access Management

Budget Controls

Single Sign-On (SSO)

Organization Management

Access Rules & Audit Logs

​6. Reliability Features

Fallbacks

Conditional Routing

Load Balancing

Caching

Smart Retries

Budget Limits

​7. Advanced Guardrails

Guardrails

​FAQs

​Next Steps

1. Setting up Portkey

2. Integrate Portkey with LiveKit

Installation

Configuration

End-to-End Example using Portkey and LiveKit

3. Set Up Enterprise Governance for Livekit

Enterprise Features Now Available

Portkey Features

1. Comprehensive Metrics

2. Advanced Logs

3. Unified Access to 1600+ LLMs

4. Advanced Metadata Tracking

5. Enterprise Access Management

6. Reliability Features

7. Advanced Guardrails

FAQs

Next Steps