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.

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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.

1

Connect your LLM`

To create a new integration:

1. Go to Integrations in the Portkey App
2. Click “Add Integration” and select OpenAI
3. 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.

2

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:

1. Go to Configs in Portkey dashboard
2. Create new config with:
   Copy

   Ask AI

   {
       "provider":"@YOUR_PROVIDER_FROM_STEP1"
   }
   

3. 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.

3

Configure Portkey API Key

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

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

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

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2. Integrate Portkey with LiveKit

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

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Installation

Install the required packages:

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

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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
               ),

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End-to-End Example using Portkey and LiveKit

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

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

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>

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))

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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

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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.

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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.

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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…

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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.

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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.

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5. Enterprise Access Management

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6. Reliability Features

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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

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FAQs

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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