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Build a voice agent using OpenAI’s GPT-4o Realtime API for native speech-to-speech processing. OpenAI Realtime processes audio directly without intermediate text conversion, delivering the lowest latency voice conversations. Best for: Applications requiring minimal latency and native multimodal AI capabilities.

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How Speech-to-Speech Differs

Standard Pipeline (STT → LLM → TTS): 
Audio → Deepgram → Text → OpenAI → Text → Cartesia → Audio
Speech-to-Speech (Direct): 
Audio → OpenAI Realtime → Audio
Speech-to-speech models process audio natively, preserving tone, emotion, and context that may be lost in text transcription.

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Prerequisites

ServiceWhat You Need
PlivoAuth ID, Auth Token, Voice-enabled phone number
OpenAIAPI key from platform.openai.com with Realtime API access

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Installation

pip install "pipecat-ai[openai]"

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Environment Variables

# Plivo credentials
PLIVO_AUTH_ID=your_auth_id
PLIVO_AUTH_TOKEN=your_auth_token
PLIVO_PHONE_NUMBER=+1234567890

# OpenAI credentials
OPENAI_API_KEY=sk-your_openai_key

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Pipeline Configuration

from pipecat.services.openai import OpenAIRealtimeLLMService

# Speech-to-Speech service
llm = OpenAIRealtimeLLMService(
    api_key=os.getenv("OPENAI_API_KEY"),
    # voice="alloy",  # Choose voice: alloy, echo, fable, onyx, nova, shimmer
)

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OpenAI Realtime Features

FeatureDescription
Minimal latencyDirect audio processing for fastest response times
Voice activity detectionMultiple VAD options including semantic-based
Function callingSeamless integration with external APIs
Multiple voicesChoose from built-in voice personalities
Context managementAdvanced conversation flow handling

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Available Voices

VoiceDescription
alloyNeutral, balanced
echoWarm, friendly
fableExpressive, storytelling
onyxDeep, authoritative
novaBright, energetic
shimmerClear, professional

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Architecture

With OpenAI Realtime, the pipeline is simplified: 
Phone Call ↔ Plivo ↔ WebSocket ↔ Pipecat ↔ OpenAI Realtime
A single service handles:
  • Speech recognition
  • Language understanding
  • Response generation
  • Voice synthesis

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Quick Start

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Inbound Calls

git clone https://github.com/pipecat-ai/pipecat-examples.git
cd pipecat-examples/plivo-chatbot/inbound

# Configure environment
cp env.example .env
# Edit .env with Plivo and OpenAI credentials

# Modify bot.py to use OpenAIRealtimeLLMService
# Start server
uv sync && uv run server.py

# Expose with ngrok (development)
ngrok http 7860
Configure your Plivo number’s Answer URL to your ngrok URL.

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Outbound Calls

cd pipecat-examples/plivo-chatbot/outbound

cp env.example .env
uv sync && uv run server.py

# Initiate a call
curl -X POST http://localhost:7860/start \
  -H "Content-Type: application/json" \
  -d '{"phone_number": "+1234567890"}'

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When to Use OpenAI Realtime

Choose OpenAI Realtime when:
  • Latency is your top priority
  • You want the simplest integration
  • Built-in voices meet your needs
  • You’re already using OpenAI
Choose standard STT → LLM → TTS when:
  • You need specific voice characteristics (ElevenLabs cloning, Cartesia emotion)
  • You want to mix providers for cost optimization
  • You need fine-grained control over each component