In this step-by-step guide, you’ll build a production-ready voice agent using the OpenAI Realtime API, WebRTC for real-time audio, MCP (Model Context Protocol) for tool integration, and SIP calling via Twilio or a CPaaS provider—all in under 48 hours. The result is a low-latency, scalable AI voice agent capable of handling live calls, executing backend tools, and seamlessly routing to human agents when needed. Start by setting up your development environment. Install Node.js, npm, and a local server (e.g., using Express). Then, create a new project folder and initialize it with npm init. Next, install required dependencies: - openai (for Realtime API) - socket.io (for real-time communication) - twilio (for SIP and call handling) - mediasoup or peerjs (for WebRTC media handling) - dotenv (to manage environment variables) Set up your OpenAI API key and Twilio credentials in a .env file. You’ll need a Twilio phone number with SIP capabilities enabled and a SIP domain configured. Now, create the core server. Initialize a WebSocket server using socket.io to handle incoming audio streams from the caller. When a call arrives via SIP (or a browser using WebRTC), the server connects to the OpenAI Realtime API using the realtime client. Configure the API with voice: 'nova', response_format: 'json', and enable input_audio_transcription to process spoken input. Enable the Realtime API’s tools feature. Define a simple MCP-style tool schema—such as get_customer_info, create_support_ticket, or search_knowledge_base. These tools will be executed on your backend server, not in the model’s context. Use tool_calls to trigger them when the model requests data. For audio flow: - Capture incoming audio via SIP (Twilio’s SIP trunking) or WebRTC (browser mic). - Stream audio chunks to OpenAI’s Realtime API using the audio_input stream. - Receive transcribed text and processed responses in real time. - Use audio_output to send synthesized speech back to the caller. Implement DTMF fallback logic. If the caller presses a key (e.g., “1” for support, “2” for sales), capture the DTMF signal via Twilio’s Digits event or WebRTC’s onDTMF handler. Use this to trigger routing, bypassing the AI agent if needed. Add a warm transfer mechanism. When the AI determines a human is needed, use Twilio’s transfer API to connect the caller to an available agent. You can optionally send a summary of the conversation via webhook to the agent’s dashboard. To reduce latency: - Use a low-latency region for your server (e.g., US-East or Europe-West). - Optimize audio chunk size (e.g., 20ms) and avoid buffering. - Run the Realtime API client and MCP tool server in the same region. Finally, test end-to-end: - Call your Twilio SIP number. - Speak to the AI agent. - Trigger a tool (e.g., “Check my order status”). - Verify the tool runs and returns data. - Test DTMF routing and warm transfer. This setup forms the foundation of a real AI contact center. It supports scalable, secure, and low-latency voice interactions—perfect for customer service, appointment booking, or support hotlines. With this system, you’re not just building a voice agent—you’re deploying a production-grade AI-powered communication layer that integrates with CRM, ticketing systems, and live agents. The OpenAI Realtime API’s support for SIP, image input, and MCP makes it ideal for enterprise use, and with proper error handling, retry logic, and monitoring, you’re ready for rollout.