Voice-first interfaces are becoming a core interaction pattern for modern apps. Instead of typing, users can speak naturally and receive instant voice responses. This post walks through how to build an AI voice assistant that runs entirely inside a WebRTC room.
The assistant supports real-time speech recognition, AI-generated responses, and natural voice playback, while exposing clear interaction states like listening, thinking, and speaking.
How AI Voice Assistant Works
The architecture is based on a WebRTC room with an embedded AI agent:
- The user joins a WebRTC room and streams microphone audio
- Audio is processed in real time by an AI agent (ASR → LLM → TTS)
- The agent responds with both live speech and text
- The UI reflects conversation states for better feedback
All…
Voice-first interfaces are becoming a core interaction pattern for modern apps. Instead of typing, users can speak naturally and receive instant voice responses. This post walks through how to build an AI voice assistant that runs entirely inside a WebRTC room.
The assistant supports real-time speech recognition, AI-generated responses, and natural voice playback, while exposing clear interaction states like listening, thinking, and speaking.
How AI Voice Assistant Works
The architecture is based on a WebRTC room with an embedded AI agent:
- The user joins a WebRTC room and streams microphone audio
- Audio is processed in real time by an AI agent (ASR → LLM → TTS)
- The agent responds with both live speech and text
- The UI reflects conversation states for better feedback
All audio streaming is handled via WebRTC, so responses feel immediate and conversational.
Key Features of AI Voice Assistant
- Real-time voice input using WebRTC
- Live speech-to-text with partial and final transcripts
- AI-generated responses with natural voice playback
- Conversation state indicators (listening, thinking, speaking)
- Optional text input alongside voice
- Session-based conversations inside isolated rooms
Tech Stack
- WebRTC for real-time audio streaming
- ZEGOCLOUD Conversational AI for ASR, LLM, and TTS
- React + TypeScript for the frontend
- Node.js for session and agent management
All heavy audio and AI processing runs on the server side, keeping the client lightweight.
Why Use WebRTC for Voice AI?
WebRTC provides low-latency, bi-directional audio streaming, which is critical for voice assistants. Running the AI agent inside the same real-time room avoids delays caused by traditional request-response audio pipelines.
This pattern works well for:
- Voice assistants
- AI companions
- Accessibility tools
- Hands-free or screenless interfaces
Source Code
GitHub repository with full source code and setup instructions: 👉 AI voice assistant source code on GitHub
Tutorial
Full step-by-step guide covering architecture, backend setup, WebRTC integration, and UI state handling: 👉 How to Create an AI Voice Assistant
If you’re exploring how to combine WebRTC with conversational AI for real-time voice experiences, this project provides a practical reference implementation.