Goal: Build low-latency, conversational Voice AI agents capable of full-duplex communication.

1. Transport: WebRTC (preferred for browser) or WebSocket (server-server).
2. VAD (Voice Activity Detection): Detect when user starts/stops speaking.
   • Tools: Silero VAD, WebRTC VAD.
3. STT (Speech-to-Text): Transcribe audio to text.
   • Tools: Deepgram (fastest), Whisper (high accuracy but slower), AssemblyAI.
4. LLM (Brain): Process text and generate response.
   • Tools: Groq (Llama 3), GPT-4o, Claude 3.5 Sonnet.
5. TTS (Text-to-Speech): Convert response to audio.
   • Tools: ElevenLabs (Quality), Cartesia (Speed), OpenAI TTS.

• Streaming Pipeline: DO NOT wait for full transcription or full generation. Stream everything.
  • User Audio Stream -> VAD -> STT Stream -> LLM Stream -> TTS Stream -> Audio Output.
• Interruption Handling (Barge-in):
  • If VAD detects user speech while AI is talking -> Immediately CUT text generation and audio playback. Clear buffers.

• Backend: Python (FastAPI) or Node.js. Python ecosystem is stronger for audio processing (numpy/scipy).
• Frameworks:
  • Pipecat: Open source framework for building voice agents.
  • LiveKit: WebRTC infrastructure for real-time audio/video.
  • Twilio: For telephony integration.

• Optimistic VAD: Tune VAD to be sensitive to start, but careful with "silence" timeout (usually 500ms-800ms) to detect end of turn.
• Prompt Engineering: Instruct LLM to be concise and conversational.
  • System Prompt: "You are a helpful voice assistant. Keep responses short (1-2 sentences). Do not use markdown or emojis."
• Audio Formats: Use OPUS or PCM (16khz/24khz/48khz) for transmission. Avoid MP3 transcoding latency.

• WER (Word Error Rate): For STT accuracy.
• TTFT (Time to First Token): LLM speed.
• TTA (Time to Audio): The critical metric. Time from user silence to first AI sound.

• Echo cancellation issues (User hears themselves). Use WebRTC's built-in AEC.
• Hallucination in STT (Whisper transcribing silence).
• Race conditions during interruptions.