voice-ai-development

Build real-time voice AI applications using OpenAI Realtime API, Vapi, Deepgram, and ElevenLabs.

2|Updated Jan 10, 2026
One-click install
npx skills add https://github.com/Shubh2310-developer/ENGUNITYCORE --skill voice-ai-development-shubh2310-developer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: voice-ai-development
Source: https://github.com/Shubh2310-developer/ENGUNITYCORE/tree/main/.claude/skills/voice-ai-development
Command: npx skills add https://github.com/Shubh2310-developer/ENGUNITYCORE --skill voice-ai-development-shubh2310-developer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Building voice AI applications requires orchestrating speech-to-text, LLM reasoning, and text-to-speech with tight latency budgets, and choosing the wrong provider combination or non-streaming architecture makes voice experiences feel slow and broken. ## Core Features & Use Cases - OpenAI Realtime API Integration: Native voice-to-voice sessions with GPT-4o over WebSockets, including server-side voice activity detection and function calling. - Vapi Voice Agents: Hosted phone and web voice agents with webhook handling for tool calls and end-of-call transcripts. - Custom STT/TTS Pipelines: Deepgram real-time transcription with interim results paired with ElevenLabs streaming synthesis for low-latency output. - Use Case: Build a customer support phone agent that transcribes callers with Deepgram, reasons with an LLM, responds with ElevenLabs voices, and supports barge-in interruption handling. ## Quick Start Help me build a real-time voice agent using Deepgram for transcription and ElevenLabs for speech synthesis with streaming throughout.

Frequently Asked Questions about voice-ai-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a real-time voice agent with low latency?

Stream every stage of the pipeline: use interim STT results from Deepgram, stream LLM tokens, and start ElevenLabs TTS before the LLM finishes. Non-streaming pipelines add seconds of delay and break conversational flow.

OpenAI Realtime API vs Deepgram plus ElevenLabs, which should I use?

Use the OpenAI Realtime API when you want integrated voice-to-voice without managing separate STT and TTS providers. Choose Deepgram plus ElevenLabs when you need best-in-class transcription accuracy and voice quality in a custom pipeline.

How do I handle user interruptions in a voice agent?

Implement barge-in detection using voice activity detection to notice when the user starts speaking. Stop TTS playback immediately and clear the audio queue so the agent feels responsive rather than robotic.

Can I use Vapi for phone-based voice agents?

Yes, Vapi provides hosted voice agents that connect to phone numbers via Twilio and handle calls through webhooks. You configure the assistant's model, voice, and transcriber, then process function calls and end-of-call reports in your webhook endpoint.

What are the limitations of real-time voice AI pipelines?

Latency varies by provider and network conditions, per-minute costs accumulate quickly, and debugging distributed streaming pipelines is complex. Mixing providers avoids lock-in but increases integration effort.