voice-agents

Installation
Summary

Natural conversation with AI through speech, balancing latency against control.

  • Choose between speech-to-speech models (lowest latency, less controllable) or pipeline architectures (STT→LLM→TTS for fine-grained control)
  • Core challenges: latency budgeting across all components, voice activity detection, barge-in handling, and turn-taking to avoid awkward pauses or overlaps
  • Requires semantic VAD, response length constraints in prompts, and noise handling to achieve natural conversational flow
  • Works alongside agent orchestration, tool builders, and LLM architects for multi-modal agent systems
SKILL.md

Voice Agents

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance.

This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Humans expect responses in 500ms. Every millisecond matters.

84% of organizations are increasing voice AI budgets in 2025. This is the year voice agents go mainstream.

Principles

  • Latency is the constraint - target <800ms end-to-end
Related skills
Installs
564
GitHub Stars
37.3K
First Seen
Jan 19, 2026