livekit-agents

Orchestrate offline realtime voice pipelines with local STT, LLM, and TTS.

Updated Jun 6, 2026
One-click install
npx skills add https://github.com/J4Joshua/manuaI --skill livekit-agents-j4joshua
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: livekit-agents
Source: https://github.com/J4Joshua/manuaI/tree/main/.agents/skills/livekit-agents
Command: npx skills add https://github.com/J4Joshua/manuaI --skill livekit-agents-j4joshua

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the complexity of building a reliable offline voice copilot by coordinating realtime audio transport, local speech recognition, local reasoning, and speech synthesis without cloud dependencies.

Core Features & Use Cases

  • Offline Voice Orchestration: Connects self-hosted LiveKit Agents with local Whisper STT, Qwen LLM, and Kokoro or Piper TTS pipelines for air-gapped deployments.
  • Realtime Operator Interaction: Implements push-to-talk workflows, sentence-level streaming, transcript delivery, SOP display updates, and safety or escalation states for factory-floor assistants.
  • Use Case: Build and debug ManuAI's voice loop where an operator speaks a machine fault, retrieves grounded SOP context through Moss, receives a cited response from Qwen, and hears the answer through local TTS.

Quick Start

Use the livekit-agents skill to configure a fully offline voice agent pipeline with local STT, LLM, TTS, push-to-talk controls, and SOP-aware screen updates.

Frequently Asked Questions about livekit-agents

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

FAQPage Schema
How do I build an offline voice agent with local speech recognition and text to speech?

Build an offline voice agent by orchestrating local speech recognition, reasoning, and text to speech through LiveKit Agents, connecting Whisper STT, Qwen LLM, and Kokoro or Piper TTS pipelines without cloud dependencies.

Can I use local Whisper and Qwen models for an air-gapped voice interface?

Yes, you can use local Whisper and Qwen models for air-gapped voice interfaces by connecting self-hosted LiveKit Agents with local inference services to enable offline speech pipelines.

How do I implement push-to-talk workflows with realtime streaming in a voice pipeline?

Implement push-to-talk workflows with realtime streaming by configuring sentence-level streaming, transcript delivery, and safety states within the LiveKit Agents voice pipeline orchestration.

What's the best way to create a factory-floor voice assistant with grounded SOP retrieval?

Create a factory-floor voice assistant by configuring a realtime operator interaction pipeline where spoken machine faults trigger grounded SOP context retrieval, delivering cited responses through local text to speech.

Does this offline voice agent approach work without internet connectivity?

Yes, the offline voice agent approach works without internet connectivity by relying entirely on local inference services for speech recognition, language model reasoning, and speech synthesis.

What are the limitations of using local AI models for realtime voice orchestration?

Limitations of local AI models for realtime voice orchestration include requiring sufficient local compute for Whisper STT and Qwen LLM inference, and needing self-hosted LiveKit Agents infrastructure to maintain air-gapped deployments.