Stack for Real-Time Video, Audio, and Data | LiveKit

LiveKit real-time video is a developer-friendly stack for building real-time video, audio, and data experiences using WebRTC. If you’re building AI agents that can join calls, live copilots, voice assistants, or multi-user streaming apps, LiveKit gives you the infrastructure layer: an SFU server, client SDKs, and production features like auth, TURN, and webhooks.

TL;DR

• LiveKit is an open-source, scalable WebRTC SFU (selective forwarding unit) for multi-user conferencing.
• It ships with modern client SDKs and supports production needs: JWT auth, TURN, webhooks, multi-region.
• For AI apps, it’s a strong base for real-time voice/video agents and copilots.

Table of Contents

• What is LiveKit?
• Key features (SFU, SDKs, auth, TURN)
• Use cases (AI voice/video agents)
• Reference architecture
• Getting started
• Tools & platforms (official + GitHub links)
• Related reads on aivineet

What is LiveKit?

LiveKit is an open-source project that provides scalable, multi-user conferencing based on WebRTC. At its core is a distributed SFU that routes audio/video streams efficiently between participants. Around that, LiveKit provides client SDKs, server APIs, and deployment patterns to run it in production.

Key features (SFU, SDKs, auth, TURN)

• Scalable WebRTC SFU for multi-user calls
• Client SDKs for modern apps
• JWT authentication and access control
• Connectivity: UDP/TCP/TURN support for tough networks
• Deployment: single binary, Docker, Kubernetes
• Extras: speaker detection, simulcast, selective subscription, moderation APIs, webhooks

Use cases (AI voice/video agents)

• Real-time voice agents that join calls and respond with low latency
• Meeting copilots: live transcription + summarization + action items
• Live streaming copilots for creators
• Interactive video apps with chat/data channels

Reference architecture

Clients (web/mobile)
  -> LiveKit SFU (WebRTC)
     -> Webhooks / Server APIs
     -> AI services (ASR, LLM, TTS)
     -> Storage/analytics (optional)

Getting started

Start with the official docs and demos, then decide whether to use LiveKit Cloud or self-host (Docker/K8s). For AI assistants, the key is designing a tight latency budget across ASR → LLM → TTS while your agent participates in the call.

Tools & platforms (official + GitHub links)

• LiveKit (official): livekit.io
• Docs: docs.livekit.io
• LiveKit (GitHub): github.com/livekit/livekit
• Pion WebRTC: github.com/pion/webrtc

Related reads on aivineet

• LiveCC: Video LLM for real-time commentary
• LLM Agent Observability & Audit Logs