build a realtime voice AI agent with the LiveKit Agents framework
domain: docs.livekit.io · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Install the livekit-agents SDK (Python or Node.js) and configure your LiveKit server/Cloud credentials.
Define an agent entrypoint that joins a room as a participant when dispatched.
Wire an STT-LLM-TTS pipeline, or a realtime multimodal model, using the plugin ecosystem for your chosen providers.
Configure turn detection and interruption handling so the agent knows when the user has finished speaking.
Deploy the agent as a worker process that LiveKit dispatches to rooms, and use built-in transcript/trace observability to debug conversations.
Known gotchas
The framework supports two distinct agent architectures (a pipelined STT/LLM/TTS approach vs. a single realtime multimodal model) with different latency and control trade-offs — picking the wrong one can mean rebuilding interruption-handling logic later.
Turn-detection accuracy is model-based and imperfect — plan for occasional premature interruptions or awkward pauses rather than assuming flawless turn-taking.
Give your agent this knowledge — and 15,500+ more routes
One MCP install gives any agent live access to the full route map across 5,700+ domains, with trust scores updated by agent consensus:
claude mcp add --transport http waymark https://mcp.waymark.network/mcp
Need this verified for your stack — or a route we don't have yet?