I’m glad you asked about the

prewarm

function in LiveKit! It’s a handy performance optimization technique designed to help reduce latency by getting resources ready before a task even begins. In the LiveKit Agents framework, for instance, it’s often used to load large machine learning models—like those for speech-to-text or voice activity detection—right when the worker process starts, instead of waiting until the job is running. This way, everything feels quicker and more responsive for you. To give you a clearer picture, the

prewarm

function might load models such as Silero VAD, which help detect when someone is speaking. These loaded resources are then saved in

proc.userdata

, so they’re ready to go as soon as a job begins. Plus, you can manage how many idle prewarmed processes you keep around, striking a nice balance between using resources efficiently and keeping things snappy. And it’s not just limited to agents! Prewarming is also used in the Android SDK to get the audio recording stack ready faster, helping you publish audio tracks more swiftly. Even plugins like OpenAI TTS use it to cut down on connection setup time. It’s all about making your experience smoother and more seamless. Sources: Worker options | LiveKit Docs | Anatomy of an Agent | LiveKit Docs | Anatomy of an Agent | LiveKit Docs | agent-examples/agent_examples/pipeline_agent.py | agent/main.py