To help reduce LLM latency in LiveKit, here are some friendly suggestions you might find useful: • Enable Preemptive Generation: Try using the

preemptive_generation

feature in

AgentSession

. It kicks off LLM processing as soon as partial transcription is ready, which can really help cut down on waiting time. • Choose Low-Latency LLM Providers: Consider providers that are known for speed, like Groq’s inference platform, which offers a quick Time To First Token (TTFT). This can make a noticeable difference. • Streamline Audio and Data Flow: Minimizing batching by using streaming services for STT, LLM, and TTS with small audio chunks (around 20–50ms) can keep things moving smoothly. • Optimize Network and Deployment: Placing your agents closer to the LLM service helps reduce network round-trip time, speeding up the whole process. • Monitor Metrics: Keep an eye on latency indicators like TTFT and LLM Response Time using LiveKit’s Agents Metrics API. This way, you can spot any bottlenecks and address them promptly. • Provider-Specific Tuning: Don’t forget to check your LLM provider’s optimization tips, such as using faster inference endpoints or fine-tuning prompts, to get the best performance. Following these steps can really enhance responsiveness and help reduce delays in your LLM pipeline. If you have any questions or want to dive deeper into any of these, I’m here to help! Sources: Agent speech and audio | LiveKit Docs | Agent Testing & Evaluation | livekit/livekit_composite | DeepWiki | Speech & Audio Processing | livekit/livekit_composite | DeepWiki | Live Conversations with AI using ChatGPT and WebRTC | Live Conversations with AI using ChatGPT and WebRTC