how can i reduce the following metrics:end_of_utterance_delay + ttft + ttfb

Thanks for reaching out, Relency. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

im referring to livekit agents

To help you reduce end_of_utterance_delay, ttft (Time to First Token), and ttfb (Time to First Byte) for your LiveKit agents, here are some friendly strategies you can try: 1. Reduce end_of_utterance_delay: • Try lowering the

min_endpointing_delay

parameter to cut down the delay after VAD detects silence. For instance, setting it to 0.2 seconds instead of the default 0.5 seconds can make a difference. • Consider using advanced turn detection modes like

realtime_llm

for more precise detection of when turns end. • If your STT provider supports it, enabling adaptive end-of-utterance detection can really help. • Also, keeping the logic in the

on_user_turn_completed

callback minimal can prevent extra delays. 2. Reduce ttft: • Opt for low-latency LLM providers and models, such as the optimized ones from OpenAI. • Simplifying your prompts by cutting down unnecessary input tokens can speed things up. • Implementing prompt caching to reuse tokens you've already computed is a great way to save time. • Keep an eye on

ttft

with metrics APIs so you can spot and fix any bottlenecks. 3. Reduce ttfb: • Pick TTS services that are optimized for low latency and enable streaming TTS output when possible. • Using smaller audio chunk sizes, like 50ms during streaming, can improve responsiveness. • Tracking

ttfb

via metrics APIs will help you fine-tune performance. 4. General Optimization: • Deploy your agents in the same region as your LLM, STT, and TTS services to keep network latency low. • Gather and monitor metrics across sessions to spot trends and optimize your setup. • Consider integrating with external monitoring tools or using OpenTelemetry for deeper observability. I hope these tips help boost your LiveKit agents' responsiveness and performance! If you'd like, I'm here to walk you through any of these steps or provide more details—just let me know. Sources: agents/src/metrics/utils.ts | Capturing metrics | LiveKit Docs | How can I reduce latency in voice agents using STT, TTS and LLM? | How does end-of-utterance detection work in conversations? | Agent Testing & Evaluation | livekit/livekit_composite | DeepWiki

for a proper enterprise level agents what should be the recommended values for these

I understand you're looking for specific recommended values for end_of_utterance_delay, ttft, and ttfb for enterprise-level LiveKit agents. While the documentation doesn't provide exact numbers, a helpful way to approach this is by calculating and monitoring total latency using the formula: total_latency = end_of_utterance_delay + ttft + ttfb From there, you can find the best values by running some latency benchmarks tailored to your particular LLM, STT, and TTS providers. If you'd like, I’m here to guide you through measuring or optimizing these metrics—just let me know! Sources: Speechmatics STT integration guide | LiveKit Docs | Speechmatics STT integration guide | LiveKit Docs | tests/test_agent_session.py | agents/src/metrics/utils.ts | Capturing metrics | LiveKit Docs

what do you recommend i should have for each of these metrics

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!

total_latency = end_of_utterance_delay + ttft + ttfb whats a good total latency value