Initial delay for answering call is high as it creates a session then agent joins in can we reduce it?

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we are using self hosted agents how we can improve this any code suggestions?

I'm glad you're looking to optimize your self-hosted agents! To help reduce that initial delay, here are some practical strategies and code-level tips you can try: • Optimize Infrastructure: A great way to avoid runtime delays is to pre-download models during the Docker build process. For example:

RUN python -c "from silero import VAD; VAD.load()"

• Also, structuring your Dockerfiles to separate dependency installation from code changes can speed up rebuilds significantly. • Minimize Connection Delays: Consider generating long-lived tokens right at user login. This helps eliminate token-fetching delays. You can even dispatch agents while tokens are being generated, allowing connections to happen simultaneously. • Optimize Agent Initialization: Using the

prewarm

function to load static models just once per process can be a big help:

def prewarm(proc):
    proc.userdata["vad"] = silero.VAD.load()

• And don’t forget to configure

num_idle_processes

to keep some pre-initialized agent processes ready to go:

cli.run_app(
    WorkerOptions(
        entrypoint_fnc=entrypoint,
        prewarm_fnc=prewarm,
        num_idle_processes=2,
    )
)

• Optimize AI Pipeline: Enabling

preemptive_generation

lets your system start LLM/TTS inference as soon as partial transcripts come in, which really speeds things up:

session = AgentSession(
    preemptive_generation=True,
    stt=deepgram.STT(),
    llm=openai.LLM(),
    tts=cartesia.TTS()
)

• Processing audio in 50ms chunks is also a good practice for smooth streaming STT/TTS. • Monitor and Measure Performance: To keep an eye on how things are running, use

UsageCollector

to log metrics and spot any bottlenecks:

usage_collector = metrics.UsageCollector()
@agent.on("metrics_collected")
def on_metrics_collected(agent_metrics):
    metrics.log_metrics(agent_metrics)

I hope these tips help you see a noticeable improvement! If you’d like, I’m here to assist you further with implementation or troubleshooting any specific issues you encounter. Just let me know! Sources: Builds and Dockerfiles | LiveKit Docs | Web and mobile frontends | LiveKit Docs | agent-examples/agent_examples/pipeline_agent.py | livekit-agents/livekit/agents/voice/agent_session.py | External data and RAG | LiveKit Docs

preemptive_generation explain more with example

Preemptive generation in LiveKit is a wonderful feature that allows an AI agent to start crafting a response even before the user has fully finished speaking. This helps reduce the wait time you might feel and makes the conversation flow much more smoothly by using early transcription signals or partial user input. To get this up and running, you simply need to set the

preemptive_generation

parameter to

True

when you initialize the `AgentSession`:

session = AgentSession(
    preemptive_generation=True,
    stt=deepgram.STT(),
    llm=openai.LLM(),
    tts=cartesia.TTS()
)

Here’s a little overview of how it works: • The agent starts generating a response as soon as it gets a final transcript from the speech-to-text (STT) system, even if the user hasn’t fully finished their turn yet. • If anything changes in the context or parameters during the

on_user_turn_completed

callback, the agent will cancel the early response and create a new one based on the updated information. For instance, in testing, turning on preemptive generation helped cut down the total response time from 1.1 seconds to 0.8 seconds. That said, it might use a bit more computing power if the user interrupts or changes what they’re saying mid-turn, since some predictions might end up being discarded. It’s a good idea to use logging and metrics to see how this feature performs in your particular setup. If you’d like, I’m here to help with any questions or guide you through the implementation—just let me know! Sources: Agent speech and audio | LiveKit Docs | livekit-plugins/livekit-plugins-google/livekit/plugins/google/beta/realtime...

for sip if we specify

roomConfig: new RoomConfiguration({
    agents: [
      new RoomAgentDispatch({
        agentName: "inbound-agent",
        metadata: 'dispatch metadata',
      }),
    ],

will it improve any performance?

Using

RoomConfiguration

with

RoomAgentDispatch

can really help you gain better control and scalability for SIP integrations by allowing explicit agent assignment and cutting down on unnecessary resource use. That said, the impact on performance can vary depending on what you're aiming to improve. Are you hoping to speed up call setup, handle more agents smoothly, enhance media quality, or focus on something else? Just let me know, and I’ll be happy to guide you more specifically! Sources: Dispatching agents | LiveKit Docs | protobufs/livekit_sip.proto | Docs::Home | Authentication | LiveKit Docs | Agent dispatch | LiveKit Docs