is faster to create room + dispatch agent server side than let the agent dispatch on client connect?

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

tokens are minted server side

Thanks — helpful to know tokens are minted server‑side. That makes the optimal flow straightforward. Recommendation (fastest, lowest latency) • Do NOT call

CreateRoom()

server‑side. • Mint the access token server‑side and include the agent dispatch in

room_config.agents

on the token so the agent is dispatched when the client’s first connection causes the room to be created. Why • Explicit

CreateRoom()

adds cross‑region synchronization and an extra round‑trip. • Including agents in the token triggers dispatch as part of room creation, avoiding that extra API call and lowering latency. If you’d like, tell me which SDK/language you use for token generation and I’ll give a concise server‑side token example showing where to set

room_config.agents

. , with interactive elements

assume we dispatch agent on request and not auto join (like client is in the room idle until it decides to join an agent), in that case what's the fastest way to dispatch the agent? how to reduce agent connection latency?

Thanks — I’m responding to your scenario where the client is idle in the room and you dispatch an agent on demand. Short answer • The fastest on‑demand method is an explicit server‑side dispatch via the AgentDispatchService API (create_dispatch). This gives immediate control and avoids waiting for token-based room-creation paths. How to reduce agent connection latency (practical checklist) • Deploy agents in the same region as your AI services (LLM/STT/TTS) and LiveKit region to avoid cross‑region sync. • Keep agents running (pre‑warmed) instead of cold-starting — run warm worker processes or a small pool ready to accept dispatch. • Use streaming mode for STT → LLM → TTS so audio and tokens flow as soon as available. • Run multiple agent worker instances (same agent_name) with load‑balancing and autoscale to handle spikes. • Reduce model startup time by choosing low‑latency model instances or smaller footprints for fast TTFT/TTFB. • Monitor the key metrics (e2e_latency, llm_node_ttft, tts_node_ttfb) and profile which component adds the most delay. • If you must pre-create rooms, avoid explicit CreateRoom() across regions — prefer letting the room be created implicitly when needed. Minimal example (Python) — explicit dispatch

from livekit import api, LiveKitAPI

lkapi = LiveKitAPI()
resp = lkapi.agent_dispatch.create_dispatch(
    api.CreateAgentDispatchRequest(agent_name="my-agent", room="my-room")
)

If you want, tell me which SDK/language your agents and server use and whether you can keep agents warm; I’ll give a tailored dispatch pattern and concrete tuning suggestions. Sources: Agent dispatch | LiveKit Documentation | Building Performant Voice Agents in India | LiveKit | Building Performant Voice Agents in India | LiveKit | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit | Understand and Improve Agent Latency | LiveKit, with interactive elements

agents is python, server is node, client is python we need to dispatch agent really fast but at the same time, need some setup calls that add latency (for fetching backend info for the agent)

Thanks — I’m responding to your setup: agents are Python, server is Node, client is Python, and you must dispatch very fast but need some backend setup data. Recommendation (fastest dispatch with required setup) 1. Dispatch explicitly from your Node server using the AgentDispatch API with minimal metadata (e.g., user_id, job_id). This gives the fastest, on‑demand control. 2. Defer heavy setup: have the Python agent fetch backend info after it connects using the minimal metadata passed. This minimizes dispatch latency. 3. Pre‑warm agents or keep a small pool of warm workers ready to accept dispatch to avoid cold starts. 4. Co‑locate agents with your LLM/STT/TTS and LiveKit region to reduce network latency. 5. Use streaming STT→LLM→TTS and multiple worker instances for low TTFT/TTFB and high availability. 6. Monitor e2e_latency, llm_node_ttft, and tts_node_ttfb to find the biggest bottleneck. Minimal Node dispatch pattern (send minimal metadata) • send: { room, agent_name, metadata: '{"user_id":"123","job_id":"abc"}' } Agent-side (Python) startup pattern • read

ctx.metadata

for IDs • start any lightweight streams immediately (accept audio, send ack) • asynchronously fetch backend details (DB/API) in background • only block the user flow when absolutely necessary; stream interim feedback If you want, tell me: • which backend info you must fetch (size/type), • whether you can pre-cache that info for some users, and I’ll provide a concrete Node.js dispatch snippet and a Python agent pattern showing non-blocking fetch and progress notifications. Sources: Agent dispatch | LiveKit Documentation | Building Performant Voice Agents in India | LiveKit | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit | Understand and Improve Agent Latency | LiveKit | Agent speech and audio | LiveKit Documentation, with interactive elements

is there a way to connect, receive audio but block response processing until everything is already processed?

Yes — you can connect and receive (or buffer) audio immediately while blocking response processing until your backend setup is finished. How to do it (high‑level) • Use manual turn control: set turn handling to manual and disable preemptive generation so nothing is processed automatically. • Keep the agent connected and subscribe to audio (or use the pre‑connect audio buffer) so user audio is captured. • While connected, have the agent read minimal metadata (IDs) and asynchronously fetch backend data. • When setup is complete, enable audio processing and commit the user turn to start STT → LLM → TTS. Concrete Python patterns 1. Disable preemptive generation and use manual turns in your agent config. 2. On agent start: ◦ Read

ctx.metadata

for IDs. ◦ Immediately accept audio but call

session.input.set_audio_enabled(False)

(or keep processing disabled). ◦ Start async task to fetch backend info. 1. After fetch finishes: ◦

session.input.set_audio_enabled(True)

◦

session.clear_user_turn()

if needed, then

session.commit_user_turn()

to begin processing. 1. Optionally stream interim acknowledgements to the client while setup runs. Notes and tips • Use the pre‑connect audio buffer/topic to capture early audio before the agent fully starts. • Pre‑warm agents to avoid cold‑start delays when you finally enable processing. • Monitor e2e_latency, llm_node_ttft, and tts_node_ttfb to see which step dominates latency. If you want, I can produce a short Python agent code snippet showing

session.input.set_audio_enabled(False)

+ async fetch +

commit_user_turn()

tailored to your agent SDK version. Which agent SDK version are you using? Sources: Agent speech and audio | LiveKit Documentation | Turns overview | LiveKit Documentation | Job lifecycle | LiveKit Documentation | Capturing the First Few Seconds of a Call for Processing, with interactive elements

is a reatlime model, so vad is server side, not in agent

Thanks — I’m responding to your constraint that VAD is server‑side (realtime model) and not in the agent. Short answer: you can still connect, receive audio, and delay response processing even with server‑side VAD. Use server VAD to detect voice, but keep agent processing paused until backend setup completes. Recommended pattern • Have the server continue doing VAD and mark turns as detected (so audio frames are available). • Dispatch the agent explicitly with minimal metadata once you decide to start the agent. • In the Python agent, disable preemptive generation and use manual turn control: ◦ Start with

preemptive_generation.enabled = False

and

turn_detection = "manual"

. ◦ Immediately accept/subscribe to audio but call

session.input.set_audio_enabled(False)

(or equivalent) so the agent does not run STT/LLM/TTS. ◦ Asynchronously fetch backend info using IDs from

ctx.metadata

. ◦ When setup is finished, enable processing:

session.input.set_audio_enabled(True)

then

session.clear_user_turn()

(if needed) and

session.commit_user_turn()

to trigger STT → LLM → TTS. • Optionally use the pre‑connect audio buffer (

lk.agent.pre-connect-audio-buffer

) so early audio captured by the client/server is available to the agent when you enable processing. Why this works • Server VAD ensures voice segments are detected and buffered without moving processing into the agent. • Manual turn control and disabled preemptive generation prevent any inference until your backend is ready, minimizing wasted work and reducing perceived latency once you commit the turn. If you want, I can provide a short Python agent snippet showing

session.input.set_audio_enabled(False)

+ async backend fetch +

commit_user_turn()

, and a Node.js dispatch example that sends minimal metadata. Which would you prefer? , with interactive elements

but how you make this?

Got it — here’s a concrete, minimal implementation plan with Node.js dispatch and a Python agent that connects, receives audio (server VAD), defers processing while it fetches backend data, then enables processing and commits the turn. 1) Node.js — explicit dispatch with minimal metadata • Dispatch quickly, include just IDs needed for backend lookup.

// Node.js (pseudo, use your LiveKit server SDK)
const { AgentDispatchClient } = require('livekit-server-sdk');

async function dispatchAgent(room, agentName, userId, jobId) {
  const client = new AgentDispatchClient(process.env.LIVEKIT_URL, process.env.API_KEY, process.env.API_SECRET);
  const resp = await client.createDispatch(room, agentName, {
    metadata: JSON.stringify({ user_id: userId, job_id: jobId })
  });
  return resp;
}

2) Python agent — connect, accept audio, defer processing, async fetch, then commit • Use manual turns, disable preemptive generation, start with audio processing off, fetch backend info in background, then enable and commit.

# Python agent (pseudo / adapt to your agent SDK)
from livekit_agents import server, JobContext, Session

@server.rtc_session(agent_name="my-agent",
                    turn_handling={"turn_detection": "manual", "preemptive_generation": {"enabled": False}})
async def my_agent(ctx: JobContext, session: Session):
    # 1. Read minimal metadata passed at dispatch
    meta = {}
    if ctx.metadata:
        import json
        meta = json.loads(ctx.metadata)
    user_id = meta.get("user_id")
    job_id = meta.get("job_id")

    # 2. Immediately accept connection but disable audio processing
    session.input.set_audio_enabled(False)   # stop STT/LLM/TTS from running

    # 3. Optionally acknowledge client so they know agent connected
    await session.send_system_message("Agent connecting, fetching data...")

    # 4. Start backend fetch asynchronously (non-blocking to audio capture)
    async def fetch_backend():
        # do your DB/API calls here
        # return whatever initialization info agent needs
        return await get_backend_info(user_id, job_id)

    fetch_task = asyncio.create_task(fetch_backend())

    # 5. While fetch runs, server-side VAD can keep buffering audio frames
    #    (audio frames will be available once processing is enabled)

    init_info = await fetch_task  # wait for backend initialization to finish

    # 6. When ready, enable audio processing and start the turn
    session.input.set_audio_enabled(True)
    session.clear_user_turn()   # if you need to reset any partial state
    await session.commit_user_turn()   # triggers STT -> LLM -> TTS for buffered audio

    # 7. Proceed with normal agent loop (handle turns, generate responses)
    await run_conversation_loop(session, init_info)

3) Key notes and tips • Server-side VAD: LiveKit/VAD detects voice and buffers frames; agent enabling processing will consume those buffered frames if using the pre-connect buffer or server-side buffering. • Pre-connect audio buffer: ensure your client/server pipeline retains early audio until the agent enables processing. • Keep dispatch metadata minimal to reduce dispatch latency; do heavy fetches inside the agent after connect. • Pre-warm agents or keep a small pool of warm workers to avoid cold-start delays. • Monitor metrics (e2e_latency, llm_node_ttft, tts_node_ttfb) to find bottlenecks. If you want, I can adapt the snippets to the exact SDK methods you’re using (names differ slightly between SDK versions). Which LiveKit agent SDK version and Python libraries are you using? , with interactive elements

but is input is disabled, we're missing client audio right?

Good question — disabling

session.input

only stops STT/LLM/TTS from running, it does NOT have to drop or lose incoming audio if you use buffering on the server side. How to avoid losing audio • Keep VAD and audio buffering on the server (your realtime model). VAD will detect voice and buffer frames even while the agent’s processing pipeline is disabled. • Use LiveKit’s pre‑connect / pre‑connect‑audio‑buffer mechanism or your own server buffer so audio frames are retained until the agent enables processing. • When the agent finishes setup, re-enable input and commit the turn so STT consumes the buffered audio. Short sequence (what actually happens) 1. Client streams audio to the server; server VAD detects and buffers voice segments. 2. Node server dispatches the agent with minimal metadata. 3. Agent connects and calls

session.input.set_audio_enabled(False)

(prevents processing) but the server continues buffering. 4. Agent fetches backend data asynchronously. 5. When ready:

session.input.set_audio_enabled(True)

→

session.clear_user_turn()

if needed →

session.commit_user_turn()

— STT/LLM/TTS then process the buffered audio. Minimal Python snippet (illustrative)

# after connect
session.input.set_audio_enabled(False)    # pause processing, not reception
fetch_task = asyncio.create_task(fetch_backend_info(user_id))

init_info = await fetch_task               # backend ready
session.input.set_audio_enabled(True)      # let STT consume buffered audio
await session.commit_user_turn()           # start processing buffered turn

If you want, I can: • Show how to implement server-side buffering with your realtime VAD, or • Provide a Node.js snippet to ensure the server retains audio until the agent commits. Which would you like? , with interactive elements

no you're wrong, server only dispatches, audio then flows only in the room where client and agent live

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!