After the job finishes, the worker either: Returns to the idle pool (if you keep it alive), or Terminates (if it was created for that job and not kept idle). how can we keep the worker alive after the job has been finished?

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the same worker is reused again, right now i am not setting any specific value for

num_idle_processes

Thanks — I’m responding to your question about whether the same worker is reused when you haven’t set

num_idle_processes

. Short answer • Yes. When you run the agent in production (

start

) mode and don’t explicitly set

num_idle_processes

, the server will still keep and reuse worker processes (prewarmed/idle processes) rather than terminating them after each job. That means the same process — along with any

proc.userdata

you populated in your

setup_fnc

— can be reused for subsequent jobs. What you can do next • If you want to control how many prewarmed workers are kept ready, set

num_idle_processes

to the desired value. That determines the size of the idle pool and how many processes will be retained for reuse. • Keep using a

setup_fnc

to load heavy resources into

proc.userdata

so reused workers don’t need to reinitialize those resources. Quick clarifying question • How are you starting the agent now (development vs. production command)? If you tell me that, I can suggest the exact place to add

num_idle_processes

or show a config snippet for your setup.

can we get the info of workers that are getting reused?

Thanks — I’m responding to your question about getting info on which worker processes are being reused. Short answer • There isn’t a built‑in API that returns a list of “reused workers.” • You can reliably observe and track reuse by instrumenting your worker processes: log the process ID, store reuse metadata in

proc.userdata

during

setup_fnc

, and increment a job counter each time the process runs a job. What to add (quick implementation) 1. Prewarm / initialize per-process state:

from datetime import datetime

def prewarm(proc: JobProcess):
    proc.userdata["initialized_at"] = datetime.utcnow().isoformat()
    proc.userdata["job_count"] = 0
    <http://logger.info|logger.info>(f"Prewarmed process {proc.pid}")
server.setup_fnc = prewarm

1. Increment and log reuse in your entrypoint:

@server.rtc_session()
async def entrypoint(ctx: JobContext):
    ctx.proc.userdata["job_count"] += 1
    job_count = ctx.proc.userdata["job_count"]
    <http://logger.info|logger.info>(f"Job started in process {ctx.proc.pid}; reused {job_count} times")
    # ... job work ...

1. Optional lifecycle hooks / shutdown logging:

def on_shutdown():
    <http://logger.info|logger.info>(f"Shutting down process {ctx.proc.pid}")
ctx.add_shutdown_callback(on_shutdown)

What this gives you • Repeated logs for the same

pid

show reuse. •

proc.userdata

gives persistent per-process metadata (init time, job count, cached resources). • Combined with

num_idle_processes

, you can infer pool size and reuse behavior. If you want more visibility • I can show how to push these events to a metrics system (Prometheus/Grafana) or a structured logging/trace pipeline so you can query reuse counts over time. • Or, tell me how you run the agent (command/host) and your preferred logging/metrics stack and I’ll provide a tailored snippet. Sources: Server options | LiveKit Documentation

can we use n8n with livekit?

Thanks — I’m responding to your question about using n8n with LiveKit. Short answer • Yes. You can use n8n with LiveKit. There’s a dedicated LiveKit node for n8n, and a full tutorial (“No‑Code Voice AI Agent with LiveKit + n8n”) that walks through building an end‑to‑end voice agent using LiveKit and n8n. What you can do with the integration • Manage rooms and participants from n8n workflows. • Trigger agent workflows or orchestrate jobs when specific events happen. • Connect telephony (SIP) flows and recordings into automated processes. • Build voice AI pipelines (speech→AI→response) without writing backend code. If you want to get started • Follow the “No‑Code Voice AI Agent with LiveKit + n8n (Full Tutorial)” to see a complete example and recommended node usage: https://livekit.io/field-guides/guide/no-code-voice-agent-livekit-n8n • Check the Agents field guide for additional examples and context: https://livekit.io/field-guides/agents Would you like: • A short step‑by‑step for a specific workflow (e.g., create room → invite participant → start recording)? • Help wiring LiveKit events into an n8n flow or an example n8n workflow JSON you can import? Tell me which use case you have in mind and your n8n setup (self‑hosted or n8n cloud), and I’ll give a tailored next step. Sources: No-Code Voice AI Agent with LiveKit + n8n (Full Tutorial) | LiveKit | Agents - Field Guides | LiveKit

can we do customization in n8n, at the niche level?

Thanks — I’m responding to your question about whether you can do niche-level customizations in n8n (especially when integrating with LiveKit). Short answer • Yes. n8n is highly extensible and supports niche/custom behavior. You can customize workflows using built-in nodes (Function, HTTP Request), create custom n8n nodes, and call LiveKit APIs directly. That gives you fine-grained control over logic, data transformation, event handling, and integration with other services. How you can customize (options) • Use the Function / Function Item nodes: ◦ Write custom JavaScript to transform data, implement conditional logic, or compute values per item inside a workflow. • Use the HTTP Request node: ◦ Call LiveKit REST endpoints or your agent endpoints to create rooms, manage participants, start/stop recordings, or trigger jobs. • Build custom n8n nodes: ◦ Author a custom node when you need reusable, strongly-typed functionality (e.g., a node that wraps specific LiveKit actions). This is ideal for packaged, repeatable integrations. • Use Webhook and Event triggers: ◦ Receive LiveKit webhooks/events in n8n to drive workflows when rooms start, participants join, recordings complete, etc. • Combine with external services: ◦ Send events to logging/metrics, push to databases, or call AI services for speech transcription/processing as part of the flow. • Error handling & retries: ◦ Implement retry logic, branching, and notification steps inside workflows for robust, production-ready automation. Concrete examples of niche customizations • Automatically call an AI transcription service only for participants flagged with a certain role. • Route calls to different agent profiles based on caller metadata (location, language). • Add per-room custom initialization (load different models) by calling your agent server with specific parameters when a room is created. • Create a custom n8n node that performs a multi-step LiveKit workflow (create room → set permissions → prewarm agent) and exposes concise parameters for non-developers. Quick question to tailor help • What specific niche customization do you have in mind? For example: ◦ Per-caller routing, per-room model selection, conditional recording/transcription, or custom telemetry? ◦ Are you using self-hosted n8n or n8n cloud? ◦ Do you prefer writing small JS snippets in Function nodes or building reusable custom nodes? Next steps I can provide • A short example n8n workflow (importable JSON) showing a LiveKit webhook → conditional logic → HTTP calls to LiveKit. • A sample Function node snippet to implement per-participant routing. • Guidance on building a custom n8n node that wraps LiveKit actions. Tell me the specific customization you want and your n8n setup, and I’ll give a tailored example.