idle processes per agent what if i set it at 1 idle process per agent.ts to prvent this error {"level":40,"time":1766024864080,"pid":141,"hostname":"deployment-p-551rhlf5aqv-ca-ubuzhpvxljvl-7db696798f-29jkh","memoryUsageMB":501.71484375,"memoryWarnMB":500,"memoryLimitMB":0,"msg":"process memory usage is high"} in livekit cloud, would we still eb able to scale for 250 workers on livekit cloud people

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

what should be our target per process for pcoesss memory

You're asking what target memory to aim for per job process to avoid the high-memory warning and keep scale predictable. Aim for a practical target and monitor/adjust: Recommended targets • Baseline target: ~100–250 MB per conversation/process for lightweight voice-only jobs. • Memory-heavy jobs (large STT/LLM/TTS models): plan 500 MB+ per process; profile specific models to get exact numbers. • Agent server sizing: start with ~8 GB RAM per agent server (handles ~10–25 concurrent jobs depending on models). How to apply this • Profile one representative job process under load to measure realistic memory usage (include model loads and buffering). • Use the measured per-process number to calculate capacity: e.g., if a process uses 300 MB, an 8 GB node supports ~20–22 concurrent processes with headroom. • Keep per-process memory well below your warning threshold (500 MB) or raise instance sizes/thresholds if processes legitimately need more. Monitoring and tuning • Monitor process memory over time and during spikes. • If many processes exceed ~500 MB, move to larger instance types or reduce per-process concurrency / offload heavy models. • Use prewarm/shared initialization where possible to avoid redundant model loads. If you want, share one example process profile (what models/libraries it loads and measured RSS in MB) and I’ll calculate recommended instance size and concurrency targets for 250 workers. Sources: Deploying to custom environments | LiveKit docs | examples/drive-thru | livekit/livekit_composite | DeepWiki | Deploy and scale agents on LiveKit Cloud

gpt 4o, vad, assembly ai, eleven labs 2.5 flash

Thanks — with GPT‑4o, VAD (Silero), AssemblyAI STT, and ElevenLabs TTS (2.5 Flash), here’s a concise target and plan. Recommended per-process memory target • Aim for ~300–500 MB per job process as a practical target. ◦ Lightweight conversation work ~100–250 MB. ◦ With GPT‑4o + AssemblyAI + ElevenLabs + VAD, expect each process to be toward the upper end (300–500 MB). Instance sizing & concurrency • Start with 4‑core / 8 GB RAM compute‑optimized instances (c6i/c7i). One 8 GB node realistically supports ~10–25 concurrent jobs depending on measured per‑process usage. • If your measured per‑process is ~350 MB, plan ~18–20 processes per 8 GB node with headroom; if ~500 MB, plan ~12–14 processes. Scaling & autoscaling settings • Autoscale based on queue depth/load and set scale‑up earlier (e.g., at ~50% CPU) to avoid overloading nodes. • Keep cooldowns short for scale‑up; use longer cooldowns for scale‑down. Memory optimizations to hit those targets • Prewarm or preload heavy components (VAD) so they’re not repeatedly loaded at runtime. • Pre-download model assets in the image/build step to avoid runtime spikes. • Limit per‑process concurrency (one conversation per process if memory tight). • Profile a representative process under load to get precise RSS numbers (then recalc capacity). Quick capacity calc for 250 concurrent workers • If target = 350 MB/process → ~71500 MB total ≈ 9–10 x 8 GB nodes (plus safety buffer → 11–12 nodes). • If target = 500 MB/process → ~125000 MB total ≈ 16 x 8 GB nodes (plus buffer → 18–20 nodes). If you’d like, share one measured memory sample (RSS in MB) from a representative process and I’ll calculate exact node counts and autoscale thresholds for your target. Sources: Deploying to custom environments | LiveKit docs | LiveKit turn detector plugin | LiveKit docs | Deploy and scale agents on LiveKit Cloud | examples/voice_agents | livekit/livekit_composite | DeepWiki | Silero VAD plugin | LiveKit docs

we are using livekit cloud

Thanks — since you're on LiveKit Cloud, here’s a concise plan you can apply immediately. Summary recommendation • Target per-process memory: ~300–500 MB for processes using GPT‑4o + VAD + AssemblyAI + ElevenLabs (aim lower if you can profile to optimize). • Agent instance: 4 vCPU / 8 GB RAM compute‑optimized (c6i/c7i) is a good starting point. • Concurrency per instance: plan ~10–25 concurrent jobs depending on measured per-process usage (use the lower end if closer to 500 MB). • Autoscaling: scale up earlier (e.g., at ~50% CPU) and keep short scale‑up cooldowns. Quick capacity math for 250 concurrent workers (LiveKit Cloud) • If per-process = 350 MB → ~18–20 x 8GB nodes (11–12 nodes after accounting typical headroom is incorrect; correct: total memory ≈ 87.5 GB → ~11 full 8GB nodes + buffer — use 12). • If per-process = 500 MB → ~16 x 8GB nodes (total ≈ 125 GB → ~16 nodes; add buffer → 18–20). Practical actions to avoid the 500 MB warning • Prewarm/preload VAD and other heavy components in the container image or at startup. • Profile one representative job under load to capture RSS memory; use that value to recalc node counts. • Limit per-process concurrency (one conversation per process if needed). • Use compute‑optimized instances (not burstable) to avoid throttling-induced spikes. • Monitor process RSS and set alerts lower than 500 MB if you want earlier warning. If you want, I can: • Recalculate exact node counts given a measured RSS number (paste the RSS MB), or • Suggest autoscale thresholds and an AgentServer

load_threshold

example tuned for LiveKit Cloud. Which would you like?

it has 16 gb per worker

Great — with 16 GB per LiveKit Cloud worker, you have more headroom. Here’s a concise plan tailored to that: Per-process target • Aim for 300–500 MB per job process for GPT‑4o + VAD + AssemblyAI + ElevenLabs. Use measured RSS to refine. Concurrency per 16 GB worker • If process ≈ 350 MB → ~40–42 concurrent jobs (16 GB ≈ 16,384 MB → 16,384 / 350 ≈ 46; allow headroom → 40–42). • If process ≈ 500 MB → ~28–30 concurrent jobs (16,384 / 500 ≈ 32; allow headroom → 28–30). Capacity for 250 concurrent workers (LiveKit Cloud) • At 350 MB/process → ~6 workers (250 / ~42 ≈ 6). • At 500 MB/process → ~9 workers (250 / ~28 ≈ 9). Include at least one extra worker as buffer for spikes and system overhead. Autoscaling and safety • Scale up at ~50% CPU or when job queue depth increases; keep short scale-up cooldowns. • Set process memory alerts below 500 MB if you want earlier warnings. • Prewarm/preload VAD and other heavy components to reduce per-process spikes. If you want, paste a measured RSS (MB) from one representative process and I’ll calculate exact worker counts and a suggested autoscale policy. Which would you like?

so idle processes per agents ts is what

You're asking what

idle_processes_per_agent

does and what it should be for your stack. Short answer and recommendation: What it is •

idle_processes_per_agent

sets how many warm (idle) job processes each agent keeps ready to accept new sessions without cold-starting. Effects • Memory: each idle process consumes RAM, so higher values increase baseline memory usage per agent. • Latency: more idle processes reduces cold-start latency when traffic spikes. • Scaling: it does not change per-process memory usage; it only changes how many prewarmed processes sit in RAM. Scaling capacity on LiveKit Cloud is independent of this setting. Recommended value for your stack (GPT‑4o + VAD + AssemblyAI + ElevenLabs) • Set to 1–2. ◦ Use 1 to minimize baseline memory if you need max density. ◦ Use 2 if you want lower cold-start latency for brief spikes. • Combine with autoscaling and 16 GB workers (your current setup) so you still have headroom. Would you like me to suggest exact

idle_processes_per_agent

plus autoscale thresholds based on a measured RSS number (paste one sample RSS in MB)?

│ ID │ Version │ Region │ Status │ CPU │ Mem │ Replicas │ Deployed At │ ├─────────────────┼─────────────────┼─────────┼─────────┼────────────┼─────────────┼────────────┼──────────────────────┤ │ CA_ubuZhpVxLJvL │ v20251218022643 │ us-east │ Running │ 6m / 8000m │ 0.82 / 16GB │ 4 / 1 / 16 │ 2025-12-18T023016Z │ └─────────────────┴─────────────────┴─────────┴─────────┴────────────┴─────────────┴────────────┴──────────────────────┘ -> what is going on here what does this mean

You're asking what the deployment row means. Briefly explained: • ID: deployment identifier (CA_ubuZhpVxLJvL) — the specific deployment instance. • Version:

v20251218022643

— deployed release tag/timestamp. • Region:

us-east

— where the deployment runs. • Status:

Running

— deployment is active. • CPU:

6m / 8000m

— current CPU usage is 6 millicores; quota/limit is 8000 millicores (8 vCPU). • Mem:

0.82 / 16GB

— current memory usage is 0.82 GB; limit is 16 GB per worker. • Replicas:

4 / 1 / 16

— shows replica counts in this format: ◦ first number (4) = desired replicas currently running, ◦ second (1) = minimum replicas configured, ◦ third (16) = maximum replicas allowed. • Deployed At: timestamp of when this version was deployed. What this implies for your stack • Each worker has 16 GB memory (matches earlier). Current usage is low (0.82 GB) across the deployment, so you have plenty of headroom now. • Desired replicas = 4 means four worker pods are running. Autoscaler can scale between 1 and 16 pods currently. • CPU quota=8 vCPU per worker; current CPU draw is minimal. Suggested next actions • If you plan for 250 concurrent jobs, calculate required replicas from measured per-process RSS and set the max replicas accordingly (you can use the earlier calculations I provided). • Ensure autoscale policy (scale-up threshold, cooldown) is set to react before pods reach high CPU/memory. • Monitor per-pod RSS to verify the ~300–500 MB per-process target; if memory per process rises, increase replica max or use larger instance types. If you want, paste a measured RSS (MB) from a representative process and I’ll compute exact replica targets and a recommended max replicas value for 250 concurrent workers.

calculate what we need to do on livekit cloud to get 250 concurrent workers we want to dial 250 people concurrently

Thanks — below is a concise, actionable plan for LiveKit Cloud to support 250 concurrent people using GPT‑4o + VAD + AssemblyAI + ElevenLabs TTS on your 16 GB workers. 1) Per-process memory target • Target: 300–500 MB per job process. Use measured RSS to refine. 2) Concurrency per 16 GB worker (safe headroom) • If 350 MB/process → ~42 concurrent jobs per 16 GB worker (use 40 for safety). • If 500 MB/process → ~28–30 concurrent jobs per 16 GB worker (use 28 for safety). 3) Worker count to hit 250 concurrent sessions • At 350 MB target → 250 / 40 ≈ 7 workers (use 7–8 to include buffer). • At 500 MB target → 250 / 28 ≈ 9 workers (use 9–10 to include buffer). Recommendation: plan for 8–10 workers of 16 GB each to cover variance and spikes. 4) Autoscaling & config (LiveKit Cloud) • Keep

idle_processes_per_agent = 1–2

. Use 1 to maximize density or 2 to reduce cold starts. • Scale-up trigger: ~50% CPU or job-queue depth threshold (scale early). • Short scale-up cooldowns; longer scale-down cooldowns. • Prewarm heavy components (VAD) and pre-download model assets in the image/build step. • Set process memory alert below 500 MB for early warning. 5) Capacity & safety • Add at least one extra worker as buffer during deploys/spikes. • Monitor per-pod RSS and adjust worker count upward if RSS > planned target. 6) Quick next steps I can do for you • If you paste one representative process RSS (MB), I’ll compute the exact recommended worker count and a sample autoscale policy (scale thresholds and cooldowns). Sources: examples/drive-thru | livekit/livekit_composite | DeepWiki | Deploying to custom environments | LiveKit docs | Deploy and scale agents on LiveKit Cloud | Agent server options | LiveKit docs | LiveKit Pricing

no 250 concurrent agents, each one in a room 1 on 1 with a person

Got it — 250 concurrent 1:1 rooms (each room = 1 agent + 1 person). The previous calculations apply — each “session” = one job/process. Final concise plan for 250 concurrent agents (1:1 rooms) on LiveKit Cloud with GPT‑4o + VAD + AssemblyAI + ElevenLabs: 1) Per-process target • Aim 300–500 MB per agent process. Profile to refine. 2) Worker sizing (you have 16 GB workers) • If process ≈ 350 MB → plan ~40 safe concurrent jobs per 16 GB worker. • If process ≈ 500 MB → plan ~28 safe concurrent jobs per 16 GB worker. 3) Workers required for 250 concurrent agents • At 350 MB → 250 / 40 ≈ 7 workers (use 7–8 for buffer). • At 500 MB → 250 / 28 ≈ 9 workers (use 9–10 for buffer). Recommendation: provision 8–10 workers (16 GB each) and let LiveKit Cloud autoscale within that range. 4) Runtime settings •

idle_processes_per_agent = 1–2

(use 1 for max density, 2 to reduce cold starts). • Scale-up trigger: ~50% CPU or job-queue depth; short scale-up cooldown, longer scale-down. • Prewarm/load VAD and other heavy components at build/startup. • Set memory alerts below 500 MB for earlier warning. 5) Safety buffer • Keep at least one extra worker for deploys and spikes; monitor per-pod RSS and increase workers if measured RSS > target. If you want exact numbers and a sample autoscale policy, paste one representative process RSS (MB) and I’ll compute the precise worker count and thresholds.

how do i provision correctly for livekit cloud im confused how do we make sure it is provisioned

You're asking how to correctly provision LiveKit Cloud so your deployment reliably supports 250 concurrent 1:1 agent rooms. Here’s a short, actionable checklist tailored to LiveKit Cloud. 1) Confirm plan and concurrency limits • Upgrade to the Scale (or Enterprise) plan so your project allows ≥250 concurrent agent sessions. 2) Deploy agents to LiveKit Cloud • Deploy your agent with the CLI:

lk agent create

/

lk agent deploy

. • LiveKit Cloud manages underlying VMs and autoscaling for you; you do not provision nodes manually. 3) Set runtime/prewarm behavior • Use

idle_processes_per_agent = 1–2

(1 for max density, 2 if you want fewer cold starts). • Pre-download and prewarm heavy components (VAD, any model assets) in your image/startup so processes don’t spike memory on first use. 4) Capacity planning (you have 16 GB workers) • Target per-process memory: 300–500 MB (profile to refine). • Plan for ~8–10 workers (16 GB each) to cover 250 concurrent agents with buffer: ◦ ~7–8 workers if per-process ≈ 350 MB ◦ ~9–10 workers if per-process ≈ 500 MB • Keep at least one extra worker for deploys/spikes. 5) Autoscaling & thresholds • Rely on LiveKit Cloud autoscaling; ensure your dashboard shows max replicas >= planned workers. • Use early scale-up (around 50% CPU or job-queue triggers) and short scale-up cooldowns; longer scale-down. 6) Monitoring & verification • Use the LiveKit Cloud dashboard /

lk agent status

and

lk agent logs

to check: ◦ Current replicas, CPU, and per-pod RSS memory. ◦ Concurrent sessions metric reaches 250 in tests. • Set memory alerts below 500 MB to catch rising usage early. 7) Test with load • Run a staged load test to 250 concurrent 1:1 rooms and observe: ◦ Per-process RSS, pod count, latency, and cold starts. • If RSS > target, increase worker count or reduce per-process load (prewarm, lower concurrency). If you want, paste a measured RSS (MB) from one representative agent process and I’ll compute the exact recommended worker count and a suggested autoscale policy (thresholds and cooldowns) for your LiveKit Cloud setup. Sources: Deploy and scale agents on LiveKit Cloud | Quotas and limits | LiveKit docs | LiveKit Pricing | Deploying to production | LiveKit Docs | Deploying to custom environments | LiveKit docs

Baseline Memory (Before Prewarm) | Worker | Heap | RSS | |----------|------|-------| | Worker 1 | 43MB | 396MB | | Worker 2 | 43MB | 441MB | After Prewarm Complete | Worker | Heap | RSS | RSS Delta | |----------|------|-------|-----------| | Worker 1 | 43MB | 420MB | +24MB | | Worker 2 | 44MB | 462MB | +21MB |

Thanks — this data is perfect. You measured per-worker RSS after prewarm of ~420–462 MB. I’ll treat each agent process ≈ 450 MB (safe midpoint) and compute precise recommendations for 250 concurrent 1:1 agent sessions on your 16 GB LiveKit Cloud workers. 1) Per-process baseline to use • Use 450 MB RSS per process (midpoint of 420–462 MB) for safe planning. 2) Concurrency per 16 GB worker (realistic safe capacity) • Usable memory per worker ≈ 16,384 MB. Reserve ~10% for OS/agent overhead → available ≈ 14,700 MB. • Concurrency = 14,700 / 450 ≈ 32 processes per worker (safe rounded value). 3) Workers required for 250 concurrent agents • 250 / 32 ≈ 7.8 → provision 8 workers. • Add one extra for safety/rolling deploys → recommend 9 workers total. 4) Autoscaling & runtime config • Set autoscaler max replicas ≥ 9 (preferably 10) and min replicas = 1–2 depending on cost tolerance. •

idle_processes_per_agent = 1

to maximize density; set to 2 only if you need lower cold-start latency. • Scale-up trigger: aggressive (e.g., ~50% CPU or increasing job-queue depth). Short scale-up cooldown (1–2 min), longer scale-down (5–10 min). • Prewarm is working (RSS increased only ~20–24 MB); keep prewarm to avoid cold loads. 5) Monitoring & operational checks • Monitor per-pod RSS during a load test to confirm ~450 MB holds under peak. • If observed RSS rises above 500 MB, increase workers to 10–11 and/or move to larger instances. • Set memory alert threshold at ~480 MB for early warning. If you want, I can: • Recalculate with a different per-process RSS (paste measured RSS), or • Provide suggested autoscale thresholds and sample

lk

dashboard checks. Which would you like?

what is the best way to implement assembly ai in typescript sdk ?

You're asking the best way to use AssemblyAI from a TypeScript app with LiveKit. Short answer: use AssemblyAI via LiveKit Inference (the TypeScript SDK supports this). The AssemblyAI plugin is Python-only, so direct plugin integration in TS isn't available. Recommended approaches 1) Use LiveKit Inference (recommended) • Configure STT on AgentSession with the AssemblyAI model string or

inference.STT

. • Example (basic):

import { AgentSession } from "@livekit/agents";

const session = new AgentSession({
  stt: "assemblyai/universal-streaming:en",
  llm: "openai/gpt-4.1-mini",
  tts: "cartesia/sonic-3:YOUR_TTS_ID",
});

• Example (advanced options):

import { AgentSession, inference } from "@livekit/agents";

const session = new AgentSession({
  stt: new inference.STT({
    model: "assemblyai/universal-streaming",
    language: "en",
    modelOptions: {
      format_turns: true,
      end_of_turn_confidence_threshold: 0.4,
      min_end_of_turn_silence_when_confident: 400,
      max_turn_silence: 1280,
    },
  }),
  llm: "openai/gpt-4.1-mini",
});

• Benefits: no AssemblyAI API key needed in your code, LiveKit handles the STT integration and billing; you can pass provider-specific options via

modelOptions

. 2) Use AssemblyAI directly (if you need full control) • Use AssemblyAI’s Realtime WebSocket/API from your Node/TS app. • Example (websocket):

const ws = new WebSocket('<wss://api.assemblyai.com/v2/realtime/ws?token=YOUR_TOKEN>');
ws.onmessage = (ev) => {
  const data = JSON.parse(ev.data.toString());
  if (data.text) console.log("Transcript:", data.text);
};

• Use this if you need features not exposed by LiveKit Inference or want to use your own AssemblyAI account. Best practices • Prefer LiveKit Inference for simplicity, managed scaling, and compatibility with AgentSession flows. • If using direct AssemblyAI: stream audio with small frames, handle reconnects/backpressure, and secure your API key (server-side only). • Profile latency and accuracy settings via

modelOptions

. • Test end-to-end in a staging load to confirm memory and latency behavior. If you want, tell me: • Whether you prefer LiveKit-managed Inference or direct AssemblyAI, and • One example use case (live transcription, diarization, low-latency captions), and I’ll give a tailored TypeScript sample and recommended

modelOptions

. Sources: AssemblyAI plugin guide | LiveKit Docs | AssemblyAI STT | LiveKit Docs | Models | LiveKit Docs

livekit infrence is always buggy and i get error connecting to websocket almost everytime

Sorry you’ve run into flaky Inference websockets — that’s frustrating. I’ll keep this short and focused on immediate, practical fixes and a reliable fallback path. What to try right now (quick triage) • Run the LiveKit Connection Tester to confirm WebSocket/TURN issues: https://livekit.io/connection-test • Check browser/agent logs for websocket errors (error code, close reason, timing). Note if failures occur behind a corporate proxy or NAT. • If failures correlate with cold starts or many simultaneous connects, increase prewarmed processes briefly to confirm whether load spikes cause the disconnects. Reliable production approach (recommended) 1. Use a fallback adapter: try Inference first, then fall back to direct AssemblyAI if Inference websocket fails. This minimizes user impact. ◦ In TS, wrap STT providers in a simple fallback that attempts Inference and on connect failure switches to a direct AssemblyAI websocket. 1. Use the AssemblyAI plugin / direct AssemblyAI for production STT if Inference remains unreliable: ◦ Plugin gives you direct control and a stable WS endpoint and avoids the gateway layer that’s failing. ◦ This requires your AssemblyAI API key and handling reconnection/backpressure, but is more predictable. Operational best practices • Keep

idle_processes_per_agent = 1

(or 2 briefly during rollout) so prewarm memory impact is small but you can reduce cold-start spikes during tests. • Pre-download and prewarm VAD/STT assets in your image/startup. You already saw only ~20–24MB RSS increase on prewarm — prewarm helps. • Implement exponential backoff + jitter and a short retry window for websocket connects; failover to AssemblyAI after N failed attempts. • Instrument and alert on websocket failure rate and session connect latency so you can detect gateway regressions quickly. If you want, I will: • Provide a minimal TypeScript fallback pattern (pseudo-code + reconnect logic) you can drop into your AgentSession STT wiring, or • Provide a sample using the AssemblyAI plugin/direct WS connect with reconnection/backpressure handling. Which would help you most right now? Sources: Deploy and scale agents on LiveKit Cloud | Quotas and limits | LiveKit docs | LiveKit Pricing | Deploying to custom environments | LiveKit docs | Models | LiveKit Docs

any other tips for outbound calling with livekit cloud

Good — here are focused, practical tips to ensure reliable outbound calling on LiveKit Cloud for 250 concurrent 1:1 agent calls. 1) Plan & account limits • Confirm your project is on the Scale/Enterprise plan so concurrency ≥250 is allowed. • Ask LiveKit sales to pre-provision capacity if you expect sustained peaks. 2) Use proper SIP trunk config • Create an outbound trunk with provider domain, credentials, and numbers. Use

numbers: ["*"]

if you’ll set

sip_number

per call. • Use username/password auth (not IP auth) and enable TLS if provider supports it. 3) Dial flow best practices • Use CreateSIPParticipant with

wait_until_answered: true

to avoid starting agent logic before answer. • Include helpful metadata (caller id, attempt id) so logs correlate to calls. 4) Capacity & worker sizing (practical) • With your measured ~450 MB RSS/process and 16 GB workers: plan ~32 concurrent processes per worker. • For 250 agents, provision 8 workers and keep a 9th as buffer (autoscaler max ≥9–10). • Ensure

idle_processes_per_agent = 1

(or 2 briefly during rollout) and prewarm heavy components. 5) Autoscale & thresholds • Scale-up early (e.g., ~50% CPU or increasing job-queue depth). Short scale-up cooldown (1–2 min); longer scale-down (5–10 min). • Ensure dashboard max replicas ≥ planned workers and set min replicas = 1–2 if you want baseline capacity. 6) STT reliability • If LiveKit Inference websocket is flaky, use a fallback: try Inference first, then fall back to direct AssemblyAI (plugin or direct WS). • For production stability prefer using AssemblyAI plugin/direct WS (with your API key) if Inference remains unreliable. 7) Connection resilience & retries • Implement exponential backoff + jitter for websocket reconnects; fail to fallback STT after N attempts. • Retry SIP participant creation with idempotency keys and backoff on transient SIP errors. 8) Observability & alerts • Monitor per-pod RSS, connect latency, websocket failure rate, and SIP call status. • Set memory alert ~480 MB, and alert on >X% increase in websocket failures or call drop rate. 9) Call failure handling • Detect call states (

sip.callStatus

→

active

,

failed

) and implement retry/callbacks and escalation logic. • Log SIP codes and provider responses for troubleshooting (Telnyx/Plivo logs are invaluable). 10) Testing & rollout • Do staged load tests: start at 25%, 50%, 75% of target concurrency, verify RSS, CPU, latency, and cold-start behavior. • Use short ramp-up windows before full production; keep one or two warm extra workers during deploys. If you want, I can: • Provide a minimal TypeScript snippet that creates SIP participants with

wait_until_answered

and retry logic, or • Draft a fallback STT pattern (Inference → AssemblyAI) in TypeScript you can drop into your agent session. Which would you like? Sources: SIP outbound trunk | LiveKit docs | Make outbound calls | LiveKit docs | Agents telephony integration | LiveKit docs | Deploy and scale agents on LiveKit Cloud | Deploying to custom environments | LiveKit docs

should we do a slow rollout with 10 workers dialing every minute to begin scaling up to 250 workers or just scale up earl yim cnfused

Do a controlled, phased rollout — not an immediate full jump. That balances safety and user experience while letting autoscaling and monitoring prove your assumptions. Recommended phased plan 1. Pilot: start with 8–10 workers, dialing at low rate (e.g., 1 call/min/worker). Validate call flow, STT/TTS stability, SIP trunk behavior, and per-pod RSS/CPU. 2. Ramp stages: increase concurrency in steps (e.g., 25% → 50% → 75% → 100% of target). At each stage run for 30–60 minutes and verify metrics before moving on. ◦ Example stages: 25 callers, 62, 125, 187, 250. 1. Autoscale enablement: ensure LiveKit Cloud autoscaling is active and configured with conservative scale‑up thresholds (scale at ~50% CPU or queue depth). Let autoscaler add pods as load increases. 2. Safety buffer: keep at least 1–2 extra workers available during each stage for spikes and deployments. 3. Failure & fallback: implement STT fallback (Inference → direct AssemblyAI) and retry/backoff for SIP creation so partial failures don’t cascade. 4. Monitoring gates (must pass before next stage): ◦ Per-pod RSS within expected bounds (your ~450MB target). ◦ CPU < 70% on average. ◦ Websocket/STT error rate < acceptable threshold (e.g., <1–2%). ◦ Call answer rate and audio latency within SLAs. 1. Rollback plan: if any gate fails, stop the ramp and either hold or roll back to previous stage; investigate logs and SIP provider responses. Why this approach • Gradual ramps surface problems (network, trunk, STT) without impacting all calls. • Autoscaling handles resource needs, but staged load shows autoscaler responsiveness and config gaps. • Keeps user experience acceptable while you tune thresholds and instance counts. If you want, I’ll produce: • A concrete ramp schedule (times, counts) tailored to your worker RSS (450MB) and desired safety buffer, or • A sample autoscale policy and monitoring thresholds you can paste into your dashboard. Which do you prefer?

like how do we set this up for livekit cloud ) Autoscale & thresholds • Scale-up early (e.g., ~50% CPU or increasing job-queue depth). Short scale-up cooldown (1–2 min); longer scale-down (5–10 min). • Ensure dashboard max replicas ≥ planned workers and set min replicas = 1–2 if you want baseline capacity. -> how do i make sure this is enabled Autoscale enablement: ensure LiveKit Cloud autoscaling is active and configured with conservative scale‑up thresholds (scale at ~50% CPU or queue depth). Let autoscaler add pods as load increases.

Good question — here’s exactly how autoscaling works on LiveKit Cloud and how you can confirm it’s active and behaving as you expect. Quick facts • LiveKit Cloud autoscaling is automatic — you do not manually set CPU thresholds, replica counts, or cooldowns. The platform scales worker instances based on effective load (CPU, memory, job queue, session count). • You still control plan limits (make sure you’re on Scale/Enterprise for 250+ concurrent sessions) and can verify min/max capacity via the dashboard. How to confirm autoscaling is enabled and healthy 1. Confirm plan and quota ◦ Verify your project is on Scale/Enterprise and supports ≥250 concurrent agent sessions in the Cloud dashboard. 1. Check deployment status & replica limits ◦ Use the LiveKit Cloud dashboard or

lk agent status

to see current replica count, min/max replicas, and worker memory/CPU. Ensure max replicas ≥ the number of workers you plan to reach. 1. Observe autoscaling in a ramp test ◦ Run a staged load (25% → 50% → 75% → 100%) and watch: ▪︎ Replica count increasing as load rises. ▪︎ Per-pod RSS and CPU remain within expected bounds. ▪︎ New pods become Ready and accept sessions without errors. ◦ Use

lk agent logs

and dashboard metrics to correlate scaling events with load. 1. Validate behavior you expect ◦ Cold-starts: verify

idle_processes_per_agent

prewarm behavior is working (1–2). ◦ Drain/rollout: perform a deployment and confirm rolling update + drain windows allow active calls to finish. What you can configure (and what you can’t) • You can: choose plan, set

idle_processes_per_agent

, prewarm assets, and set min replicas (via dashboard if desired). You can also request higher max concurrency from LiveKit if needed. • You cannot: set explicit scale-up CPU% or cooldown timers — LiveKit Cloud manages those internals. If autoscaling seems slow or you hit limits • Verify plan/concurrency quota (upgrade or request pre-provisioning). • Increase min replicas to keep baseline capacity during ramps. • Add one or two warm extra workers before expected spikes (short-term manual buffer). • Run rolling load tests and capture timestamps/logs showing scale events — share them and I can interpret whether scaling lag is platform-related or caused by policy/usage. If you’d like, I can: • Provide a step-by-step ramp test script and what to watch in the dashboard, or • Review your

lk agent status

output and recent logs and point out any scaling issues. Which would you prefer? Sources: Deploying to production | LiveKit Docs | Deploying to custom environments | LiveKit docs | AssemblyAI plugin guide | LiveKit Docs | Agents Framework | livekit/livekit_composite | DeepWiki | Resolving &amp;#34;InvalidState - failed to capture frame&amp;#34; errors in AudioSource

Run a staged load (25% → 50% → 75% → 100%) and watch: how do i set up a staged result for testing is there a easy way to do this

Yes — the easiest way is to run the LiveKit CLI load test in stages (25 → 50 → 75 → 100%) and watch the dashboard/metrics between stages. Here’s a minimal, safe plan and a ready-to-run script. Plan • Choose full target (e.g., 250 rooms). Stages: 25% (62), 50% (125), 75% (187), 100% (250). • Run each stage for 5–10 minutes, then wait 2–5 minutes to observe autoscaling and pod readiness. • Pass/fail gates before moving to next stage: per-pod RSS ~450MB, CPU <70%, websocket/STT error rate acceptable, call answer rate OK. Script (bash) — update URL/keys/target counts/duration:

#!/usr/bin/env bash
URL="<wss://your-domain.livekit.cloud>"
KEY="YOUR_API_KEY"
SECRET="YOUR_API_SECRET"
ROOM="staged-load-test"
DURATION="5m"         # per stage
PAUSE="180"           # pause between stages (seconds)
STAGES=(62 125 187 250)  # 25%,50%,75%,100% for 250 target

for SUBS in "${STAGES[@]}"; do
  echo "Starting stage: $SUBS subscribers"
  lk load-test \
    --url "$URL" \
    --api-key "$KEY" \
    --api-secret "$SECRET" \
    --room "$ROOM" \
    --video-publishers 0 \
    --subscribers "$SUBS" \
    --duration "$DURATION" \
    --render-type scalable

  echo "Stage complete. Waiting $PAUSE seconds to observe autoscaling..."
  sleep "$PAUSE"
done

echo "Staged load test finished."

What to watch during and between stages • Dashboard: replica count, pod readiness, concurrent sessions. • Per-pod metrics: RSS, heap, CPU. Confirm RSS ~450MB and CPU <70%. • Error rates: websocket/STT failures, call drops, SIP errors. • Cold starts: check whether new pods accept sessions quickly. • Autoscaling behavior: replica count should increase before pods hit capacity. If anything fails • Pause ramp, diagnose logs (

lk agent logs

), check websocket/STT errors, increase prewarm to 2 briefly or add one extra worker, then resume after fixes. Optional enhancements • Automate gates: script API checks to read metrics and only continue when gates pass. • Run load-test from multiple machines if the single client becomes a bottleneck. • Increase

ulimit -n

on test machine (e.g., 65535) to avoid file descriptor limits. Would you like: • A version of the script that auto-aborts on a failed gate (CPU/RSS thresholds), or • A sample Node/TS harness for more control and parallel generation? Sources: Benchmarking | LiveKit docs | Self-hosting the Ingress Service | LiveKit docs | Self-hosting the Egress Service | LiveKit docs

how do i setup You can: choose plan, set

idle_processes_per_agent

, prewarm assets, and set min replicas (via dashboard if desired). You can also request higher max concurrency from LiveKit if needed.

You're asking how to actually configure those items on LiveKit Cloud. Here's a concise, step‑by‑step checklist with where to act (dashboard, agent code, or LiveKit team). 1) Choose plan (dashboard / sales) • Go to LiveKit Cloud dashboard → Billing/Plan and confirm you’re on Scale or Enterprise (supports 250+ concurrent agents). • If limits are insufficient, request capacity increase via the dashboard’s contact/sales flow. 2) Set

idle_processes_per_agent

and prewarm assets (agent code / image) • In your agent code (AgentServer/AgentSession), set

idle_processes_per_agent

(or

num_idle_processes

) to 1–2: ◦ 1 = maximize density, 2 = fewer cold starts. • Pre-download heavy assets in your container build or startup script so they’re already on disk before process start (example: run your model download script in Dockerfile during build). • Call your prewarm logic at agent start so processes load VAD/STT weights and hold them warmed. 3) Set min replicas / view max replicas (dashboard) • Open LiveKit Cloud dashboard → Agents/Deployment view. You can: ◦ See current replica count and the configured max replicas. ◦ Set a sensible min replicas (1–2) via the dashboard if you want baseline always-warm capacity. • If you don’t see controls for max replicas or need higher concurrency limits, request it from LiveKit (sales/support contact in dashboard). 4) Verify autoscaling behavior (dashboard + tests) • Autoscaling is automatic on LiveKit Cloud; it uses effective load (job count, CPU/memory). You cannot set CPU% thresholds directly. • Verify autoscaling by running staged load tests (CLI script I provided earlier) and watching replica changes in the dashboard. 5) Practical actions to perform now • Upgrade to Scale/Enterprise if not already done. • Update agent image to pre-download models in Dockerfile and call prewarm in startup. • Set

idle_processes_per_agent = 1

in your server config and deploy. • In dashboard set min replicas = 1–2 and confirm max replicas ≥ planned workers (or request higher). • Run staged load test and observe autoscaling; adjust min replicas or ask LiveKit to pre-provision capacity if you see slow scale-up. If you want, I can: • Provide a small Dockerfile snippet for pre-downloading assets and a short AgentServer config example with

idle_processes_per_agent

, or • Walk through where to click in your LiveKit Cloud dashboard if you paste a screenshot of the Agents/Deployment page. Which would help most?

any other agent server tips for outbound callers

Great — here are concise, high-value agent server tips specifically for outbound callers on LiveKit Cloud (quick actionable items you can apply right away). 1) Use explicit dispatch & ROOM agents • Give outbound agents a name and dispatch them explicitly so calls only start when you want: ◦ Use

agent_name

on dispatch and

ServerType.ROOM

so one agent = one call. 2) Prewarm heavy components • Pre-download model assets in your Dockerfile/build step. • Run a

prewarm

function at agent start to load VAD/STT into memory once per worker (reduces cold-start spikes). 3) Start muted / wait until answered • When creating SIP participant, use

wait_until_answered: true

. • Keep agent audio input disabled until answer, then enable listening; prevents premature speech. 4) Retry & idempotency for SIP creation • Wrap

CreateSIPParticipant

in retry with exponential backoff and idempotency keys. • Log SIP status codes and provider responses for debugging. 5) Keep calls isolated • Use ROOM type (one agent per room) to avoid resource contention and simplify scaling/observability. 6) Tune idle processes and prewarm counts •

idle_processes_per_agent = 1

to maximize density; use 2 during ramp if you need fewer cold starts. • Prewarm only the necessary models to limit RSS growth. 7) Drain/rollout safety • Ensure drain timeout is long enough for typical call length (default 30m is usually fine). • Keep at least one extra worker during deploys to avoid capacity gaps. 8) Observability & alerts • Monitor per-pod RSS, CPU, websocket/STT error rate, and call answer rate. • Set memory alert around ~480 MB (your measured ~450 MB/process). • Alert on websocket failure rate and SIP failure rate. 9) STT fallback & resilience • If LiveKit Inference is flaky, implement fallback: try Inference first, then switch to direct AssemblyAI (plugin or WS) after N failures. • Use exponential backoff + jitter for reconnects and fallback switching. 10) Load testing & safe ramp • Do staged ramps (25/50/75/100%) using

lk load-test

script. Pause between stages to verify autoscaling and metrics. • Validate gates before proceeding (RSS, CPU, error rate, answer rate). 11) Separate inbound vs outbound deployments • Run distinct agent deployments for inbound and outbound traffic for isolated scaling and easier troubleshooting. 12) Region & compliance • Pin trunk destination region when necessary for regulatory or latency reasons (

destination_country

). If you’d like, I can: • Provide a short TypeScript or Node snippet that: ◦ Creates SIP participants with retries and

wait_until_answered

, and ◦ Implements STT fallback (Inference → AssemblyAI). • Or produce a small Dockerfile + prewarm example you can use today. Which would you prefer? Sources: Agent server options | LiveKit docs | Introducing LiveKit Inference: A unified model interface for voice AI | AssemblyAI plugin guide | LiveKit Docs | Deploy and scale agents on LiveKit Cloud | AssemblyAI STT | LiveKit Docs