why is wait_until_answered giving issues on self hosted livekit SIP?

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why my self hosted livekit is taking time in dispatching the calls on prod? but when i do it from my local the call gets triggered instantly?

Thanks — I understand you’re seeing slow call dispatch in your self‑hosted production LiveKit (calls fire instantly locally but take time in prod). I can help track this down. I don’t yet have enough details to pinpoint the root cause, but here are the most likely causes and the exact information/logs I need to diagnose quickly. Likely causes to check (what you can do now) • Network latency / proximity: If your agent, LiveKit server, or SIP gateway are in different regions or behind slow network hops, dispatch will be slower than a local run. • Agent worker availability: If your production agent has only one worker or is under-provisioned, dispatch requests can queue or time out. Run multiple agent workers per agent_name for redundancy. • Burstable instance throttling: If you’re using burstable cloud instances (t3/t4), CPU credit exhaustion can throttle CPU and cause multi‑second delays. Use non‑burstable instance types (m5/c5/c6i) or enable unlimited credits. • Explicit dispatch / registration delays: If you use explicit dispatch, ensure agents are registered and ready before dispatch. Misconfigured dispatch rules or API latency can delay room assignment. • Cross‑region or multi‑node sync: Cross-region creation or CreateRoom() calls can add overhead. Let rooms auto‑create on join where possible to reduce latency. • RTP/media or SIP handshake issues: If the SIP endpoint never reaches a final answered state or ACK/200 OK sequences are missing, waits can appear like dispatch delays. What I need from you to investigate 1. Symptom details ◦ Exact behavior: are calls queued, eventually dispatched after N seconds, failing, or returning an error? ◦ Any error messages or stack traces you see (LiveKit, agent, or app). 1. Environment & versions ◦ LiveKit server version, agent version, and whether this is the standard agent/telephony integration or a custom agent. ◦ Cloud provider and instance types used for LiveKit/agent in prod. 1. Agent & scaling config ◦ Number of agent workers running per agent_name in prod. ◦ Any autoscaling configuration or resource limits. 1. Logs (short, redacted) ◦ LiveKit server logs around the delayed dispatch attempt (include timestamps). ◦ Agent logs showing dispatch attempts. ◦ SIP gateway/provider logs (INVITE, 180/200/ACK) if available. 1. Network details ◦ Are LiveKit, agents, and SIP gateway in the same region/VPC? Any NAT/firewalls between them? ◦ TURN/STUN usage and whether media paths are working. 1. Test comparison ◦ Do you see the same delay if you run a production‑like agent locally (same version/config) pointed at the prod LiveKit URL? ◦ Is the delay consistent or intermittent under load? Quick troubleshooting steps you can run now 1. Confirm agent workers: increase worker count temporarily and retry dispatch. 2. Check instance type: switch a test agent to a non‑burstable instance and compare latency. 3. Compare logs for a fast (local) vs slow (prod) dispatch attempt to find where lag starts. 4. Use a connection test from the prod host to verify WebSocket/turn connectivity and RTT. 5. Ensure timeouts are sufficiently long so calls aren’t aborted while waiting for answer signals. If you send: • A short excerpt of the LiveKit and agent logs around a delayed dispatch, • The

CreateSIPParticipantRequest

(redact secrets) or dispatch call you’re making, • Your prod instance type and agent worker count, I’ll analyze them and point to the exact step that’s introducing delay and recommend a concrete fix. Which of the requested items can you share first? Sources: Agents Framework | livekit/livekit_composite | DeepWiki | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | Agent dispatch | LiveKit Documentation | CreateRoom() is Taking Too Long | Diagnosing Connection Errors with Connection Test Utility

my prod server and self-livekit are under same VPC, and when i try an outbound call from local it disptached. I can see that my instance on AWS for livekit agents has a huge network-in and network-out can this be the reason of dispatch taking soo much time?

Thanks — that’s helpful context. Since your prod server and LiveKit agent are in the same VPC and you can dispatch instantly from local, high network traffic on the AWS agent instance could indeed contribute to dispatch delays, but it’s not the only possible cause. Let’s narrow this down with a few checks and targeted troubleshooting steps. What to check next (quick wins) • Instance network saturation: Look at NetworkIn/NetworkOut metrics in CloudWatch at the time of delayed dispatch. If sustained near the NIC bandwidth limit, packets (including signaling) can be delayed or dropped. • CPU and I/O usage: High network can coincide with high CPU or disk I/O. Check CPUUtilization, DiskRead/Write metrics, and packet drop counters. Burstable instance throttling (t3/t4) can also slow processing even if network looks busy. • Packet drops and retransmits: Check for network interface errors, retransmits, or ENI drops on the instance. High retransmits cause SIP/WebSocket/RTP delays. • Socket/ephemeral port exhaustion: Very high connection volume can exhaust available ports or cause slow accepts. Monitor ephemeral port usage and TCP connection states (TIME_WAIT). • Agent worker saturation: If worker processes are busy processing other tasks, they may be slow to pick up new dispatches even if network capacity exists. Immediate troubleshooting steps 1. Correlate timestamps: Match a delayed dispatch timestamp with CloudWatch metrics (NetworkIn/Out, CPU, Disk I/O) and agent process metrics. That will show whether network or CPU is peaking at the same time. 2. Temporarily scale up network/CPU: Launch a test agent on a larger instance family (non‑burstable, higher network bandwidth) in the same VPC and rerun calls to compare latency. 3. Rate test from within VPC: From the agent host, run a small network test to the LiveKit server (ping, traceroute, iperf) to measure latency and throughput during normal and delayed periods. 4. Check OS network stats: Run

netstat -antp | wc -l

,

ss -s

, and `ifconfig`/`ethtool` to spot errors, high connection counts, or drops. 5. Review agent logs: Look for queueing, “room connection not established” timeouts, or long GC/processing pauses around delays. What I need from you to help further • Instance type (e.g., t3.medium, m5.large) and whether it’s burstable. • CloudWatch snapshots around a delayed dispatch (NetworkIn/Out, CPU, Disk I/O) for the time window. • Agent worker count and agent process CPU usage at the time. • A short redacted agent log excerpt around one delayed dispatch (timestamps help). If you want a fast test: spin up a non‑burstable instance (m5/c5) in the same VPC, register an agent there, and try dispatching — if dispatches are instant from that instance, the issue is likely instance resource or NIC saturation. Tell me the instance type and share one CloudWatch snapshot you mentioned and I’ll point to the most likely bottleneck and a recommended fix.

i am using a c4 type instance for my lievkit agents server, can the cause be too many agents created?

Thanks — that’s a useful detail. Yes, having too many agents running on a single c4 instance can definitely contribute to dispatch delays in prod. I’ll explain why and then give concrete next steps to confirm and fix it. Why many agents on a single c4 can slow dispatch • Resource contention: Each agent consumes CPU, memory, and network. When many agents share a single instance, CPU and memory can become saturated and new dispatch jobs wait for an available worker. • Processing latency: Agent work (signaling, room setup, STT/LLM/TTS, media handling) can be CPU/network heavy. If workers are busy, dispatch can be delayed or time out even though the instance is not “bursting.” • Network saturation: Even on non‑burstable instances, high NetworkIn/Out can lead to packet drops or retransmits that slow signaling and SIP/WebSocket flows. • Limits in job acceptance: Agents may stop accepting new jobs if load crosses configured thresholds (so new dispatches queue or timeout). What I recommend you check now (quick diagnostics) 1. Agent count and process metrics ◦ How many agent processes (workers) are you running on that c4 instance? ◦ Run `top`/`htop` or check your process manager to see per‑agent CPU and memory. 1. CloudWatch correlation ◦ Pull NetworkIn, NetworkOut, CPUUtilization, and NetworkPacketsDropped around a delayed dispatch timestamp. ◦ If NetworkIn/Out is near the NIC limit or you see increased packet drops/retransmits, that’s a likely contributor. 1. Check for dispatch timeout logs ◦ Search agent and LiveKit server logs for messages like “room connection not established within 10 seconds” or any queueing/timeout errors. 1. OS/network state ◦ Inspect ephemeral port usage and TCP states (

ss -s

,

netstat -antp | wc -l

). ◦ Check for interface errors (`ifconfig`/`ethtool`) or high retransmits. Quick experiments to isolate cause • Temporarily reduce the number of agents on the c4 instance and re-run a few calls. If dispatch latency improves, you’ve identified capacity as the issue. • Spin up one test agent on a larger instance (c5/c6i or an instance with higher network bandwidth) in the same VPC and compare dispatch latency. If it’s faster there, upgrade or scale out in prod. • Register an additional agent on a different host (horizontal scale) to spread load and see if dispatch improves immediately. Recommended fixes • Scale horizontally: Run fewer agents per instance and add more instances so each agent has adequate CPU/memory. • Upgrade instance type: Move to a newer compute family (c5/c6) with better CPU and network performance if you want vertical scaling. • Add auto‑scaling or more workers to handle burst traffic and avoid single-point overload. • Monitor key metrics and alert when CPU/network usage or agent queue depth is high. What I can help with next • If you share: instance type details (exact c4 flavor), number of agent workers running, one CloudWatch snapshot around a delayed dispatch, and a short redacted agent log around a delayed call, I’ll point to the exact bottleneck and give a concrete sizing recommendation. Which item can you share first — number of agents running on that instance or a CloudWatch snapshot for a recent delayed dispatch? Sources: Agents Framework | livekit/livekit_composite | DeepWiki | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | Server options | LiveKit Documentation

what is the role of _initialize_process_timeout in agent server?_

Thanks — good question. I don’t have a direct reference describing

initialize_process_timeout

in the available LiveKit agent docs, so I can’t state its exact behavior from those sources. To help you accurately, I need one quick clarification: • Where did you see

initialize_process_timeout

(agent config file, deployment manifest, or a docs link)? If you can paste the surrounding config snippet (redact secrets), that will let me confirm its role and how it affects your agents. While you grab that, here’s how we’ll investigate and what I’ll check once you share the snippet: • I will look for whether it controls how long the agent process waits for worker/process initialization before marking the agent unavailable for dispatch. • I will check if it affects agent registration/health checks with the LiveKit server and whether exceeding the timeout causes dispatch delays or worker rejection. • I’ll correlate any timeout behavior with your agent logs to see if initialization is failing or taking too long (helpful for your dispatch delays). If you’re seeing symptoms now (e.g., agents not registering, dispatches delayed with “connection not established within 10 seconds”, or agent restart loops), tell me which symptom you observe and share the agent logs around startup — that will let me confirm whether this setting is the cause and recommend the right timeout value or mitigation. Sources: Server options | LiveKit Documentation

and what is the role of _num_idle_processes?_

Thanks — good question. Here’s a concise explanation of what

num_idle_processes

does and how it affects your agent behavior and call dispatch latency. What

num_idle_processes

controls • It sets how many prewarmed (idle) worker processes the agent keeps ready to handle incoming jobs. • Each incoming job (SIP call, room session) is assigned to a worker process; prewarmed processes have already run the

prewarm

routine (loading models, initializing libraries, etc.) so they can start handling work immediately. How it affects dispatch latency and resource use • Higher

num_idle_processes

reduces cold-start latency because calls are assigned to already-initialized processes instead of waiting for a new process to start and load resources. • Idle processes consume memory and some CPU; increasing this value improves responsiveness at the cost of higher baseline resource usage. • If

num_idle_processes

is too low for your traffic, dispatches will queue while new processes spin up, causing the delays you see in production (but not locally where load is lower). When to increase it • If you see dispatch delays under load (calls waiting while processes initialize) or long cold starts for features like STT/LLM/TTS, raise

num_idle_processes

. • Balance it against instance capacity: ensure the instance has enough RAM/CPU to host the configured number of idle workers without swapping or CPU contention. Recommended steps 1. Check current utilization (CPU/memory) and the number of concurrent calls during peak times. 2. Start by increasing

num_idle_processes

modestly and observe dispatch latency and resource usage. 3. If resource limits are reached, scale horizontally (more agent instances with fewer idle processes each) or upgrade instance type (higher memory/CPU and better network). 4. Correlate any improvements with agent logs showing immediate worker assignment vs. worker creation/cold-start messages. If you share your current

num_idle_processes

value, agent worker count, and one recent agent log snippet showing a delayed dispatch, I’ll recommend a concrete number to try and show what to watch in logs/metrics when you test. Sources: Server options | LiveKit Documentation | SIP Lifecycle Management Agent | LiveKit Documentation | examples/dtmf | livekit/livekit_composite | DeepWiki | Playing Audio | LiveKit Documentation

I have a muli client application, with over 100 agents what sould i do in AgentServer to make sure everything runs smoothly

Thanks — great question. Running 100+ agents is a large scale and you’ll want to tune AgentServer and your infra to avoid dispatch delays, failures, or resource exhaustion. Below are focused, practical recommendations you can apply right away plus the most important metrics and checks to watch. High-level strategy • Favor horizontal scaling and multi‑tenant agents rather than 1:1 dedicated agents for every client. A single multi‑tenant agent that loads tenant config at session start reduces deployment and operational overhead. • Prewarm worker processes so calls use already-initialized processes instead of paying cold-start cost per job. • Keep agent instances healthy and avoid over-subscribing CPU, memory, or NIC capacity. Concrete configuration & operational recommendations 1. num_idle_processes (prewarming) ◦ Keep several prewarmed processes ready to handle instant traffic. Each idle process removes cold-start latency at the cost of memory. ◦ Start small (e.g., 2–5) and increase while observing memory usage and latency. 1. initialize_process_timeout ◦ I don’t have a direct doc snippet for this parameter, but if present it likely controls how long agent waits for worker initialization. If initialization can take several seconds (model loads), raise this timeout to avoid premature failures. Share the config snippet and I’ll confirm exact behavior. 1. Workers & horizontal scaling ◦ Run multiple agent worker instances under the same agent_name for redundancy and load distribution. ◦ Aim for at least 3 workers per agent type for high availability; scale out more as load grows. 1. Instance sizing and placement ◦ Use modern compute families (c5/c6) with good network bandwidth and CPU rather than older/smaller types. ◦ Avoid overloading a single instance with too many agents—each agent process consumes CPU/memory/network. ◦ Place agents close to the LiveKit server and any external AI/STT providers to reduce RTT. 1. Autoscaling & load gating ◦ Use load-based autoscaling (based on a

load_fnc

or system metrics) and configure thresholds conservatively so you scale up before the instance becomes saturated. ◦ Make sure agents reject new jobs when load is high rather than accepting and causing long queueing. 1. Networking and NIC sizing ◦ Watch NetworkIn/NetworkOut and packet drop/retransmit metrics. High sustained throughput or drops cause signaling and RTP delays. ◦ If you see NIC saturation, move to larger instances or spread agents across more instances. 1. Observability & metrics to monitor ◦ CPUUtilization, MemoryUsed, NetworkIn/Out, NetworkPacketsDropped ◦ Agent process counts, active jobs, queue depth, and time-to-assign job ◦ Logs for timeouts like “room connection not established within 10 seconds” ◦ TTFT / time-to-first-media and cold-start indicators in your app logs 1. Diagnostics to run now ◦ Temporarily reduce agents on a crowded host and see if dispatch latency improves. ◦ Spin up a test agent on a larger host (c5/c6) in the same VPC and compare dispatch times. ◦ Correlate delayed dispatch timestamps with CloudWatch metrics (CPU, network, packet drops). ◦ Check OS-level socket and TCP stats (ss/netstat), ephemeral port usage, and interface errors. 1. Scaling pattern suggestions ◦ Multi-tenant single agent for many clients (hydrate tenant config at session start). ◦ For heavy/isolated clients, use dedicated agent pools and autoscale them separately. ◦ Keep prewarm processes small per instance and favor adding more instances to handle scale. Next steps I can help with • Recommend exact

num_idle_processes

values given your instance sizes, average concurrent sessions, and memory footprint—share those numbers and a sample process memory profile. • Review a CloudWatch snapshot (CPU, NetworkIn/Out, packet drops) and an agent log excerpt around a delayed dispatch and I’ll point to the bottleneck. • If you want, paste your agent config (redact secrets) and I’ll review specific settings like prewarm, worker count, and timeouts. Which would you like to start with — CloudWatch snapshot + a short agent log, or your current agent config and instance sizes? Sources: How much can LiveKit scale? | LiveKit | Can you increase agent deployment limits? | LiveKit | Server options | LiveKit Documentation | Agents Framework | livekit/livekit_composite | DeepWiki | Self-hosted deployments | LiveKit Documentation

I am observing a strange issue, I have self-hostel livekit server which is taking too long to Dispatch outbound calls, but if i am using the same credentials locally that calls dispatch instantly. I had to apply retry on my LivekitAgents server to overcome this problem but its becoming bottleneck as even making a single call from an agent takes around 4-5mins and can go into retries but the same LivekitAgent server running on my local using the same self-hostel livekit endpoint works perfectly fine.

I’m sorry you’re dealing with this — 4–5 minute dispatch delays in prod are definitely urgent to fix. You’ve already done useful troubleshooting (same credentials work locally), so let’s focus on targeted diagnostics and the most likely root causes so you can get immediate relief. Quick summary of what I’m responding to • Self‑hosted LiveKit endpoint. Agent server in production experiences very long outbound dispatch times (4–5 min, retries), while the same agent+credentials run locally dispatches instantly. Most likely causes (ranked) 1. Instance resource exhaustion (CPU/memory) or process contention on the prod agent host. 2. Network saturation, packet drops, or retransmits on the prod instance (high NetworkIn/Out, NIC limits). 3. Agent worker configuration: too many agents/processes on one host, low

num_idle_processes

, or load gating rejecting/queuing jobs. 4. Cold-starts / prewarm missing: heavy model loads per job causing long initialization. 5. SIP signaling or ACK/200 sequence issues (rare given local works, but possible if prod network alters packets). 6. Timeouts/thresholds:

initialize_process_timeout

or job acceptance thresholds causing retries. Immediate diagnostic steps (run now) 1. Correlate a delayed call timestamp with CloudWatch (or your metrics): ◦ Check CPUUtilization, MemoryUsed, NetworkIn, NetworkOut, NetworkPacketsDropped, and CloudWatch’s CPUCreditBalance (if burstable). ◦ Look for spikes or sustained high values at the delay time. 1. Inspect agent process/state at the moment of a delayed dispatch: ◦ How many agent worker processes are running? (ps/top/htop) ◦ Per‑process CPU and memory. ◦

ss -s

or

netstat -an

to check TCP states (lots of TIME_WAIT or SYN_RECV can indicate connection churn). 1. Check NIC/errors and retransmits: ◦

ifconfig

or

ethtool -S <iface>

to look for rx/tx errors. ◦ High retransmits = signaling delays and potential retransmission backoffs. 1. Look for agent/server logs around the attempt: ◦ Agent logs showing job assignment or “creating process” vs “assigned prewarmed process”. ◦ LiveKit server logs for dispatch timing and any “room connection not established” messages. 1. Try a quick isolation test: ◦ Temporarily spin up a single new agent on a larger instance (c5/c6, same VPC) and register it under the same agent_name. Use that to place a few calls from prod. If dispatch is instant from that agent, prod instance resource/NIC is the bottleneck. ◦ Alternatively, reduce number of agent processes on the current prod host and re-run tests; if latency improves, it’s capacity related. Configuration checks to run • Instance type: confirm exact flavor (you previously said c4 — confirm the size, e.g., c4.large). c4 family can be limited for network; consider c5/c6. • Number of agents/processes on the host and

num_idle_processes

value. • Whether

initialize_process_timeout

is set low — if initialization takes time, increase it. • Are you using prewarm/setup_fnc to load heavy models once per process? If not, cold starts can be expensive. Short-term mitigations (fast to try) • Scale horizontally: add at least one additional agent instance and register it. This often reduces queueing immediately. • Increase

num_idle_processes

slightly (if memory allows) so new calls hit prewarmed processes. • If CPU or NIC is saturated, move to a larger instance (c5/c6) or split agents across hosts. • Add an autoscaling rule (or manual scale) to bring up new workers when load rises. What I need from you to pinpoint this now Please share the following (redact secrets): • Exact prod instance type (e.g., c4.large) and how many agent worker processes run on it. • CloudWatch (or equivalent) snapshots for a delayed call window: CPUUtilization, MemoryUsed, NetworkIn/Out, and NetworkPacketsDropped. • One short agent log excerpt (timestamps) around a delayed dispatch — specifically from job receive to job assignment/failure. • The value of

num_idle_processes

and whether you use `prewarm`/`setup_fnc`. • Does the delay affect all outbound calls or only under load / for specific destinations? If you want, start by pasting one CloudWatch snapshot and a short log excerpt and I’ll analyze and give the exact next action (scale, config change, or deeper network checks).