What is the recommended way to obtain latency induced by SIP telephony

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

how do i do this in a self-hosted setup. I'm using python

Thanks — I’m responding to your question about measuring SIP‑induced latency in a self‑hosted setup using the Python SDK. Short answer • Use the Python Agents SDK (v1.3.0+) to emit the same observability data (metrics, traces, transcripts, and logs) from your self‑hosted agent, capture STT/LLM/TTS metrics (TTFT/TTFB and EOU timings), and log SIP lifecycle events (participant connect, SIP attributes). Save/forward those artifacts yourself since self‑hosted agents don’t automatically upload Agent Insights to LiveKit Cloud. What you can measure and where to look • SIP transport/connection latency: time from SIP call initiation to the SIP participant joining the LiveKit room (participant lifecycle events and SIP attributes such as

sip.callID

,

sip.phoneNumber

,

sip.trunkID

). • RTP → STT latency: time between SIP media arrival (RTP ingress or the conversion point) and STT processing start / end‑of‑utterance detection. • Model and response latency: LLM Time To First Token (TTFT) and TTS Time To First Byte (TTFB). • End‑to‑end latency: combine the above to see how much latency is introduced by the SIP transport vs. processing. Practical steps (self‑hosted Python) 1) Use the correct SDK • Run LiveKit Python Agents SDK v1.3.0 or later. 2) Ensure observability is generated • Observability (traces, transcript, metrics, logs) is generated by the agent by default unless disabled. • In self‑hosted mode you must persist or forward these artifacts yourself; they are not presented in LiveKit Cloud automatically. 3) Attach listeners for metrics and EOU • Hook into STT, LLM, and TTS metric events to capture TTFT, TTFB, and EOU durations programmatically. Example pattern

from livekit.agents.metrics import LLMMetrics, TTSMetrics, EOUMetrics

class MyAgent(Agent):
    async def on_enter(self):
        self.session.llm.on("metrics_collected", self.on_llm_metrics)
        self.session.tts.on("metrics_collected", self.on_tts_metrics)
        self.session.stt.on("eou_metrics_collected", self.on_eou_metrics)

    async def on_llm_metrics(self, metrics: LLMMetrics):
        # TTFT indicates LLM responsiveness after utterance
        print(f"LLM TTFT: {metrics.ttft:.3f}s")

    async def on_tts_metrics(self, metrics: TTSMetrics):
        # TTFB indicates TTS audio generation delay
        print(f"TTS TTFB: {metrics.ttfb:.3f}s")

    async def on_eou_metrics(self, metrics: EOUMetrics):
        print(f"EOU detection duration: {metrics.duration:.3f}s")

4) Track SIP participant lifecycle and timestamps • Record time at SIP call start, and log when the SIP participant joins the room. • Use participant events to get the join/connect timestamp and SIP attributes. Example pattern

import time

call_start = time.time()

def on_participant_connected(participant):
    elapsed = time.time() - call_start
    print(f"Participant {participant.identity} connected in {elapsed:.3f}s")
    # inspect SIP attributes if present
    print("SIP attrs:", participant.attributes.get("sip.callID"))

ctx.room.on("participant_connected", on_participant_connected)

5) Correlate timestamps to isolate SIP latency • Correlate: ◦ SIP-side timestamps (from your SIP infrastructure/SBC or CDRs) ◦ Agent session timestamps (RTP ingress, STT start, EOU, LLM start, TTS audio send) • The gap between SIP media arrival and STT start (or EOU detection) is the SIP‑transport + conversion latency. • TTFT/TTFB measure model/TTS processing latency. 6) Persist and analyze observability data • Save metrics, traces, transcripts, and logs to local storage, a database, or forward to monitoring services (Datadog, CloudWatch, Sentry, etc.). • Build queries/dashboards for trends: average SIP connect time, median RTP→STT latency, 95th percentile TTFT, etc. Practical tips to reduce noise • Pin agents to the same region as your SIP endpoint to cut network hops and re-measure. • Ensure clocks are synchronized (NTP) across SIP infrastructure and agents to make timestamp correlation reliable. • Capture multiple calls and compute percentiles (p50/p90/p95) rather than relying on single samples. Limitations to be aware of • Self‑hosted deployments generate observability data locally — you must implement storage/forwarding and an analysis UI if you want a dashboard similar to Agent Insights in LiveKit Cloud. • If you need long‑term retention, export session reports to your chosen storage. If you’d like, I can: • Provide a complete Python agent template that logs and exports the observability artifacts (metrics, traces, transcripts) for SIP latency analysis. • Help map the exact SIP timestamps available from your SBC/IMS to the agent events you have so we can pinpoint measurement points. Which would you prefer, and do you have SIP-side timestamps or a preferred logging/monitoring backend to export metrics to? Sources: Where to find your Agent Logs | LiveKit | examples/bank-ivr | livekit/livekit_composite | DeepWiki | STT Metrics | LiveKit Documentation | SIP Lifecycle Management Agent | LiveKit Documentation | Agents (Python) | livekit/livekit_composite | DeepWiki

I dont have exact SIP timestamps but i do get callstatus. Can i use this to calculate the telephony latency?

Thanks — I’m responding to your question about using

callstatus

(without exact SIP timestamps) to estimate telephony latency. Short answer • Yes, you can use

callstatus

to get a rough estimate of telephony latency, but it has important limitations.

callstatus

events can indicate lifecycle milestones (e.g., ringing, answered, connected, ended) that let you measure coarse delays (call setup and connect time). They won’t give precise media‑path timing (RTP arrival → STT start). Use

callstatus

together with agent-side events (participant connected, RTP/ingress timestamps, STT/EOU, LLM/TTS metrics) to approximate SIP-induced latency. How to estimate latency with

callstatus

1. Identify which

callstatus

values you receive and what they represent in your SIP flow (e.g., RINGING, ANSWERED, CONNECTED, COMPLETED). 2. Log the local timestamp each time you receive a

callstatus

update in your agent. 3. Correlate those timestamps with agent events you do have: ◦ Participant connected in room (room participant_connected event) ◦ RTP/media ingress moment (if your agent exposes when audio frames start arriving) ◦ STT EOU detection and STT start ◦ LLM

metrics_collected

(TTFT) and TTS

metrics_collected

(TTFB) Example estimation approaches • Call setup (coarse SIP latency): time from

callstatus=RINGING

(or call initiation) to

callstatus=ANSWERED

or to

participant_connected

. This measures how long it took for the call to be answered and for the SIP session to reach the agent. • Media startup latency (approximate): time from

callstatus=ANSWERED

or

participant_connected

to the first STT activity or first EOU event recorded by the agent. This gap approximates how long until audio actually reached and was processed by the agent. • End-to-end influence on response time: measure from

participant_connected

or first audio frame timestamp to LLM TTFT / TTS TTFB. Subtract model/TTS processing time (from metrics) to estimate transport/conversion portion that includes SIP. What you cannot get reliably from

callstatus

• Precise RTP transport delays (packet-level latency, jitter, packet loss). • Exact timestamp of the first media RTP packet arrival unless your agent exposes an ingress timestamp. • Per-packet or sub-second SIP signaling delays (callstatus updates can be delayed or batched). Practical suggestions to improve accuracy • Capture agent-side audio ingress time: instrument your agent to record a local timestamp when it receives the first audio frame for that participant. Correlate that with

callstatus

events. • Ensure clock sync (NTP) across SIP infrastructure, SBC, and your agent hosts. Even coarse estimates need consistent clocks to be meaningful. • Capture multiple calls and compute percentiles (p50/p90/p95). Single-call estimates can be noisy. • Add small test calls where you generate a known tone or spoken phrase at known times so you can better correlate media arrival and STT events. • If possible, request additional SIP attributes from your SIP gateway (e.g.,

callStartTime

,

answeredTime

, or CDR timestamps) — even approximate fields improve accuracy. Example logging flow (Python pseudocode) 1. On

callstatus

event: ◦ log: event name, callstatus value, local timestamp 1. On `participant_connected`: ◦ log: participant id, local timestamp 1. On first audio frame / RTP ingress: ◦ log: local timestamp (first_media_ts) 1. On STT EOU and LLM/TTS metrics: ◦ log: STT start/EOU time, LLM TTFT, TTS TTFB Later analysis: • setup_time = timestamp_answered - timestamp_call_initiated (from callstatus) • media_start_delay = first_media_ts - timestamp_answered (or participant_connected) • processing_delay = TTFT (from LLM metrics) + TTFB (from TTS metrics) — attribute rest to transport/conversion Offer to help • If you share the list of

callstatus

values you receive and a sample of the agent events you already log (participant_connected, STT/EOU, LLMMetrics/TTSMetrics), I can give a concrete mapping and a small script to compute those latency estimates and percentiles for your call logs.

How does the time between RTP arrival and STT start indicate telephony latency?

Thanks — I’m responding to your question about why measuring the interval between RTP arrival and STT start is a good indicator of telephony (SIP) latency. Short answer • The time from RTP arrival to STT start captures the delay introduced by the telephony side of the system: network transport (SIP/RTP), any RTP→WebRTC or codec conversion, buffering/packet reassembly, and the moment audio becomes available to the speech recognizer. Those pieces are all part of “telephony latency” before your speech pipeline actually begins processing audio. Why that interval maps to telephony latency (step‑by‑step) • RTP arrival = first media available at the agent edge: ◦ This marks when the media packets carrying the caller’s audio reach your agent process or gateway. ◦ It already includes any SIP signaling and media routing delays upstream (SBC, trunking, carrier network). • Pre‑STT work that is still telephony‑related: ◦ Codec conversion: the audio may be in a telephony codec (G.711, G.729, etc.) and needs decoding or re-encoding for STT. ◦ Packet buffering / jitter compensation: the system may buffer a small window to smooth jitter or recover lost packets before handing contiguous audio to STT. ◦ RTP→WebRTC or internal transport conversion: if you bridge RTP into WebRTC or another internal stream format, that conversion step takes time. ◦ Media demultiplexing and decoding CPU work that happens before STT receives frames. • STT start = when speech recognition actually begins: ◦ This is the moment your STT engine first receives enough decoded/assembled audio to perform recognition or detect end-of-utterance. ◦ Any time after STT start (LLM and TTS delays) is processing latency, not transport latency. What the interval includes vs what it excludes • Included (part of telephony-induced delay): network transit time from caller → SBC → your agent, any RTP packetization and reassembly delays, codec decode/convert time, jitter buffer delay, and conversion overhead (RTP→internal audio). • Excluded (not telephony): STT model processing time after it receives audio, LLM inference time, TTS generation and delivery. These are processing/system latencies inside your application stack. How to measure it accurately (practical points) • Capture a precise local timestamp when you receive the first RTP packet for that participant (or for a given utterance). • Capture a precise local timestamp when STT begins processing that same utterance (e.g., when the STT engine receives first decoded frames or when EOU detection begins). • Ensure host clocks are stable (use NTP). Prefer local timestamps in the same process to avoid cross-host clock skew. • Prefer “first meaningful audio frame” rather than first packet if early packets are silence or comfort noise; that reduces noise in the measurement. • Average across many calls and compute percentiles (p50/p90/p95) to avoid single-call noise. What to watch out for / common pitfalls • Jitter buffers vary: some deployments add larger buffers to handle poor networks; that increases the RTP→STT interval but reflects real telephony quality tradeoffs, not a measurement error. • Codec processing variability: heavy CPU load or expensive codec conversions can inflate this time and may look like telephony latency even though it’s an agent-side resource issue. • “RTP arrival” source matters: if you log the server’s network receive timestamp that’s ideal. If you only have an upstream SIP gateway timestamp, that will be less accurate. • Silence / VAD behavior: Some STT systems wait for voice activity before starting recognition; that will make the recorded interval longer but still reflects when audio is actually ready to be processed. How to use the number • Use it to isolate transport/conversion delays from model/TTS delays. • If RTP→STT is high, investigate network path, SBC/trunking, codec handling, jitter buffer size, or agent CPU. • If RTP→STT is low but end‑to‑end is high, focus on STT/LLM/TTS processing and queuing. Quick example (self‑hosted Python) • On first RTP packet receipt: record

first_rtp_ts = time.time()

. • When STT receives first decoded audio (or when

stt.on("eou_metrics_collected")

or

stt.on("start")

fires): record

stt_start_ts = time.time()

. • Telephony_latency ≈

stt_start_ts - first_rtp_ts

. • Log these per call and compute percentiles across calls. If you’d like, I can: • Provide a small Python snippet that hooks into your agent to log RTP arrival and STT start timestamps. • Help interpret a few sample logs to separate telephony vs. processing delays. Which do you prefer, and do you currently have access to an event for “first RTP packet” or just the participant connection time?