why am i seeing EOUs hitting endpointing. in pristine environments where i say 1 word like yes and then mute the mic i'm seeing 6 seconds for eou... which makes perceived latency very high. this is unacceptable. what am i missing...

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

But if I reduce the max and pointing delay then does that mean that I'm cutting off the user?

Great question — short answer: yes, lowering

max_endpointing_delay

increases the risk of cutting off users, but you can manage the trade-off and minimize interruptions with the right approach. Why reducing the delay can cut users off • Endpointing delay is a safety buffer that lets the system wait through hesitations, trailing words, or slow speakers. • If you shorten that buffer, the system will conclude a turn ended sooner. That can produce faster responses but may also truncate a user’s final syllable or stop them mid-phrase when they speak more slowly or with pauses. How to reduce that risk while improving responsiveness • Use a hybrid approach rather than only lowering the timeout: ◦ Prefer

stt

or

realtime_llm

turn detection over

vad

alone. Transcript punctuation or semantic models give stronger evidence the speaker is finished, allowing you to safely lower delays. ◦ If you must use VAD, lower

max_endpointing_delay

incrementally (e.g., 6.0 → 1.5 → 1.0) and test with real users to find the sweet spot. ◦ Keep a modest

min_endpointing_delay

(0.2–0.5s) so high-confidence immediate endings still have a small buffer. ◦ Consider adaptive logic: use a shorter timeout for short utterances (e.g., single-word responses) and longer for longer turns. ◦ Enable or add the semantic/transformer-based EOU detector — it reduces false cut-offs by predicting completion from content rather than silence alone. ◦ Provide a manual override in your UI/flow (e.g., a commit button or explicit “done” signal) for critical paths where cut-offs are unacceptable. Practical configuration examples 1. Conservative responsiveness (lower latency, low risk): ◦

min_endpointing_delay = 0.3s

◦

max_endpointing_delay = 1.5s

◦ Use

turn_detection = "stt"

or enable semantic turn-detector. 1. Aggressive responsiveness (lower latency, higher risk): ◦

min_endpointing_delay = 0.2s

◦

max_endpointing_delay = 0.8–1.0s

◦ Only recommended if you also have STT/realtime model support or manual user cues. How to validate safely • Run A/B tests with representative users and measure: ◦ Frequency of mid-speech cut-offs (manual review or user complaints) ◦ End-to-end latency and user satisfaction • Log cases where EOU triggered shortly after speech and sample audio to verify whether those were legitimate turns. If you tell me: • Which

turn_detection

mode you’re using now (

vad

,

stt

,

realtime_llm

), and • How tolerant your application is to occasional cut-offs (e.g., unacceptable vs. tolerable with retries), I’ll recommend a concrete config and testing plan tailored to your use case.

Okay so you're saying I should use Deepgram flux and then I guess my question is does max endpoint delay still apply for Deepgram flux or is that only if I'm using life kits built in stuff?

Thanks — great follow-up. Short answer: when you use Deepgram Flux for turn detection, LiveKit’s

max_endpointing_delay

does not apply. Deepgram Flux handles endpointing itself using its own parameters, so you should tune those instead. What that means • LiveKit’s

max_endpointing_delay

only affects LiveKit’s internal turn-detection modes (e.g.,

vad

, LiveKit

stt

, or LiveKit’s built-in models). • With Deepgram Flux, endpointing and end-of-turn decisions are delegated to Deepgram. You control behavior with Deepgram’s settings such as: ◦

eot_timeout_ms

— maximum time to wait after detected speech ends before forcing a turn end (milliseconds). ◦

eot_threshold

— confidence threshold required to end a turn. ◦

eager_eot_threshold

— lower threshold for early hints if you want faster but riskier decisions. How to tune for lower latency without excessive cut-offs • Reduce

eot_timeout_ms

to cap the longest wait (e.g., 500–1500 ms) to improve perceived latency. • Keep a conservative

eot_threshold

(higher value) if you want fewer premature cut-offs; lower it if you need faster responses and can tolerate occasional interruptions. • Use

eager_eot_threshold

for earlier hints, combined with application-level confirmation if needed. • Combine Deepgram’s endpointing with local logic (e.g., a short

min_endpointing_delay

in LiveKit or UI manual commit) for critical flows where mistakes are costly. Recommended starting config (example) • For balanced responsiveness:

eot_timeout_ms = 1000

and

eot_threshold ≈ 0.7

. • For aggressive low-latency:

eot_timeout_ms = 500

and

eot_threshold ≈ 0.6

(test carefully). If you want, tell me: • Which

turn_detection

mode you’ve configured now, and • Your tolerance for occasional cut-offs (unacceptable vs. tolerable), and I’ll suggest precise Deepgram parameter values and a safe test plan you can run to validate changes. Sources: How does end-of-utterance detection work in conversations? | examples/voice_agents | livekit/livekit_composite | DeepWiki | Deepgram STT | LiveKit Documentation

Okay so can you show me what it looks like in the voice dot agent session code? Like what would that look like with DeepGram Flux in what you recommend here?

Thanks — I’ll show a concise, practical example of configuring a LiveKit voice AgentSession to use Deepgram Flux for turn detection with recommended endpointing settings. This example uses a balanced starting config (1s timeout, moderate threshold) that reduces the 6s wait while keeping cut-off risk reasonable. Notes before you paste: • Replace placeholders like

YOUR_DEEPGRAM_API_KEY

and any model names with your actual values. • If your environment uses a different client library or language, tell me which and I’ll adapt the snippet. Example (Python-like pseudocode using AgentSession)

from livekit_agent import AgentSession
from livekit_agent.integrations import DeepgramSTT

# Create a Deepgram STT config using Flux for turn detection/endpointing
deepgram_config = DeepgramSTT(
    api_key="YOUR_DEEPGRAM_API_KEY",
    model="flux-general-en",        # pick the Flux model you want
    # Deepgram endpointing params (tune as needed)
    eot_threshold=0.7,             # confidence required to end a turn
    eot_timeout_ms=1000,           # max wait after speech ends (1000 ms = 1s)
    eager_eot_threshold=0.4        # optional: hint threshold for earlier EOT
)

# Create the AgentSession and instruct LiveKit to use STT-level turn detection.
# LiveKit will delegate endpointing to Deepgram Flux (so LiveKit's max_endpointing_delay is ignored).
session = AgentSession(
    turn_detection="stt",          # use STT-level (Deepgram) turn detection
    stt=deepgram_config,
    # Optional: keep local VAD around for quick interruption handling or metrics
    vad_enabled=True
)

# Example: start session (actual method names may differ per SDK)
session.start()

Why this setup •

turn_detection="stt"

tells LiveKit to rely on the STT provider (Deepgram) for end-of-turn decisions. LiveKit’s

max_endpointing_delay

will not be used in this flow. •

eot_timeout_ms=1000

caps the longest wait to ~1s after Deepgram detects silence; this reduces perceived latency compared to 6s. •

eot_threshold=0.7

errs toward avoiding premature cut-offs while still allowing faster decisions. •

eager_eot_threshold

can provide earlier hints if you want faster but slightly riskier responses; combine with app logic if necessary. Testing and iteration • Start with

eot_timeout_ms = 1000

and

eot_threshold = 0.7

. Run a small user test with short replies (e.g., “yes”, “no”) and longer replies to validate cut-off frequency. • If you still see too many waits, lower

eot_timeout_ms

to 500ms and/or reduce

eot_threshold

slightly. If you see cut-offs, raise the threshold or increase the timeout. • For deterministic flows, add a UI “done” or call

commit_user_turn()

when you know the user finished speaking. If you want, I can: • Provide the same snippet adapted to your exact SDK/language (Node, TypeScript, or actual LiveKit client names), or • Suggest a short A/B test script and metrics to validate changes in production.

Yeah, node, please. And then I don't think I still understand like what should this be changed to? "const session = new voice.AgentSession({ vad, stt: 'deepgram/nova-3:en', llm: 'google/gemini-3-flash', tts: 'cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc', turnDetection: new livekit.turnDetector.EnglishModel(), userData: createInitialSessionData(), voiceOptions: { maxToolSteps: 10, userAwayTimeout: 5.5 }, });"

Thanks — I’ll show a Node.js example that fits your existing snippet and explain exactly what to change. I’ll also confirm what each change does so you can balance latency vs. cut-offs. Key points up front • When you use Deepgram Flux for turn detection, LiveKit’s

max_endpointing_delay

is ignored. Deepgram controls endpointing via its own parameters (e.g.,

eot_timeout_ms

,

eot_threshold

,

eager_eot_threshold

). • My recommendation: delegate turn detection to Deepgram (STT-level), set

eot_timeout_ms

≈ 1000 ms and

eot_threshold

≈ 0.7 as a balanced starting point, and test from there. Node.js example based on your config • Replace placeholders (API keys, model names) with your real values. • This example assumes the LiveKit voice SDK exposes similar constructors as in your snippet and that you can pass a Deepgram config object as

stt

. Adjust names if your SDK differs.

const livekit = require('livekit-agent'); // adjust to your actual package name
const deepgram = require('deepgram-sdk'); // placeholder if you use a helper lib

// Deepgram Flux config (endpointing handled by Deepgram)
const deepgramFluxConfig = {
  provider: 'deepgram',
  model: 'flux-general-en',            // or your chosen Flux model
  apiKey: process.env.DEEPGRAM_API_KEY,
  // Deepgram endpointing params
  eot_timeout_ms: 1000,                // max wait after speech ends (1s)
  eot_threshold: 0.7,                  // confidence required to end a turn
  eager_eot_threshold: 0.4,            // optional early hint threshold
  // any other Deepgram-specific options your integration supports
};

const vad = { enabled: true }; // keep local VAD for quick interruption handling if desired

const session = new livekit.voice.AgentSession({
  vad,
  // Tell LiveKit to use the STT provider (Deepgram) for turn detection
  stt: deepgramFluxConfig,
  llm: 'google/gemini-3-flash',
  tts: 'cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc',
  // Remove or replace LiveKit's built-in turnDetector so Deepgram handles EOU.
  // If your SDK requires a field name, set turnDetection to "stt" or leave out the built-in detector.
  turnDetection: 'stt',
  userData: createInitialSessionData(),
  voiceOptions: { maxToolSteps: 10, userAwayTimeout: 5.5 },
});

// Start session (API may differ)
await session.start();

What changed from your original snippet • stt: swapped from a string to a Deepgram config object so LiveKit delegates endpointing to Deepgram Flux. • turnDetection: set to

'stt'

(or removed the

new livekit.turnDetector.EnglishModel()

), so LiveKit won’t use its built-in endpointing. • Added Deepgram endpointing params (

eot_timeout_ms

,

eot_threshold

,

eager_eot_threshold

) — these control the maximum wait and the confidence needed to end a turn. Suggested starting values and trade-offs • eot_timeout_ms = 1000 (1s) — cuts the long 6s wait down to at most ~1s after detected silence. • eot_threshold = 0.7 — conservative enough to avoid many premature cut-offs. Lowering the threshold makes endpointing faster but riskier. • eager_eot_threshold = 0.4 — optional, for earlier hints the app can act on if you accept a bit more risk. Testing checklist • Run tests with short single-word replies ("yes", "no") and with longer, pausey replies. • Log/record cases where the user was cut off and adjust

eot_threshold

upward if cut-offs occur. • If you need deterministic behavior for parts of your flow, still call

session.commit_user_turn()

from the app when you detect explicit user completion (button press, DTMF, etc.). If you want, I can: • Convert this snippet to the exact Node.js SDK you’re using (send me the package name/import pattern), or • Propose a small A/B test plan and metrics (cut-off rate, mean latency) to validate safe settings in production. Which would you prefer next?

How does pre-emptive generation work here? Is that still a thing or no?

Great question — yes, pre-emptive generation is still supported and very useful when you use Deepgram Flux. It’s a key tool to hide EOU wait time by starting LLM work on interim transcripts before the turn is finalized. Quick summary • Pre-emptive generation starts LLM inference from high-confidence interim transcripts (before Deepgram confirms EOU). • If the final transcript matches the interim context, the pre-generated response is used immediately. If not, the agent regenerates or adjusts the response. • This runs independently of Deepgram’s endpointing settings and significantly reduces perceived latency by overlapping LLM work with the endpointing wait. What to change in your Node code • Keep your STT as Deepgram Flux and set Deepgram endpointing params (

eot_timeout_ms

,

eot_threshold

, etc.) to control how quickly Flux finalizes turns. • Enable pre-emptive generation in the AgentSession voice options. • Continue to use

turnDetection: 'stt'

(or omit built-in detectors) so Deepgram handles EOU. Example Node.js snippet (fit to your original shape) • Replace placeholders and adapt to your SDK import style.

const { voice } = require('livekit-agent'); // adjust import to your SDK
const deepgramConfig = {
  provider: 'deepgram',
  model: 'flux-general-en',
  apiKey: process.env.DEEPGRAM_API_KEY,
  eot_timeout_ms: 1000,   // 1s max wait after speech (tune as needed)
  eot_threshold: 0.7,     // conservative confidence for final EOT
  eager_eot_threshold: 0.4
};

const vad = { enabled: true }; // optional: keep local VAD for interruptions

const session = new voice.AgentSession({
  vad,
  stt: deepgramConfig,            // delegate turn detection to Deepgram Flux
  llm: 'google/gemini-3-flash',
  tts: 'cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc',
  turnDetection: 'stt',           // ensure LiveKit uses STT-level EOU
  userData: createInitialSessionData(),
  voiceOptions: {
    maxToolSteps: 10,
    userAwayTimeout: 5.5,
    preemptiveGeneration: true   // <-- enable pre-emptive generation
  }
});

await session.start();

How this behaves in practice • While Deepgram is still deciding EOU (up to your

eot_timeout_ms

), interim transcripts (and eager hints) can trigger pre-emptive LLM generation. • If the final transcript changes meaningfully, the pre-generated output is validated and replaced if needed. • This reduces user-perceived latency without requiring you to shorten endpointing aggressively (so you don’t have to trade as much safety for speed). Recommended starting tuning • eot_timeout_ms: 1000 ms (1s) — reduces the long 6s wait while giving Flux time to finalize. • eot_threshold: ~0.7 — conservative to limit cut-offs. • preemptiveGeneration: true — let LLM work during the endpointing window. Testing suggestions • Test with short single-word answers and longer, pausey responses. • Track two metrics: cut-off incidents (user truncated mid-speech) and end-to-end response latency. Adjust

eot_*

values and preemptive logic based on those results. • For critical flows, add an explicit client-side “done” signal or

commit_user_turn()

so you have deterministic control. If you want, I can: • Convert this snippet to your exact Node SDK import names, or • Propose a small A/B test plan (metrics and sample size) to validate the settings in your app. Which would you prefer? Sources: Deepgram STT | LiveKit Documentation | Agent speech and audio | LiveKit Documentation

model: 'flux-general-en', eagerEotThreshold: 0.4, eotThreshold: 0.6, eotTimeoutMs: 500,

Thanks — this is a useful concrete set of parameters. I’ll show exactly how to plug them into your Node session and explain the practical impact on latency vs. cut-off risk so you know what to expect. Node snippet (using your values) • Replace imports and API-key handling to match your SDK.

const { voice } = require('livekit-agent'); // adjust to your actual SDK import

const deepgramFluxConfig = {
  provider: 'deepgram',
  model: 'flux-general-en',
  apiKey: process.env.DEEPGRAM_API_KEY,
  // Your parameters:
  eagerEotThreshold: 0.4,   // hint threshold for early interim-triggered generation
  eotThreshold: 0.6,        // confidence threshold to finalize an EOT
  eotTimeoutMs: 500         // max wait after speech ends (500 ms)
};

const vad = { enabled: true }; // optional: keep for interruption handling

const session = new voice.AgentSession({
  vad,
  stt: deepgramFluxConfig,        // delegate EOU to Deepgram Flux
  llm: 'google/gemini-3-flash',
  tts: 'cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc',
  turnDetection: 'stt',           // ensure LiveKit uses STT-level EOU
  userData: createInitialSessionData(),
  voiceOptions: {
    maxToolSteps: 10,
    userAwayTimeout: 5.5,
    preemptiveGeneration: true    // keep pre-emptive generation enabled
  }
});

await session.start();

What these settings do (practical impact) • eotTimeoutMs = 500 ms ◦ Deepgram will wait at most 500 ms of silence after detected speech before forcing an end-of-turn. That dramatically lowers perceived worst-case latency compared to 6s, but gives very little buffer for long pauses. • eotThreshold = 0.6 (lower confidence) ◦ The model will finalize EOT with lower confidence than the 0.7 example. This speeds up finalization but increases the chance of premature cut-offs when users hesitate or trail off. • eagerEotThreshold = 0.4 ◦ Interim transcripts crossing this lower threshold can trigger pre-emptive generation (early LLM work), improving perceived speed even if final EOT arrives slightly later or changes. Expected trade-offs • Latency: Perceived latency should improve significantly (often responses can begin within ~500ms after a speaker stops or even earlier via pre-emptive generation). • Cut-off risk: Because

eotThreshold

is fairly low and

eotTimeoutMs

is short, risk of cutting off slow or pausey speakers increases. Single-word replies will usually be fine; speakers who pause mid-sentence or speak with hesitation may be truncated. • Pre-emptive generation: With

eagerEotThreshold

at 0.4 and

preemptiveGeneration

enabled, interim transcripts can start LLM generation early — this often masks the short timeout and reduces perceived interruptions. How to validate and tuned safely • Run a small test set: ◦ Short replies (single words) ◦ Long replies with natural pauses ◦ Hesitations and trailing words • Metrics to collect: ◦ Cut-off rate (how often users are truncated) ◦ Mean end-to-end latency ◦ User complaints or manual review samples • If cut-offs are too frequent: ◦ Increase

eotThreshold

to 0.65–0.75, or ◦ Increase

eotTimeoutMs

to 800–1200 ms. • If latency still feels high and cut-offs are rare: ◦ You can keep

eotTimeoutMs = 500

and further lower

eotThreshold

— test carefully. Quick recommendation given your values • These settings are aggressive and good for a low-latency experience where occasional cut-offs are acceptable or where you have UI/manual signals to recover. • If your application cannot tolerate cut-offs, raise

eotThreshold

to ~0.7 or increase

eotTimeoutMs

to ~1000 ms. Would you like: • This snippet adapted to your exact Node package import names, or • A suggested A/B test plan (sample sizes and specific metrics) to validate these values in production? Tell me which and I’ll prepare it.