I'm using livekit agent for calling, the primary issue i have is interruption loop. If the user interrupts the agent, the agent stops to generate response, when it starts speaking the user also speaks and the agent again registers it as an interruption and stop to generate the response and same thing happens 3-4 times resulting in user cutting the call how can I handle this ??

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

# 🔽 Latency tuning — makes assistant feel more responsive min_endpointing_delay=0.25, # Wait this long before deciding speech has ended max_endpointing_delay=2.0, # Hard stop for silence detection min_consecutive_speech_delay=0.15, # Time between two speech segments # 🔽 Transcript handling use_tts_aligned_transcript=aligned_script, # Donot wait for TTS metadata to finalize transcript # 🔽 Responsiveness preemptive_generation=True, # Start generating replies while user is still talking # 🧏 Interruptions : smoother and less aggressive allow_interruptions=True, min_interruption_duration=0.15, # require 150ms of user speech to count as real interruption min_interruption_words=1, # only if STT is enabled discard_audio_if_uninterruptible=False, # keep audio even if agent is mid-sentence agent_false_interruption_timeout=2.0, # quicker recovery if user stopped here is the interruption configuration of the agent

Thanks — I reviewed your interruption config. You’re close, but a few settings likely cause the loop when users talk briefly while the agent speaks. Below are concise, prioritized changes and why, plus an updated config you can try. Why the loop happens • Low values for

min_interruption_duration

(0.15s) and

min_interruption_words

(1) let very short noises or overlapping speech trigger true interruptions. •

preemptive_generation=True

makes the agent begin generating while the user is still speaking, which increases chances of partial restarts. •

discard_audio_if_uninterruptible=False

keeps playing audio even when the agent can’t be safely interrupted, creating overlapping detection. Recommended quick changes (apply together) 1. Increase interruption thresholds: ◦

min_interruption_duration=1.0

(or 0.8–1.2s) ◦

min_interruption_words=2

1. Make false-interruption handling more conservative: ◦

agent_false_interruption_timeout=3.0

1. Reduce preemption (avoid generation while user is mid-speech): ◦

preemptive_generation=False

(or set to True only with stricter interruption thresholds) 1. Change audio discard behavior: ◦

discard_audio_if_uninterruptible=True

so agent audio stops if a true interruption occurs 1. Slightly increase endpointing delay to avoid rapid restart: ◦

min_endpointing_delay=0.7

Updated config to try

min_endpointing_delay = 0.7
max_endpointing_delay = 2.0
min_consecutive_speech_delay = 0.15

use_tts_aligned_transcript = aligned_script
preemptive_generation = False

allow_interruptions = True
min_interruption_duration = 1.0
min_interruption_words = 2
discard_audio_if_uninterruptible = True
agent_false_interruption_timeout = 3.0

If you still want low-latency responsiveness later, we can: • Re-enable

preemptive_generation=True

but keep the stricter interruption thresholds. • Use a model-based turn detector (e.g., MultilingualModel) for smarter endpointing. • Or switch to

turn_detection="manual"

and commit user turns from the frontend (push-to-talk or clear end-of-speech signals). Tell me which runtime you’re using (Node or Python) and whether you want me to produce exact code snippets for your environment or try the stricter thresholds first.

I'm using python, can't switch to manual turn detection

Thanks — since you’re on Python and can’t use manual turn detection, here’s a focused Python snippet and a short explanation of the key changes to stop the interruption loop. Key changes applied • Make interruptions harder to trigger: longer

min_interruption_duration

and require more words. • Prevent rapid resume/restarts: increase

agent_false_interruption_timeout

and

min_endpointing_delay

. • Avoid overlapping audio causing re-detection: stop playing agent audio when it can’t be safely interrupted (

discard_audio_if_uninterruptible=True

). • Disable preemptive generation so the agent doesn’t start generating while the user is still speaking. Python snippet

from livekit.agent import AgentSession
# adjust imports as needed for your setup

session = AgentSession(
    # turn detection stays automatic (model or vad)
    turn_detection="vad",              # or your current model
    vad=silero.VAD.load(),             # if you use VAD

    # endpointing / latency tuning
    min_endpointing_delay=0.7,
    max_endpointing_delay=2.0,
    min_consecutive_speech_delay=0.15,

    # transcript handling (keep as you had)
    use_tts_aligned_transcript=aligned_script,

    # responsiveness
    preemptive_generation=False,       # avoid generating mid-user-speech

    # interruptions (less aggressive)
    allow_interruptions=True,
    min_interruption_duration=1.0,     # require ~1s of user speech
    min_interruption_words=2,          # require >=2 recognized words
    discard_audio_if_uninterruptible=True,
    agent_false_interruption_timeout=3.0,
)

If you prefer slightly lower latency, try these milder settings instead:

min_interruption_duration=0.8

,

min_interruption_words=2

,

agent_false_interruption_timeout=2.5

, and keep

preemptive_generation=False

until you confirm stability. Want me to generate this snippet adapted to your exact AgentSession instantiation (show me your current

AgentSession(...)

call)?

if i use this configuration, another issue arise, since user need to speak more than a word to get it registered as response, when agent finishes speaking and user says "Yes" only the agent doesnt respond it waits for proper user input

You’re right — raising interruption thresholds prevents loops but can make short replies like “Yes” get ignored. You can balance both with one of these practical approaches. Which would you like to try first? Options (pick one) 1) Short-phrase shortcut + confirm window (recommended) • Detect short replies in the final few seconds after agent TTS ends: accept 1-word replies (like “yes”, “no”, “okay”) if they occur within a small confirm window (e.g., 1.0–1.5s) after agent speech stops. • Keep conservative interruption thresholds during agent speech to avoid loops, but when TTS finishes switch to a short confirm mode that uses

min_interruption_duration=0.15

and

min_interruption_words=1

for that window only. 2) Two-stage thresholds (automatic) • While agent is speaking: strict thresholds (e.g.,

min_interruption_duration=1.0

,

min_interruption_words=2

,

discard_audio_if_uninterruptible=True

). • After agent stops: immediately lower thresholds for a brief period (e.g., 0.8–1.5s) so short confirmations register. 3) Use STT-boost + intent check • Keep stricter audio thresholds but always run a fast STT pass on the small audio immediately after agent TTS ends; if STT returns a high-confidence short token like “yes” map it to an intent and proceed. • This avoids changing global interruption logic and reduces false positives while letting one-word replies pass. 4) Conservative preemptive generation with stricter interruption • Re-enable

preemptive_generation=True

only if you keep

min_interruption_duration>=1.0

and

min_interruption_words>=2

— reduces restarts, but still may miss single-word confirmations unless combined with 1 or 2 above. Example Python pattern for option 1 (conceptual)

# while agent speaking:
session.min_interruption_duration = 1.0
session.min_interruption_words = 2
session.discard_audio_if_uninterruptible = True

# when TTS finishes, open short confirm window:
open_confirm_window(1.2)  # listen for 1.2s
session.min_interruption_duration = 0.15
session.min_interruption_words = 1
# after window, restore strict settings

What I need to tailor a snippet for you • Do you control when TTS finishes on the server (can you detect TTS-end event), or only on the client? • Which STT engine are you using (built-in agent STT / external)? • Share your current

AgentSession(...)

call if you want an exact drop-in snippet. Tell me which option you prefer and those details and I’ll produce the exact Python code you can use.

1) Short-phrase shortcut + confirm window (recommended) • Detect short replies in the final few seconds after agent TTS ends: accept 1-word replies (like “yes”, “no”, “okay”) if they occur within a small confirm window (e.g., 1.0–1.5s) after agent speech stops. • Keep conservative interruption thresholds during agent speech to avoid loops, but when TTS finishes switch to a short confirm mode that uses

min_interruption_duration=0.15

and

min_interruption_words=1

for that window only. give me sample code for this case

Below is a focused, ready-to-use Python example that implements the "short-phrase shortcut + confirm window" pattern for LiveKit Agents. It: • Keeps strict interruption thresholds while the agent speaks to avoid loops. • Detects when agent TTS finishes via playback events. • Opens a short confirm window (1.2s) after TTS ends with relaxed thresholds to accept one-word replies like "yes". • Restores strict thresholds after the window. Adjust variable names and imports to match your project.

import asyncio
from livekit.agents import JobContext, WorkerOptions
from livekit.agents.voice import AgentSession, PlaybackFinishedEvent
# import your VAD/STT/LLM/TTS as needed, e.g. silero, llm, tts

CONFIRM_WINDOW_SECONDS = 1.2

class ShortPhraseConfirm:
    def __init__(self, session: AgentSession,
                 strict_min_duration=1.0,
                 strict_min_words=2,
                 relaxed_min_duration=0.15,
                 relaxed_min_words=1,
                 confirm_window=CONFIRM_WINDOW_SECONDS):
        self.session = session
        self.strict_min_duration = strict_min_duration
        self.strict_min_words = strict_min_words
        self.relaxed_min_duration = relaxed_min_duration
        self.relaxed_min_words = relaxed_min_words
        self.confirm_window = confirm_window
        self._confirm_task: asyncio.Task | None = None

        # store current values to restore reliably
        self._orig_min_duration = session.min_interruption_duration
        self._orig_min_words = session.min_interruption_words

        # listen for playback finished (agent TTS ended)
        self.session.output.audio.on("playback_finished", self._on_playback_finished)

    def _set_strict(self):
        # enforce conservative thresholds
        self.session.update_options(
            min_interruption_duration=self.strict_min_duration,
            min_interruption_words=self.strict_min_words,
            discard_audio_if_uninterruptible=True,
            preemptive_generation=False,
        )

    def _set_relaxed(self):
        # temporarily allow short confirmations
        self.session.update_options(
            min_interruption_duration=self.relaxed_min_duration,
            min_interruption_words=self.relaxed_min_words,
            # keep other safety options as needed
        )

    def _on_playback_finished(self, ev: PlaybackFinishedEvent):
        # Cancel any existing window and open a new one
        if self._confirm_task and not self._confirm_task.done():
            self._confirm_task.cancel()

        self._confirm_task = asyncio.create_task(self._confirm_window())

    async def _confirm_window(self):
        try:
            self._set_relaxed()
            # wait for the short confirm window
            await asyncio.sleep(self.confirm_window)
        except asyncio.CancelledError:
            # reopened window - just exit and let the new one run
            return
        finally:
            # always restore strict settings afterwards
            self.session.update_options(
                min_interruption_duration=self._orig_min_duration,
                min_interruption_words=self._orig_min_words,
            )

async def entrypoint(ctx: JobContext):
    # create session with conservative defaults
    session = AgentSession(
        stt=...,         # your STT
        llm=...,         # your LLM
        tts=...,         # your TTS
        vad=...,         # your VAD
        turn_detection="vad",
        min_interruption_duration=1.0,
        min_interruption_words=2,
        discard_audio_if_uninterruptible=True,
        preemptive_generation=False,
        # other options...
    )

    await session.start(...)

    # attach the short-phrase handler
    confirm = ShortPhraseConfirm(session,
                                 strict_min_duration=1.0,
                                 strict_min_words=2,
                                 relaxed_min_duration=0.15,
                                 relaxed_min_words=1,
                                 confirm_window=1.2)

    # Example: agent asks a question (set allow_interruptions as desired)
    await session.say("Do you want to continue?", allow_interruptions=False)

    # keep running
    await ctx.wait_for_shutdown()

if __name__ == "__main__":
    from livekit.agents import agents
    worker = agents.cli.ServiceWorker(
        worker_name="short_phrase_agent",
        worker_type=agents.JobType.JOB_ROOM,
        auto_subscribe=agents.AutoSubscribe.AUDIO,
        entrypoint=entrypoint,
    )
    agents.cli.run_app(WorkerOptions(worker))

Notes: • Ensure your STT returns short words reliably; you may optionally add a quick STT confidence check inside the confirm window to ignore low-confidence tokens. • If your frontend signals TTS-end instead of the SDK event, trigger the same confirm window logic from that signal. • Tune

CONFIRM_WINDOW_SECONDS

and the relaxed thresholds to your UX needs. Sources: Agents (Python) | livekit/livekit_composite | DeepWiki | How to detect when an agent has finished speaking