"Using LiveKit Agents + OpenAI gpt-realtime. Any user sound mid-response — even short words like 'haan', 'ok' — cancels the agent speech and it goes quiet then repeats. We've tried semantic_vad with interrupt_response=False, manual audio commit with VAD disabled, nothing works. What is the correct way to disable interruptions completely in AgentSession?"

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

Thanks — I appreciate the details and your patience. You already tried the two obvious fixes, so let’s walk through what else can cause that replay and the next diagnostic steps to find the root cause. Quick summary of what I’m responding to • You set

interrupt_response=False

in

TurnDetection

and

allow_interruptions=False

on every

session.say()

/

session.generate_reply()

, but the agent still goes quiet and then restarts from the last sentence whenever the user makes any short sound. Why this can still happen •

interrupt_response=False

prevents the model from automatically creating a new assistant response after an interruption, but it does not fully stop the realtime service from reacting to voice activity (VAD) in ways that can resynchronize or rebuffer the synthesis stream. That resynchronization can cause the model to re-synthesize and replay segments. • Some server-side behaviors (buffering, resuming synthesis, or state resync) are outside client-only interruption flags — so the replay often indicates the OpenAI realtime engine detected audio and restarted/resumed synthesis internally. • Lastly, other parts of your agent logic (event handlers, transcription hooks, tool invocations) can inadvertently trigger a repeat if they react to user-speech events. What to check next (diagnostic steps) 1. Confirm TurnDetection flags in the actual Realtime session payload ◦ Log the exact

RealtimeModel

/

TurnDetection

object that is being passed when the session is created. Make sure

create_response

and

interrupt_response

are both set as you expect. 1. Try

create_response=False

for a test ◦ If you haven’t already, set

create_response=False

in

TurnDetection

(in addition to

interrupt_response=False

).

create_response=False

prevents the service from creating any interrupted assistant response automatically — useful to confirm server-side interruption handling is the cause. 1. Switch to

semantic_vad

+ lower eagerness for a test ◦ If you’re using

server_vad

or default VAD, try

semantic_vad

with

eagerness="low"

and

create_response=False

. Semantic VAD is content-aware and can reduce false positives from short words/noise. 1. Confirm no client-side code re-triggers speech on events ◦ Search your event handlers for anything that responds to: ▪︎

user_state_changed

,

transcript_received

,

conversation_item_added

, or similar events ▪︎ If you have logic that says “on user audio → pause/resume or re-say last message,” disable it and re-test. 1. Capture and inspect logs/events during a reproduce: ◦ Log Realtime events and any LiveKit/AgentSession events with timestamps: ▪︎ When playback starts/ends ▪︎ When user audio is detected / transcription arrives ▪︎ Any server-sent “response restarted” or similar event ◦ This will show whether the replay is triggered by the server (re-synthesis) or by your client logic. 1. Minimal reproducible test ◦ Create a minimal test app that only: ▪︎ Creates a session with your

TurnDetection

settings, ▪︎ Calls

session.say(..., allow_interruptions=False)

, ▪︎ Streams mic input (or simulates a brief noise) and logs events. ◦ This isolates whether any other app code triggers repeat behavior. If you’d like, I can help with concrete snippets or a short checklist to add to your logs. To help quickly, please share one of the following (whichever is easiest): • The exact

TurnDetection

config you’re passing when creating the Realtime model (copy-paste), and the

session.say()

call example you use. • A trimmed event log showing playback start, user audio detection, and when the agent replays (timestamps help). • Whether you’re using

server_vad

or

semantic_vad

. If you prefer, I can provide a ready-to-run minimal Python or TypeScript test case that uses

create_response=False

+

interrupt_response=False

so you can reproduce and confirm whether the server still triggers a replay. Which language would you like? Sources: OpenAI and LiveKit partner to turn Advanced Voice into an API | OpenAI and LiveKit partner to turn Advanced Voice into an API | OpenAI Realtime API plugin guide | LiveKit Documentation | xAI Grok Voice Agent API plugin | LiveKit Documentation | Interrupt User | LiveKit Documentation

using gpt-realtime speech to speech via livekit, there is no way to make the model not interuput to small feedback words when having a conversation for example ai is speaking and users just acknolwdges like "okay", "I see", but ai stops as it takes a interupttion and its frustrating as user is just normal acknowlding how we speak in real world

Thanks — I hear your frustration. Short acknowledgements like “okay”, “I see”, or “haan” are very natural in conversation, and having the agent stop and repeat makes interactions feel awkward. I’ll outline what’s possible, what isn’t, and practical next steps you can take to minimize or eliminate those false interruptions. What’s happening • The OpenAI Realtime model runs server-side turn detection (VAD / semantic VAD). Even very short user sounds can trigger that server-side logic and cause the model to resynchronize or restart synthesis. • You can control both client-side interruption (LiveKit’s

allow_interruptions

) and server-side behavior (

TurnDetection

settings), but some server-side VAD effects can still cause replay/resynthesis unless further measures are taken. What you should try (in order) 1) Ensure both layers are set (you may already have these) • On every speak call: use

allow_interruptions=False

. • In the Realtime model `TurnDetection`: set

interrupt_response=False

. 2) Add

create_response=False

in TurnDetection (critical test) • Setting

create_response=False

prevents the realtime service from creating a new assistant response when it detects user audio. This is the most impactful change for preventing server-driven restarts/replay. • Trade-off: the model will not auto-respond to user speech; you must manually trigger replies when appropriate. 3) Use

semantic_vad

with low eagerness (preferred for conversational flow) • Switch to

type="semantic_vad"

and set

eagerness="low"

. Semantic VAD is content-aware and less likely to treat short acknowledgements as a real “turn”. • Combine with

create_response=False

+

interrupt_response=False

to reduce false positives. 4) Tune thresholds and paddings (if available) • Increase brief thresholds like

interruption_threshold

,

prefix_padding_ms

, or

silence_duration_ms

to make the server less sensitive to short sounds. • Example approach: small increase to interruption threshold and some prefix padding so brief overlaps are tolerated. 5) Prevent client logic from re-triggering speech • Confirm no handlers in your app re-invoke the last message on transcription events. Disable or add debounce logic on events like

transcript_received

,

conversation_item_added

, or

user_state_changed

. 6) Consider manual turn control (if you can manage triggers yourself) • Use

turn_detection="manual"

combined with server

create_response=False

. Perform your own logic to decide when to call

session.generate_reply()

after the user genuinely finishes speaking. Practical config examples • Python sketch (test mode):

from openai.types.beta.realtime.session import TurnDetection
turn_detection = TurnDetection(
    type="semantic_vad",
    eagerness="low",
    create_response=False,      # prevents automatic interrupted responses
    interrupt_response=False,   # don't interrupt an in-progress response
)
# When speaking:
await session.say("Agent message...", allow_interruptions=False)

When the above still shows replay • Capture event logs (timestamps): playback start/end, user audio/transcript events, any server-sent response/restart events. That will show whether the server resynthesized audio or your client re-played it. • Try a minimal repro: single session, single speak call, simulate a short “ack” noise — log everything. This isolates client code. Realistic limitation to understand • If server-side VAD treats short user utterances as relevant audio, the server can still affect synthesis timing.

create_response=False

plus semantic VAD/eagerness tuning is the best available way to stop replays, but it changes behavior (you will need to manually trigger responses when you do want the model to reply). If you want, I can: • Provide a minimal Python or TypeScript test case you can drop into your app to reproduce the issue with

create_response=False

+

semantic_vad

so we can confirm whether the server is still resynthesizing. • Review the exact

TurnDetection

payload and one

session.say()

call from your code to point out any remaining misconfigurations. Which would you prefer — a TypeScript or Python test snippet, or paste of the TurnDetection + one speak call for me to review?

Developer used this python

turn_detection=TurnDetection(
    type="semantic_vad",
    eagerness="low",
    create_response=True,
    interrupt_response=True,    # ← back to True
),

python

session = AgentSession(
    llm=realtime_model,
    allow_interruptions=False,  # ← this is now the only interruption guard
    min_endpointing_delay=2.0,
    max_endpointing_delay=6.0,
)

The logic now is: OpenAI side:

interrupt_response=True

— model behaves normally, no confusion • LiveKit side:

allow_interruptions=False

— LiveKit blocks any interruption before it even reaches OpenAI

Thanks — that’s a very clear example. I can see why you tried to rely on LiveKit’s

allow_interruptions=False

as the single guard, but this configuration can still allow server-side behavior (OpenAI Realtime) to cause replay or resumed/resynthesized audio. What you shared • OpenAI side:

semantic_vad

,

eagerness="low"

, but

create_response=True

and

interrupt_response=True

. • LiveKit side:

AgentSession(allow_interruptions=False, ...)

so you’re relying on LiveKit to block interruptions. Why this can still fail •

interrupt_response=True

(OpenAI) allows the Realtime model to react to detected user audio and create/interrupt responses. Even if LiveKit blocks client-side interruption, the OpenAI Realtime service can still detect the short ack, change internal synthesis state, and either restart or rebuffer audio. That server-side resynchronization is what commonly produces the “agent goes quiet then repeats last sentence” behavior. •

allow_interruptions=False

only prevents LiveKit from stopping playback locally and triggering a new speak call, but it cannot prevent the OpenAI server from altering the stream it’s producing if the server-side turn detection is allowed to interrupt/create responses. What will actually stop server-driven replay • Disable the server from creating interrupted responses. Specifically, set: ◦

create_response=False

AND ◦

interrupt_response=False

in your

TurnDetection

. That prevents the Realtime API from generating any automatic interrupted response or interrupting an in-progress response on brief user audio. • Use

semantic_vad

with

eagerness="low"

to reduce false-positive turn detection (you already have the right idea there). • Continue to use

allow_interruptions=False

client-side to ensure LiveKit won’t locally stop playback. Recommended test configuration • TurnDetection:

TurnDetection(
    type="semantic_vad",
    eagerness="low",
    create_response=False,
    interrupt_response=False,
    # optional: tune silence/prefix padding if your environment provides those properties
)

• AgentSession speak:

await session.say("...", allow_interruptions=False)

Trade-offs to know • Setting

create_response=False

means the server will not automatically create a follow-up response when it detects user speech. You will need to manually trigger generation (for instance, call

session.generate_reply()

after you’ve decided the user has finished speaking). This is usually acceptable for use-cases that need uninterrupted announcements or long agent utterances. • If you need the model to be interruptible in some contexts but not others, you can switch

create_response

/

interrupt_response

or use

turn_detection="manual"

and implement your own logic to commit user audio and trigger responses only when appropriate. Additional steps to ensure robustness • Confirm no app-side handlers re-trigger speech on

transcript_received

,

conversation_item_added

, or other events. • Add logging for server events and your client events (playback start/end, user audio/transcript timestamps) to see whether the replay comes from server resynthesis or client re-play logic. • If you want a less disruptive approach than fully disabling server responses, you can experiment with slightly higher

interruption_threshold

/ longer

prefix_padding_ms

/ longer

silence_duration_ms

(if available) so short acknowledgments are ignored more often. If you like, I can: • Review the exact

TurnDetection

payload you are sending to the Realtime model and one

session.say()

call to spot any mismatch. • Provide a minimal Python or TypeScript test case that uses

create_response=False

+

semantic_vad

so you can reproduce and confirm behavior in isolation. Which would help you most — a quick payload review or a runnable test snippet (Python or TypeScript)?