i m having intermittent issues with self interrupting agent LiveKit Community #ask-ai

i'm having intermittent issues with self-interrupt...

narrow-pager-84057

12/20/2025, 1:12 AM

i'm having intermittent issues with self-interrupting agent when using OpenAIs realtime model, any tips on python agents SDK?

tall-airline-16756

12/20/2025, 1:12 AM

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

tall-airline-16756

12/20/2025, 1:13 AM

Thanks — I hear you’re seeing intermittent self-interruptions with an agent using OpenAI’s Realtime model via the Python agents SDK. Below are targeted tips to stabilize turn-taking and reduce premature interruptions. 1. Confirm you’re using server-side turn detection • Use the Realtime API’s server VAD instead of mixing client-side VAD or STT-based turn detection. Mixing methods causes conflicting end-of-turn signals. • Example:

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from livekit.plugins.openai import realtime

llm = realtime.RealtimeModel(
    turn_detection=realtime.TurnDetection(type="server_vad")
)

1. Tune VAD timing • Increase

silence_duration_ms

(longer silence required to end a turn) if interruptions happen mid-sentence; lower it only if responses feel too slow. 1. Try semantic VAD for fewer false positives •

semantic_vad

uses context to decide turn end and reduces mid-sentence cuts. Set lower eagerness to allow longer user speech:

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llm = realtime.RealtimeModel(
    turn_detection=realtime.TurnDetection(type="semantic_vad", eagerness="low")
)

1. Avoid redundant STT during realtime mode • Don’t attach a separate STT plugin unless you need interim transcripts. Duplicate or delayed transcriptions can trigger extra turn events. 1. Log and monitor agent state transitions • Listen for

agent_state_changed

events to capture when interruptions occur (e.g., during

speaking

) and correlate with audio/transcript timing. 1. Use observability tools • Inspect Trace and Logs to see timing of end-of-turn events vs LLM or TTS processing delays; this often reveals whether VAD or processing latency causes the cutoffs. 1. Prevent interruptions for critical replies • For responses that must finish, call

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

to block interruptions during playback. If you want, share a short snippet of how you currently configure

RealtimeModel

narrow-pager-84057

12/20/2025, 1:14 AM

yes - it appears to be a regression when i upgraded to 1.3.6

tall-airline-16756

12/20/2025, 1:14 AM

Thanks — I understand this started after upgrading to v1.3.6. I can help pinpoint the regression and suggest concrete fixes, but I need a bit more info. Could you share: • The snippet where you construct

RealtimeModel

AgentSession

(turn_detection, VAD params,

allow_interruptions

false_interruption_timeout

, etc.). • Whether you added or removed any STT/TTS plugins or client-side VAD when upgrading. • A brief description of when interruptions happen (mid-sentence, right after TTS starts, after silence) and one trace/log timestamp if you have Agent Observability traces. With those I’ll recommend specific config changes or workarounds (e.g., adjust

silence_duration_ms

, switch to

semantic_vad

, set

allow_interruptions=False

, or tune

false_interruption_timeout

). Sources: Streamline Troubleshooting with Agent Observability | xAI Grok Voice Agent API plugin | LiveKit docs | Turns overview | LiveKit docs

narrow-pager-84057

12/20/2025, 1:16 AM

here's the main boostrap:

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super().__init__(
            instructions = self.prompts.instructions,
            # half pipe (LLM audio input + STT)
            llm=openai.realtime.RealtimeModel(
                modalities=["text"],
                turn_detection=TurnDetection(
                    type="semantic_vad",
                    eagerness="medium",
                    create_response=True,
                    interrupt_response=False,
                )
            ),
            tts=elevenlabs.TTS(
                voice_id=config.ELEVENLABS_VOICE,
                model=config.ELEVENLABS_MODEL
            ),
            
        )

tall-airline-16756

12/20/2025, 1:16 AM

Thanks — I see your bootstrap uses

semantic_vad

with

eagerness="medium"

create_response=True

, and

interrupt_response=False

. That’s helpful. A few targeted changes to try that often stop self-interruptions after the 1.3.6 upgrade: • Lower semantic eagerness (less aggressive end-of-turn detection) ◦ Set

eagerness="low"

so the VAD waits longer before concluding a turn.

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turn_detection=TurnDetection(type="semantic_vad", eagerness="low", ...)

• Ensure you’re not creating interim responses that trigger interrupts ◦

create_response=True

can cause the agent to begin composing while audio is still being processed. Try

create_response=False

to avoid partial-response interrupts. • Keep

interrupt_response=False

(you already have this) but also protect critical TTS playback ◦ When speaking, use

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

for replies that must finish. • Tune false interruption handling on the session ◦ If you control

AgentSession

params, increase

false_interruption_timeout

and enable

resume_false_interruption

so brief spurious VAD triggers are ignored and the agent resumes speaking when appropriate. • Verify no client-side VAD/STT overlap ◦ Make sure you aren’t running a client VAD or separate STT that could emit extra end-of-turn events alongside server-side semantic VAD. If you want, I can suggest the exact code changes — do you control

AgentSession

creation in this class, and can you paste that snippet (or confirm you want me to propose defaults I can drop into your bootstrap)?

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