Hey

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

We do sales call. Please check the following configurations and let me know after doing a check i teh flow very conversational or not ?

AgentSession(
    llm=openai_plugin.LLM(
        model="gpt-4.1-mini",
        api_key=...,
    ),
    tts=cartesia_plugin.TTS(
        model="sonic-2",
        api_version="2024-11-13",  # sonic-3 uses "2025-04-16"
        voice="c35946ea-f4b0-40fa-b1ba-1530d87b9c58",
        api_key=...,
        speed=None,           # set per-call via tts_speed
        emotion=None,         # set per-call via tts_emotion
        volume=None,          # set per-call via tts_volume
        sample_rate=22050,    # voice trained at 22-24kHz; LK Cloud downsamples to 8k μ-law
    ),
    stt=deepgram_plugin.STT(
        model="nova-3",
        api_key=...,
        language="en",
        interim_results=True,
        endpointing_ms=100,
        no_delay=True,
        smart_format=True,
        filler_words=False,
        keyterm=[          # ~12 domain words — nova-3 keyterm prompting
            "Evolv", "Talkstone", "Buyer's Choice",
            "payment processing", "credit card processing",
            "POS", "point of sale", "Heartland", "Square",
            "Stripe", "PCI", "interchange",
        ],
        sample_rate=8000,
    ),
    turn_handling={
        "turn_detection": <in-process LiveKit turn-detector ONNX>,
        "endpointing": {
            "min_delay": 0.4,
            "max_delay": 0.8,
        },
        "interruption": {
            "enabled": True,
            "mode": "adaptive",        # Adaptive Interruption Handling
            "min_duration": 0.3,
            "min_words": 2,
            "resume_false_interruption": True,
            "false_interruption_timeout": 0.5,
        },
        "preemptive_generation": {
            "enabled": True,
            "preemptive_tts": True,
        },
    },
    vad=<silero.VAD loaded in prewarm>,
    aec_warmup_duration=0.0,
)

Thanks — I’m reviewing the config you shared and will help you assess whether the flow will feel conversational. Quick clarification before I review: do you want the agent to sound highly natural and human-like (longer responses, interruptions handled like a sales rep), or more concise and transactional? That will change a few recommendations. Assuming you want a natural, conversational sales flow, initial observations and targeted suggestions: • LLM:

gpt-4.1-mini

can produce natural responses; tune prompts/system messages for friendly, consultative tone and brevity control if you want shorter replies. • TTS: sonic-2 with that voice and 22k sample rate is fine. To be more conversational: ◦ Enable per-call

tts_emotion

and

tts_speed

to vary prosody (e.g., slight enthusiasm for upsell, calm for objections). ◦ Use natural pauses and sentence-level SSML (if supported) to avoid robotic pacing. • STT (Deepgram nova-3): interim_results=True + no_delay=True supports low-latency turn detection, which helps flow. Keyterm list is good for domain accuracy. • Turn handling: ◦ Turn detector ONNX + VAD means fast local turn decisions — good. ◦ Endpointing min_delay 0.4 / max_delay 0.8: these are short and will make the agent respond quickly; for more natural pacing consider slightly larger max_delay (e.g., 1.0s) so the agent doesn’t cut off brief thoughtful pauses. ◦ Interruption adaptive + resume_false_interruption True: this supports natural interruptions. Suggest increasing

false_interruption_timeout

to ~0.6–0.8s if you observe premature resumes. ◦ min_duration 0.3 and min_words 2 are reasonable to avoid reacting to short noises; lower them only if you need ultra-fast handoffs. ◦ Preemptive generation + preemptive_tts enabled: good for responsiveness. Ensure the LLM prompt accounts for possible mid-speech cutoffs to avoid half sentences. • VAD & AEC: silero VAD and aec_warmup_duration=0.0: ◦ VAD is good; consider small AEC warmup (>0s) if echoes occur at session start. ◦ If participants use speakerphones, tune VAD thresholds to avoid false triggers. Potential issues to watch for (test cases to run): • Short pauses during the human’s thinking causing agent to interrupt—test with 0.5–1.0s pauses. • Overlapping speech: verify how the system prioritizes human vs agent when STT interim + preemptive TTS triggers. • False interruptions when brief background noises occur—adjust false_interruption_timeout and VAD sensitivity. • Prosody monotony—test with varied

tts_emotion

values and confirm voice handles emotional cues. Concrete next steps I can help with: • Provide recommended numeric tweaks for endpointing/interruption based on your preferred conversational style. • Draft system and user prompt examples for a warm, consultative sales tone. • Suggest test scenarios and metrics to measure conversational naturalness (interruptions per call, latency, user satisfaction questions). Which of the next steps would you like me to do now? , with interactive elements