Can u tell me by default 1. turn_detection 2. input_audio_noise_reduction - what is near_field and far_field - its significance for openai realtime model

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

pre-warming the tts-cartesia and openai-realtime

Thanks — I’ll answer about pre-warming TTS‑Cartesia and OpenAI Realtime. Short answer: • The prewarm pattern in LiveKit Agents (Python

setup_fnc

/ Node

prewarm

) is designed to load long‑loading models into

proc.userdata

so agent jobs start faster. • The docs do not explicitly show examples that prewarm Cartesia TTS or the OpenAI Realtime model, but the same prewarm pattern can be used for any model that needs loading time. If you want to implement this, tell me which runtime you’re using (Node or Python) and whether you want: • an example that preloads Cartesia TTS, • an example that preloads an OpenAI Realtime client/connection, or • both — and I’ll provide concise code showing how to load them into

proc.userdata

and access them from

entry

. Sources: Server options | LiveKit Documentation | Understand and Improve Agent Latency | LiveKit | Cartesia TTS | LiveKit Documentation | Realtime models overview | LiveKit Documentation, with interactive elements

python • Cartesia TTS, • OpenAI Realtime client/connection,

Thanks — I’ll provide a concise Python example and notes for pre-warming Cartesia TTS and an OpenAI Realtime client in LiveKit Agents. Notes • Use the AgentServer

setup_fnc

(prewarm) to load models/clients into

proc.userdata

. • Store the loaded objects on

proc.userdata

so

entry

can reuse them without reloading. • Ensure any clients you pre-warm are safe to reuse across jobs in your environment (e.g., thread/process safety). If a client requires per-job state, pre-warm a factory or connection helper instead. Example (Python) 1. Minimal pattern using

setup_fnc

to pre-warm Cartesia TTS and an OpenAI Realtime client:

from livekit_agents import AgentServer, JobProcess, JobContext

# Pseudo-imports - replace with actual Cartesia & OpenAI client libraries
# from cartesia import CartesiaTTS
# from openai_realtime_client import OpenAIRealtimeClient

server = AgentServer()

def prewarm(proc: JobProcess):
    # Pre-warm Cartesia TTS (replace with actual Cartesia init)
    proc.userdata["cartesia_tts"] = CartesiaTTS(api_key="YOUR_CARTESIA_KEY", model="cartesia-model")

    # Pre-warm OpenAI Realtime client/connection (replace with actual client init)
    proc.userdata["openai_realtime"] = OpenAIRealtimeClient(api_key="YOUR_OPENAI_KEY", model="gpt-realtime")

server.setup_fnc = prewarm

async def entry(ctx: JobContext):
    # Access pre-warmed instances
    tts = ctx.proc.userdata["cartesia_tts"]
    openai_rt = ctx.proc.userdata["openai_realtime"]

    # Example usage (pseudo-code)
    audio_bytes = tts.synthesize_text("Hello from prewarmed Cartesia")
    # send audio or use openai_realtime client for realtime interactions
    await openai_rt.send_audio(audio_bytes)
    # ... rest of job logic ...

server.entry_fnc = entry

if __name__ == "__main__":
    server.run()

1. If the OpenAI realtime client requires an async connection handshake, pre-warm a ready-to-use client or a lightweight factory that opens connections on demand. Example pattern:

async def prewarm_async(proc: JobProcess):
    proc.userdata["cartesia_tts"] = CartesiaTTS(api_key="KEY")
    client = OpenAIRealtimeClient(api_key="OPENAI_KEY")
    await client.connect()           # if connect is async and reusable
    proc.userdata["openai_realtime"] = client

server.setup_fnc = lambda p: None   # keep sync signature, call async prewarm elsewhere if needed
# Or if framework supports async setup, register prewarm_async accordingly.

Recommendations • Test reuse safety: verify the Cartesia TTS instance and OpenAI realtime client can be reused across jobs or processes in your deployment. • If clients are not safe to share, pre-warm a heavy-weight artifact (model weights, tokenizer, etc.) and create per-job client instances quickly using those artifacts. • Monitor memory: pre-warming large models increases resident memory per process. If you want, I can: • Provide a fully runnable example with a specific Cartesia TTS Python SDK and a real OpenAI Realtime client API (if you share which SDKs/libs you plan to use), or • Show an async-compatible setup that fits your exact agent server version., with interactive elements

in the agent example - hwete the start the recording if it is telephony call - do we call post ctx.connect or when?

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!