What's the best way to create an agent on the fly - for example with a different voice or model? Say its deployed on a remote server via docker.

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

So once my python script runs in my docker container, and a fastApi gets all the data to replace; will everything work as expected..? My new agent instance will be created and scaled by itself?

Hey there! Let me break down how LiveKit makes scaling your agents super smooth. Think of it like having a smart assistant that automatically creates and assigns workers exactly when you need them. When you deploy in Docker, each worker (running

python agent.py start

) basically raises its hand and says, "I'm ready!" to the LiveKit server, which then distributes jobs like room creation across available workers. Now, if you're using FastAPI for configuration, you'll want to adjust your approach a bit. Instead of treating this like a typical web server, your containers should act more like LiveKit agent workers that connect via WebSocket. It's like making sure your team is ready to jump in and help, rather than just waiting for someone to knock on the door. Want to scale things up? Container orchestration platforms like Kubernetes are your best friend here. As demand increases, you can spin up more worker containers, and LiveKit will automatically distribute the workload. Just make sure your agent creation logic fits into the LiveKit job handler, not as standalone processes. It's all about working smarter, not harder! Got questions about adapting your FastAPI workflow to the LiveKit worker model? I'm all ears! Tell me about your container setup or target orchestration platform, and I'll help you navigate this. Sources: An open source stack for real-time multimodal AI | Deploying to production | LiveKit Docs | Deployment and scaling | LiveKit Docs | Dispatching agents | LiveKit Docs

Give me a short and concise example of a vad based voice agent that can be deployed to render.com with autoscaling features.

Hey there! Let me walk you through a super cool example of a voice agent that you can easily deploy on render.com with smart autoscaling. It's like having a friendly AI assistant that's ready to help you out!

from livekit.agents import JobContext, JobProcess, WorkerOptions, AutoSubscribe, cli, llm
from livekit.agents.pipeline import VoicePipelineAgent
from livekit.plugins import silero, openai, deepgram

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()

async def entrypoint(ctx: JobContext):
    await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
    participant = await ctx.wait_for_participant()
    agent = VoicePipelineAgent(
        vad=ctx.proc.userdata["vad"],
        stt=deepgram.STT(),
        llm=openai.LLM(model="gpt-4o-mini"),
        tts=openai.TTS(),
        chat_ctx=llm.ChatContext().append(role="system", text="You are a voice assistant.")
    )
    agent.start(ctx.room, participant)
    await agent.say("Hello! How can I help you today?", allow_interruptions=True)

if __name__ == "__main__":
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, prewarm_fnc=prewarm))

render.yaml (autoscaling configuration):

services:
- type: pserv
  name: voice-agent
  runtime: image
  plan: starter
  envVars:
  - fromGroup: voice-agent-env
  scaling:
    minInstances: 1
    maxInstances: 4
    targetCPUPercent: 60
    targetMemoryPercent: 60
  maxShutdownDelaySeconds: 300
version: "1"

• Don't forget to set up these environment variables: LIVEKIT_URL, LIVEKIT_API_KEY, LIVEKIT_API_SECRET, OPENAI_API_KEY, DEEPGRAM_API_KEY. They're like the secret sauce that makes everything work! • Render.com's got your back with autoscaling that adjusts based on CPU and memory usage. Pretty smart, right? • The

maxShutdownDelaySeconds

is a nice touch – it makes sure your voice conversations can wrap up smoothly without getting cut off. Sources: examples/src/pipeline_voice_agent.ts | agent/main.py | render.com/render.yaml | Deployment and scaling | LiveKit Docs