I'd be happy to help you manually trigger a response from an agent without any user input! Here are a couple of friendly methods you can use: • session.say(): This is great when you want the agent to speak a predefined message using Text-to-Speech (TTS). It works perfectly for fixed responses like greetings or status updates. For example:

await self.session.say("Hello! How can I help you today?", allow_interruptions=False)

• session.generate_reply(): This method lets the LLM create a dynamic response based on instructions or simulated user input. For example:

await self.session.generate_reply(instructions="Greet the user warmly and ask how they're doing today.")

Both of these methods return a

SpeechHandle

, which you can use to manage when the speech finishes or handle any interruptions. If you'd like, I’m here to provide more details or examples—just let me know! Sources: pipeline-llm/openai_llm.py | examples/voice_agents/fast-preresponse.py | Agent speech and audio | LiveKit Docs | Voice activity detection (VAD) parameters | LiveKit Docs | Agent speech and audio | LiveKit Docs

I'd love to help you get started with using the Agent class to send a message and generate a response! You can use the

session.generate_reply()

method inside the Agent's lifecycle methods. This is a great way to create dynamic responses based on instructions or even simulated user input. For instance, here’s how you might do it within the Agent class:

class MyAgent(Agent):
    async def on_enter(self):
        await self.session.generate_reply(instructions="Greet the user warmly and ask how you can assist.")

This approach works smoothly whether your agent is text-only or voice-enabled. If you have any questions or want me to walk you through more examples, just let me know—I’m here to help! Sources: Agent speech and audio | LiveKit Docs | Workflows | LiveKit Docs | Voice AI quickstart | LiveKit Docs | examples/voice_agents/realtime_load_chat_history.py | Text and transcriptions | LiveKit Docs