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It seems likely that using a state machine with LiveKit agents involves managing the agent's state transitions and objectives within your application logic. You can manipulate the chat context and function context to control the agent's behavior dynamically. According to LiveKit documentation, you can manage agent states like "initializing," "thinking," "listening," and "speaking." Here's a basic example of how you might implement a state machine using LiveKit's `VoicePipelineAgent`:

from livekit import VoicePipelineAgent, JobContext

class MyStateMachine:
    def __init__(self):
        self.state = "initializing"

    def transition(self, new_state):
        print(f"Transitioning from {self.state} to {new_state}")
        self.state = new_state

async def entrypoint(ctx: JobContext):
    state_machine = MyStateMachine()
    state_machine.transition("listening")

    # Connect to the room
    await ctx.connect(auto_subscribe=True)

    # Wait for a participant
    participant = await ctx.wait_for_participant()
    print(f"Participant {participant.identity} connected")

    # Transition to speaking state
    state_machine.transition("speaking")
    await ctx.say("Hello, how can I assist you today?")

    # Example of transitioning to another state
    state_machine.transition("thinking")
    # Perform some logic here

    # Transition back to listening
    state_machine.transition("listening")

# Define the agent
agent = VoicePipelineAgent(entrypoint=entrypoint)

Would you like more detail on how to integrate this with your specific use case? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.