Certainly! Here's a step-by-step breakdown of potential workarounds for dynamically adjusting the

min_endpointing_delay

during a conversation with LiveKit's `VoicePipelineAgent`: 1. Reinitialize the Agent: One approach is to stop the current

VoicePipelineAgent

and reinitialize it with a new

min_endpointing_delay

value. This would involve: - Stopping the current agent session. - Creating a new instance of

VoicePipelineAgent

with the desired

min_endpointing_delay

. - Starting the new agent session. This method, however, may introduce a brief interruption in the conversation flow. 2. Use Callbacks for Dynamic Adjustments: If reinitializing the agent is not feasible, consider using the

before_llm_cb

or

before_tts_cb

callbacks to adjust the conversation context or behavior dynamically. While these callbacks do not directly change

min_endpointing_delay

, they can be used to modify how the agent processes input and output, potentially simulating a change in endpointing behavior. 3. Custom Implementation: For more advanced control, you might consider implementing a custom agent that allows for dynamic adjustments. This would involve: - Extending the

VoicePipelineAgent

class. - Adding logic to adjust the endpointing delay based on specific triggers or conditions during the conversation. 4. Feedback Mechanism: Implement a feedback mechanism where the agent can adjust its behavior based on user input or detected conversation patterns. This could involve monitoring the conversation for specific cues and adjusting the agent's response timing accordingly. These workarounds require a deeper integration with the LiveKit framework and may involve trade-offs in terms of complexity and performance. If you need further assistance with implementation details, feel free to ask! Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1731432250.159119 - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1731059458.698089 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.