I understand how the warning "no candidates in the response" can be a bit confusing. It happens when the LLM API doesn’t return any valid candidate outputs. Don’t worry—here are some friendly strategies to help you handle this smoothly: • Retry Mechanisms: It’s a good idea to use exponential backoff for errors that can be retried, like rate limiting (status code 429). You can add retries right in your application code to manage these situations gracefully. • Fallback Strategies: Consider setting up fallbacks to other LLM models or even using Text-to-Speech (TTS) for audio generation when you get text-only responses. This way, your app keeps running without a hitch. • Error Event Handling: Subscribing to error events can really help. You’ll be able to create custom recovery steps, and by checking the recoverable field in error objects, you can smartly decide when to retry. • Model Configuration: Tweaking parameters like temperature, top_p, and candidate_count can often reduce the chance of empty responses. It’s a helpful way to fine-tune your results. • Default Responses: Having some default responses at the application level is a great safety net when no valid response comes through. These tips should make handling this warning a lot easier and more reliable. If you’d like, I’m here to help with more details or examples on how to put these into practice. Just let me know! Sources: Events and error handling | LiveKit Docs | plugins/google/src/beta/gemini_tts.ts | livekit-agents/livekit/agents/voice/agent_activity.py