Thanks — I’m responding to your question about whether LiveKit can detect a user’s emotion and then respond accordingly. Short answer: LiveKit Agents do not include a built-in emotion-detection feature for user speech, text, or video. The platform supports capabilities like customizing agent responses and sentiment-aware TTS instructions, but it does not provide automatic emotion recognition from user input. What you can do instead (options you can implement) • Integrate a third-party emotion/affect detection service: ◦ Use a speech-emotion API (third-party) to analyze audio for emotion, or a text-emotion API to analyze transcripts. ◦ Feed the emotion label or confidence score into your agent’s decision logic and choose an appropriate response or voice instruction. • Use emotion-aware rules in your agent logic: ◦ Add a preprocessing step that converts incoming audio to text (if needed) and runs sentiment/emotion analysis. ◦ Map analysis results to response strategies (e.g., escalate to empathetic script when “sad” or “angry”; use upbeat language when “happy”). • Use LiveKit’s existing response customization: ◦ Return structured outputs from your LLM or agent tools (for example:

{emotion: "frustrated", action: "apologize_and_offer_help"}

) and have your client or server apply the mapped behavior (TTS voice, wording, UI prompts). Questions that will help me guide you next • Do you plan to detect emotion from audio, text, or video? • Do you already have a sentiment/emotion model or vendor in mind (or would you like recommendations)? • Do you want the agent itself to decide the response, or do you want your application server/client to mediate the response selection? If you tell me which input type and whether you prefer a managed vendor or open-source approach, I can outline a concrete integration flow and sample logic for mapping detected emotions to agent responses. Sources: Tool definition and use | LiveKit Documentation | Introducing the Grok Voice Agent API in partnership with xAI | Improving voice AI's turn detection with transformers