Build a LiveAvatar integration end-to-end — assesses the user's existing stack, recommends the optimal path, and guides implementation. Use when: (1) Building a new LiveAvatar integration, (2) Adding a real-time avatar to an app or site, (3) Connecting LiveAvatar to an existing AI pipeline, (4) User

LiveAvatar Integration LiveAvatar gives your product a human face — real time, lip synced video avatars that speak, react, and maintain eye contact. This skill assesses what you have, recommends the best integration path, and walks you through building it. Step 1: Discover What the User Has Before recommending a path, gather context. Check the codebase and conversation for signals. Do not ask questions the codebase already answers. Signals to look for in the codebase Scan for these automatically — do not ask the user if you can detect them: Signal Where to look What it means OpenAI / Anthropic / LLM SDK imports package.json , requirements.txt , imports User has their own LLM ElevenLabs / PlayHT / Deepgram TTS SDK dependencies, imports User has their own TTS Deepgram / Whisper / AssemblyAI STT SDK dependencies, imports User has their own STT LiveKit SDK ( livekit server sdk , @livekit/ ) dependencies User has LiveKit infra Agora SDK dependencies User has Agora infra Pipecat imports dependencies, imports User has a Pipecat pipeline ElevenLabs Agent / Conversational AI dependencies, config User has an ElevenLabs agent HEYGEN API KEY / LIVEAVATAR API KEY .env , config files User already has an API key Existing LiveAvatar code imports, API calls to api.liveavatar.com Existing integration (debug, not new setup) No backend / static site file structure (pure HTML/CSS/JS, no server) Embed is the only option Questions to ask (only what's still unknown) If the codebase scan leaves gaps, ask the user. Frame as a concise checklist — do not ask these one at a time : Skip any question the codebase or conversation already answered. Step 2: Route to the Golden Pathway Based on what you've gathered, match to ONE pathway. Always pick the simplest path that works. Do not offer multiple options — make the call. Decision tree Golden pathways (pick one, then implement) Pathway When Implementation guide Embed No backend, or no custom logic needed [references/embed guide.md](references/embed guide.md) FULL standard No existing AI stack [references/full mode guide.md](references/full mode guide.md) FULL + Custom LLM Has own LLM, wants LiveAvatar's ASR + TTS [references/full mode guide.md](references/full mode guide.md) (Custom LLM section) FULL + Custom TTS Has own ElevenLabs voice [references/full mode guide.md](references/full mode guide.md) (Custom TTS section) FULL + Push to Talk Needs explicit mic control [references/full mode guide.md](references/full mode guide.md) (Push to Talk section) LITE standard Has complete STT + LLM + TTS pipeline [references/lite mode guide.md](references/lite mode guide.md) LITE + ElevenLabs Plugin Has ElevenLabs Conversational AI agent [references/lite mode guide.md](references/lite mode guide.md) (ElevenLabs Plugin section) LITE + BYO WebRTC Has own LiveKit / Agora [references/lite mode guide.md](references/lite mode guide.md) (BYO WebRTC section) Step 3: Present the Recommendation Once you've picked a pathway, tell the user what you recommend and why, in 2 3 sentences. Example: Based on your setup, I recommend FULL Mode with Custom LLM . You already have an OpenAI integration for your LLM, so we'll plug that in and let LiveAvatar handle ASR, TTS, and video. This gets you a conversational avatar without rebuilding your audio pipeline. Then proceed directly to implementation using the corresponding guide in references/ . Step 4: Implement Read the appropriate reference guide and implement. Every guide follows the same structure: 1. Prerequisites — what to create/gather before writing code 2. Session lifecycle — step by step with curl commands and code 3. Events — what to send and receive 4. Add ons — mode specific optional features 5. Sandbox testing — free testing before going live 6. Gotchas — what breaks and how to avoid it Principles that apply to ALL paths Backend / frontend split is non negotiable. X API KEY is a secret — backend only. Frontend only gets livekit client token (safe for browsers). If you see the API key in client code, stop and restructure. Context makes the avatar conversational. In FULL Mode, no context id = silent avatar. No error thrown. Always create a context first, even a minimal "You are a helpful assistant." . FULL and LITE are completely different protocols. FULL = LiveKit data channels ( avatar. / user. ). LITE = WebSocket ( agent. / session. ). Never mix them. Start with sandbox. is sandbox: true , avatar ID dd73ea75 1218 4ef3 92ce 606d5f7fbc0a . Free, ~1 min sessions. Swap to production avatar when ready. LITE Mode: Fitting into an existing pipeline LITE users almost always have a working conversational system already. Do not ask them to rebuild their pipeline. Instead, map their existing components onto the LITE turn cycle: 1. Identify their current flow. Read their code to understand how conversation turns work today — where does user audio come in, how does it reach the LLM, how does TTS output get delivered? Look for their event loop, message handler, or turn manager. 2. Find the integration points. You need to hook into three moments in their existing flow: User starts/stops speaking → add agent.start listening / agent.stop listening TTS produces audio → route PCM output to agent.speak chunks over WebSocket instead of (or in addition to) their current audio output Response finishes → send agent.speak end and wait for agent.speak ended 3. Adapt, don't replace. If they have a working turn manager, add LiveAvatar calls into it. If they stream TTS to a browser via WebSocket already, tap into that same stream. The goal is the minimum change to get avatar video synced to their existing audio flow. 4. Verify audio format last. Once the wiring is in place, confirm their TTS outputs PCM 16 bit 24KHz. If not, either configure the TTS provider's output format or add resampling at the integration point. Step 5: Collect Feedback Trigger this immediately after you finish writing the integration code — do not wait for the user to confirm it works or say they're done. Also trigger if the user gives up or you've seen 3+ failed attempts at the same step. What to do: Follow the liveavatar feedback skill. It will walk you through gathering context from the conversation, showing the user what you'd send, getting explicit consent, and submitting the feedback. Keep it lightweight. One brief prompt at the end of your implementation message — don't push if the user declines or ignores it. What to consult [references/embed guide.md](references/embed guide.md) — Embed implementation [references/full mode guide.md](references/full mode guide.md) — FULL Mode implementation + events + add ons [references/lite mode guide.md](references/lite mode guide.md) — LITE Mode implementation + events + audio format + add ons

liveavatar-integrate