# Examples metadata consumed by livekit/agents-jukebox. # # Each top-level key under `examples` is the agent name — used as # - the directory name under examples/ # - the LIVEKIT_AGENT_NAME injected at deploy time # - the dispatch target in the jukebox's token endpoint # # Pixel art icons: `1` = accent color, `2` = lighter accent (highlight # / secondary detail), `0` = transparent. version: 1 # All examples deploy to this LiveKit Cloud project. The CI regenerates # each example's livekit.toml from this subdomain + the per-example # agent_id below before invoking `lk agent deploy`. project: subdomain: examples-wfxyig8v examples: frontdesk: title: Front Desk description: A receptionist agent wired up to a live calendar. Answers questions about availability, books appointments in real time, and confirms back to the caller. # brand palette — indigo-500 accent: "#1F44F9" agent_id: CA_9TqkLsnwhjmE entry: agent.py github: "https://github.com/livekit/agents/tree/main/examples/frontdesk" tags: [tools, scheduling] icon: # Calendar with the top header strip in the highlight tone. size: 12 pixels: | 000000000000 000100010000 001111111000 012222222100 011111111100 010000000100 010000000100 010000000100 010000000100 010000000100 001111111000 000000000000 views: - rpc: set_appointment_status title: "\uf073 Schedule" healthcare: title: Healthcare description: A medical front-desk agent that handles intake, authenticates returning patients, books appointments, and hands the conversation off to a human when it needs to. # LiveKit brand palette (raw-colors.ts) — green-500 accent: "#009E4F" agent_id: CA_uFGnwowV2PMS entry: agent.py github: "https://github.com/livekit/agents/tree/main/examples/healthcare" tags: [multi-agent, scheduling] icon: # Heart with a top-left specular highlight on the left lobe. size: 12 pixels: | 000000000000 002100011000 022210111100 211111111110 111111111110 111111111110 011111111100 001111111000 000111110000 000011100000 000001000000 000000000000 survey: title: Survey description: A structured interview agent that walks candidates through a software engineer screening flow, captures answers as they go, and writes a graded summary to disk. # brand palette — amber-500 accent: "#EF8B01" agent_id: CA_APDyCSXwMUNS entry: agent.py github: "https://github.com/livekit/agents/tree/main/examples/survey" tags: [multi-agent] icon: # Speech bubble outline with two highlight "text lines" inside. size: 12 pixels: | 000000000000 001111111100 010000000010 010000000010 010222200010 010000000010 010220000010 010000000010 010111111100 011000000000 010000000000 000000000000 drive-thru: title: Drive Thru description: A drive-thru ordering agent that takes the order over voice, builds a structured cart with dynamically-generated tools per menu item, and reads back the total. # brand palette — red-500 accent: "#FA4C39" agent_id: CA_cn3jfgKTxXDL entry: agent.py github: "https://github.com/livekit/agents/tree/main/examples/drive-thru" tags: [tools] icon: # Side-view car: cabin pillars in accent, windshield + side # windows in the lighter tone, body and wheels in accent. size: 12 pixels: | 000000000000 000000000000 001111110000 012221221000 012222111110 100000000001 101110011101 011011110110 001110011100 000000000000 000000000000 000000000000 # The playground reads these `views` and renders a read-only # markdown card per entry on the right of the orb. The agent fills # them at runtime by RPC'ing the matching `id` with a markdown # payload — the card stays hidden until the first payload arrives. # The agent fills these `views` at runtime by RPC'ing the # declared method name (e.g. `set_cart_content`) with a markdown # payload. Each entry renders as a card on the right of the orb, # hidden until the first non-empty payload, re-hidden on empty. views: - rpc: set_cart_content # Card title is rendered in the body font, which has the # FA Solid range merged in — so YAML `\u` escapes for any # FA codepoint render inline. `\uf07a` is shopping-cart. title: "\uf07a Current order" inference: title: Inference description: A voice agent powered end-to-end by LiveKit Inference. Pick the STT, LLM, and TTS provider live to hear how each one feels in the same conversation. # brand palette — purple-500 accent: "#9B5CFF" agent_id: CA_K9e3yQ3RPNKQ entry: agent.py github: "https://github.com/livekit/agents/tree/main/examples/inference" tags: [inference] icon: # Three stacked bars: the STT → LLM → TTS pipeline, with the # middle bar in the highlight tone. size: 12 pixels: | 000000000000 000000000000 011111111110 011111111110 000000000000 022222222220 022222222220 000000000000 011111111110 011111111110 000000000000 000000000000 # The playground reads these `controls` and renders one widget per # entry. `type` selects the widget — defaults to `dropdown` for # backwards-compat with the other examples. Picking an option fires # an RPC on the agent participant: `rpc` is the method name, # payload is `{"value": "…"}`. Defaults match the agent's # DEFAULT_* constants in agent.py. # # Supported types: # - dropdown (default): pill with options list # - textarea: multiline string editor. Fires `rpc` with the # current text on every edit-commit (debounced by the FE) # so the agent can apply it live. # - link: a button-styled action. On click fires `rpc` on the # agent (no payload) and opens the returned string as a URL # in a new tab. The agent owns the URL — useful for deep # links into Builder, dashboards, etc. where the params # depend on the agent's current state. controls: - rpc: set_system_prompt type: textarea label: System prompt # Keep in sync with INSTRUCTIONS in examples/inference/agent.py. default: "You're a friendly agent in the LiveKit Playground. The person talking to you is prototyping their own voice agent — they can edit this prompt in the side panel and swap the STT / LLM / TTS models live. Keep replies short, natural, and conversational. If asked which models you're using, answer honestly." rows: 8 - rpc: set_stt_model label: STT default: deepgram/nova-3 options: - { value: deepgram/nova-3, label: Deepgram Nova-3 } - { value: deepgram/nova-3-medical, label: Deepgram Nova-3 Medical } - { value: deepgram/flux-general-en, label: Deepgram Flux (English) } - { value: deepgram/flux-general-multi, label: Deepgram Flux (Multilingual) } - { value: assemblyai/u3-rt-pro, label: AssemblyAI Universal-3 Pro } - { value: assemblyai/universal-streaming, label: AssemblyAI Universal-Streaming } - { value: assemblyai/universal-streaming-multilingual, label: AssemblyAI Universal-Streaming Multilingual } - { value: cartesia/ink-whisper, label: Cartesia Ink Whisper } - { value: elevenlabs/scribe_v2_realtime, label: ElevenLabs Scribe v2 Realtime } - { value: speechmatics/enhanced, label: Speechmatics Enhanced } - { value: speechmatics/standard, label: Speechmatics Standard } - { value: xai/stt-1, label: xAI Speech-to-Text } - rpc: set_llm_model label: LLM default: openai/gpt-4.1-mini options: - { value: openai/gpt-4.1, label: OpenAI GPT-4.1 } - { value: openai/gpt-4.1-mini, label: OpenAI GPT-4.1 mini } - { value: openai/gpt-4.1-nano, label: OpenAI GPT-4.1 nano } - { value: openai/gpt-4o, label: OpenAI GPT-4o } - { value: openai/gpt-4o-mini, label: OpenAI GPT-4o mini } - { value: openai/gpt-5, label: OpenAI GPT-5 } - { value: openai/gpt-5-mini, label: OpenAI GPT-5 mini } - { value: openai/gpt-5-nano, label: OpenAI GPT-5 nano } - { value: openai/gpt-5.1, label: OpenAI GPT-5.1 } - { value: openai/gpt-5.1-chat-latest, label: OpenAI GPT-5.1 Chat } - { value: openai/gpt-5.2, label: OpenAI GPT-5.2 } - { value: openai/gpt-5.2-chat-latest, label: OpenAI GPT-5.2 Chat } - { value: openai/gpt-5.3-chat-latest, label: OpenAI GPT-5.3 Chat } - { value: openai/gpt-5.4, label: OpenAI GPT-5.4 } - { value: openai/gpt-5.4-mini, label: OpenAI GPT-5.4 mini } - { value: openai/gpt-5.4-nano, label: OpenAI GPT-5.4 nano } - { value: openai/gpt-5.5, label: OpenAI GPT-5.5 } - { value: openai/gpt-oss-120b, label: OpenAI GPT OSS 120B } - { value: google/gemini-2.0-flash, label: Gemini 2.0 Flash } - { value: google/gemini-2.0-flash-lite, label: Gemini 2.0 Flash-Lite } - { value: google/gemini-2.5-flash, label: Gemini 2.5 Flash } - { value: google/gemini-2.5-flash-lite, label: Gemini 2.5 Flash-Lite } - { value: google/gemini-2.5-pro, label: Gemini 2.5 Pro } - { value: google/gemini-3-flash-preview, label: Gemini 3 Flash } - { value: google/gemini-3-pro-preview, label: Gemini 3 Pro } - { value: google/gemini-3.1-flash-lite, label: Gemini 3.1 Flash Lite } - { value: google/gemini-3.1-pro-preview, label: Gemini 3.1 Pro } - { value: xai/grok-4-1-fast-non-reasoning, label: Grok 4.1 Fast } - { value: xai/grok-4-1-fast-reasoning, label: Grok 4.1 Fast Reasoning } - { value: xai/grok-4.20-0309-non-reasoning, label: Grok 4.20 } - { value: xai/grok-4.20-0309-reasoning, label: Grok 4.20 Reasoning } - { value: xai/grok-4.20-multi-agent-0309, label: Grok 4.20 Multi-Agent } - { value: deepseek-ai/deepseek-v3, label: DeepSeek-V3 } - { value: deepseek-ai/deepseek-v3.1, label: DeepSeek-V3.1 } - { value: deepseek-ai/deepseek-v3.2, label: DeepSeek-V3.2 } - { value: moonshotai/kimi-k2-instruct, label: Kimi K2 Instruct } - { value: moonshotai/kimi-k2.5, label: Kimi K2.5 } - rpc: set_tts_model label: TTS default: cartesia/sonic-3 options: - { value: cartesia/sonic, label: Cartesia Sonic } - { value: cartesia/sonic-2, label: Cartesia Sonic-2 } - { value: cartesia/sonic-3, label: Cartesia Sonic-3 } - { value: cartesia/sonic-3-latest, label: Cartesia Sonic-3 Latest } - { value: cartesia/sonic-turbo, label: Cartesia Sonic Turbo } - { value: deepgram/aura, label: Deepgram Aura } - { value: deepgram/aura-2, label: Deepgram Aura-2 } - { value: elevenlabs/eleven_flash_v2, label: ElevenLabs Flash v2 } - { value: elevenlabs/eleven_flash_v2_5, label: ElevenLabs Flash v2.5 } - { value: elevenlabs/eleven_multilingual_v2, label: ElevenLabs Multilingual v2 } - { value: elevenlabs/eleven_turbo_v2, label: ElevenLabs Turbo v2 } - { value: elevenlabs/eleven_turbo_v2_5, label: ElevenLabs Turbo v2.5 } - { value: elevenlabs/eleven_v3, label: ElevenLabs v3 } - { value: inworld/inworld-tts-1, label: Inworld TTS 1 } - { value: inworld/inworld-tts-1-max, label: Inworld TTS 1 Max } - { value: inworld/inworld-tts-1.5-mini, label: Inworld TTS 1.5 Mini } - { value: inworld/inworld-tts-1.5-max, label: Inworld TTS 1.5 Max } - { value: inworld/inworld-tts-2, label: Inworld TTS 2 } - { value: rime/arcana, label: Rime Arcana } - { value: rime/mist, label: Rime Mist } - { value: rime/mistv2, label: Rime Mist v2 } - { value: rime/mistv3, label: Rime Mist v3 } - { value: xai/tts-1, label: xAI Text-to-Speech } # "Open in Builder" CTA. `type: link` fires `rpc` on the agent # and opens the returned string as a URL in a new tab. The # agent (examples/inference/agent.py :: open_in_builder) reads # its current instructions + STT/LLM/TTS model strings and # builds the Cloud Builder deep-link from there, so the # frontend doesn't have to know the schema. - rpc: open_in_builder type: link label: "Open in Builder \u2192" primary: true # Live conversation transcript. The agent doesn't push to this view # by RPC the way other examples push markdown cards — the frontend # subscribes to LiveKit `TranscriptionReceived` events and renders # both sides of the conversation as text. `source: transcript` is # the trigger; `rpc` is a sentinel value and isn't actually called. views: - rpc: __transcript source: transcript title: "\uf075 Transcript" avatar: title: Avatar description: Talk to a LemonSlice avatar. Pick anyone from the dropdown to swap who you're chatting with, from Leila to a Pixar fox. # LemonSlice brand \u2014 yellow-500 from the LK palette. accent: "#F9E71F" agent_id: CA_dzjZwsBsRKzZ entry: agent.py github: "https://github.com/livekit/agents/tree/main/examples/avatar" tags: [avatar, video, multi-persona] # Tells the jukebox frontend to render the AvatarFrame and drive # the connect-time orb \u2192 video reveal animation. Without this # flag the example renders like every other one (orb only). avatar: true icon: # Head + shoulders. Head is filled with '2' (highlight tone) and # the shoulders are '1' (full accent), so the two regions read as # two distinct shades instead of a single blob. size: 12 pixels: | 000000000000 000022220000 000222222000 002222222200 002222222200 002222222200 000222222000 000022220000 000000000000 001111111100 011111111110 111111111111 # The playground reads these `controls` and renders one widget # per entry. The persona dropdown fires `set_avatar` on the # agent with the chosen id as the payload value. The agent # updates voice + system prompt live; the avatar image stays # on the current persona until the session reconnects. controls: - rpc: set_avatar label: Persona default: leila options: - { value: leila, label: Leila } - { value: jess, label: Jess } - { value: software_engineer, label: Software Engineer } - { value: social_worker, label: Social Worker } - { value: ai_therapist, label: AI Therapist } - { value: management_consultant, label: Management Consultant } - { value: shopping_assistant, label: Shopping Assistant } - { value: cat_girl, label: Cat Girl } - { value: mr_fox, label: Mr Fox }