hi

Thanks for reaching out, Rahib. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

Are there any examples where agent can speak fluently in languages that it receives from the llm ? For example in a language learning agent it might be talking in english but as it is correcting someone it might say some words in that language.

You're asking if agents can speak fluently in languages they receive from an LLM — for example, a language-learning agent that speaks mostly English but inserts words or corrections in the target language. Yes — LiveKit supports that pattern and there are examples and building blocks you can reuse. What exists and how to use it • Example: the "Change Language" agent demonstrates switching the agent's output language during a session, so an agent can dynamically speak different languages. • Turn detection: the

MultilingualModel

supports mixed-language conversations (13 languages) and correctly detects turns when users and agents switch languages. • TTS support: examples use ElevenLabs TTS (and other multilingual TTS) that can be configured per segment to render the correct language and pronunciation. • Prompting pattern: prepend or include language instructions (e.g.,

en-US

or target language) in the LLM prompt so the model outputs the desired mix of English and foreign-language phrases. How you’d build a language-learning agent (high level) 1. Use an LLM that can output multilingual text and instruct it to produce corrections or target-language phrases inline with English explanations. 2. Send the LLM output to your TTS engine. For each spoken segment, set the TTS language/voice to match the text (switch dynamically when the LLM outputs target-language snippets). 3. Use the turn detector to segment user/agent turns reliably when languages switch. 4. Optionally, control pronunciation or phonetic hints via prompt instructions or by sending small phonetic fragments to a TTS engine that supports SSML/phonemes. If you want, I can: • Point you to the "Change Language" recipe and the turn-detector example. • Sketch a short prompt template that mixes English explanations and target-language corrections. Which would you like? Sources: LiveKit Docs | Building the all-in-one platform for voice AI agents | LiveKit turn detector plugin | LiveKit docs | Live Conversations with AI using ChatGPT and WebRTC

Can you tell me if my thinking is correct for the architecture: We have a frontend ui which uses nextjs We have a backend agent in python We can have a api app that can use fast api So basically there are three layers of framework in order to build a full stack agent? for example a fastAPI can handle db calls and all that backend agent handles the main agent logic and nextjs ui shows the UI interface ? Am i correct?

You're on the right track — that three-layer split is a common, sensible architecture for a full-stack agent. Quick confirmation and a few practical notes: • Frontend (Next.js) ◦ Handles UI, captures microphone/camera input, plays TTS audio, and displays transcripts/feedback. ◦ Connects to LiveKit for real-time audio/video and to your API for agent requests, session state, or transcripts. • API layer (FastAPI) ◦ Handles authentication, DB access, session/room management, and exposes endpoints the frontend calls. ◦ Orchestrates calls to external services (LLM, TTS, speech-to-text), stores conversation history, and returns structured data to the frontend. • Backend agent (Python) ◦ Implements the agent logic: prompt engineering, dialogue state, turn management, deciding when to call LLM/TTS/ASR, and post-processing. ◦ Can run as part of the FastAPI app or as a separate service that the API calls (both are valid; separate service helps scale and isolate heavy LLM work). Additional practical points • LiveKit: Use it for real-time media routing and to connect users and agent audio streams; agent can join rooms as a participant or receive media via an audio bridge/TTS integration. • Where to host LLM/TTS/ASR: these are typically called from the API or the agent service. Keep latency in mind — colocate heavy model calls near the agent service. • Scaling: separate services (API, agent worker, background job queue) make horizontal scaling and retries easier. • Security & state: keep DB, secrets, and user identity logic in the API layer; agent logic should receive only the data it needs. • Simpler option: For small projects, you can combine agent logic into the FastAPI app to reduce complexity, then split later. If you want, I can: • Sketch a minimal deployment layout (which services talk to which) or • Provide a short example flow: user speech → frontend → ASR → agent → TTS → LiveKit playback. Which would help you next?

• Sketch a minimal deployment layout (which services talk to which) or

Acknowledged — you want a minimal deployment layout showing which services talk to which. Below is a simple, focused diagram and a short explanation of each connection. Minimal deployment layout (components and connections) • Browser (Next.js UI) ◦ ↔️ LiveKit (real-time audio/video & data channels) ◦ ↔️ FastAPI (REST/WebSocket calls for auth, session metadata, transcripts, UI actions) • FastAPI (API layer) ◦ ↔️ Browser (auth, session management, DB reads/writes) ◦ ↔️ Backend Agent (RPC or HTTP calls to request agent processing) ◦ ↔️ Database (conversation history, user profiles, secrets) ◦ ↔️ LiveKit Admin API (create/join rooms, tokens) • Backend Agent (Python service) ◦ ↔️ FastAPI (receives requests to run agent logic) ◦ ↔️ LLM provider (generate responses / corrections) ◦ ↔️ ASR provider (optional: transcribe audio if not using LiveKit pipeline) ◦ ↔️ TTS provider (synthesize agent speech; can return audio stream or files) ◦ ↔️ Storage/DB (optional: store transcripts, artifacts) • LiveKit (media routing) ◦ ↔️ Browser (publish/subscribe audio & video) ◦ ↔️ Backend Agent or TTS gateway (agent can join as a participant or receive/send audio via a bridge) • External services ◦ LLM, ASR, TTS providers called by Backend Agent (or FastAPI if agent is co-located) Flow examples (short) 1. Real-time voice flow: ◦ User speaks → Browser publishes audio to LiveKit → Agent subscribes to audio (or FastAPI pulls recorded chunks) → Agent calls ASR → Agent calls LLM → Agent calls TTS → Agent publishes synthesized audio back into LiveKit → Browser plays agent audio. 1. API-driven flow (low-latency text interactions or control): ◦ Browser sends message → FastAPI → Backend Agent (LLM) → FastAPI returns structured response → Browser displays text or triggers TTS. Deployment notes (concise) • The Backend Agent can be co-located inside FastAPI for simplicity; split into a separate service for better scaling. • Use LiveKit for real-time media; agent can either join rooms as a participant or receive media via a bridge. • Keep DB and secrets behind FastAPI; only expose necessary tokens to the client. • Place LLM/TTS close to the agent service to reduce latency. Would you like a sample sequence diagram for the real-time voice flow or a minimal file list for deploying these services?