for low resource environment, which language do you recommend for client?

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

raspberry pi. actually, tried with Python, but heavy

Thanks — you're using a Raspberry Pi and found Python too heavy. Good call. Recommendation (short) • Use a native C/C++ client — the LiveKit ESP32 SDK (C/C++) runs efficiently on Raspberry Pi and is much lighter than Python/aiortc. • Consider the Rust SDK if you want safe memory management with low runtime overhead. • C++ (native LiveKit client) is also a solid option if you prefer a more conventional native SDK. Why • Python’s runtime and aiortc add CPU/memory overhead on Pi. • The ESP32 SDK and Rust/C++ options are built for minimal memory/CPU usage and have been used on Raspberry Pi Zero-class devices. Next step • Do you want links to the ESP32 SDK repo and docs, or help choosing between C/C++ vs Rust for your Pi model and use case (audio-only vs audio+video)? Sources: LiveKit SDK for ESP32: bringing voice AI to embedded devices | LiveKit | LiveKit SDK for ESP32: bringing voice AI to embedded devices | LiveKit | Introducing data tracks: realtime streaming beyond audio and video | LiveKi... | Introducing data tracks: realtime streaming beyond audio and video | LiveKi... | An open source stack for real-time multimodal AI | LiveKit, with interactive elements

how about go

Thanks — you're asking whether Go is a good choice for a lightweight LiveKit client on Raspberry Pi. Short answer: maybe, but not ideal. Facts to help you decide • LiveKit provides a Go Server SDK that embeds a Go WebRTC stack (Pion). It's intended for backend/CLI use and automated participants, not specifically optimized as a low‑resource embedded client. • Pion is pure Go and can run on Raspberry Pi, but the Go SDK is heavier than a minimal native client and is not positioned as the most resource‑efficient option for constrained devices. • The lightest options are the native C/C++ ESP32 SDK (works on Raspberry Pi) or the Rust SDK — both are designed for low memory/CPU usage and are better choices if Python felt too heavy. Recommendation • If you need lowest footprint and best performance on Pi: use the C/C++ (ESP32) SDK or Rust. • If you prefer Go for easier development and can accept more CPU/memory than the C/Rust options, try the Go SDK (Pion) in a prototype and measure resource use; move to C/Rust if it’s too heavy. Would you like links to the Go SDK repo and docs, or help testing a small Go prototype on your Pi (commands to build/run and what to measure)? Sources: LiveKit SDK for ESP32: bringing voice AI to embedded devices | LiveKit | LiveKit SDK for ESP32: bringing voice AI to embedded devices | LiveKit, with interactive elements

i don’t have enough experience of c/c++. learning curve is impirtant

Totally understandable — learning C/C++ can be a big lift. Let’s pick the best tradeoff between ease-of-learning and lower resource use. Quick recommendation and trade-offs • Go: Good balance — easier than C/C++, strong standard tooling, and uses Pion WebRTC. Likely lighter than Python but heavier than C/Rust. Good if you prefer fast development and are okay measuring resource use. • Rust: Lower-level performance close to C with safer memory handling and a gentler long-term maintenance story than C/C++. Steeper learning curve than Go but worth it if you want safety + performance. • C/C++ (ESP32 SDK): Best runtime footprint but highest learning cost — avoid if learning curve is your main constraint. • Continue with Python only if development speed matters more than resource use. Two quick questions so I can be specific 1. Is your app audio-only or audio+video? 2. Which languages are you already comfortable with (Go, Rust, JavaScript, etc.)? If you want, I can: • Suggest a Go prototype plan with exact repos and commands to build/run on Raspberry Pi and what metrics to measure, or • Outline a minimal Rust path (starter repo + build steps) if you’re open to Rust., with interactive elements

audio only. have experience of perl/python mainly. nidejs a little bit.

Thanks — audio-only on Raspberry Pi and preference to avoid C/C++ makes sense. Given your background (Perl/Python, some Node.js), here are practical options and my recommendation. Recommendation (best tradeoff) • Try Go first. It’s easier to learn than C/C++ or Rust, has good tooling, and uses the Pion WebRTC stack which can run on Raspberry Pi. It should be lighter than Python for audio-only use while keeping development straightforward. Alternate (if you prefer JS) • Node.js: Familiar if you know JavaScript. Likely easier to prototype, but may not be as efficient as Go. Good if you want fastest iteration and accept some resource overhead. If you want to proceed, I can: • Provide a concise Go prototype plan (repos, build/run commands for Raspberry Pi, and which metrics to measure), or • Give a Node.js audio-only prototype plan instead. Which would you like to try first — Go or Node.js? , with interactive elements

trying go. are there any feature which go sdk not support?

Thanks — you’re asking if the Go SDK supports all client features for joining rooms and publishing audio on Raspberry Pi. Short answer • The LiveKit Go SDK is server-side tooling and does NOT provide a WebRTC client: it cannot join rooms as a participant, publish/subscribe media tracks, or handle local media streams. • For an audio-only Raspberry Pi participant you should use a client SDK (e.g., JavaScript/Node.js, the ESP32 C/C++ SDK, or Rust) that implements WebRTC client functionality. Recommended next steps • If you want to keep using Go for development ease, you can: ◦ Prototype a small Node.js (JavaScript) audio client on the Pi to validate resource use, or ◦ Build a lightweight Go service that controls a separate client process (Node/Rust/C) to handle WebRTC media. • I can provide a concise Node.js audio prototype (repo, install/build/run steps) or a minimal Go+Node orchestration pattern—which would you prefer? Sources: An open source stack for real-time multimodal AI | LiveKit, with interactive elements

ok. nodejs sounds nice also

Great — Node.js is a good choice for audio‑only on Raspberry Pi given your background. I’ll outline a concise plan you can follow and offer help next. What to use • SDK: use the LiveKit JavaScript Node SDK (

@livekit/rtc-node

) for non-browser/node environments. • Requirements: Node >= 20, pnpm >= 10.15.0 (or npm/yarn), Raspberry Pi OS (Linux). Minimal plan (prototype) 1. Generate an access token on your server (JWT) to join the room. 2. On the Pi: ◦ Install dependencies:

@livekit/rtc-node

, an audio capture library (e.g.,

node-record-lpcm16

) and a playback library (e.g.,

speaker

). ◦ Connect to LiveKit with

connect(url, token, { rtcConfiguration })

. ◦ Capture microphone, create a LocalAudioTrack, and

localParticipant.publishTrack(...)

. ◦ Subscribe to remote audio tracks and pipe them to the speaker for playback. 1. Keep it audio-only: skip any video setup to reduce CPU/memory. Starter resources • Node starter example: https://github.com/livekit-examples/agent-starter-node • LiveKit docs: https://docs.livekit.io Would you like a minimal code snippet and exact install/run commands for your Pi model, or prefer I provide a ready-to-run small example (package.json + script) you can paste and test? Sources: Job lifecycle | LiveKit Documentation | Building a voice agent frontend on custom ESP32 hardware | LiveKit | Voice AI quickstart | LiveKit Documentation | LiveKit SDK for ESP32: bringing voice AI to embedded devices | LiveKit, with interactive elements