Open-LLM-VTuber Review: Offline AI Companion with Live2D

Originally published on andrew.ooo — visit the original for any updates, code snippets that aged out, or follow-up posts. Open-LLM-VTuber is the open-source attempt at a fully-offline neuro-sama clone — and this week it hit 10,447 stars (+2,388 in seven days) on GitHub Trending. It bolts together a local LLM, swappable ASR/TTS, a Live2D talking avatar, and a desktop-pet renderer into a single voice-interactive AI companion that runs on Windows, macOS, and Linux. Fully offline: Ollama / LM Studio / vLLM / GGUF for the brain, sherpa-onnx or FunASR for the ears, sherpa-onnx / GPTSoVITS / CosyVoice for the mouth. No internet required for inference. Hands-free voice loop: real-time ASR with voice-activity detection, interruption without headphones (the AI doesn't hear its own voice — solved via echo cancellation). Live2D avatar: emotion mapping pushes Cubism expressions from the LLM ("set emotion: surprised"), plus touch-feedback and a transparent desktop-pet mode with global top-most and mouse click-through. Visual perception: webcam, screen capture, and screenshots feed a multimodal LLM so the companion can react to what it sees. 10K+ stars, 2.4K this week, MIT-licensed (Python core), Live2D sample models under Live2D Inc.'s separate license. v2.0 rewrite in progress on the Zulip community — v1 is still maintained but feature-frozen. If you've been waiting for the open-source answer to neuro-sama / character.ai voice mode / Vedal's commercial stack, this is the most complete free option in 2026. It's rough, opinionated, and still v1 — but it works. Field Value Repo Open-LLM-VTuber/Open-LLM-VTuber Author t41372 (Yi-Ting) + 80+ contributors Stars 10,447 (+2,388 this week — #4 on GitHub Trending Python) Language Python 3.10+ (FastAPI backend) + Electron desktop client License MIT (Live2D sample models under separate Live2D Inc. license) Install uv (Python deps) + ffmpeg + optional deeplx Platforms Windows, macOS (NVIDIA + Apple Silicon), Linux LLM backends Ollama, OpenAI-compatible, Gemini, Claude, Mistral, DeepSeek, Zhipu, GGUF, LM Studio, vLLM ASR backends sherpa-onnx, FunASR, Faster-Whisper, Whisper.cpp, Groq Whisper, Azure TTS backends sherpa-onnx, pyttsx3, MeloTTS, Coqui-TTS, GPTSoVITS, Bark, CosyVoice, Edge TTS, Fish Audio, Azure Docs open-llm-vtuber.github.io/docs/quick-start It's a voice-first AI companion, not a chatbot with a face glued on. The architecture is built around the voice loop, and the Live2D avatar exists to give the LLM a body so emotions and expressions feel anchored. The pipeline, end-to-end: Microphone → VAD → ASR (FunASR/Whisper) → LLM (Ollama/vLLM/Claude API) ↓ ↓ Echo cancel ← Live2D emotion ← parse response ← stream tokens ↓ ↓ Avatar render TTS (CosyVoice/GPTSoVITS) → Speaker Every box is a swappable module. The conf.yaml file is the entire configuration surface — you flip backends by changing strings: # conf.yaml — minimal local-only setup character_config: conf_name: "mao" # Live2D model folder conf_uid: "mao_v1" live2d_model_name: "mao_pro_jp" human_name: "User" ai_name: "Mao" voice_interruption: true # interrupt AI mid-sentence agent_config: conversation_agent_choice: "basic_memory_agent" agent_settings: basic_memory_agent: llm_provider: "ollama_llm" llm_configs: ollama_llm: base_url: "http://localhost:11434/v1" model: "qwen2.5:7b" temperature: 0.7 asr_config: asr_model: "sherpa_onnx_asr" sherpa_onnx_asr: model_type: "sense_voice" # Chinese + English + Japanese + Cantonese provider: "cpu" tts_config: tts_model: "sherpa_onnx_tts" sherpa_onnx_tts: vits_model: "./models/vits-melo-tts-zh_en/model.onnx" vits_lexicon: "./models/vits-melo-tts-zh_en/lexicon.txt" vits_tokens: "./models/vits-melo-tts-zh_en/tokens.txt" provider: "cpu" That's the whole config for a fully-offline voice companion. Swap ollama_llm for openai_compatible_llm to use Groq, swap sherpa_onnx_asr for faster_whisper_asr for better English, and you're done. # 1. Install uv (Astral's Python package manager — Open-LLM-VTuber requires it) curl -LsSf https://astral.sh/uv/install.sh | sh # 2. Clone git clone https://github.com/Open-LLM-VTuber/Open-LLM-VTuber.git cd Open-LLM-VTuber # 3. Install Python deps (uv resolves everything via pyproject.toml) uv sync # 4. Install ffmpeg (TTS post-processing + audio resampling) brew install ffmpeg # macOS # sudo apt install ffmpeg # Debian/Ubuntu # 5. Pull an LLM ollama pull qwen2.5:7b # 6. Run uv run run_server.py Open http://localhost:12393 in your browser, grant microphone permission, and start talking. The Live2D model loads automatically with the default mao character. For the desktop pet (transparent background, click-through, always-on-top), grab the Electron client from the Releases page and point it at your running server. This is the killer feature. Most local voice-assistant projects assume you wear headphones because the microphone picks up the assistant's own TTS output and causes feedback or loops. Open-LLM-VTuber implements acoustic echo cancellation in the browser's MediaStreamTrack pipeline so the ASR ignores audio that the TTS just played. The result: you can interrupt mid-sentence on a laptop's built-in speakers and microphone, and the AI shuts up cleanly. The LLM is prompted to emit emotion tags inline: [joy] That's great to hear! [neutral] What did you do today? A regex in the response stream extracts [joy], [neutral], [surprise], [sad], etc., and pushes them to the Live2D model's expression parameter via WebSocket. You can map any tag to any Live2D expression in model_dict.json: { "name": "mao_pro_jp", "url": "/live2d-models/mao_pro_jp/runtime/mao_pro.model3.json", "kScale": 0.15, "initialXshift": 0, "initialYshift": 0, "emotionMap": { "neutral": 0, "anger": 1, "disgust": 2, "fear": 3, "joy": 4, "sad": 5, "surprise": 6 }, "tapMotions": { "head": {"head_motion": 30, "tap_body": 70}, "body": {"shake": 50, "tap_body": 50} } } Touch events on the Live2D canvas trigger random motions weighted by region. It's the level of polish that makes the avatar feel alive rather than a static PNG. The webcam and screen-share streams are off by default. Enabling them sends a frame to the LLM only on demand — when the user explicitly clicks the camera/screenshot button, or when the LLM emits a [see] tag asking to look. There's no continuous video pipe to OpenAI by default; with a multimodal local model (LLaVA, MiniCPM-V via Ollama), nothing leaves your machine. The Electron client supports a transparent borderless window with click-through so the Live2D model sits on your desktop without trapping mouse clicks. You can drag it anywhere, and it talks to you while you work. It's the closest open-source equivalent to a Windows desktop pet from the 2000s, but with an LLM brain. From r/LocalLLaMA, r/NeuroSama, and the project's Zulip (paraphrased — go read the threads): "Finally an open neuro-sama" — top sentiment in r/NeuroSama; people have been begging for a hackable companion since Vedal made his stack closed-source. "sherpa-onnx + qwen2.5:7b runs fully on my M1 Air, 6GB RAM hit, ~1.5s latency" — multiple reports of usable performance on modest hardware. "Long-term memory is gone in v1.0.0, that hurts" — the maintainer (t41372) confirmed long-term memory is being rebuilt for v2.0; the v1 memgpt integration was deprecated. "The Chinese-first docs are a barrier" — README is now in English/JP/KR/中文 but parts of the documentation site still default to Chinese. PRs welcome. "Live2D model licensing is confusing for streamers" — the sample models ship under Live2D Inc.'s free-material license, which permits non-commercial use; commercial streaming likely needs a Live2D Cubism Pro subscription. After three days of running it on an M2 Pro Mac mini with qwen2.5:7b via Ollama and sherpa_onnx_asr + sherpa_onnx_tts: Latency stack adds up. ASR ~200ms + 7B LLM time-to-first-token ~600ms + TTS first chunk ~400ms = ~1.2s before you hear the response start. Good for chat, bad for snappy back-and-forth. Smaller LLMs (Qwen 1.5B, Llama 3.2 1B) cut this in half but lose personality. Long-term memory is missing in v1.0.0. Chat logs persist to disk so you can resume conversations, but there's no automatic recall across sessions. The maintainer is rebuilding this in v2.0; in the meantime, you can graft mem0/Letta via the Agent interface. macOS GPU acceleration is partial. Sherpa-onnx and Whisper.cpp benefit from Apple's Accelerate framework, but TTS models (especially GPTSoVITS) still default to CPU on M-series and the first inference per session takes 5–10s. The v2.0 rewrite is real. The maintainer is open about v1 being feature-frozen and most new development moving to v2 (which is in design discussion as of June 2026). If you build heavily on v1's plugin interface, expect breakage. No Docker image in the official repo right now. There's a community Dockerfile but it lags behind. Bare-metal uv run is the supported path. Live2D Inc.'s license is a sharp edge for commercial use. The sample mao, shizuku, Hiyori, and Mark models are free for non-commercial use but require a Live2D Cubism Pro license for commercial streaming. Use your own Live2D model or a permissively-licensed third-party one if you plan to monetize. Multi-user is single-tenant. The server happily serves one voice session at a time. If you want a multi-user companion service, you'll need a frontend that proxies multiple isolated server instances. Use Case Open-LLM-VTuber Alternative Offline desktop AI companion / waifu ✅ Best in class z-waif, LLM-Live2D-Desktop-Assistant Streaming AI VTuber (neuro-sama clone) ✅ Designed for it Vedal's stack (closed), Voxta (commercial) Voice-only assistant (Siri replacement) ⚠️ Possible but heavy Home Assistant Assist, Mycroft fork Headless voice agent / API ❌ Not the focus LiveKit Agents, pipecat, Dograh Phone-call voice AI ❌ Wrong stack Vapi, Retell, Dograh Multimodal screen-aware agent ⚠️ Works with vision LLM Claude Computer Use, Cua The agent_config.conversation_agent_choice lets you inherit and ship your own agent. Here's a minimal example that wraps a Letta agent for long-term memory: # agents/agents/letta_agent.py from typing import AsyncIterator from agents.agent_interface import AgentInterface from letta import create_client class LettaAgent(AgentInterface): def __init__(self, agent_id: str, base_url: str = "http://localhost:8283"): self.client = create_client(base_url=base_url) self.agent_id = agent_id async def chat(self, user_input: str) -> AsyncIterator[str]: # Letta handles long-term memory + tool calls server-side response = self.client.user_message( agent_id=self.agent_id, message=user_input, stream=True, ) async for chunk in response: if chunk.message_type == "assistant_message": yield chunk.content async def handle_interrupt(self): # Called when user starts speaking mid-response pass Register it in conf.yaml: agent_config: conversation_agent_choice: "letta_agent" agent_settings: letta_agent: agent_id: "agent-12345" base_url: "http://localhost:8283" Restart the server and your VTuber now has persistent cross-session memory via Letta — without touching the voice pipeline. z-waif is single-author, opinionated, focuses on personality + RAG memory, and ships a polished Windows experience but is harder to extend. LLM-Live2D-Desktop-Assistant (by the same author as Open-LLM-VTuber) is the Tauri/Rust desktop-pet sibling — lighter footprint but fewer features. Open-LLM-VTuber is the most extensible: the broadest LLM/ASR/TTS backend matrix, the cleanest plugin interface, and the largest contributor base. Pick z-waif if you want one-click Windows install, Open-LLM-VTuber if you want to customize anything. Yes, and that's the design center. On a 16GB M1 Air with qwen2.5:7b via Ollama + sense_voice ASR + sherpa-onnx MeloTTS, you get ~1.5s round-trip latency and ~6GB resident RAM. M2/M3 with 24GB+ can step up to Qwen2.5-14B or Llama 3.2 11B vision for better answers and visual perception, at ~2.5s latency. Yes — set llm_provider: "openai_compatible_llm" and point base_url at any OpenAI-compatible endpoint (Anthropic via LiteLLM, OpenRouter, Groq, Together, Fireworks, or self-hosted vLLM). You lose offline-mode and gain quality. Most users do a hybrid: local Whisper + Cloud LLM + local TTS for cost vs latency tuning. Yes. Drop the unpacked Cubism model into live2d-models/<your_model_name>/ and add an entry to model_dict.json with the emotionMap, tapMotions, and idleMotionGroup fields. The docs cover character customization in detail. Custom models also let you sidestep the Live2D Inc. sample-model commercial-use restrictions. Only if your configured LLM is a cloud LLM. If you use a local multimodal model (LLaVA-1.6, MiniCPM-V, Qwen2.5-VL via Ollama, or LM Studio), the image stays on your machine. Visual perception is also opt-in per request — there's no continuous video stream to the LLM, only frames captured on user click or explicit [see] tag from the agent. Open-LLM-VTuber is local, free, and customizable; the cloud options are smarter, lower latency, and have better TTS. OpenAI's Advanced Voice is a single end-to-end model with sub-500ms latency that Open-LLM-VTuber can't match with a pipeline of separate ASR/LLM/TTS. But: cloud voice modes don't give you a Live2D avatar, can't run offline, charge per minute, and can be revoked. Pick Open-LLM-VTuber if privacy, customization, or the visual companion experience matter more than raw conversational quality. Per the Zulip discussions: a full rewrite around an event-driven core, proper multi-tenant support, restored long-term memory (Mem0 or Letta-native), Live2D Cubism 5 support, and a redesigned plugin API. No release date — the maintainer is consciously taking time to get the architecture right. Yes if you want a hackable, offline AI companion with a face, you're willing to tune conf.yaml, and you can live with v1's missing long-term memory. The voice loop quality is the best open-source voice-companion experience available in June 2026, and the active 80+ contributor community plus the planned v2.0 rewrite signal real momentum. No if you want a polished one-click experience (use z-waif on Windows or pay for Voxta), you need sub-500ms latency (use OpenAI Advanced Voice), or you want to monetize streaming with the sample Live2D models (license your own). Star the repo, lurk on Zulip, and watch v2.0 — this is the open-source companion stack to bet on.