Configs2026年7月21日·1 分钟阅读

Open-LLM-VTuber — Build Your Own AI Companion with Live2D and Voice

Open-LLM-VTuber is a self-hosted framework for creating interactive AI companions that combine large language models with Live2D avatars and real-time speech synthesis for natural voice conversations displayed through animated characters.

Agent 就绪

Agent 可直接安装

这个资产可安装;Agent 先选择当前运行时、检查安装计划,再运行匹配命令。

Native · 98/100策略:允许
Agent 入口
任意 MCP/CLI Agent
类型
Skill
安装
Single
信任
信任等级:Established
入口
Open-LLM-VTuber
直接安装命令
npx -y tokrepo@latest install cc97db30-849d-11f1-9bc6-00163e2b0d79 --target codex

先 dry-run 确认安装计划,再运行此命令。

Introduction

Open-LLM-VTuber lets developers and creators build AI characters that listen, think, and speak with an animated Live2D avatar. It connects any LLM backend to speech recognition and text-to-speech engines, producing a real-time conversational experience rendered through expressive 2D character models.

What Open-LLM-VTuber Does

  • Drives Live2D character animations synchronized with AI-generated speech
  • Supports multiple LLM backends including OpenAI, Ollama, and local models
  • Integrates speech-to-text engines for real-time voice input from microphone
  • Connects to various TTS providers for natural-sounding AI voice output
  • Provides a web-based interface for interacting with the AI companion in a browser

Architecture Overview

The system runs as a Python server with a web frontend. Audio from the user is captured by the browser, transcribed via a configurable ASR engine (Whisper, Faster Whisper, or cloud APIs), and sent to the chosen LLM for response generation. The LLM output is streamed to a TTS engine, and the resulting audio is synchronized with Live2D lip-sync and expression data using a motion mapping layer. The frontend renders the Live2D model via PixiJS and plays audio in sync.

Self-Hosting & Configuration

  • Clone the repo and install Python dependencies from requirements.txt
  • Configure your preferred LLM provider in conf.yaml (OpenAI, Ollama, or any OpenAI-compatible API)
  • Set up a TTS engine such as Edge TTS, VITS, or ElevenLabs in the config
  • Choose an ASR provider for voice input (Whisper, Faster Whisper, or cloud services)
  • Place Live2D model files in the models directory and reference them in the config

Key Features

  • Modular architecture lets you swap LLM, TTS, and ASR components independently
  • Real-time lip-sync and expression mapping for natural character animation
  • Supports both local and cloud AI backends for flexibility in deployment
  • Browser-based interface requires no client installation
  • Extensible persona system for defining character personality and behavior

Comparison with Similar Tools

  • SillyTavern — focuses on text-based character chat; Open-LLM-VTuber adds voice and animated avatar interaction
  • AI Vtuber — typically relies on specific platforms; Open-LLM-VTuber is fully self-hosted and provider-agnostic
  • GPT-SoVITS — specializes in voice cloning; Open-LLM-VTuber integrates voice with visual avatars and conversation
  • Character.AI — proprietary cloud service; Open-LLM-VTuber runs entirely on your own infrastructure

FAQ

Q: Which LLMs work with Open-LLM-VTuber? A: Any model accessible via OpenAI-compatible API, including Ollama for local models and cloud providers.

Q: Can I use my own Live2D model? A: Yes. Place your model files in the models directory and update the configuration to reference them.

Q: Does it work offline? A: Yes, if you use local models for LLM (Ollama), TTS (VITS), and ASR (Whisper).

Q: What are the hardware requirements? A: A modern CPU handles the pipeline. GPU is recommended only if running local LLM or TTS models.

Sources

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