LibreAI / Chat IA / Open-LLM-VTuber
// ai-chat · Open-LLM-VTuber/Open-LLM-VTuber

Open-LLM-VTuber

Open-LLM-VTuber est un projet de chat IA open source, créé par Open-LLM-VTuber (Open-LLM-VTuber/Open-LLM-VTuber sur GitHub). Il compte 14 011 étoiles sur GitHub et 1 682 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 15 mai 2026.

Talk to any LLM hands-free with voice interruption and a Live2D avatar, running locally on your computer.

★ 14,0 k étoiles⑂ 1,7 k forksMITmis à jour mai 2026
▦ visuel — le README de ce projet ne contient aucune capture intégrable,
voici donc un emplacement réservé. Le guide ci-dessous est complet malgré tout.
// détails du projet

Détails du projet

Étoiles GitHub
14 011
Forks
1 682
Licence
MIT
Dernière mise à jour sur GitHub
15 mai 2026
Catégorie
Chat IA
Type de fiche
Guide d'installation complet
Site officiel
Official site
// fonctionnement

Comment ça marche

  1. Decide your companion's shape

    Choose a character, persona prompt and whether everything must run offline with local models, or cloud APIs are acceptable for heavier components.

  2. Install from the quick-start guide

    Clone the project and follow the documentation's Quick Start for your platform; recent versions use uv to manage Python dependencies.

  3. Configure the modules

    Edit the configuration file to pick your LLM, ASR and TTS engines and import a Live2D model - no code changes needed for module swaps.

  4. Start talking

    Launch the server and open the web UI or desktop client, then hold a hands-free conversation - you can interrupt the avatar mid-sentence by speaking.

// fonctionnalités

Pourquoi on l'adore

Real-time voice interaction

Hands-free voice conversation with voice interruption that works without headphones - the AI does not hear its own voice.

Live2D avatar with expressions

A lively Live2D character with emotion-mapped expressions, touch feedback via clicks and drags, and visible inner thoughts that are not spoken aloud.

Visual perception

The companion can see you and your screen through camera, screen recording and screenshots.

Fully offline mode

Run everything with local models on NVIDIA or non-NVIDIA GPUs or CPU, keeping conversations private on your device.

Huge engine choice

Many interchangeable LLM, ASR and TTS backends - from Ollama and Faster-Whisper to GPT-SoVITS voice cloning and cloud APIs - selected by config file.

Desktop pet mode

The desktop client can switch to a transparent-background, always-on-top, click-through pet that sits anywhere on your screen, and chat logs persist between sessions.

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// lancez-le

Lancez-le vous-même

  1. Clone the repository

    Clone Open-LLM-VTuber to your machine.

    git clone https://github.com/Open-LLM-VTuber/Open-LLM-VTuber.git
    cd Open-LLM-VTuber
  2. Install dependencies

    Follow the documentation Quick Start to install dependencies for your platform; v1.x uses uv to manage the Python environment.

    uv sync  # see the Quick Start guide in the docs for the exact steps
  3. Launch the companion

    Start the server and open the web or desktop client to begin a voice conversation.

    uv run run_server.py  # see README/docs for the exact command
// cas d'usage

Ce que vous pouvez créer

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// faq

Questions fréquentes

Who maintains Open-LLM-VTuber?

It is a community open-source project; the maintainers note v1 is in bug-fix mode while a v2.0 rewrite is discussed with the developer community.

Is it free to use?

The project code is MIT-licensed and free. Note that bundled Live2D sample models are licensed separately by Live2D Inc., which matters for commercial use - you can substitute your own model.

Does it need a paid API key?

No if you run local engines (for example Ollama for the LLM and local ASR/TTS). Cloud LLM, ASR or TTS options use your own API keys.

How does it compare with Neuro-sama or commercial AI companions?

Neuro-sama is a closed, single-character production; Open-LLM-VTuber is an open toolkit for building your own companion with interchangeable local or cloud components.

How do I get started?

Clone the repo and follow the Quick Start in the project documentation to install, configure conf.yaml, and launch the server.

// continuez l'exploration

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// alternatives

Alternatives en Chat IA

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// plus en ai-chat

Plus en Chat IA

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