airunner
AI Runner is a desktop application for offline AI art generation and conversational AI companions, built with privacy in mind so everything runs locally without internet or API keys. It combines image generation (SDXL, Z-Image Turbo), voice conversation (TTS/STT), memory persistence, and a layered canvas interface.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | Capsize-Games/airunner |
| Owner | Capsize-Games |
| Primary language | Python |
| License | GPL-3.0 — OSI-approved |
| Stars | 1.3k |
| Forks | 99 |
| Open issues | 5 |
| Latest release | v5.6.1 (2025-12-11) |
| Last updated | 2026-07-08 |
| Source | https://github.com/Capsize-Games/airunner |
What airunner is
Python-based offline inference engine using llama.cpp for LLM inference, stable-diffusion derivatives for image generation, and voice synthesis/recognition via local models. Desktop UI built with PySide6, with optional headless HTTP API and Docker deployment support. GPU-accelerated via CUDA for NVIDIA hardware.
Get the airunner source
Clone the repository and explore it locally.
git clone https://github.com/Capsize-Games/airunner.gitcd airunner# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- GPU memory and inference latency scale inversely with model size; RTX 3060 baseline may require quantized (Q8) models; RTX 5080+ recommended for SDXL in acceptable time.
- Installation requires system-level CUDA toolkit, MeCab (for Japanese TTS), and multiple language-pack dependencies; Docker Compose or pre-built bundle installers strongly recommended over manual setup.
- Database schema migrations via Alembic are manual if upgrading local installs; no automatic schema versioning or rollback strategy documented.
- Voice STT only confirmed for English; multi-language TTS present but STT missing for Spanish, French, Chinese, Korean despite language-aware GUI.
- Model management via HuggingFace and Civitai is manual (no auto-update); requires careful disk space management for large SDXL and LLM artifacts.
When to avoid it — and what to weigh
- Need Multi-User Collaboration — AI Runner is single-machine focused; no built-in multi-user sync, shared state, or team workspace features.
- Require Minimal Hardware Footprint — Minimum 16 GB RAM, 22–100 GB+ storage, and dedicated GPU (RTX 3060+) make this unsuitable for embedded, mobile, or lightweight edge devices.
- Expect Production SLA/Support — Community-driven open-source project with no commercial support contract or guaranteed uptime guarantees; latest release is 7+ months old.
- GPL-Incompatible Derivative Products — GPL-3.0 license requires any derivative work to be GPL-licensed; proprietary or other-license integration is legally complex—requires legal review.
License & commercial use
GPL-3.0 (GNU General Public License v3.0). This is a copyleft license requiring any derivative work, modification, or linked executable to be distributed under GPL-3.0 with source code available. Proprietary or closed-source integrations are not permitted without legal exemption.
GPL-3.0 permits commercial use of the unmodified software itself, but any commercial product or derivative that incorporates or links AI Runner must be GPL-3.0 licensed and open-source. This severely restricts commercial SaaS, proprietary plugins, or closed-source wrappers. Internal business use of the unmodified application is permitted. Strong legal review is required before any commercial deployment or integration.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Possible |
| Assessment confidence | High |
Project includes configurable NSFW filters and prompt classifiers for illegal content; no third-party security audit documented. All inference and data remain local, eliminating cloud-side injection risks. Cryptography for local conversation storage is not explicitly mentioned. Dependency pinning and supply-chain security practices (SBOM, signature verification) are not evident. Review dependency update cadence and vulnerability disclosure process before production use.
Alternatives to consider
LM Studio / Ollama
Lighter-weight, modular LLM-only inference engines with simpler setup and smaller resource footprint; lack integrated voice/art and desktop UI.
ComfyUI / Stable Diffusion Web UI
Mature node-graph or web-based image generation workflows with larger community and plugin ecosystem; require separate LLM and voice tooling.
LocalAI
Minimal self-hosted LLM/image API server supporting OpenAI-compatible endpoints; no voice, companion, or canvas UI—better for headless backend use.
Build on airunner with DEV.co software developers
AI Runner enables offline image generation, voice-driven chat companions, and creative workflows with zero cloud dependency. Evaluate GPU requirements, GPL-3.0 licensing constraints, and operational complexity before piloting. Contact Devco for architecture review and deployment planning.
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airunner FAQ
Can I use AI Runner in a commercial product?
What hardware is realistically required?
Is multi-language STT supported?
How do I run this in production / at scale?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If airunner is part of your open-source devops roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Private AI Locally?
AI Runner enables offline image generation, voice-driven chat companions, and creative workflows with zero cloud dependency. Evaluate GPU requirements, GPL-3.0 licensing constraints, and operational complexity before piloting. Contact Devco for architecture review and deployment planning.