augint-shell 0.94.0

Launch AI coding tools and local LLMs in Docker containers

augint-shell

Launch AI coding tools (Claude Code, Codex, opencode, aider) and local LLMs (Ollama, Open WebUI, Kokoro TTS, Speaches STT) in per-project Docker containers.

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Pipeline Artifacts

Reports are published to GitHub Pages on every push to main.

Report	Link
API docs	svange.github.io/augint-shell/ai_shell.html
Test coverage	svange.github.io/augint-shell/coverage/htmlcov/
Test results	svange.github.io/augint-shell/coverage/test-report.html
Security scans	svange.github.io/augint-shell/security/
License compliance	svange.github.io/augint-shell/compliance/
PyPI package	pypi.org/project/augint-shell/

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What This Does

ai-shell is a Python CLI that stands up:

1. Per-project dev containers (augint-shell-{project}-dev) -- one per repo, runs Claude Code, Codex, opencode, or aider with your project mounted, SSH keys, AWS creds, and tool configs wired in.
2. Host-level LLM stack (shared singletons) -- Ollama, Open WebUI, Kokoro TTS, Speaches STT, a Pipecat voice agent, and n8n on a common Docker network. GPU auto-detected.

One command replaces the old Makefile + docker-compose.yml workflow.

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Getting Started

This project uses AI-assisted development. You do not need to memorize git commands or CI configuration -- your AI agent handles that.

Prerequisites

• Docker
• Python >= 3.12
• (Optional) NVIDIA GPU + drivers for local LLM acceleration

First-time setup

pip install augint-shell
# or:  uv add --dev augint-shell

Running locally

# Launch Claude Code in the current project
ai-shell claude

# Launch with extra args
ai-shell claude -- --debug

# Set up local LLM stack (first time)
ai-shell llm setup

# Launch opencode with local LLM
ai-shell opencode

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How to Contribute

Contributions are made through AI agents (Claude Code, Copilot, etc.). You describe what you want changed in plain language; the agent handles branching, coding, testing, and submitting a pull request.

1. Open Claude Code (or your AI agent) in this repo.
2. Describe the change you want -- a bug fix, a new feature, a doc update.
3. The agent will:
   • Create a feature branch
   • Make the changes
   • Run pre-commit checks and tests
   • Open a pull request
4. Review the PR when the agent is done. CI runs automatically.
5. Merge once CI is green.

If you need to work manually, see the full contributor guide (if available).

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Commands

AI Tools

Command	Description
ai-shell claude	Launch Claude Code
ai-shell claude-x	Claude Code with skip-permissions
ai-shell codex	Launch Codex
ai-shell opencode	Launch opencode
ai-shell aider	Launch aider with local LLM
ai-shell shell [bash|zsh|fish]	Interactive shell in dev container

LLM Stack

Command	Description
ai-shell llm up	Start Ollama (add --webui, --whisper, --voice-agent, --n8n, --image-gen, or --all)
ai-shell llm down	Stop LLM stack
ai-shell llm pull	Pull configured models
ai-shell llm models	Browse curated model catalog
ai-shell llm unload [MODEL]	Unload models from VRAM
ai-shell llm setup	First-time setup (up + pull + configure)
ai-shell llm status	Show status and available models
ai-shell llm logs	Tail LLM stack logs
ai-shell llm shell	Shell into Ollama container

Container Management

Command	Description
ai-shell manage status	Show dev container status
ai-shell manage stop	Stop dev container
ai-shell manage clean	Remove container and volumes
ai-shell manage logs	Tail dev container logs
ai-shell manage pull	Pull latest Docker image
ai-shell manage env [--aws]	Show resolved environment variables

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Configuration

Optional .ai-shell.yaml in your project root (YAML default, TOML also accepted). Run ai-shell init for the full template.

container:
  image: svange/augint-shell
  image_tag: latest
  extra_env:
    MY_VAR: value
  dev_ports: [3000, 4200, 5000, 5173, 5678, 8000, 8080, 8888]
  extra_ports: []

openai:
  profile: work  # resolves OPENAI_API_KEY_WORK from .env

llm:
  primary_chat_model: qwen3.5:27b
  secondary_chat_model: huihui_ai/qwen3.5-abliterated:27b
  primary_coding_model: qwen3-coder:30b-a3b-q4_K_M
  secondary_coding_model: huihui_ai/qwen3-coder-abliterated:30b-a3b-instruct-q4_K_M
  context_size: 32768
  ollama_port: 11434
  webui_port: 3000
  comfyui_port: 8188
  extra_models: []

Global config at ~/.ai-shell.yaml or ~/.config/ai-shell/config.yaml also supported.

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Local LLM stack

Four role-specific model slots, each sized for an RTX 4090 (24 GiB VRAM). All four defaults together total ~74 GB on disk.

Slot	Default	Size	Role	Routed to
primary_chat_model	qwen3.5:27b	17 GB	Best chat model that fits a 4090	Open WebUI default
secondary_chat_model	huihui_ai/qwen3.5-abliterated:27b	17 GB	Best uncensored chat (abliterated Qwen3.5)	Open WebUI (selectable)
primary_coding_model	qwen3-coder:30b-a3b-q4_K_M	19 GB	Best agentic coder with explicit Ollama tools badge	opencode / aider default
secondary_coding_model	huihui_ai/qwen3-coder-abliterated:30b-a3b-instruct-q4_K_M	19 GB	Best uncensored coder	opencode (selectable)

Optional stacks (not auto-started; opt-in with ai-shell llm up --<flag> or --all):

Flag	Service	Port	Notes
--webui	Open WebUI	3000	Implies --voice so Kokoro is wired as "read aloud" backend. Use --no-voice to skip.
--voice	Kokoro TTS	8880	OpenAI-compatible /v1/audio/speech
--whisper	Speaches STT	8001	OpenAI-compatible /v1/audio/transcriptions. GPU image auto-used when NVIDIA is detected
--voice-agent	Pipecat voice agent	8010	Push-to-talk PWA (Speaches -> Ollama -> Kokoro). See VOICE_AGENT_PLAN.md
--image-gen	ComfyUI	8188	GPU image generation. Wires into WebUI when combined with --webui
--n8n	n8n	5678	Workflow automation

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OpenAI multi-account switching (--openai-profile)

Codex and opencode support switching between multiple OpenAI accounts via named profiles in .env:

# .env
OPENAI_API_KEY_WORK=sk-proj-...
OPENAI_ORG_ID_WORK=org-...
OPENAI_API_KEY_PERSONAL=sk-proj-...

ai-shell codex --openai-profile work
ai-shell opencode --openai-profile personal

Set a default in config (openai.profile: work) or via AI_SHELL_OPENAI_PROFILE=work.

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Attaching to Windows Chrome (--local-chrome)

ai-shell claude --local-chrome bridges Claude inside the container to your real Chrome on Windows via the Chrome DevTools Protocol (using chrome-devtools-mcp). Unblocks OAuth popups, CAPTCHA pages, and "click around in a logged-in site" tasks.

• Claude drives Chrome tabs on your Windows desktop in real time.
• Separate Chrome profile per project -- your normal browsing untouched, each repo keeps its own logged-in state.
• All traffic stays on localhost.

Set [claude] local_chrome = true in ai-shell.toml (or AI_SHELL_LOCAL_CHROME=1) to persist.

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Requirements

• Docker
• Python >= 3.12
• (Optional) NVIDIA GPU for local LLM acceleration