ODS installs and wires together everything you need to run AI locally, so you do not have to assemble Ollama, Open WebUI, n8n, ComfyUI, and privacy tools by hand:

• Local model inference — run open models on your own hardware
• ChatGPT-style web UI — talk to your models from any browser
• Control dashboard — manage models, services, setup, GPU status, and extensions from one place
• Voice, agents, and workflows — build automations that can listen, speak, call tools, and get work done
• RAG and search — connect local documents, private search, and retrieval workflows
• Image generation — run local image tools without sending prompts to a hosted API
• Privacy and ops — keep service auth, secrets, observability, and diagnostics in one local stack

No cloud required. No subscriptions required. Your prompts and data stay on your machine unless you choose otherwise. Cloud and hybrid API modes are optional when you want them.

Release validation: Operational changes are checked with a release-grade fleet and distro lab: zero-prereq bootstrap, fresh installs, product flows, full-model capabilities, lifecycle recovery, and the final User Green gate. See Release Validation for what a green run proves.

Repo layout: the repository root holds the public README, installers, security policy, GitHub workflows, and project coordination docs. The ods/ directory is the product runtime: services, installer phases, compose overlays, dashboard, CLI, tests, and operator docs.

Stable consumption: v2.5.2 is the current stable release. main moves quickly; use it for active development and validation candidates. For forks, appliances, labs, or production-like installs, pin a tagged release or audited commit and keep your own validation receipt. Stable patch fixes land on release/2.5.x before being merged forward. See Release Channels, Installer Trust, and Forkability.

Get Started

Linux and macOS:

curl -fsSL https://raw.githubusercontent.com/Light-Heart-Labs/ODS/main/ods/get-ods.sh | bash

Prefer to inspect before running or pin a release tag? See Installer Trust.

Windows users should use the PowerShell installer shown below or follow the Windows Quickstart.

After install, open http://localhost:3000 and start chatting.

API endpoint: Linux Docker installs expose llama-server on http://localhost:11434 by default (OLLAMA_PORT) while containers use llama-server:8080. macOS native Metal and Windows native/Lemonade paths use http://localhost:8080 unless overridden. Open WebUI stays on http://localhost:3000.

No GPU? ODS also runs in cloud mode — same full stack, powered by OpenAI/Anthropic/Together APIs instead of local inference:

./install.sh --cloud

Port conflicts? Every port is configurable via environment variables. See .env.example for the full list, or override at install time:

WEBUI_PORT=9090 ./install.sh

New here? Read the Friendly Guide or listen to the audio version — a complete walkthrough of what ODS is, how it works, and how to make it your own. No technical background needed.

At A Glance

Current Platform Support

Platform	Status
Linux (NVIDIA + AMD + Intel Arc)	Supported — install and run today
Windows (NVIDIA + AMD)	Supported — install and run today
macOS (Apple Silicon)	Supported — install and run today

Tested Linux distros: Ubuntu 24.04/22.04, Debian 12, Linux Mint 21.3, Fedora 41+, Rocky Linux 9, Arch Linux, Manjaro, CachyOS, and openSUSE Tumbleweed. Other distros using apt, dnf, pacman, or zypper should also work — open an issue if yours doesn't.

Release validation: Operational changes run through a release-grade gate that covers zero-prereq bootstrap, clean installs, product behavior, full-model capabilities, lifecycle recovery, and User Green. See Release Validation and the Validation Matrix.

Windows: Requires Docker Desktop with WSL2 backend. NVIDIA GPUs use Docker GPU passthrough; AMD Strix Halo runs through the platform-specific accelerated path documented in the Windows installer and support matrix.

macOS: Requires Apple Silicon (M1+) and Docker Desktop. llama-server runs natively with Metal GPU acceleration; all other services run in Docker.

See the Support Matrix for supported platform claims and the Validation Matrix for the layered test surface used to test those claims.

Why ODS?

A handful of companies control the vast majority of global AI traffic — and with it, your data, your costs, and your uptime. Every query you send to a centralized provider is business intelligence you don’t own, running on infrastructure you don’t control, priced on terms you can’t negotiate.

If AI is becoming critical infrastructure, it shouldn’t be rented. Self-hosting local AI should be a sovereign human right, not a career choice.

Because running your own AI shouldn't require a CS degree and a weekend of debugging CUDA drivers. Right now, setting up local AI means stitching together a dozen projects, writing Docker configs from scratch, and praying everything talks to each other. Most people give up and go back to paying OpenAI.

We built ODS so you don't have to.

• One command — detects your GPU, picks the right model, generates credentials, launches everything
• Chatting in under 2 minutes — bootstrap mode gives you a working model instantly while your full model downloads in the background
• Full service stack, pre-wired — chat, agents, voice, workflows, search, RAG, image generation, privacy tools, observability, and developer tools. All talking to each other out of the box
• Fully moddable — every service is an extension. Drop in a folder, run ods enable, done

git clone https://github.com/Light-Heart-Labs/ODS.git
cd ODS/ods
./install.sh

Requires Docker Desktop with WSL2 backend enabled. Install Docker Desktop first and make sure it is running before you start.

Open a normal PowerShell session and run:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
git clone https://github.com/Light-Heart-Labs/ODS.git
cd ODS
.\install.ps1

The Set-ExecutionPolicy command allows the installer script to run in the current session. It does not change your system-wide policy. Running as Administrator is not recommended for the installer because user-level paths such as .opencode, data/, and .env can be created with admin-owned permissions.

The installer detects your GPU, picks the right model, generates credentials, starts all services, and creates a Desktop shortcut to the Dashboard. Manage with .\ods\installers\windows\ods.ps1 status.

Requires Apple Silicon (M1+) and Docker Desktop. Install Docker Desktop first and make sure it is running before you start.

git clone https://github.com/Light-Heart-Labs/ODS.git
cd ODS/ods
./install.sh

The installer detects your chip, picks the right model for your unified memory, launches llama-server natively with Metal acceleration, and starts all other services in Docker. Manage with ./ods-macos.sh status.

See the macOS Quickstart for details.

What's In The Box

Chat & Inference

• Open WebUI — full-featured chat interface with conversation history, web search, document upload, and 30+ languages
• llama-server — high-performance LLM inference with continuous batching, auto-selected for your GPU; Linux Docker host API defaults to localhost:11434, native macOS/Windows paths use localhost:8080, and container API runs on 8080
• LiteLLM — API gateway supporting local/cloud/hybrid modes
• TEI Embeddings — text embedding service for RAG and search workflows

Voice

• Whisper — speech-to-text
• Kokoro — text-to-speech

Agents & Automation

• Hermes Agent — default local-first autonomous/browser agent with memory, skills, and a magic-link-gated proxy
• OpenClaw — deprecated legacy autonomous agent, still opt-in during the migration window
• n8n — workflow automation with 400+ integrations (Slack, email, databases, APIs)
• APE — Agent Policy Engine for auditing and governing autonomous tool calls
• OpenCode — browser-based AI coding assistant wired to the local stack
• Memory Shepherd — host/systemd helper for agent memory lifecycle management

Knowledge & Search

• Qdrant — vector database for retrieval-augmented generation (RAG)
• SearXNG — self-hosted web search (no tracking)
• Perplexica — deep research engine
• Brave Search — optional paid Brave Search API integration

Creative

• ComfyUI — node-based image generation

Privacy & Ops

• Privacy Shield — PII scrubbing proxy for API calls
• Dashboard — real-time GPU metrics, service health, model management
• Dashboard API — service health, setup, status, metrics, and management API behind the dashboard
• Token Spy — token usage monitor for local and proxied LLM traffic
• Langfuse — optional LLM observability and tracing

Hardware Auto-Detection

The installer detects your GPU and first assigns a deterministic hardware tier. Linux and macOS then run the versioned catalog selector (ods/scripts/select-model.py), while Windows uses the PowerShell catalog selector in ods/installers/windows/lib/tier-map.ps1; both read ods/config/model-library.json to choose the best installable GGUF for the detected memory envelope. The final choice is written to .env as LLM_MODEL, GGUF_FILE, MAX_CONTEXT, and MODEL_RECOMMENDATION_*.

MODEL_PROFILE=qwen is the default non-Gemma catalog profile, so the effective pick can be Qwen, Phi, or DeepSeek depending on what fits best. MODEL_PROFILE=gemma4 forces Gemma 4 where available, and MODEL_PROFILE=auto uses Gemma 4 on NVIDIA, Apple Silicon, and Intel Arc tiers. Override tier selection with ./install.sh --tier 3; override the model family with MODEL_PROFILE=gemma4 ./install.sh or MODEL_PROFILE=auto ./install.sh.

When Hermes is enabled, which is the default agent path, installers keep the first-run bootstrap model at a 64K context floor and promote the full local model context to 128K where the selected model supports it. That avoids Hermes's hard 64K minimum while preserving the under-2-minute first chat experience. The examples below are current catalog-selector outputs for common hardware envelopes; exact installs can differ with detected VRAM/RAM, host architecture, existing downloads, or explicit profile overrides. Throughput still needs a local benchmark after first launch.

NVIDIA

AMD Strix Halo (Unified Memory)