<div align="center">

# ODS

**Osmantic Deployment System**

**Turn your PC, Mac, or Linux box into a private AI server.**

AI server and homelab setup is rapidly becoming a solved problem.
It should feel that way for everyone.

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
[![GitHub Stars](https://img.shields.io/github/stars/Light-Heart-Labs/ODS)](https://github.com/Light-Heart-Labs/ODS/stargazers)
[![Release](https://img.shields.io/github/v/release/Light-Heart-Labs/ODS)](https://github.com/Light-Heart-Labs/ODS/releases)

[![Watch the demo](https://img.shields.io/badge/Demo-Watch%20on%20YouTube-red?logo=youtube)](https://youtu.be/nO8xFNHX-HA)

</div>

---

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](ods/docs/RELEASE_VALIDATION.md) 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](ods/docs/RELEASE_CHANNELS.md),
[Installer Trust](ods/docs/INSTALLER_TRUST.md), and
[Forkability](ods/docs/FORKABILITY.md).

## Get Started

Linux and macOS:

```bash
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](ods/docs/INSTALLER_TRUST.md).

Windows users should use the PowerShell installer shown below or follow the [Windows Quickstart](ods/docs/WINDOWS-QUICKSTART.md).

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:
> ```bash
> ./install.sh --cloud
> ```

> **Port conflicts?** Every port is configurable via environment variables. See [`.env.example`](ods/.env.example) for the full list, or override at install time:
> ```bash
> WEBUI_PORT=9090 ./install.sh
> ```

![ODS Dashboard](ods/docs/images/dashboard.png)

**New here?** Read the [Friendly Guide](ods/docs/HOW-ODS-SERVER-WORKS.md) or [listen to the audio version](https://open.spotify.com/episode/40MvqJ41bC8cEgvUyOyE3K) — a complete walkthrough of what ODS is, how it works, and how to make it your own. No technical background needed.

---

## At A Glance

| Question | Answer |
|----------|--------|
| **What is it?** | A local AI server stack for your own hardware, with a one-command Linux/macOS installer and a PowerShell installer for Windows. |
| **Who is it for?** | People who want private AI at home, in a lab, or on a workstation without hand-wiring a dozen services. |
| **What do I get?** | Local inference, Open WebUI chat, a control dashboard, voice, agents, workflows, RAG, search, image generation, privacy tools, observability, and developer tools. |
| **What does it run on?** | Linux, Windows with WSL2/Docker Desktop, and macOS Apple Silicon. |
| **Is cloud required?** | No. Local mode is the default; cloud and hybrid API modes are optional. |

| If you know... | ODS adds... |
|----------------|----------------------|
| **Ollama / llama.cpp** | The surrounding server stack: chat, dashboard, voice, RAG, workflows, agents, privacy, and service management. |
| **Open WebUI** | A full installer and control plane around Open WebUI, plus pre-wired local services. |
| **AnythingLLM** | Broader local AI appliance behavior beyond RAG: inference, chat, voice, workflows, image generation, and ops. |
| **n8n self-hosted AI starter kits** | Workflow automation as one part of a larger private AI server. |

---

> **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](https://github.com/Light-Heart-Labs/ODS/issues) 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](ods/docs/RELEASE_VALIDATION.md) and the
> [Validation Matrix](ods/docs/VALIDATION-MATRIX.md).
>
> **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](ods/docs/SUPPORT-MATRIX.md) for supported
> platform claims and the [Validation Matrix](ods/docs/VALIDATION-MATRIX.md)
> 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

<div align="center">

![ODS Installer](ods/docs/images/installer-splash.gif)

*The ODSGATE installer handles everything — GPU detection, model selection, service orchestration.*

</div>

<details>
<summary><b>Manual install (Linux)</b></summary>

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

</details>

<details>
<summary><b>Windows (PowerShell)</b></summary>

Requires [Docker Desktop](https://www.docker.com/products/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:

```powershell
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`.

</details>

<details>
<summary><b>macOS (Apple Silicon)</b></summary>

Requires Apple Silicon (M1+) and [Docker Desktop](https://www.docker.com/products/docker-desktop/).
**Install Docker Desktop first and make sure it is running before you start.**

```bash
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](ods/docs/MACOS-QUICKSTART.md) for details.

</details>

---

## What's In The Box

### Chat & Inference
- **Open WebUI** — full-featured chat interface with conversation history, web search, document upload, and [30+ languages](https://docs.openwebui.com)
- **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

| Tier / envelope | Current default catalog pick | Context | Example hardware |
|------|--------------|---------|--------------|
| 0 / 8 GB CPU fallback | Qwen3.5 2B (Q4_K_M) | 8K | Low-RAM CPU-only |
| 1 / 8 GB discrete VRAM | Qwen3.5 9B (Q4_K_M) | 32K | RTX 4060, RTX 3060 12GB |
| 2 / 12 GB discrete VRAM | Phi-4 14B (Q4_K_M) | 16K | RTX 4070-class cards |
| 3 / 24 GB discrete VRAM | Qwen3.5 27B (Q4_K_M) | 32K | RTX 4090, A6000 |
| 4 / 48 GB discrete VRAM | DeepSeek R1 Distill Llama 70B (Q4_K_M) | 32K | A6000 Ada, L40S |
| NV_ULTRA / 90+ GB amd64 discrete VRAM | Qwen3 Coder Next (Q4_K_M) | 128K | Multi-GPU A100/H100 |
| NV_ULTRA / 90+ GB arm64 unified memory | Qwen3.6 35B-A3B (UD-Q4_K_M) | 128K | DGX Spark / GB10-class hosts |

### AMD Strix Halo (Unified Memory)

| Tier / envelope | Current default catalog pick | Context | Hardware |
|------|--------------|---------|----------|
| SH_COMPACT / 64 GB unified RAM | Qwen3.6 35B-A3B (UD-Q4_K_M) | 128K | Ryzen AI MAX+ 395 (64GB) |
| SH_LARGE / 96 GB unified RAM | DeepSeek R1 Distill Llama 70B (Q4_K_M) | 32K | Ryzen AI MAX+ 395 (96GB) |
| SH_LARGE / 124 GB unified RAM | Qwen3.6 35B-A3B (UD-Q4_K_M) | 128K | Ryzen AI MAX+ 395 (128GB class) |

The selector routes unified-memory hosts away from Qwen3 Coder Next when that model would otherwise be selected, because current repo policy documents correctness issues on those backends.

### Apple Silicon (Unified Memory, Metal)

| Tier / envelope | Current default catalog pick | Context | Example hardware |
|------|--------------|---------|-----------------|
| 0 / 8 GB unified RAM | Phi-4 Mini (Q4_K_M) | 128K | M1/M2 base (8GB) |
| 1 / 16 GB unified RAM | Qwen3.5 9B (Q4_K_M) | 32K | M4 Mac Mini (16GB) |
| 2 / 32 GB unified RAM | Phi-4 14B (Q4_K_M) | 16K | M4 Pro Mac Mini, M3 Max MacBook Pro |
| 3 / 48 GB unified RAM | Qwen3.5 27B (Q4_K_M) | 32K | M4 Pro (48GB), M2 Max (48GB) |
| 4 / 64+ GB unified RAM | Qwen3.6 35B-A3B (UD-Q4_K_M) | 128K | M2 Ultra Mac Studio, M4 Max (64GB+) |

### Intel Arc (Linux, SYCL)

| Tier / envelope | Current default catalog pick | Context | Example hardware |
|------|--------------|---------|------------------|
| ARC_LITE / 6 GB discrete VRAM | Phi-4 Mini (Q4_K_M) | 128K | Arc A380 |
| ARC_LITE / 8 GB discrete VRAM | Qwen3.5 9B (Q4_K_M) | 32K | Arc A750 |
| ARC / 16 GB discrete VRAM | Phi-4 14B (Q4_K_M) | 16K | Arc A770 16GB, newer Arc GPUs |

Gemma 4 profile tiers remain in the installer tier maps: E2B on entry hardware, E4B on midrange hardware, 26B-A4B on pro hardware, and 31B on large/ultra hardware.

---

## Bootstrap Mode

No waiting for large downloads. ODS uses bootstrap mode by default:

1. Downloads a tiny 1.5B model in under a minute
2. You start chatting immediately
3. The full model downloads in the background
4. Hot-swap to the full model when it's ready — zero downtime

<div align="center">

![Installer downloading modules](ods/docs/images/installer-download.png)

*The installer pulls all services in parallel. Downloads are resume-capable — interrupted downloads pick up where they left off.*

</div>

The bootstrap model starts with a 64K context window so Hermes can work during the first session. After the background download finishes, ODS swaps to the full model and restores the Hermes/full-model context target.

Skip bootstrap: `./install.sh --no-bootstrap`

---

## Switching Models

The installer picks a model for your hardware, but you can switch anytime:

```bash
ods model current              # What's running now?
ods model list                 # Show all available tiers
ods model swap T3              # Switch to a different tier
```

If the new model isn't downloaded yet, pre-fetch it first:

```bash
./scripts/pre-download.sh --tier 3    # Download before switching
ods model swap T3                    # Then swap (restarts llama-server)
```

Already have a GGUF you want to use? Drop the single `.gguf` file in
`data/models/`, then open Dashboard -> Models and load the local entry. For
older installs or headless maintenance, update `GGUF_FILE` and `LLM_MODEL` in
`.env`, then restart with the CLI:

```bash
ods restart llm
```

Or restart the container directly from the installed `ods` directory:

```bash
docker compose restart llama-server
```

Rollback is automatic — if a new model fails to load, ODS reverts to your previous model.

---

## Extensibility

ODS is designed to be modded. Every service is an extension — a folder with a `manifest.yaml` and a `compose.yaml`. The dashboard, CLI, health checks, and compose stack all discover extensions automatically.

```
extensions/services/
  my-service/
    manifest.yaml      # Metadata: name, port, health endpoint, GPU backends
    compose.yaml       # Docker Compose fragment (auto-merged into the stack)
```

```bash
ods enable my-service     # Enable it
ods disable my-service    # Disable it
ods list                  # See everything
```

The installer itself is modular — 19 library modules, a shared service registry, and 13 ordered phases. Want to add a hardware tier, swap a default model, or skip a phase? Start with the installer architecture map so you update the Linux, macOS, Windows, upgrade, and host-agent writers together.

[Full extension guide](ods/docs/EXTENSIONS.md) | [Installer architecture](ods/docs/INSTALLER-ARCHITECTURE.md)

---

## ods-cli

The `ods` CLI manages your entire stack:

```bash
ods status                # Health checks + GPU status
ods list                  # All services and their state
ods logs llm              # Tail logs (aliases: llm, stt, tts)
ods restart [service]     # Restart one or all services
ods start / stop          # Start or stop the stack

ods mode cloud            # Switch to cloud APIs via LiteLLM
ods mode local            # Switch back to local inference
ods mode hybrid           # Local primary, cloud fallback

ods model swap T3         # Switch to a different hardware tier
ods enable n8n            # Enable an extension
ods disable whisper       # Disable one

ods config show           # View .env (secrets masked)
ods preset save gaming    # Snapshot current config
ods preset load gaming    # Restore it
```

---

## How It Compares

Other tools get you part of the way. ODS gets you the whole way.

| | ODS | Ollama + Open WebUI | LocalAI |
|---|:---:|:---:|:---:|
| **Scope** | Full AI stack — inference to agents to workflows | LLM + chat | LLM only |
| One-command install | Everything, auto-configured | LLM + chat only | LLM only |
| Hardware auto-detect + model selection | NVIDIA + AMD Strix Halo + Apple Silicon + Intel Arc + CPU/cloud fallback | No | No |
| AMD APU unified memory support | Platform-specific accelerated backend, selected by installer | Partial (Vulkan) | No |
| Autonomous AI agents | Hermes Agent default; OpenClaw legacy opt-in | No | No |
| Workflow automation | n8n (400+ integrations) | No | No |
| Voice (STT + TTS) | Whisper + Kokoro | No | No |
| Image generation | ComfyUI | No | No |
| RAG pipeline | Qdrant + embeddings | No | No |
| Extension system | Manifest-based, hot-pluggable | No | No |
| Multi-GPU | Yes (NVIDIA) | Partial | Partial |

---

## Documentation

| | |
|---|---|
| [Quickstart](ods/QUICKSTART.md) | Step-by-step install guide with troubleshooting |
| [Docs Index](ods/docs/README.md) | Maintained map for operators, contributors, and reviewers |
| [Build On ODS](ods/docs/BUILD-ON-ODS-SERVER.md) | Forking, custom editions, extension templates, and downstream validation |
| [Forkability](ods/docs/FORKABILITY.md) | How to fork, audit, customize, and independently operate ODS |
| [Maintainer Runbook](ods/docs/MAINTAINER_RUNBOOK.md) | Release, rollback, validation, and operator continuity guidance for maintainers and forks |
| [High-Risk Change Map](ods/docs/HIGH_RISK_CHANGE_MAP.md) | Which changes require focused checks, fleet validation, or release-grade gates |
| [Headless Setup](ods/docs/HEADLESS-SETUP.md) | QR onboarding, first-boot setup, AP mode, mDNS, and local agent access |
| [Support Matrix](ods/docs/SUPPORT-MATRIX.md) | Current platform and GPU support status |
| [Release Validation](ods/docs/RELEASE_VALIDATION.md) | User Green gates and the release-grade fleet/distro validation policy |
| [Validation Matrix](ods/docs/VALIDATION-MATRIX.md) | Sanitized CI, distro lab, and real-hardware fleet release-readiness evidence |
| [Validation Reproducibility](ods/docs/VALIDATION_REPRODUCIBILITY.md) | How forks and operators can reproduce the validation story on their own hardware |
| [Offline And Mirroring](ods/docs/OFFLINE_AND_MIRRORING.md) | Pinning, mirroring, and preserving release artifacts for independent operation |
| [Installer Trust](ods/docs/INSTALLER_TRUST.md) | Inspect-first install paths, ref pinning, and current provenance limits |
| [Model Management](ods/docs/MODEL-MANAGEMENT.md) | Dashboard model downloads, switching, and manual GGUF workflows |
| [Hardware Guide](ods/docs/HARDWARE-GUIDE.md) | What to buy, tier recommendations |
| [FAQ](ods/FAQ.md) | Common questions and configuration |
| [Extensions](ods/docs/EXTENSIONS.md) | How to add custom services |
| [Installer Architecture](ods/docs/INSTALLER-ARCHITECTURE.md) | Modular installer deep dive |
| [Installer Phase Contracts](ods/docs/INSTALLER_PHASE_CONTRACTS.md) | Phase ownership, idempotency, failure modes, and validation expectations |
| [Compose Resolver Contracts](ods/docs/COMPOSE_RESOLVER_CONTRACTS.md) | Rules for compose layers, extensions, backends, ports, and mode overlays |
| [Changelog](ods/CHANGELOG.md) | Version history and release notes |
| [Contributing](CONTRIBUTING.md) | How to contribute |

---

## Contributors And Recognition

ODS is built by a growing group of contributors across installers, GPU support, dashboard, security, extensions, docs, and release validation. The README keeps the product overview focused; the long-form credits, upstream acknowledgements, and contributor history live in [CONTRIBUTORS.md](CONTRIBUTORS.md).

ODS has been recognized by the local AI and developer community, including AMD Featured Developer recognition, selection as a May 2026 AMD Lemonade Developer Challenge winner, and a feature at [(Co)nnect: Philly's AI Ecosystem Summit](https://luma.com/xdwih64h) at Pennovation Works.

---

## License

Apache 2.0 — Use it, modify it, ship it. See [LICENSE](LICENSE).

---

<div align="center">

*Built by [Light Heart Labs](https://github.com/Light-Heart-Labs) and the growing resistance that refuses to rent what should be owned.*

</div>
