harbor
Harbor is a CLI tool that orchestrates a complete local LLM stack—including Ollama, llama.cpp, vLLM frontends, Open WebUI, and supporting services like SearXNG and ComfyUI—via Docker Compose. A single `harbor up` command pre-wires these services so they work together without manual configuration.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | av/harbor |
| Owner | av |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 3.1k |
| Forks | 211 |
| Open issues | 56 |
| Latest release | v0.5.2 (2026-06-14) |
| Last updated | 2026-06-21 |
| Source | https://github.com/av/harbor |
What harbor is
Harbor manages Docker Compose orchestration, environment configuration, and inter-service networking for local LLM deployments. It supports multiple backends (Ollama, llama.cpp, vLLM, DMR, MLX), frontends (Open WebUI), and specialized services (web search, voice chat, image generation, coding agents). Recent versions add agentic modules and workflow presets.
Get the harbor source
Clone the repository and explore it locally.
git clone https://github.com/av/harbor.gitcd harbor# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Docker and Docker Compose installed; verify Docker daemon is running and user has appropriate permissions.
- Disk space varies by backend and models chosen; llama.cpp defaults consume less than Ollama. Plan for 10–100+ GB depending on model sizes.
- Initial `harbor up` pulls Docker images and may take several minutes on first run; subsequent starts are faster.
- Service inter-connectivity is pre-wired, but custom model loading, fine-tuning, or token/resource limits require editing YAML or `.env` files.
- Monitor port conflicts (Open WebUI defaults to 3000–3001, Ollama to 11434, SearXNG to 8888); use `harbor doctor` to detect and resolve.
When to avoid it — and what to weigh
- Production SaaS deployments — Harbor is designed for local development and homelabs, not for cloud-scale production infrastructure or multi-tenant services.
- Minimal resource environments — The full stack (multiple LLM backends, Open WebUI, SearXNG, ComfyUI, etc.) requires significant disk, memory, and CPU; unsuitable for lightweight deployments.
- Users unfamiliar with Docker or Linux CLI — Despite the simplicity of `harbor up`, troubleshooting, custom service configuration, and `.env` management assume Docker and shell command familiarity.
- Proprietary or closed-source model integration — Harbor focuses on open-source and self-hosted models; integration with commercial API-based models (OpenAI, Anthropic) requires additional wrapper layers.
License & commercial use
Apache License 2.0 (Apache-2.0): permissive OSI license allowing commercial use, modification, and distribution with attribution and liability disclaimer.
Apache-2.0 permits commercial use. However, individual bundled services (Ollama, Open WebUI, SearXNG, etc.) carry their own licenses (MIT, AGPL, GPL variants); review each service's license if incorporating into a commercial product. Harbor itself does not restrict commercial deployment.
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 | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Local-only by default (services bind to localhost/127.0.0.1 unless explicitly exposed). No built-in authentication for Open WebUI in default config; enable if exposing to network. Models are downloaded from public registries (Ollama, Hugging Face); verify provenance. Docker container isolation provides baseline process separation; host filesystem access and GPU passthrough introduce standard container security considerations. No formal security audit or vulnerability disclosure policy documented.
Alternatives to consider
Ollama + manual Docker Compose
Offers more control and minimalism; suitable if you only need Ollama backend and are comfortable writing Docker Compose YAML yourself.
LLaMA.cpp + Gradio / Streamlit UI
Lighter weight, fewer dependencies, and more portable for single-model inference; trade orchestration features for simplicity.
LM Studio (GUI desktop app)
Cross-platform GUI alternative for local inference (macOS, Windows, Linux); no CLI/Docker required, but less suitable for headless or remote access scenarios.
Build on harbor with DEV.co software developers
Install Harbor and spin up a fully integrated LLM environment in seconds. Explore the wiki, join Discord for community support, and start experimenting with local inference today.
Talk to DEV.coRelated open-source tools
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harbor FAQ
Can I run Harbor on macOS or Windows?
Do I need to download models manually?
How do I add a custom service or model?
Is Harbor suitable for production?
Software development & web development with DEV.co
Need help beyond evaluating harbor? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and mcp servers integrations — and maintain them long-term.
Ready to simplify your local LLM stack?
Install Harbor and spin up a fully integrated LLM environment in seconds. Explore the wiki, join Discord for community support, and start experimenting with local inference today.