Effortlessly run LLM backends, APIs, frontends, and services with one command.
Harbor is a containerized LLM toolkit that allows you to run LLMs and additional services. It consists of a CLI and a companion App that allows you to manage and run AI services with ease.
Open WebUI ⦁︎ ComfyUI ⦁︎ LibreChat ⦁︎ HuggingFace ChatUI ⦁︎ Lobe Chat ⦁︎ Hollama ⦁︎ parllama ⦁︎ BionicGPT ⦁︎ AnythingLLM ⦁︎ Chat Nio
Ollama ⦁︎ llama.cpp ⦁︎ vLLM ⦁︎ TabbyAPI ⦁︎ Aphrodite Engine ⦁︎ mistral.rs ⦁︎ openedai-speech ⦁︎ Speaches ⦁︎ Parler ⦁︎ text-generation-inference ⦁︎ LMDeploy ⦁︎ AirLLM ⦁︎ SGLang ⦁︎ KTransformers ⦁︎ Nexa SDK ⦁︎ KoboldCpp
Harbor Bench ⦁︎ Harbor Boost ⦁︎ SearXNG ⦁︎ Perplexica ⦁︎ Dify ⦁︎ Plandex ⦁︎ LiteLLM ⦁︎ LangFuse ⦁︎ Open Interpreter ⦁ ︎cloudflared ⦁︎ cmdh ⦁︎ fabric ⦁︎ txtai RAG ⦁︎ TextGrad ⦁︎ Aider ⦁︎ aichat ⦁︎ omnichain ⦁︎ lm-evaluation-harness ⦁︎ JupyterLab ⦁︎ ol1 ⦁︎ OpenHands ⦁︎ LitLytics ⦁︎ Repopack ⦁︎ n8n ⦁︎ Bolt.new ⦁︎ Open WebUI Pipelines ⦁︎ Qdrant ⦁︎ K6 ⦁︎ Promptfoo ⦁︎ Webtop ⦁︎ OmniParser ⦁︎ Flowise ⦁︎ Langflow ⦁︎ OptiLLM
See services documentation for a brief overview of each.
# Run Harbor with default services:
# Open WebUI and Ollama
harbor up
# Run Harbor with additional services
# Running SearXNG automatically enables Web RAG in Open WebUI
harbor up searxng
# Speaches includes OpenAI-compatible SST and TTS
# and connected to Open WebUI out of the box
harbor up speaches
# Run additional/alternative LLM Inference backends
# Open Webui is automatically connected to them.
harbor up llamacpp tgi litellm vllm tabbyapi aphrodite sglang ktransformers
# Run different Frontends
harbor up librechat chatui bionicgpt hollama
# Get a free quality boost with
# built-in optimizing proxy
harbor up boost
# Use FLUX in Open WebUI in one command
harbor up comfyui
# Use custom models for supported backends
harbor llamacpp model https://huggingface.co/user/repo/model.gguf
# Access service CLIs without installing them
# Caches are shared between services where possible
harbor hf scan-cache
harbor hf download google/gemma-2-2b-it
harbor ollama list
# Shortcut to HF Hub to find the models
harbor hf find gguf gemma-2
# Use HFDownloader and official HF CLI to download models
harbor hf dl -m google/gemma-2-2b-it -c 10 -s ./hf
harbor hf download google/gemma-2-2b-it
# Where possible, cache is shared between the services
harbor tgi model google/gemma-2-2b-it
harbor vllm model google/gemma-2-2b-it
harbor aphrodite model google/gemma-2-2b-it
harbor tabbyapi model google/gemma-2-2b-it-exl2
harbor mistralrs model google/gemma-2-2b-it
harbor opint model google/gemma-2-2b-it
harbor sglang model google/gemma-2-2b-it
# Convenience tools for docker setup
harbor logs llamacpp
harbor exec llamacpp ./scripts/llama-bench --help
harbor shell vllm
# Tell your shell exactly what you think about it
harbor opint
harbor aider
harbor aichat
harbor cmdh
# Use fabric to LLM-ify your linux pipes
cat ./file.md | harbor fabric --pattern extract_extraordinary_claims | grep "LK99"
# Open services from the CLI
harbor open webui
harbor open llamacpp
# Print yourself a QR to quickly open the
# service on your phone
harbor qr
# Feeling adventurous? Expose your Harbor
# to the internet
harbor tunnel
# Config management
harbor config list
harbor config set webui.host.port 8080
# Create and manage config profiles
harbor profile save l370b
harbor profile use default
# Lookup recently used harbor commands
harbor history
# Eject from Harbor into a standalone Docker Compose setup
# Will export related services and variables into a standalone file.
harbor eject searxng llamacpp > docker-compose.harbor.yml
# Run a build-in LLM benchmark with
# your own tasks
harbor bench run
# Gimmick/Fun Area
# Argument scrambling, below commands are all the same as above
# Harbor doesn't care if it's "vllm model" or "model vllm", it'll
# figure it out.
harbor model vllm
harbor vllm model
harbor config get webui.name
harbor get config webui_name
harbor tabbyapi shell
harbor shell tabbyapi
# 50% gimmick, 50% useful
# Ask harbor about itself
harbor how to ping ollama container from the webui?2024-09-29.17-22-06.mp4
In the demo, Harbor App is used to launch a default stack with Ollama and Open WebUI services. Later, SearXNG is also started, and WebUI can connect to it for the Web RAG r
7FDC
ight out of the box. After that, Harbor Boost is also started and connected to the WebUI automatically to induce more creative outputs. As a final step, Harbor config is adjusted in the App for the klmbr module in the Harbor Boost, which makes the output unparseable for the LLM (yet still undetstandable for humans).
- Installing Harbor
Guides to install Harbor CLI and App - Harbor User Guide
High-level overview of working with Harbor - Harbor App
Overview and manual for the Harbor companion application - Harbor Services
Catalog of services available in Harbor - Harbor CLI Reference
Read more about Harbor CLI commands and options. Read about supported services and the ways to configure them. - Compatibility
Known compatibility issues between the services and models as well as possible workarounds. - Harbor Bench
Documentation for the built-in LLM benchmarking service. - Harbor Boost
Documentation for the built-in LLM optimiser proxy. - Harbor Compose Setup
Read about the way Harbor uses Docker Compose to manage services. - Adding A New Service
Documentation on bringing more services into the Harbor toolkit.
- Convenience factor
- Workflow/setup centralisation
If you're comfortable with Docker and Linux administration - you likely don't need Harbor per se to manage your local LLM environment. However, you're also likely to eventually arrive to a similar solution. I know this for a fact, since I was rocking pretty much similar setup, just without all the whistles and bells.
Harbor is not designed as a deployment solution, but rather as a helper for the local LLM development environment. It's a good starting point for experimenting with LLMs and related services.
You can later eject from Harbor and use the services in your own setup, or continue using Harbor as a base for your own configuration.
This project consists of a fairly large shell CLI, fairly small .env file and enourmous (for one repo) amount of docker-compose files.
- Manage local LLM stack with a concise CLI
- Convenience utilities for common tasks (model management, configuration, service debug, URLs, tunnels, etc.)
- Access service CLIs (
hf,ollama, etc.) via Docker without install - Services are pre-configured to work together (contributions welcome)
- Host cache is shared and reused - Hugging Face, ollama, etc.
- Co-located service configs
- Built-in LLM benchmarking service
- Manage configuration profiles for different use cases
- Eject to run without harbor with
harbor eject