3. Harbor CLI Reference
Alias:
harbor u,harbor start,harbor s
Starts selected services. See the list of available services here. Run harbor defaults to see the default list
of services that will be started. When starting additional services, you might need to harbor down first, so that all the services can pick updated configuration. API-only services can be started without stopping the main stack.
# Start with default services
harbor up
# Start with additional services
# See service descriptions in the Services Overview section
harbor up searxng
# Start with multiple additional services
harbor up webui ollama searxng llamacpp tts tgi lmdeploy litellmup supports a few additional behaviors, see below.
You can instruct Harbor to start tailing logs of the services that are started.
# Starts tailing logs as soon as "docker compose up" is done
harbor up webui --tail
# Alias
harbor up webui -tYou can instruct Harbor to also open the service that is started with up, once the docker compose up is done.
# Start default services + searxng and open
# searxng in the default browser
harbor up searxng --open
# Alias
harbor up searxng -oYou can also configure Harbor to automatically run harbor open for the current default UI service. This is useful if you always want to have the UI open when you start Harbor. The behavior can be enabled by setting ui.autoopen config field to true.
# Enable auto-open
harbor config set ui.autoopen true
# Disable auto-open (default)
harbor config set ui.autoopen falseYou can switch the default UI service with the ui.main config field.
# Set the default UI service
harbor config set ui.main hollamaYou can instruct Harbor to only start the services you specify and skip the default ones.
# Start only the services you explicitly specify
harbor up --no-defaults searxngYou can configure Harbor to automatically start tunnels for given services when running up. This is managed by harbor tunnels command.
# Add webui to the list of services that will be tunneled
# whenever `harbor up` is run
harbor tunnels add webui[!WARN] Exposing your services to the internet is dangerous. Be safe! It's a bad idea to expose a service to the Internet without any authentication.
By default, Harbor will try to infer some capabilities of the host (and match related cross files), such as Nvidia GPU availability (nvidia capability) or presence of modern Docker Compose features (mdc capability).
If this behavior is undesirable or you want to provide a manual list of capabilities, you can disable the automatic detection.
# Disable automatic capability detection
harbor config set capabilities.autodetect falseIt's also possible to provide a manual list of capabilities to use instead of the detected ones.
# Provide a default capabilities list manually,
# as a colon-separated list
harbor config set capabilities.default 'rocm;cdi'Alias:
harbor d
Stops all currently running services.
# Stop all services
harbor down
# Pass down options to docker-compose
harbor down --remove-orphansAlias:
harbor r
Restarts Harbor stack. Very useful for adjusting the configuration on the fly.
# Restart everything
harbor restart
# Restart a specific service only
harbor restart tabbyapi
# 🚩 Restarting a single service might be
# finicky, if something doesn't look right
# try down/up cycle insteadPulls the latest images for the selected services or models. Accepts:
- Harbor service handle (e.g.
ollama,webui, etc.) - Ollama model name (e.g.
gemma3n:e4b-it-q8_0,hf.co/bartowski/SicariusSicariiStuff_Impish_LLAMA_4B-GGUF:Q8_0) - llama.cpp HuggingFace model specifier (e.g.
unsloth/GLM-4.7-Flash-GGUF:Q8_0)
# Pull the latest images for the default services
harbor pull
# Pull the latest images for additional services
harbor pull searxng
# Do not pull default services alongside the specified ones
harbor pull --no-defaults searxng
# Pull Ollama model from native registry
harbor pull gemma3n:e4b-it-q8_0
# Pull Ollama model from HuggingFace
harbor pull hf.co/bartowski/SicariusSicariiStuff_Impish_LLAMA_4B-GGUF:Q8_0
# Pull llama.cpp model from HuggingFace (with optional tag)
# Downloads the model to llama.cpp cache using ephemeral server
harbor pull microsoft/Phi-3.5-mini-instruct-gguf
harbor pull microsoft/Phi-3.5-mini-instruct-gguf:Q4_K_MNote
When pulling a llama.cpp model, Harbor starts an ephemeral llama.cpp server that downloads the model to the cache and then exits. The model format must be supported by llama.cpp's HuggingFace integration (typically GGUF files).
Builds the images for the selected services. Mostly relevant for services that have their Dockerfile local in the Harbor repository.
# HF Downloader is an example of a service that
# has a local Dockerfile
harbor build hfdownloadProxy to docker-compose ps command. Displays the status of all services.
harbor psAlias:
harbor l
Tails logs for all or selected services.
harbor logs
# Show logs for a specific service
harbor logs webui
# Show logs for multiple services
harbor logs webui ollama
# Filter specific logs with grep
harbor logs webui | grep ERROR
# Start tailing logs after "harbor up"
harbor up llamacpp --tail
# Show last 1000 lines in the initial tail chunk
harbor logs -n 1000Additionally, harbor logs accepts all the options that docker-compose logs does.
Allows executing arbitrary commands in the container running given service. Useful for inspecting service at runtime or performing some custom operations that aren't natively covered by Harbor CLI.
# This is the same folder as "harbor/open-webui"
harbor exec webui ls /app/backend/data
# Check the processes in searxng container
harbor exec searxng ps auxexec offers plenty of flexibility. Some useful examples below.
Launch an interactive shell in the running container with one of the services.
# Launch "bash" in the ollama service
harbor exec ollama bash
# You are then landed in the interactive
# container shell
$ root@279a3a523a0b:/#Access useful scripts and CLIs from the llamacpp.
# See .sh scripts from the llama.cpp
harbor exec llamacpp ls ./scripts
# Run one of the bundled CLI tools
harbor exec llamacpp ./llama-bench --helpEnsuring that the service is running might not be convenient. See harbor run and harbor cmd.
Runs (in the order of precedence):
- One of configured aliases
- A command in the Harbor services
# Configure and run an alias to quickly edit
harbor alias set env 'code $(harbor home)/.env
harbor run envAliases take precedence over services in case of a name conflict. See the harbor aliases reference for more details.
Unlike harbor exec, harbor run starts a new container with the given command. This is useful for running one-off commands or scripts that don't require the service to be running. Note that the command accepts the service handle, not the container name, main container for the service will be used.
# Run a one-off command in the litellm service
harbor run litellm --helpThis command has a pretty rigid structure, it doesn't allow you to override the entypoint or run an interactive shell. See harbor exec and harbor cmd for more flexibility.
harbor run litellm --help
# Will run the same command as
$(harbor cmd litellm) run litellm --helpLaunch interactive shell in the service's container. Useful for debug and inspection.
# Tries to launch with "bash" shell by default
harbor shell tabbyui
# You can switch to another shell by supplying
# an additional argument (must be available in the container)
harbor shell tabbyui sh
harbor shell tabbyui ash
harbor shell tabbyui fish
harbor shell tabbyui zshPrepares the same docker compose call that is used by the Harbor itself, you can then use it to run arbitrary Docker commands.
# Will print docker compose command
# that is used to start these services
harbor cmd webui litellm vllmIt's most useful to be combined with eval of the returned command.
$(harbor cmd litellm) run litellm --help
# Unlike exec, this doesn't require service to be running
$(harbor cmd litellm) run -it --entrypoint bash litellm
# Note, this is not an equivalent of `harbor down`,
# It'll only shut down default services.
$(harbor cmd) down
# Harbor has a special wildcard notation for compose commands.
# Note the quotes around the wildcard (otherwise it'll be expanded by the shell)
$(harbor cmd "*") down
# And now, this is an equivalent of
harbor downRenders Harbor's Docker Compose configuration into a standalone config that can be moved and used elsewhere. Accepts the same options as harbor up.
# Eject with default services
harbor eject
# Eject with additional services
harbor eject searxng
# Likely, you want the output to be saved in a file
harbor eject searxng llamacpp > docker-compose.harbor.ymlPrints the path to the Harbor's home directory, where the Harbor CLI is located and where the configuration and data are stored.
harbor homeMost notably, you can use this command to refer to Harbor's workspace for other commands and services that might require it.
# For example - see all files in the Harbor workspace
ls $(harbor home)
# Or, inspect a folder used by a specific service
ls $(harbor home)/services/ollamaRuns a diagnostic script to check if all requirements are met for Harbor to run properly.
Will check things like relevant Docker and Docker Compose versions, the presence of required directories, and other things that might prevent Harbor CLI or the Harbor App running as expected.
harbor doctorRuns Ollama CLI in the container against the Harbor configuration.
# All Ollama commands are available
harbor ollama --version
# Show currently cached models
harbor ollama list
# Pull a model
harbor ollama pull llama3.2
# Run a model interactively
harbor ollama run llama3.2
# Remove a model
harbor ollama rm llama3.2
# See for more commands
harbor ollama --helpGet/set the context length for Ollama (sets OLLAMA_CONTEXT_LENGTH environment variable).
# Show current context length
harbor ollama ctx
# Set context length to 8192 tokens
harbor ollama ctx 8192
# Set to 128k for large context models
harbor ollama ctx 131072# Configure ollama version, accepts a docker tag
harbor config set ollama.version 0.3.7-rc5-rocm
# Or use latest
harbor config set ollama.version latestRuns CLI tasks specific to managing llamacpp service.
List models currently loaded by the llama.cpp server.
# List loaded models
harbor llamacpp modelsGet/set the model to run via HuggingFace URL.
# Show the model currently configured to run
harbor llamacpp model
# Set a new model to run via a HuggingFace URL
# ⚠️ Note, other kinds of URLs are not supported
harbor llamacpp model https://huggingface.co/user/repo/blob/main/file.gguf
# Above command is an equivalent of
harbor config set llamacpp.model https://huggingface.co/user/repo/blob/main/file.gguf
# And will translate to a --hf-repo and --hf-file flags for the llama.cpp CLI runtimeGet/set the path to GGUF file to run (alternative to model URL).
# Show the current GGUF path
harbor llamacpp gguf
# Set path to local GGUF file
harbor llamacpp gguf /models/model.ggufGet/set extra arguments to pass to the llama.cpp CLI.
# Show current arguments
harbor llamacpp args
# Set extra arguments
harbor llamacpp args '-c 4096 -n 512'Runs CLI tasks specific to managing text-generation-inference service.
Get/set the model repository to run.
# Show the model currently configured to run
harbor tgi model
# Set model repository
harbor tgi model meta-llama/Llama-3.2-3B-InstructGet/set the quantization mode. Must match the contents of the model repository.
# Show current quantization
harbor tgi quant
# Set quantization (awq, eetq, exl2, gptq, marlin, bitsandbytes, bitsandbytes-nf4, bitsandbytes-fp4, fp8)
harbor tgi quant awqGet/set the model revision/branch to use.
# Show current revision
harbor tgi revision
# Set revision
harbor tgi revision 4.0bpwGet/set extra arguments to pass to the TGI CLI.
# Show current arguments
harbor tgi args
# Set extra arguments
harbor tgi args '--max-input-length 4096'# Unlike llama.cpp, a few more parameters are needed,
# example of setting them below
harbor tgi model TheBloke/Llama-2-7B-AWQ
harbor tgi quant awq
harbor tgi revision 4.0bpw
# Alternatively, configure all in one go
harbor config set tgi.model.specifier '--model-id repo/model --quantize awq --revision 3_5'Runs CLI tasks specific to managing litellm service.
# change default username and password to use litellm UI
harbor litellm username admin
harbor litellm password admin
# Open LiteLLM UI in the browser
harbor litellm ui
# Note that it's different from the main litellm endpoint
# that can be opened/accessed with general commands:
harbor open litellm
harbor url litellmRuns HuggingFace CLI in the container against the hosts' HuggingFace cache.
# All HF commands are available
harbor hf --help
# Show current cache status
harbor hf scan-cacheHarbor's hf CLI is expanded with some additional commands for convenience.
Parses the HuggingFace URL and prints the repository and file names. Useful for setting the model in the llamacpp service.
# Get repository and file names from the HuggingFace URL
harbor hf parse-url https://huggingface.co/user/repo/blob/main/file.gguf
# > Repository: user/repo
# > File: file.ggufManage HF token for accessing private/gated models.
# Set the token
harbor hf token <token>
# Show the token
harbor hf tokenGet/set the location of HuggingFace cache directory.
# Show current cache location
harbor hf cache
# Set cache location
harbor hf cache /path/to/cacheResolve the path to a model directory in HF cache. Useful for finding where a model is stored locally.
# Get the path to a model in cache
harbor hf path meta-llama/Llama-2-7b-hfThis is a proxy for the awesome HuggingFaceModelDownloader CLI pre-configured to run in the same way as the other Harbor services.
# See the original help
harbor hf dl --help
# EXL2 example
#
# -s ./hf - Save the model to global HuggingFace cache (mounted to ./hf)
# -c 10 - make download go brr with 10 concurrent connections
# -m - model specifier in user/repo format
# -b - model revision/branch specifier (where applicable)
harbor hf dl -c 10 -m turboderp/TinyLlama-1B-exl2 -b 2.3bpw -s ./hf
# GGUF example
#
# -s ./llama.cpp - Save the model to global llama.cpp cache (mounted to ./llama.cpp)
# -c 10 - make download go brr with 10 concurrent connections
# -m - model specifier in user/repo format
# :Q2_K - file filter postfix - will only download files with this postfix
harbor hf dl -c 10 -m TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF:Q2_K -s ./llama.cppHuggingFace's own download utility. Works great when you want to download things for tgi, aphrodite, tabbyapi, vllm, etc.
# Download the model to the global HuggingFace cache
harbor hf download user/repo
# Set the token for private/gated models
harbor hf token <token>
harbor hf download user/private-repo
# Download a specific file
harbor hf download user/repo fileTip
You can use harbor find to locate downloaded files on your system.
A shortcut from the terminal to the HuggingFace model search. It will open the search results in the default browser.
# Search for the models with the query
harbor hf find gguf gemma-2
# will open this URL
# https://huggingface.co/models?sort=trending&search=gguf%20gemma-2
# Search for the models with the query
harbor hf find exl2 gemma-2-2b
# will open this URL
# https://huggingface.co/models?sort=trending&search=exl2%20gemma-2-2bRuns CLI tasks specific to managing vllm service.
Get/set the model currently configured to run.
# Show the model currently configured to run
harbor vllm model
# Set a new model to run via a repository specifier
harbor vllm model user/repoManage extra arguments to pass to the vllm engine.
# See the list of arguments in
# the official CLI
harbor run vllm --help
# Show the current arguments
harbor vllm args
# Set new arguments
harbor vllm args '--served-model-name vllm --device cpu'Select one of the attention backends. See VLLM_ATTENTION_BACKEND in the official env var docs for reference.
# Show the current attention backend
harbor vllm attention
# Set a new attention backend
harbor vllm attention 'ROCM_FLASH'Get/set VLLM version docker tag.
# Show the current version
harbor vllm version
# Set a specific version
harbor vllm version v0.6.0Runs CLI tasks specific to managing webui service.
Get/set current version of the WebUI. Accepts a docker tag from the GHCR registry
# Show the current version
harbor webui version
# Set a new version
harbor webui version dev-cudaGet/Set the secret JWT key for the webui service. Allows Open WebUI JWT tokens to remain valid between Harbor restarts.
# Show the current secret
harbor webui secret
# Set a new secret
harbor webui secret sk-203948Get/Set the name of the service for Open WebUI (by default "Harbor").
# Show the current name
harbor webui name
# Set a new name
harbor webui name "Pirate Harbor"Get/Set the log level for the webui service. Allows to control the verbosity of the logs. See Official logging documentation.
# INFO is the default log level
harbor webui log
# Set to DEBUG for more visibility
harbor webui log DEBUGManage OpenAI-related configurations for related services.
One unusual thing is that Harbor allows setting up multiple OpenAI APIs and Keys. This is mostly useful for the services that support such a configuration, for example LiteLLM or Open WebUI.
When setting one or more Keys/URLs - the first one will be propagated to serve as "default" for services that require strictly one url/key pair.
Manage OpenAI API keys for the services that require them.
# Show the current API keys
harbor openai keys
harbor openai keys ls
# Add a new API key
harbor openai keys add <key>
# Remove an API key
harbor openai keys rm <key>
# Remove by index (zero-based)
harbor openai keys rm 0
# Underlying config option
harbor config get openai.keysWhen settings API keys, the first one is also propagated to be the "default" one, for services that require strictly one key.
Manage OpenAI API URLs for the services that require them.
# Show the current URLs
harbor openai urls
harbor openai urls ls
# Add a new URL
harbor openai keys add <url>
# Remove a URL
harbor openai keys rm <url>
# Remove by index (zero-based)
harbor openai keys rm 0
# Underlying config option
harbor config get openai.urlsWhen settings API URLs, the first one is also propagated to be the "default" one, for services that require strictly one URL.
Manage TabbyAPI-related configurations for related services.
Get/Set the model currently configured to run.
# Show the model currently configured to run
harbor tabbyapi model
# Set a new model to run via a repository specifier
harbor tabbyapi model user/repo
# For example:
harbor tabbyapi model Annuvin/gemma-2-2b-it-abliterated-4.0bpw-exl2Manage extra arguments to pass to the tabbyapi engine. See the arguments in official Configuration Wiki.
# Show the current arguments
harbor tabbyapi args
# Set new arguments
harbor tabbyapi args --log-prompt trueYou can find some other items not listed above running the tabbyapi CLI with Harbor:
harbor run tabbyapi --helpWhen tabbyapi is running - will open the Docs Swagger UI in the default browser.
harbor tabbyapi apidocTip
Similarly to the official Plandex CLI, also available with pdx alias.
Access Plandex CLI for interactions with the self-hosted Plandex instance.
See the service guide for some additional details on the Plandex service setup.
# Access Plandex own CLI
harbor pdx --helpWhenever you're running harbor pdx, the tool will have access to the current folder as if it was called directly in the terminal.
Pings the Plandex server to check if it's up and running, using the official /health endpoint.
# Check the Plandex server health
harbor pdx health # OKAllows you to verify which specific folder will be mounted to the Plandex containers as the workspace.
# Show the folder that will be mounted to the Plandex CLI
# against the current location
harbor pdx pwdA CLI to manage the mistralrs service.
Everything except the commands specified below is passed to the original mistralrs-server CLI.
Pings the MistralRS server to check if it's up and running, using the official /health endpoint.
# Check the MistralRS server health
harbor mistralrs health # OKOpen official service docs in the default browser (when the service is running).
# Open MistralRS docs in the browser
harbor mistralrs docsGet/Set the model currently configured to run. See a more detailed guide in the mistralrs service guide.
# Show the model currently configured to run
harbor mistralrs model
# Set a new model to run via a repository specifier
# For "plain" models:
harbor mistralrs model user/repo
# For "gguf" models:
harbor mistralrs model "container/folder -f model.gguf"
# See the guide above for a more detailed overviewManage extra arguments to pass to the mistralrs engine. See the full list with harbor mistralrs --help.
# Show the current arguments
harbor mistralrs args
# Set new arguments
harbor mistralrs args "--no-paged-attn --throughput"
# Reset the arguments to the default
harbor mistralrs args ""Get/Set the model type currently configured to run.
# Show the model type currently configured to run
harbor mistralrs type
# Set a new model type to run
harbor mistralrs type gguf
harbor mistralrs type plain
# See the service guide for setup on bothFor plain type, allows to set the architecture of the model. See the official reference.
# Show the model architecture currently configured to run
harbor mistralrs arch
# Set a new model architecture to run
harbor mistralrs arch mistral
harbor mistralrs arch gemma2For plain type, allows to set the in situ quantization.
# Show the ISQ status currently configured to run
harbor mistralrs isq
# Set a new ISQ status to run
harbor mistralrs isq Q2KGet/set mistral.rs version docker tag.
# Show the current version
harbor mistralrs version
# Set version (0.3, 0.4, etc.)
harbor mistralrs version 0.4Configure and run Open Interpreter CLI. (Almost) everything except the commands specified below is passed to the original interpreter CLI.
Get/set the backend service to use for Open Interpreter (e.g., ollama, vllm, litellm).
# Show the current backend
harbor opint backend
# Set backend service
harbor opint backend ollama
harbor opint backend vllmGet/Set the model currently configured to run.
# Show the model currently configured to run
harbor opint model
# Set a new model to run
# must match the "id" of a model of a backend
# that'll be used to serve interpreter requests
harbor opint model <model>
# For example, for ollama
harbor opint model codestralManage extra arguments to pass to the Open Interpreter engine.
# Show the current arguments
harbor opint args
# Set new arguments
harbor opint args "--no-paged-attn --throughput"Overrides the whole command that will be run in the Open Interpreter container. Useful for running something completely custom.
[!WARN] Resets "model" and "args" to empty strings.
# Set the command to run in the Open Interpreter container
harbor opint cmd "--profile agentic_code_expert.py"Prints the directory that will be mounted to the Open Interpreter container as the workspace.
# Show the folder that will be mounted
# to the Open Interpreter CLI
harbor opint pwdAlias:
harbor opint --profilesAlias:harbor opint -p
Works identically (hopefully) to the interpreter --profiles - open the directory storing custom profiles for the Open Interpreter.
Alias:
harbor opint --local_models
Open the directory containing local models for Open Interpreter.
OS Mode is not supported as there's no established way to have full OS host control from within a container.
Manage Aphrodite-related configurations. Aphrodite is a high-performance vLLM fork optimized for inference.
Get/set the model currently configured to run.
# Show the model currently configured to run
harbor aphrodite model
# Set a new model to run via a repository specifier
harbor aphrodite model user/repoManage extra arguments to pass to the Aphrodite engine.
# Show the current arguments
harbor aphrodite args
# Set new arguments
harbor aphrodite args '--max-model-len 4096'Get/set the Aphrodite version docker tag.
# Show the current version
harbor aphrodite version
# Set a specific version
harbor aphrodite version latestManage cmdh (Command-H) service configuration. Command-H helps generate CLI commands using AI.
Get/set the cmdh model to use.
# Show the current model
harbor cmdh model
# Set a new model
harbor cmdh model qwen2.5-coder:7bGet/set the cmdh LLM host provider.
# Show the current host
harbor cmdh host
# Set host to ollama or OpenAI
harbor cmdh host ollama
harbor cmdh host OpenAIGet/set the cmdh OpenAI API key (when using OpenAI host).
# Show the current key
harbor cmdh key
# Set a new key
harbor cmdh key sk-...Get/set the cmdh OpenAI API URL (when using OpenAI host).
# Show the current URL
harbor cmdh url
# Set a new URL
harbor cmdh url https://api.openai.com/v1Manage Fabric service configuration. Fabric is a CLI tool for applying AI patterns to text.
See Fabric Documentation for pattern details.
Get/set the Fabric model to use.
# Show the current model
harbor fabric model
# Set a new model
harbor fabric model gpt-4Open the Fabric patterns directory in your file manager.
# Open patterns directory
harbor fabric patterns
harbor fabric --patterns# List available patterns
harbor fabric -l
# Use a pattern with piped input
echo "Explain quantum computing" | harbor fabric --pattern explain
# Apply pattern to a file
harbor fabric --pattern summarize < document.txtManage Parler TTS service configuration.
Get/set the Parler TTS model.
# Show the current model
harbor parler model
# Set a new model
harbor parler model parler-tts/parler-tts-mini-v1Get/set the voice description for Parler TTS.
# Show the current voice
harbor parler voice
# Set a new voice description
harbor parler voice "A female speaker with a slightly low-pitched voice"Manage AirLLM service configuration. AirLLM enables running large models with limited VRAM.
Get/set the model to run.
# Show the current model
harbor airllm model
# Set a new model
harbor airllm model meta-llama/Llama-2-70b-hfGet/set the context length for AirLLM.
# Show the current context length
harbor airllm ctx
# Set context length
harbor airllm ctx 4096Get/set the compression level for AirLLM.
# Show the current compression
harbor airllm compression
# Set compression (4bit, 8bit, or none)
harbor airllm compression 4bit
harbor airllm compression 8bit
harbor airllm compression noneManage txtai service configuration for semantic search and RAG.
Get/set the location of global txtai cache.
# Show the current cache location
harbor txtai cache
# Set cache location
harbor txtai cache /path/to/cacheGet/set the txtai RAG model repository to run.
# Show the current RAG model
harbor txtai rag model
# Set RAG model
harbor txtai rag model user/repoGet/set the path to the embeddings file.
# Show the current embeddings path
harbor txtai rag embeddings
# Set embeddings path
harbor txtai rag embeddings /path/to/embeddingsAccess Aider AI coding assistant. Aider helps you edit code using AI.
See Aider Documentation for detailed usage.
Get/set the Aider model to use.
# Show the current model
harbor aider model
# Set a new model
harbor aider model gpt-4# Start Aider in current directory
harbor aider
# Start with specific files
harbor aider file1.py file2.py
# Use with a specific model
harbor aider --model gpt-4-turboManage HuggingFace ChatUI service configuration.
Get/set the ChatUI version docker tag.
# Show the current version
harbor chatui version
# Set a new version
harbor chatui version latestGet/set the Ollama model to target.
# Show the current model
harbor chatui model
# Set model ID
harbor chatui model llama3.2Manage ComfyUI service configuration for Stable Diffusion workflows.
Get/set the ComfyUI version docker tag.
# Show the current version
harbor comfyui version
# Set a new version
harbor comfyui version latestGet/set the ComfyUI username for authentication.
# Show the current username
harbor comfyui user
# Set username
harbor comfyui user adminGet/set the ComfyUI password for authentication.
# Show the current password
harbor comfyui password
# Set password
harbor comfyui password secret123Enable/disable ComfyUI authentication.
# Show auth status
harbor comfyui auth
# Enable authentication
harbor comfyui auth true
# Disable authentication
harbor comfyui auth falseSync installed custom nodes to persistent storage.
# Sync custom nodes
harbor comfyui workspace syncOpen folder containing ComfyUI workspace in the File Manager.
# Open workspace folder
harbor comfyui workspace openClear ComfyUI workspace, including all configurations and models.
# Clear workspace (prompts for confirmation)
harbor comfyui workspace clearOpen folder containing ComfyUI output in the File Manager.
# Open output folder
harbor comfyui outputManage aichat service. AIChat is a versatile CLI AI assistant.
See AIChat Documentation for details.
Get/set the model to run.
# Show the current model
harbor aichat model
# Set a new model
harbor aichat model gemma2:9bOpen the aichat workspace directory.
# Open workspace directory
harbor aichat workspace# Start interactive chat
harbor aichat
# Execute a single query
harbor aichat "Explain Docker volumes"
# Use with pipes
echo "Translate this to French" | harbor aichatManage omnichain service configuration.
Open the omnichain workspace directory.
# Open workspace
harbor omnichain workspaceManage SGLang service configuration. SGLang is a fast inference engine.
Get/set the sglang model repository to run.
# Show the current model
harbor sglang model
# Set a new model
harbor sglang model meta-llama/Llama-3.2-3B-InstructGet/set extra args to pass to the sglang CLI.
# Show the current arguments
harbor sglang args
# Set new arguments
harbor sglang args '--tp 2 --mem-fraction-static 0.8'Manage Jupyter service configuration.
Open the Jupyter workspace directory.
# Open workspace
harbor jupyter workspaceGet/set the Jupyter image to run.
# Show the current image
harbor jupyter image
# Set a custom image
harbor jupyter image jupyter/tensorflow-notebookManage extra dependencies to install in the Jupyter image.
# List current dependencies
harbor jupyter deps ls
# Add a dependency
harbor jupyter deps add pandas
# Remove a dependency
harbor jupyter deps rm pandasManage OL1 service configuration for o1-style reasoning.
Get/set the OL1 model repository to run.
# Show the current model
harbor ol1 model
# Set a new model
harbor ol1 model user/repoManage OL1 arguments as a dictionary.
# List current arguments
harbor ol1 args ls
# Set an argument
harbor ol1 args set key value
# Remove an argument
harbor ol1 args rm keyManage KTransformers service configuration. KTransformers optimizes inference with kernel-level optimizations.
Get/set the --model_path for KTransformers.
# Show the current model
harbor ktransformers model
# Set model path
harbor ktransformers model /models/Qwen2-7BGet/set the --gguf_path for KTransformers.
# Show the current GGUF path
harbor ktransformers gguf
# Set GGUF path
harbor ktransformers gguf /models/model.ggufGet/set KTransformers version.
# Show the current version
harbor ktransformers version
# Set version
harbor ktransformers version 0.1.0Get/set KTransformers docker image.
# Show the current image
harbor ktransformers image
# Set custom image
harbor ktransformers image custom/ktransformers:latestGet/set extra args to pass to KTransformers.
# Show current args
harbor ktransformers args
# Set args
harbor ktransformers args '--max_tokens 2048'Access OpenHands (formerly OpenDevin) AI coding agent. Provides autonomous software development capabilities.
# Run OpenHands in current directory
harbor openhands
# The workspace is mounted at /opt/workspace_baseManage Speech-to-Text service configuration.
Get/set the STT model to run.
# Show the current model
harbor stt model
# Set a new model
harbor stt model openai/whisper-large-v3Get/set the STT docker tag.
# Show the current version
harbor stt version
# Set version
harbor stt version latestManage Speaches service configuration (combined STT/TTS).
Get/set the STT model to run.
# Show the current STT model
harbor speaches stt_model
# Set STT model
harbor speaches stt_model openai/whisper-baseGet/set the TTS model to run.
# Show the current TTS model
harbor speaches tts_model
# Set TTS model
harbor speaches tts_model facebook/mms-tts-engGet/set the TTS voice to use.
# Show the current voice
harbor speaches tts_voice
# Set voice
harbor speaches tts_voice en-US-Neural2-AGet/set the Speaches version docker tag.
# Show the current version
harbor speaches version
# Set version
harbor speaches version latestManage Boost service configuration. Boost provides advanced reasoning modules for LLMs.
Manage OpenAI API URLs to boost.
# List URLs
harbor boost urls ls
# Add a URL
harbor boost urls add https://api.openai.com/v1
# Remove a URL
harbor boost urls rm https://api.openai.com/v1Manage OpenAI API keys to boost.
# List keys
harbor boost keys ls
# Add a key
harbor boost keys add sk-...
# Remove a key
harbor boost keys rm sk-...Manage Boost modules to enable.
# List modules
harbor boost modules ls
# Add a module
harbor boost modules add klmbr
# Remove a module
harbor boost modules rm klmbrManage KLMBR (Knowledge-enhanced Language Model with Bayesian Reasoning) module.
# Access KLMBR module configuration
harbor boost klmbrManage RCN (Recursive Cognitive Network) module.
# Access RCN module configuration
harbor boost rcnManage G1 reasoning module.
# Access G1 module configuration
harbor boost g1Manage R0 reasoning module.
# Access R0 module configuration
harbor boost r0Access Nexa SDK CLI. Nexa provides efficient model inference.
See Nexa Documentation for details.
Get/set the Nexa model to use.
# Show the current model
harbor nexa model
# Set a new model
harbor nexa model gemma-2b# Run Nexa CLI
harbor nexa
# Generate text
harbor nexa gen "Once upon a time"Access Repopack CLI. Repopack helps package repository contents for AI context.
See Repopack Documentation for details.
# Pack current directory
harbor repopack
# Pack with specific output
harbor repopack -o output.txtAccess K6 load testing CLI with Grafana visualization.
See K6 Documentation for test script details.
# Run a load test
harbor k6 script.js
# Run with specific options
harbor k6 run --vus 10 --duration 30s script.jsWhen running K6 tests, Harbor automatically displays the Grafana dashboard URL.
Access Promptfoo CLI for LLM testing and evaluation.
See Promptfoo Documentation for details.
Open the Promptfoo UI in browser.
# Open Promptfoo UI
harbor promptfoo view
harbor promptfoo open
harbor promptfoo o# Initialize a new config
harbor promptfoo init
# Run evaluations
harbor promptfoo eval
# View results in UI
harbor promptfoo viewManage Webtop service (full Linux desktop in browser).
Delete Webtop workspace and reset to fresh state.
# Reset Webtop (stops service and clears data)
harbor webtop resetManage Langflow service configuration for visual LLM workflow building.
Open Langflow UI in browser.
# Open Langflow UI
harbor langflow ui
harbor langflow openGet the Langflow URL.
# Print Langflow URL
harbor langflow urlGet/set the Langflow version docker tag.
# Show the current version
harbor langflow version
# Set version
harbor langflow version 1.0.0Get/set the Langflow superuser username.
# Show username
harbor langflow auth username
# Set username
harbor langflow auth username adminGet/set the Langflow superuser password.
# Show password
harbor langflow auth password
# Set password
harbor langflow auth password secret123Manage KoboldCPP service configuration.
Get/set the Kobold model repository to run.
# Show the current model
harbor kobold model
# Set a new model
harbor kobold model user/repoGet/set Kobold arguments.
# Show current args
harbor kobold args
# Set args
harbor kobold args '--contextsize 4096'Manage Morphic service configuration (AI-powered search interface).
Get/set the default model for Morphic.
# Show the current model
harbor morphic model
# Set model
harbor morphic model llama3.2Get/set the tool calling model for Morphic.
# Show the current tool model
harbor morphic tool_model
# Set tool model
harbor morphic tool_model qwen2.5-coderAccess GPTme AI assistant CLI.
See GPTme Documentation for details.
Get/set the GPTme model repository to run.
# Show the current model
harbor gptme model
# Set model
harbor gptme model gpt-4# Start interactive session
harbor gptme
# Execute with specific prompt
harbor gptme "Explain Docker networking"Manage Model Context Protocol tools.
Launch MCP Inspector for debugging MCP servers.
# Run MCP Inspector
harbor mcp inspectorManage ModularMax service configuration (Modular MAX Engine inference).
Get/set the ModularMax model repository to run.
# Show the current model
harbor modularmax model
# Set model
harbor modularmax model meta-llama/Llama-3.2-1B-InstructGet/set extra args to pass to the ModularMax CLI.
# Show current args
harbor modularmax args
# Set args
harbor modularmax args '--max-length 2048'Manage PhotoPrism service and run PhotoPrism CLI commands.
See PhotoPrism CLI Documentation for available commands.
Get/set the vision model for Ollama integration.
# Show the current vision model
harbor photoprism model
# Set vision model (for use with Ollama)
harbor photoprism model llava# List configured vision models
harbor photoprism vision ls
# Run caption generation
harbor photoprism vision run -m caption
# Run label generation
harbor photoprism vision run -m labels
# Reset user password
harbor photoprism passwd admin
# List users
harbor photoprism users lsNote: PhotoPrism must be running to execute CLI commands.
Manage LM Eval Harness for evaluating language models.
See lm-evaluation-harness for task details.
Open the results directory in file manager.
# Open results folder
harbor lmeval resultsOpen the cache directory in file manager.
# Open cache folder
harbor lmeval cacheGet/set the evaluation type.
# Show current type
harbor lmeval type
# Set type
harbor lmeval type localGet/set the model for evaluation.
# Show current model
harbor lmeval model
# Set model
harbor lmeval model meta-llama/Llama-3.2-3BManage Harbor's integrated benchmark suite.
Open the benchmark results directory.
# Open results folder
harbor bench resultsGet/set benchmark tasks to run.
# Show current tasks
harbor bench tasks
# Set tasks
harbor bench tasks "hellaswag,winogrande"Get/set debug mode for benchmarks.
# Show debug status
harbor bench debug
# Enable debug
harbor bench debug trueGet/set the model to benchmark.
# Show current model
harbor bench model
# Set model
harbor bench model gpt-4Get/set the API endpoint for benchmarks.
# Show current API
harbor bench api
# Set API
harbor bench api http://localhost:8000/v1Get/set the API key for benchmarks.
# Show current key
harbor bench key
# Set key
harbor bench key sk-...Access Parllama CLI (Ollama GUI client).
# Launch Parllama
harbor parllamaAccess oterm CLI (Ollama terminal UI).
# Launch oterm
harbor otermOpens the service URL in the default browser. In case of API services, you'll see the response from the service main endpoint.
# Without any arguments, will open
# the service from main.ui config field
harbor open
# `harbor open` will now open hollama
# by default
harbor config set main.ui hollama
# Open a specific service
# using its handle
harbor open ollamaAdditionally, harbor open can be configured to open a custom URL for a given handle. This is done by using <service>.open_url config field. For example, to open Ollama /api/ps instead of the default / endpoint, you can run:
# Set the new config
harbor config set ollama.open_url http://localhost:33821/api/ps
# Now, running `harbor open ollama` will open the `/api/ps` endpoint
harbor open ollamaNote that custom open_url configs might be reset during Harbor updates.
Prints the URL of the service to the terminal.
# With default settings, this will print
# http://localhost:33831
harbor url llamacppHarbor will try to determine multiple additional URLs for the service:
# URL on local host
harbor url ollama
# URL on LAN
harbor url --lan ollama
harbor url --addressable ollama
harbor url --a ollama
# URL on Docker's intranet
harbor url -i ollama
harbor url --internal ollamaGenerates a QR code for the service URL and prints it in the terminal.
# This service will open by default
harbor config get ui.main
# Generate a QR code for default UI
harbor qr
# Generate a QR code for a specific service
# Makes little sense for non-UI services.
harbor qr ollama
Alias:
harbor t
Opens a cloudflared tunnel to the local instance of the service. Useful for sharing the service with others or accessing it from a remote location.
[!WARN] Exposing your services to the internet is dangerous. Be safe! It's a bad idea to expose a service without any authentication whatsoever.
# Open a tunnel to the default UI service
harbor tunnel
# Open a tunnel to a specific service
harbor tunnel ollama
# Stop all running tunnels
harbor tunnel down
harbor tunnel stop
harbor t s
harbor t dThe command will print the URL of the tunnel as well as the QR code for it.
Let's say that you are absolutely certain that you want a tunnel to be available all the time you run Harbor. You can set up a list of services that will be tunneled automatically.
# See list config docs
harbor tunnels --help
# Show the current list of services
harbor tunnels
harbor tunnels ls
# Add a new service to the list
harbor tunnels add ollama
# Remove a service from the list
harbor tunnels rm ollama
# Remove by index (zero-based)
harbor tunnels rm 0
# Remove all services from the list
# Don't confuse with stopping the tunnels (see above)
harbor tunnels rm
harbor tunnels clear
# Stop all running tunnels
harbor tunnel down
harbor tunnel stop
harbor t s
harbor t dYou can also edit this setting directly in the .env:
HARBOR_SERVICES_TUNNELS="webui"Whenever a harbor up is run - these tunnels will be established, Harbor will print their URLs as well as QR codes in the terminal.
Alias:
harbor ln
Creates a symlink to the harbor.sh script in the user's home bin directory. This allows you to run the script from any directory.
# Puts the script in the bin directory
harbor lnIf you're me and have to run harbor hundreds of times a day, ln comes with a --short option.
# Also links the short alias
harbor ln --shortYou can adjust where harbor is linked and the names for the symlinks:
# Assuming it's not linked yet
# See the defaults
./harbor.sh config get cli.path
./harbor.sh config get cli.name
./harbor.sh config get cli.short
# Customize
./harbor.sh config set cli.path ~/bin
./harbor.sh config set cli.name ai
./harbor.sh config set cli.short ai
# Link
./harbor.sh ln --short
# Use
ai up
ai downAn antipode to harbor link. Removes previously added symlinks. Note that this uses current links configuration, so if it was changed since the link was added, it might not work as expected.
# Removes the symlink(s)
harbor unlinkDisplays or sets the list of default services that will be started when running harbor up. Will include one LLM backend and one LLM frontend out of the box.
# Show the current default services
harbor defaults
harbor defaults ls
# Add a new default service
harbor defaults add tts
# Remove a default service
harbor defaults rm tts
# Remove by index (zero-based)
harbor defaults rm 0
# Remove all services from the default list
harbor defaults rm
# This is an alias for the
# services.default config field
harbor config set services.default 'webui ollama searxng'
# You can also configure it
# via the .env file
cat $(harbor home)/.env | grep HARBOR_SERVICES_DEFAULTAllows configuring additional aliases for the harbor run command. Any arbitrary shell command can be added as an alias. Aliases are managed in a key-value format, where the key is the alias name and the value is the command.
# Show the current list of aliases
harbor aliases
# Show aliases help
harbor aliases --help
# Same as above
harbor alias
harbor aThe alias is managed by harbor config internally, and is linked to the aliases config field.
# Will be empty, unless some aliases are configured
harbor config get aliases
# Placement in the `.env`:
cat $(harbor home)/.env | grep HARBOR_ALIASESLists all the currently set aliases.
harbor aliases ls
# Running without any args
# defaults to "ls" behavior
harbor aliases
harbor alias
harbor aAdds a new alias to the list.
# Note the single quotes on the outside
# and double quotes on the inside
harbor alias set echo 'echo "I like $PWD!"'You can then see the set alias:
harbor alias
echo: echo "I like $PWD!"
harbor alias get echo
# echo "I like $PWD!"You can run aliases with harbor run:
harbor run echo
# I like /home/user/harborObtain a command for a specific alias.
harbor alias get echoRemoves an alias from the list.
harbor alias rm echoPrint basic help information to the console.
harbor help
harbor --helpPrints the current version of the Harbor script.
harbor version
harbor --version# Show the help for the config command
harbor config --helpAllows working with the harbor configuration via the CLI. Mostly useful for the automation and scripting, as the configuration can also be managed via the .env file variables.
Translating CLI config fields to .env file variables:
# All three version are pointing to the same
# environment variable in the .env file
webui.host.port -> HARBOR_WEBUI_HOST_PORT
webui_host_port -> HARBOR_WEBUI_HOST_PORT
WEBUI_HOST_PORT -> HARBOR_WEBUI_HOST_PORTAlias:
harbor config ls
# Show the current configuration
harbor config listThis will print all the configuration options and their values. List could be quite long, so it's handy to pipe it to grep or less.
# Show the current configuration
harbor config list | grep WEBUIYou will see that configuration options have a namespace hierarchy, for example - everything related to the webui service will be under the WEBUI_ namespace.
Unprefixed variables will either be global or will be related to the Harbor CLI itself.
# Get a specific configuration value
# All versions below are equivalent and will return the same value
harbor config get webui.host.port
harbor config get webui.host_port
harbor config get WEBUI_HOST.PORT
harbor config get webui.HOST_PORT# Set a new configuration value
harbor config set webui.host.port 8080Resets the current .env configuration to its original form, based on the default.env file.
# You'll be asked to confirm the reset
harbor config resetWill merge default.env with the current local .env in order to add new configuration options. Typically used after updating Harbor when new variables are added. Most likely, you won't need to run this manually, as it's done automatically after harbor update.
This process won't overwrite user-defined variables, only add new ones.
# Merge the default.env with the current .env
harbor config updateAll subcommands support some extra options listed below.
# Point to another .env file
harbor config ls --env-file /path/to/another.env
# Mute logging
harbor config ls --silent
# Use custom prefix for env vars instead of "HARBOR_"
harbor config ls --prefix "HARBOR_WEBTOP_"Alias:
harbor profiles,harbor p
Allows creating and managing configuration profiles. It's attached to the .env file under the hood and allows you to switch between different configurations easily.
# Show the help for the profile command
harbor profile --helpNote
There are a few considerations when using profiles. Please read below.
- When the profile is loaded, modifications are not saved by default and will be lost when switching to another profile (or reloading the current one). Use
harbor profile save <name>to persist the changes after making them - Profiles are stored in the Harbor workspace and can be shared between different Harbor instances
- Profiles are not versioned and are not guaranteed to work between different Harbor versions
- You can also edit profiles as
.envfiles in the workspace, it's not necessary to use the CLI - Profiles can be partial, meaning that you can only specify the options you want to change in a profile, without needing to include everything
Alias:
harbor profile ls
Lists currently saved profiles.
harbor profile list
harbor profile lsAlias:
harbor profile save
Creates the new profile from the current configuration.
# Create a new profile named "dev"
harbor profile add devAlias:
harbor profile load,harbor profile set
Loads the profile with the given name.
# Load the "dev" profile
harbor profile use devIt's also possible to "import" a remote profile from a URL:
# Load the profile from a remote URL
harbor profile use https://example.com/path/to/harbor-profile.envAlias:
harbor profile rm
Removes the profile with the given name.
# Remove the "dev" profile
harbor profile remove devThis is a helper command to similar configuration experience provided by the harbor config to the service-specific environment variables, that are not directly managed by the Harbor CLI.
This command writes to the override.env file for a given service, you can also do that manually, if more convenient.
# List current override env vars
# Note, that it doesn't include the ones from main "harbor config"
harbor env <service>
# Get a specific env var
harbor env <service> <key>
# Set a new env var
harbor env <service> <key> <value>The <key> supports same naming convention as used by the harbor config command.
# All keys below are equivalent
# and will write to the same env var: "N8N_SECURE_COOKIE"
harbor env n8n N8N_SECURE_COOKIE # original notation
harbor env n8n n8n_secure_cookie # underscore notation
harbor env n8n n8n.secure_cooke # mixed dot/underscore notation# Show the current environment variables for the "n8n" service
harbor env n8n
# Get a specific environment variable
# for the dify service (LOG_LEVEL under the hood)
harbor env dify log.level
# Set a brand new environment variable for the service
# All three are equivalent
harbor env cmdh NODE_ENV development
harbor env cmdh node_env development
harbor env cmdh node.env developmentHarbor remembers a number of most recently executed CLI commands. You can search/re-run the commands via the harbor history command.
This is an addition to the native history in your shell, that'll persist longer and is specific to the Harbor CLI.
Use history.size config option to adjust the number of commands stored in the history.
# Get/set current history size
harbor history size
harbor history size 50
# Same, but with harbor config
harbor config get history.size
harbor config set history.size 50History is stored in the .history file in the Harbor workspace, you can also edit/access it manually.
# Using a built-in helper
harbor history ls | grep ollama
# Manually, using the file
cat $(harbor home)/.history | grep ollamaYou can clear the history with the harbor history clear command.
# Clear the history
harbor history clear
# Empty
harbor history lsLaunched a Docker container with the Dive CLI to inspect the given image layers and sizes.
Might be integrated with service handles in the future.
# Dive into the latest image of the webui service
harbor dive ghcr.io/open-webui/open-webuiPulls the latest version of the Harbor script from the repository.
# Pull the latest version of the Harbor script
harbor updateNote
Updates implementation is likely to change in the future Harbor versions.
Note
Harbor needs to be running with ollama backend to use the how command.
Harbor can actually tell you how to do things. It's a bit of a gimmick, but it's also surprisingly useful and fun.
# Ok, I'm cheesing a bit here, this is one of the examples
$ harbor how to ping a service from another service?
✔ Retrieving command... to ping a service from another service?
desired command: harbor exec webui curl $(harbor url -i ollama)
assistant message: The command 'harbor exec webui curl $(harbor url -i ollama)' will ping the Ollama service from within the WebUI service's container. This can be useful for checking network connectivity or testing service communication.
# But this is for real
$ harbor how to filter webui error logs with grep?
✔ Retrieving command... to filter webui error logs with grep?
setup commands: [ harbor logs webui -f ]
desired command: harbor logs webui | grep error
assistant message: You can filter webui error logs with grep like this. Note: the '-f' option is for follow and will start tailing new logs after current ones.
# And this is a bit of a joke
$ harbor how to make a sandwich?
✔ Retrieving command... to make a sandwich?
desired command: None (harbor is a CLI for managing LLM services, not making sandwiches)
assistant message: Harbor is specifically designed to manage and run Large Language Model services, not make physical objects like sandwiches. If you're hungry, consider opening your fridge or cooking an actual meal!
# And this is surprisingly useful
$ harbor how to run a command in the ollama container?
✔ Retrieving command... to run a command in the ollama container?
setup commands: [ docker exec -it ollama bash ]
desired command: harbor exec ollama <command>
assistant message: You can run any command in the running Ollama container. Make sure that command is valid and doesn't try to modify the container's state, because it might affect the behavior of Harbor services.A simple wrapper around the find command that allows you to search for files in the service's cache directories. Uses a substring match on a file path.
# Find all GGUFs
harbor find .gguf
# Use wildcards for more complex searches
harbor find Q8_0*.gguf
# Find all files from bartowski repos
harbor find bartowski
# Find all .safetensors files
harbor find .safetensorsAn alias for nvtop on the host system. Will display the GPU usage and processes running on the GPU, including those in the containers of the Harbor services.
# Show the GPU usage
harbor topWalks all CACHE and WORKSPACE directories from harbor config ls and prints their sizes, additionally displays a size for $(harbor home) directory.
# Show the sizes of the cache and workspace directories
harbor size
Harbor size:
----------------------------------
/home/user/.cache/huggingface: 277G
/home/user/.cache/llama.cpp: 64G
/home/user/.ollama: 241G
/home/user/.cache/vllm: 8.0K
/home/user/.cache/txtai: 92K
/home/user/.cache/nexa: 1.9G
/home/user/.parllama: 80K
./lmeval/cache: 2.5M
./langfuse/data: 89M
./comfyui/workspace: 33G
./omnichain: 108K
./jupyter/workspace: 1.5M
./n8n: 48M
./promptfoo/data: 356K
./webtop/data: 152M
./flowise/data: 176K
./langflow: 3.1M
./optillm/data: 4.0K
./kobold/data: 5.3G
./agent: 6.6M
/home/user/code/harbor: 72GLaunch development scripts from .scripts folder in the Harbor's workspace. Requires deno to be installed and available in the system's PATH.
# Scaffold a template for a new service
harbor dev scaffold <service>
# Seed release values
harbor dev seed