harbor-cli

name

harbor-cli

description

Harbor CLI command reference and usage patterns. Covers harbor run, harbor jobs, harbor trials, harbor datasets, harbor adapters, harbor tasks, harbor view, harbor sweeps, harbor traces, harbor cache, and harbor admin commands. Use this skill whenever running Harbor evaluations, managing datasets, viewing results, debugging tasks, exporting traces, or working with any harbor CLI command. Also use when constructing harbor command lines, looking up flag names, or troubleshooting CLI errors.

Harbor CLI Reference

Harbor CLI manages the full evaluation lifecycle: creating tasks, running agents, viewing results. Install with uv tool install harbor. Global option: --version / -v.

For complete flag tables with types and defaults for every command, read references/flags.md.

Quick Reference

Command	Description
harbor run	Run evaluations (alias for harbor jobs start)
harbor jobs start	Start an evaluation job
harbor jobs resume	Resume an interrupted job
harbor jobs summarize	AI-powered failure summaries for a job
harbor trials start	Run a single trial (debugging)
harbor trials summarize	AI-powered summary of a single trial
harbor datasets list	List available datasets
harbor datasets download	Download a dataset
harbor adapters init	Scaffold a new benchmark adapter
harbor adapters review	Structural + AI review of an adapter
harbor tasks init	Scaffold a new task
harbor tasks check	AI quality assessment of a task
harbor tasks start-env	Launch task environment interactively
harbor tasks debug	Analyze failing trials for a task
harbor tasks migrate	Convert Terminal-Bench tasks to Harbor
harbor view	Browse job/trial results in web UI
harbor sweeps run	Run successive evaluation sweeps
harbor traces export	Export trace data in ATIF format
harbor cache clean	Clean Docker images and cache directory
harbor admin	Administrative commands (hidden)

harbor run / harbor jobs start

The primary command. harbor run is an alias for harbor jobs start.

Defaults: agent = oracle, n-concurrent = 1, environment = docker, output = ./jobs.

Essential flags:

Flag	Short	Description
--path	-p	Local path to task directory
--dataset	-d	Dataset from registry (e.g., terminal-bench@2.0)
--config	-c	Job config YAML/JSON file
--agent	-a	Agent name (default: oracle). Repeatable
--model	-m	Model identifier. Repeatable
--n-concurrent	-n	Concurrent trials (default: 1)
--env	-e	Environment backend (default: docker)
--jobs-dir	-o	Output directory (default: ./jobs)
--agent-env	--ae	Pass env var to agent: KEY=VALUE. Repeatable
--agent-kwarg	--ak	Agent kwarg: key=value. Repeatable
--task-name	-t	Include tasks by glob pattern. Repeatable
--exclude-task-name	-x	Exclude tasks by glob. Repeatable
--n-tasks	-l	Max tasks to run
--quiet	-q	Suppress trial progress
--debug		Enable debug logging
--dry-run		Print config and exit
--disable-verification		Skip running tests
--no-cache		Skip Docker build cache
--artifact		Download path from env after trial. Repeatable
--timeout-multiplier		Scale all timeouts (default: 1.0)

Additional flags for environment kwargs (--ek), environment env vars (--ee), verifier kwargs (--vk), orchestrator kwargs (--ok), agent images, retry config, trace export, and per-phase timeout multipliers are in references/flags.md.

Examples

# Run local task with oracle (validates solution + tests)
harbor run -p ./my-task

# Run with a real agent
harbor run -p ./my-task -a claude-code -m anthropic/claude-sonnet-4-1

# Dataset evaluation with concurrency
harbor run -d terminal-bench@2.0 -a claude-code -m anthropic/claude-sonnet-4-1 -n 8

# Cloud environment
harbor run -d my-dataset -a claude-code -m anthropic/claude-sonnet-4-1 -e daytona

# From config file
harbor run -c eval-config.yaml

# Pass API key to agent
harbor run -p ./my-task -a claude-code -m anthropic/claude-sonnet-4-1 \
  --ae ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY

# Run subset of tasks
harbor run -d terminal-bench@2.0 -a claude-code -m anthropic/claude-sonnet-4-1 \
  -t "bash-*" -l 10

# Dry run
harbor run -d terminal-bench@2.0 -a claude-code -m anthropic/claude-sonnet-4-1 --dry-run

harbor jobs resume

Resume an interrupted or partially completed job.

harbor jobs resume -p ./jobs/my-job-2025-01-15
harbor jobs resume -p ./jobs/my-job -f AgentTimeoutError

Flag	Short	Description
--job-path	-p	Path to job directory with config.json (required)
--filter-error-type	-f	Remove trials matching this error type before resuming. Repeatable. Default: CancelledError

harbor jobs summarize

Generate AI-powered failure summaries for trials in a job.

harbor jobs summarize ./jobs/my-job
harbor jobs summarize ./jobs/my-job -m sonnet --all --overwrite

Flag	Short	Description
job_path		Path to job dir or parent (positional)
--model	-m	Model: haiku, sonnet, opus (default: haiku)
--n-concurrent	-n	Max concurrent queries (default: 5)
--all / --failed		Analyze all or only failed trials (default: --failed)
--overwrite		Overwrite existing summary.md files

harbor trials start

Run a single trial. Useful for debugging and task development.

Key difference from harbor jobs start: the environment flag is --environment-type (not --env), and output goes to --trials-dir (default: ./trials).

Flag	Short	Description
--path	-p	Path to local task directory
--config	-c	Trial config YAML/JSON
--agent	-a	Agent name (default: oracle)
--model	-m	Model for the agent
--environment-type	-e	Environment type (default: docker)
--trials-dir		Output directory (default: ./trials)
--agent-env	--ae	Env var for agent: KEY=VALUE. Repeatable
--agent-kwarg	--ak	Agent kwarg: key=value. Repeatable
--no-cleanup		Keep environment after trial
--no-verify		Skip running tests

Full flag list (task kwargs, git options, etc.) in references/flags.md.

# Test with oracle (validates solution + tests)
harbor trials start -p ./my-task

# Test with real agent
harbor trials start -p ./my-task -a claude-code -m anthropic/claude-sonnet-4-1

# Keep container for inspection
harbor trials start -p ./my-task -a claude-code -m anthropic/claude-sonnet-4-1 --no-cleanup

The oracle agent runs solution/solve.sh inside the environment, then the verifier runs tests/test.sh. If oracle does not get reward 1.0, your tests or solution have a bug.

harbor trials summarize

harbor trials summarize ./trials/my-trial
harbor trials summarize ./trials/my-trial -m sonnet --overwrite

Flag	Short	Description
trial_path		Path to trial directory (positional)
--model	-m	Model: haiku, sonnet, opus (default: haiku)
--overwrite		Overwrite existing summary.md

harbor datasets

harbor datasets list

harbor datasets list
harbor datasets list --registry-url https://custom-registry.example.com
harbor datasets list --registry-path ./local-registry.json

Flags --registry-url and --registry-path are mutually exclusive. Default: Harbor's public registry.

harbor datasets download

harbor datasets download terminal-bench@2.0
harbor datasets download terminal-bench@2.0 -o ./my-tasks --overwrite

Flag	Short	Description
DATASET		Name or name@version (positional)
--output-dir	-o	Download dir (default: ~/.cache/harbor/tasks)
--overwrite		Re-download even if cached

harbor adapters

harbor adapters init

harbor adapters init my-benchmark

Interactive wizard that prompts for benchmark name, adapter ID, class name, description, source URL, and license. Creates the adapter directory with template files.

harbor adapters review

harbor adapters review -p adapters/my-benchmark
harbor adapters review -p adapters/my-benchmark --skip-ai -o report.md

harbor tasks

harbor tasks init

harbor tasks init my-task
harbor tasks init my-task -p ./custom-tasks

Flag	Short	Description
name		Task name (positional)
--tasks-dir	-p	Output directory (default: .)
--no-pytest		Skip pytest in test.sh
--no-solution		Skip solution directory
--include-canary-strings		Add anti-cheating canary strings
--include-standard-metadata		Add full metadata template to task.toml

harbor tasks check

AI-powered quality assessment against a rubric.

harbor tasks check ./my-task
harbor tasks check ./my-task -m opus -o results.json
harbor tasks check ./my-task -r custom-rubric.toml

Flag	Short	Description
task		Task name or path (positional)
--model	-m	Model: sonnet, opus, haiku (default: sonnet)
--output-path	-o	Write JSON results here
--rubric_path	-r	Custom rubric (.toml, .yaml, .yml, .json)

harbor tasks start-env

Launch task environment interactively for manual inspection.

harbor tasks start-env -p ./my-task
harbor tasks start-env -p ./my-task -e daytona
harbor tasks start-env -p ./my-task --agent claude-code -m anthropic/claude-sonnet-4-1

Flag	Short	Description
--path	-p	Path to task directory
--env	-e	Environment type (default: docker)
--all	-a	Include solution and tests (default: true)
--interactive / --non-interactive	-i	Interactive shell (default: true)
--agent		Agent to install in environment
--model	-m	Model for the agent

Full flag list in references/flags.md.

harbor tasks debug

Analyze failing trials for a task using AI.

harbor tasks debug my-task-id -m sonnet
harbor tasks debug my-task-id --job-id my-job --jobs-dir ./jobs

Flag	Short	Description
task_id		Task ID (positional)
--model	-m	Model for analysis
--job-id		Specific job to analyze
--jobs-dir		Directory containing jobs

harbor tasks migrate

Convert Terminal-Bench tasks to Harbor format.

harbor tasks migrate -i ./old-tasks -o ./harbor-tasks
harbor tasks migrate -i ./old-tasks -o ./harbor-tasks --cpus 2 --memory-mb 4096

Flag	Short	Description
--input	-i	Terminal-Bench task dir or parent
--output	-o	Output directory
--cpus		Override CPUs for all tasks
--memory-mb		Override memory (MB)
--storage-mb		Override storage (MB)
--gpus		Override GPUs

harbor view

Browse job/trial results in a web UI.

harbor view ./jobs/my-job
harbor view ./jobs --port 9000
harbor view ./jobs --dev

Flag	Short	Description
folder		Directory with trajectories (positional)
--port	-p	Port or range (default: 8080-8089)
--host		Bind host (default: 127.0.0.1)
--dev		Hot-reload development mode
--build		Force rebuild viewer
--no-build		Skip auto-build

Shows job summaries, per-trial reward values, agent trajectories, and test output.

harbor sweeps run

Run successive sweeps. Each sweep drops tasks that already have at least one success, focusing effort on remaining failures.

harbor sweeps run -c sweep-config.yaml
harbor sweeps run -c sweep-config.yaml --max-sweeps 5 --trials-per-task 3
harbor sweeps run -c sweep-config.yaml --hint "Try grep to find the file first"

Flag	Short	Description
--config	-c	Job config file (YAML/JSON)
--max-sweeps		Max sweeps (default: 3)
--trials-per-task		Trials per task per sweep (default: 2)
--hint		Hint string passed to agent kwargs
--hints-file		JSON mapping task name to hint
--push / --no-push		Push exported datasets to HF Hub

Additional export flags in references/flags.md.

harbor traces export

Export ATIF trajectories as training datasets.

harbor traces export -p ./jobs/my-job
harbor traces export -p ./trials --filter success --push --repo my-org/traces
harbor traces export -p ./jobs/my-job --sharegpt --episodes last

Flag	Short	Description
--path	-p	Path to trial dir or root containing trials
--recursive / --no-recursive		Search recursively (default: recursive)
--episodes		all or last per trial (default: all)
--filter		success, failure, or all (default: all)
--sharegpt / --no-sharegpt		ShareGPT-formatted conversations
--push / --no-push		Push to Hugging Face Hub
--repo		HF repo id (org/name)

Additional flags (--subagents, --instruction-metadata, --verifier-metadata, --verbose) in references/flags.md.

harbor cache clean

Remove Harbor Docker images and ~/.cache/harbor.

harbor cache clean
harbor cache clean --force
harbor cache clean --dry
harbor cache clean --no-docker      # Only clean ~/.cache/harbor
harbor cache clean --no-cache-dir   # Only clean Docker images

Flag	Short	Description
--force	-f	Skip confirmation
--dry		Preview without deleting
--no-docker		Skip Docker image removal
--no-cache-dir		Skip ~/.cache/harbor removal

harbor admin upload-images

Build and push task Docker images to a container registry. Updates task.toml with docker_image for pre-built image workflows. Hidden from harbor --help but fully functional.

harbor admin upload-images -r my-registry.io --tag 20260317
harbor admin upload-images -r my-registry.io --dry-run -f "bash-*"

Key flags: --tasks-dir / -t (default: tasks), --registry / -r (required), --tag (default: today YYYYMMDD), --dry-run, --filter / -f, --push/--no-push (default: push), --delete/--no-delete, --parallel / -n (default: 1), --update-config/--no-update-config, --override-config/--no-override-config. Full details in references/flags.md.

Supported Agents

Agent	CLI value	Notes
Oracle	oracle	Runs solution/solve.sh. Default agent
Nop	nop	Does nothing (baseline)
Claude Code	claude-code	Needs ANTHROPIC_API_KEY
Aider	aider
Codex	codex
Cline CLI	cline-cli	Model format: provider:model-id
Cursor CLI	cursor-cli
Gemini CLI	gemini-cli	Needs GOOGLE_API_KEY or GEMINI_API_KEY
Goose	goose	Model format: provider/model_name
Mini SWE-agent	mini-swe-agent
SWE-agent	swe-agent
OpenCode	opencode
OpenHands	openhands	Needs LLM_API_KEY and LLM_MODEL
OpenHands SDK	openhands-sdk
Qwen Coder	qwen-coder
Terminus	terminus	Harbor's built-in agent
Terminus 1	terminus-1
Terminus 2	terminus-2

Environment Backends

Backend	CLI value	Description
Docker	docker	Default. Local containers
Daytona	daytona	Cloud sandbox with Docker-in-Docker
E2B	e2b	E2B cloud sandboxes
GKE	gke	Google Kubernetes Engine
Modal	modal	Serverless with GPU support
Runloop	runloop	Runloop cloud environments

Note: harbor run uses --env / -e. harbor trials start uses --environment-type / -e.

Common Workflows

Create and test a new task

harbor tasks init my-task
# Edit: task.toml, instruction.md, Dockerfile, tests/test.sh, solution/solve.sh
harbor tasks check ./my-task
harbor trials start -p ./my-task                    # oracle validates tests
harbor trials start -p ./my-task -a claude-code -m anthropic/claude-sonnet-4-1
harbor view ./trials

Run a benchmark evaluation

harbor datasets list
harbor datasets download terminal-bench@2.0
harbor run -d terminal-bench@2.0 -a claude-code -m anthropic/claude-sonnet-4-1 -n 8
harbor view ./jobs
harbor traces export -p ./jobs/my-job --push --repo my-org/traces

Debug a failing task

harbor tasks start-env -p ./my-task         # interactive shell in the container
# Inside: bash /tests/test.sh && bash /solution/solve.sh
harbor tasks debug my-task-id -m sonnet     # AI analysis of failures
harbor trials start -p ./my-task            # re-test with oracle

Resume a failed job

harbor jobs resume -p ./jobs/my-job-2025-01-15
harbor jobs resume -p ./jobs/my-job -f AgentTimeoutError
harbor jobs summarize ./jobs/my-job

Common Gotchas

API keys: Most agents need keys passed via --ae. Claude Code needs ANTHROPIC_API_KEY, OpenHands needs LLM_API_KEY, Goose needs provider-specific keys. If the agent fails immediately, check the key.

Docker must be running. Harbor uses Docker for sandboxed environments. Network exhaustion from many concurrent trials can cause failures -- harbor cache clean helps.

Model name format varies by agent. Claude Code uses anthropic/claude-sonnet-4-1, Cline CLI uses provider:model-id (e.g., openrouter:anthropic/claude-opus-4.5), Goose uses provider/model_name. A ValueError about model format usually means the wrong format for that agent.

--env vs --environment-type: harbor run uses --env / -e. harbor trials start uses --environment-type / -e. Same short flag, different long name.

Default agent is oracle. Forgetting -a gives you the oracle agent, which just runs solve.sh. Fine for task validation, not for real evaluations.

--no-cleanup for debugging. Pass this to harbor trials start to keep the container running after a failed trial so you can inspect it.

Retry defaults skip common errors. --retry-exclude defaults to AgentTimeoutError, VerifierTimeoutError, RewardFileNotFoundError, RewardFileEmptyError, VerifierOutputParseError. These usually indicate task bugs, not transient failures.