@withgauge/cli

gauge — the Gauge command-line interface. Manage your eval sets, Agent Preference prompts, run presets, and agent runs, and watch runs live from your terminal.

npm install -g @withgauge/cli
gauge onboard             # sign in, create a workspace, seed starter measurements
gauge auth login          # (or just sign in)
gauge orgs use <org>      # set your default organization

Requires Node.js ≥ 22.12.

Usage

gauge status                      # the org at a glance
gauge evals list                  # judged agent-experience measurements
gauge preference list             # Agent Preference prompts and measurements
gauge presets list                # run configurations (repo × persona × agents × skills × MCP)
gauge models list                 # supported model × harness × provider targets
gauge evals attach <id> --preset <presetId>   # schedule a preset on an eval (prints the bill first)
gauge evals run <id>              # launch now (ORG-funded, credit-gated)
gauge runs watch <run-id>         # follow a run as the agent works
gauge stats rankings -o json      # brand rankings (share of voice) as JSON
gauge query -d ecosystem -m installs,install_rate -o json   # a flexible report

Run gauge --help (or gauge <command> --help) for the full command set: evals, preference, presets, models, personas, skills, mcp, cycles, repos, runs, experiments, batches, brands, tags, actions, impl, dashboards, members, org, provider keys, billing, usage, stats, query, and more.

Run presets are the unit of configuration: an eval set or preference prompt runs whatever presets are attached to it, every cycle. gauge presets create --name api-main --repo https://github.com/acme/api --agent claude-code:claude-opus-4-8 --skill [email protected] authors one; gauge evals attach / gauge preference attach schedules it. Attaching spends money on a schedule, so attach and run print the per-cycle run arithmetic and ask before acting (--yes skips the prompt for scripts).

Use gauge models list before pinning models. It reads the organization's live catalog and shows every selectable logical model with its harness, provider, and effective capabilities. Open harnesses use --agent pi:<model> and --agent opencode:<model> just like the frontier harnesses.

gauge query builds flexible reports over your event warehouse (a GA4 run_report analog): pick a --dataset, group by --dimensions (including run_preset), and aggregate --metrics, or pass a whole spec with --json. gauge query datasets / gauge query fields --dataset <name> list what you can ask for.

To launch runs declaratively, gauge apply -f <spec>.json. Discover the spec format without leaving the terminal: gauge apply --example prints a starter spec and gauge apply --schema prints its JSON Schema.

Every read supports -o json for scripting. GAUGE_API_TOKEN overrides stored credentials (useful in CI). Exit codes: 0 ok · 1 error · 2 usage · 3 billing-blocked.

Support

Questions or problems: [email protected].