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tracer cloud: drive Tracer Cloud from the terminal

tracer cloud mirrors the Tracer Cloud web app from your shell: log in once, then create and train tracers, run live routing queries, manage tests, models, observability keys, billing, and more. It talks to the same API the dashboard uses, as you.

pip install tracer-llm
tracer cloud login          # opens your browser, pick your account
tracer cloud tracers        # list your tracers

Auth

tracer cloud login runs a browser device flow: it opens a page where you pick your account and approve, then hands the session back to the CLI on a localhost callback. The session is stored only on this machine in ~/.tracer/cloud.json and refreshed automatically. Sign out with tracer cloud logout.

Headless / CI: pass credentials to skip the browser.

tracer cloud login --email you@example.com --password "$TRACER_PW"

Point at a different deployment or a local dev server with --url (or the TRACER_CLOUD_URL env var). The package hardcodes no infrastructure details; the client config is fetched from <url>/api/cli/config at login.

Commands

Account & workspaces

tracer cloud whoami                 # the logged-in user + workspaces
tracer cloud tenants                # list workspaces
tracer cloud tenant-delete <ref> --confirm "<name>"   # delete an EMPTY company workspace

Tracers

tracer cloud tracers                # list
tracer cloud get <id|slug>          # a tracer + its versions
tracer cloud rename <id|slug> "New name"
tracer cloud delete <id|slug> [--confirm <name>]

# create + train
tracer cloud create "support router" --mode quick \
    --file traces.jsonl --teacher my-provider:my-model \
    --label-space "billing,fraud,general" --wait
tracer cloud create "watch mode" --mode auto --teacher my-provider:my-model --threshold 200

--mode quick trains on a labelled upload, unlabelled lets the teacher label it, auto starts in watch mode (no data needed). --wait blocks until training finishes. --teacher takes provider:model_id or a JSON object.

Training, retraining, promotion

tracer cloud training <id>                      # job status
tracer cloud training-cancel <id>
tracer cloud retrain <id> --mode reuse|merge|replace [--trace-ids a,b,c] [--wait]
tracer cloud auto-retrain <id> --threshold 500  # or --disable
tracer cloud promote <id> [--version <version_id>]   # promote (or roll back)

Live routing

tracer cloud route <id> "my card was declined"     # the dashboard's Live Query
tracer cloud route <id> "..." --lite               # skip the teacher call
tracer cloud route <id> "..." --show-exemplars

Tests (batteries)

tracer cloud batteries list <id>
tracer cloud batteries create <id> --name "smoke" --cases cases.json
tracer cloud batteries run <id> --battery <battery_id> [--version <id>]

Models

tracer cloud models list
tracer cloud models add --name my-llm --endpoint https://… --upstream model-id \
    --api-key "$KEY" --kind llm --cost-in 0.15 --cost-out 0.6
tracer cloud models delete --id <model_id>

Observability

tracer cloud ingest-keys create --name "my app"     # mint a workspace ingest key
tracer cloud ingest-keys list
tracer cloud ingest-keys revoke --id <key_id>
tracer cloud ingest --key trobs_… --events events.json   # send observed events

Most teams stream live traffic with tracer.watch instead of posting batches by hand.

Keys, analytics, billing, scan, label space

tracer cloud keys list|create|revoke <id>     # per-tracer gateway API keys
tracer cloud analytics <id> --range 24h|7d|30d
tracer cloud label-space <id> [--set a,b,c] [--strict]
tracer cloud billing [--tenant <ref>]
tracer cloud scan path/to/traces.jsonl        # public scan

Add --json to any read command for raw JSON output.

Environment variables

Var Effect
TRACER_CLOUD_URL Tracer Cloud base URL (default https://app.tracerml.ai)
TRACER_TENANT default workspace id/slug/name when a command needs one
TRACER_CLOUD_CONFIG path to the stored session (default ~/.tracer/cloud.json)