Cargo CLI — Storage

Data layer management: inspecting and modifying models, datasets, columns, relationships, unification, and records, and running SQL queries against workspace storage.

See references/response-shapes.md for full JSON response structures. See references/troubleshooting.md for common errors and how to fix them. See references/examples/models.md for model CRUD, DDL inspection, and schema discovery examples. See references/examples/datasets.md for dataset listing and navigation examples. See references/examples/columns.md for column creation and management examples. See references/examples/queries.md for storage query execute / storage query download SQL examples (WHERE, aggregations, joins, pagination, exports). See references/examples/ingest-webhook.md for ingest (webhook-fed) models — deriving the webhook URL and POSTing records.

Bootstrap

Already signed in (cargo-ai whoami returns a workspace)? Skip to the next section.

npm install -g @cargo-ai/cli            # no global install? prefix every command with `npx @cargo-ai/cli`
cargo-ai login --email you@company.com  # emailed code, no browser; creates the account on first use
                                        # alternatives: --oauth (browser) · --token <api-token> (CI)
cargo-ai whoami                         # confirm the active workspace before any write

Every command prints JSON to stdout; failures exit non-zero with {"errorMessage": "..."}. Anything that creates a run or a batch is async — pass --wait-until-finished or poll the matching get. When the full skill bundle is installed, ../cargo/references/prerequisites.md adds the CLI version pin, token scopes, and the admin-only surface.

Discover resources first

Always list before inspecting or modifying.

cargo-ai storage dataset list              # all datasets (uuid, slug)
cargo-ai storage model list                # all models (uuid, name, slug, columns, datasetUuid)
# `model list` takes no flags — filter its output instead:
cargo-ai storage model list | jq '[.models[] | select(.datasetUuid == "<uuid>")]' 

Retrieve in the UI: models live at app.getcargo.io/workspaces/<WORKSPACE_UUID>/models/<MODEL_UUID>. Get <WORKSPACE_UUID> from cargo-ai whoami under workspace.uuid.

Quick reference

cargo-ai storage model list
cargo-ai storage model get <model-uuid>
cargo-ai storage model get-ddl <model-uuid>
cargo-ai storage dataset list
cargo-ai storage column list --model-uuid <uuid>
cargo-ai storage relationship list
cargo-ai storage record list --model-uuid <uuid>
cargo-ai storage query execute "SELECT * FROM default.companies LIMIT 10"
cargo-ai storage query download --query "SELECT * FROM default.companies"

Models

Models are structured tables in your workspace (e.g. Companies, Contacts).

# List all models
cargo-ai storage model list

# List models in a dataset — every model carries `datasetUuid`, and
# `model list` has no flags of its own, so filter client-side
cargo-ai storage model list | jq '[.models[] | select(.datasetUuid == "<uuid>")]' 

# Get a single model (includes columns)
cargo-ai storage model get <model-uuid>

# Get the DDL (full schema, table name and SQL dialect)
cargo-ai storage model get-ddl <model-uuid>
# → Useful for column discovery and SQL dialect (BigQuery vs Snowflake) before writing queries

# Create a model
cargo-ai storage model create \
  --slug contacts \
  --name "Contacts" \
  --dataset-uuid <uuid> \
  --extractor-slug <extractor-slug> \
  --config '{}'

# Update a model
cargo-ai storage model update --uuid <model-uuid> --name "New Name"

# Remove a model
cargo-ai storage model remove <model-uuid>

Querying: Use cargo-ai storage query execute "<sql>" (or storage query download --query "<sql>" for full exports) to run SQL against storage. Tables are referenced as <datasetSlug>.<modelSlug> (e.g. default.companies) and rewritten to the underlying storage table under the hood. See Query with SQL below.

Ingest models (webhook-fed)

A model whose extractor has mode.kind === "ingest" — http.listenHook and friends — is filled by pushing records to Cargo. The app shows a "Webhook URL" on the model settings screen; no CLI command or API field returns it, but it's assembled from values the CLI already exposes:

<baseUrl>/v1/models/<model-uuid>/records/ingest?token=<api-token>

MODEL_UUID=<model-uuid>
BASE=$(cargo-ai whoami | jq -r '.baseUrl')
TOKEN=$(cargo-ai workspaceManagement token list | jq -r '.tokens[0].token')
echo "$BASE/v1/models/$MODEL_UUID/records/ingest?token=$TOKEN"

Not for workflows. A Cargo workflow writes to a model with the native modelUpsert / modelInsert action (see cargo-orchestration → references/nodes.md → "Storage"). An HTTP node calling this URL costs an extra node, a payload script, and an API token pasted into the node config. The webhook is for systems outside Cargo.

Check the extractor's mode first — when it reports "autoIngest": true (calendly, smartlead, instantlyV2, heyReach, cargo signals) Cargo registers the hook with the provider itself and the URL must not be handed out. Full flow, payload shapes, and limits: references/examples/ingest-webhook.md.

Datasets

Datasets are logical groupings of models.

# List all datasets
cargo-ai storage dataset list

# Get a single dataset
cargo-ai storage dataset get <dataset-uuid>

Columns

Columns define the schema of a model.

# List columns for a model
cargo-ai storage column list --model-uuid <uuid>

# Create a column
cargo-ai storage column create \
  --model-uuid <uuid> \
  --column '{"slug":"my_column","type":"string","label":"My Column","kind":"custom"}'

# Update a column (pass the full column object — columns are identified by slug, not UUID)
cargo-ai storage column update \
  --model-uuid <uuid> \
  --column '{"slug":"my_column","type":"string","label":"Updated Label","kind":"custom"}'

# Remove a column
cargo-ai storage column remove --model-uuid <uuid> --column-slug <slug>

# Reorder a column (move to a specific index)
cargo-ai storage column reorder --model-uuid <uuid> --column-slug <slug> --to-index 2

Column types: string, number, boolean, date, object, array, vector, any.

Column kinds: custom (user-defined), computed (expression over other columns), metric (aggregated from a related model), lookup (single field pulled from a related model via a join).

Preview what you built

A column list doesn't tell the user whether the model is right — rows do. Two checkpoints (the pack-wide convention lives in ../cargo/references/interaction.md §4):

1. Right after model create / column create — show the schema, not rows. A new model is empty; a LIMIT 10 here returns nothing and reads as failure. Echo the columns as a compact table instead (column, type, what will fill it).

2. As soon as data lands — show the rows. After a batch, play, or import writes into the model, preview it:

cargo-ai storage query execute \
  "SELECT * FROM <dataset-slug>.<model-slug> LIMIT 10"

Show ~10 rows and only the columns that carry meaning. Storage queries are free, so this costs nothing but a few lines of output — and it's the first moment the user can actually see what they built. When a play fills a new column, preview that column next to the record's identifying fields (name, domain) so filled vs. empty is obvious.

If the preview comes back empty or all-null when it shouldn't, that's a finding — surface it rather than reporting the write as a success. See cargo-diagnostics to trace why.

Relationships

Relationships link models together (e.g. Contacts belong to Companies). They are authored from the CLI, not just the UI.

relationship list takes no flags — it returns every relationship in the workspace. Filter client-side on fromModelUuid / toModelUuid.

cargo-ai storage relationship list

relationship set replaces the dataset's whole relationship set. It takes a dataset and the complete list that should exist within it: entries carrying a uuid are updated, entries without one are created, and any existing relationship whose uuid is absent from the payload is deleted. Sending one relationship to a dataset that has five removes the other four. Always list first, then send back the full array with your addition:

cargo-ai storage relationship set \
  --dataset-uuid <dataset-uuid> \
  --relationships '[
    {"uuid":"<existing-uuid>","fromModelUuid":"<contacts-uuid>","fromColumnSlug":"account_id","toModelUuid":"<companies-uuid>","toColumnSlug":"id","relation":"manyToOne"},
    {"fromModelUuid":"<deals-uuid>","fromColumnSlug":"company_id","toModelUuid":"<companies-uuid>","toColumnSlug":"id","relation":"manyToOne"}
  ]'

relation is oneToOne, manyToOne, or oneToMany. Both models must live in the dataset you pass — relationships never span datasets, so fromDatasetUuid and toDatasetUuid on the response always equal --dataset-uuid.

Failure reasons: datasetNotFound; invalidRelationships (a column slug or model UUID that doesn't resolve, or a duplicate — including the same pair stated in reverse); modelNotCompatible (see below).

Unify models refuse manual relationships. In the native dataset, a unify model's relationships are generated during sync, so naming one as fromModelUuid or toModelUuid returns modelNotCompatible. Those auto-generated rows are also excluded from the replace above, so a set call cannot delete them.

Unification

Unification is what merges records from several source models into one canonical account/contact — and it is configurable from the CLI, via --unification on model update. Pass null to clear it.

# Connector-driven: the integration decides how records unify
cargo-ai storage model update --uuid <model-uuid> --unification '{"source":"integration"}'

# Custom: you name the type, the matching keys, and optionally a parent
cargo-ai storage model update --uuid <model-uuid> --unification '{
  "source": "custom",
  "type": "account",
  "uniqueColumns": [{"slug":"domain","reference":"domain"}],
  "selectedColumnSlugs": ["name","industry","employee_count"],
  "parent": {"kind":"model","columnSlug":"account_id","parentModelUuid":"<accounts-uuid>"}
}'

Writing the config does not recompute anything. The unified rows are rebuilt by the model's sync run, so follow the update with a run and poll it:

cargo-ai storage run create --model-uuid <model-uuid>
cargo-ai storage run list --model-uuid <model-uuid>

Get the current config from storage model get <uuid> → unification (null when the model doesn't unify). Once the run finishes, check the row count with storage query execute before treating the change as done — a too-narrow uniqueColumns under-merges and a too-broad one collapses distinct entities, and both look like a successful run.

Records

# List records in a model
cargo-ai storage record list --model-uuid <uuid>

For advanced record queries (filtering, sorting, pagination), use segmentation segment fetch from the cargo-orchestration skill.

Query with SQL

Run SQL against workspace storage with storage query execute. Tables are referenced as <datasetSlug>.<modelSlug> (e.g. default.companies) and rewritten to the underlying storage table under the hood — no DDL lookup is needed for the table name.

cargo-ai storage query execute \
  "SELECT name, domain FROM default.companies LIMIT 10"
# → { "rows": [...] } on success; non-zero exit with { "errorMessage": "..." } on error

For full exports, use storage query download — it returns a signed URL to a CSV (default) or Parquet file:

cargo-ai storage query download \
  --query "SELECT name, domain, revenue FROM default.companies ORDER BY revenue DESC"

cargo-ai storage query download \
  --query "SELECT * FROM default.companies" --format parquet

Get column slugs from storage column list --model-uuid <uuid> (or run storage model get-ddl <model-uuid> for the full schema and SQL dialect). Page through large result sets with LIMIT / OFFSET directly in the SQL.

See references/examples/queries.md for WHERE clauses, aggregations, joins, date queries, pagination, and the failure shapes returned on error.

Help

Every command supports --help:

cargo-ai storage model list --help
cargo-ai storage column create --help
cargo-ai storage relationship set --help
cargo-ai storage query execute --help
cargo-ai storage query download --help