cargo-orchestration

name	cargo-orchestration
description	Interact with the Cargo platform via CLI. Use when the user wants to execute an action, run a workflow, trigger a batch, message an AI agent, query orchestration runtime tables (runs/batches/spans/records) with SQL, fetch segment records, resolve an action's output schema, or inspect a model schema.
version	1.5.1
compatibility	Requires @cargo-ai/cli (npm) and a Cargo account (browser sign-in via --oauth, or an API token)
homepage	https://github.com/getcargohq/cargo-skills
metadata	{"author":"getcargo","openclaw":{"requires":{"bins":["cargo-ai"]},"install":[{"kind":"node","package":"@cargo-ai/cli@latest","bins":["cargo-ai"]}],"homepage":"https://github.com/getcargohq/cargo-skills"}}

Cargo CLI — Orchestration

Runtime operations for the Cargo platform.

What do you want to run?

Need to run something?
├── One action, one record       → action execute
├── One action, many records     → action execute-batch
├── Multiple actions chained
│   ├── One-off / ad-hoc         → run create --nodes (one record)
│   │                              batch create --nodes (many records)
│   └── Reusable workflow        → build a tool, then run create --workflow-uuid
│                                  or batch create --workflow-uuid
└── Conversational AI agent      → message create

Terminology: An orchestration tool is a saved on-demand workflow (listed via tool list). An action is a single operation you execute without building a workflow — it can embed a saved orchestration tool (kind: "tool"), call a third-party connector (kind: "connector"), invoke an AI agent (kind: "agent"), or run a built-in platform operation (kind: "native").

Composing a node graph? Prefer built-in actions + expressions. Use the actions Cargo already provides plus template expressions; avoid python, script (JS), and raw HTTP nodes unless you truly have no alternative. Reshape data → variables; call an LLM and get parsed JSON → native agent node; call an API → the integration's dedicated connector action; route → branch/filter/switch. See references/node-selection.md.

References:

references/examples/actions.md — action execute and execute-batch examples references/examples/tools.md — tool (on-demand workflow) examples references/examples/plays.md — play (segment-driven automation) examples references/examples/agents.md — AI agent chat examples references/examples/templates.md — pre-built workflow templates references/examples/queries.md — orchestration query execute (ClickHouse: runs/batches/spans/records) SQL examples. For storage query (workspace storage), see the cargo-storage skill. references/examples/segments.md — segment fetch and filter examples references/nodes.md — full node creation guide (kinds, native actions, expressions, validation, routing) references/node-selection.md — how to pick the right node and avoid unnecessary python nodes (decision table, native LLM agent node, template-expression limits, the silent-undefined footgun, inspecting node data via runContext, Pyodide sandbox limits, what survives a delay, group result access) references/filter-syntax.md — complete filter condition reference references/polling.md — async polling patterns, error handling, retry strategies references/response-shapes.md — full JSON response structures references/troubleshooting.md — common errors, plus a "Debugging a workflow run" section for runs that succeed but produce wrong output (wrong-branch routing, empty downstream values)

Diagnosing after the fact? For the ordered forensic runbooks built on these surfaces — trace one run, sweep a batch for errors grouped by root cause, profile a play's credit spend — load the cargo-diagnostics skill.

Prerequisites

See ../cargo/references/prerequisites.md for install, login (--oauth / --token), JSON output conventions, and error shapes. Verify the session with cargo-ai whoami before running any of the commands below.

Discover resources first

Most commands require UUIDs. Always discover them before acting.

cargo-ai orchestration play list            # all plays (name, workflowUuid, modelUuid, segmentUuid)
cargo-ai orchestration tool list            # all tools (name, workflowUuid, description)
cargo-ai orchestration workflow list        # all workflows (uuid only — no name)
cargo-ai orchestration template list       # all workflow templates (slug, name, kind)
cargo-ai ai agent list                     # all agents (uuid, name)
cargo-ai ai template list                  # all AI agent templates (slug, name, languageModelSlug)
cargo-ai storage model list                # all models (uuid, name, slug, columns)
cargo-ai storage dataset list              # all datasets
cargo-ai segmentation segment list         # all segments (uuid, name, modelUuid)
cargo-ai connection connector list         # all connectors

Plays vs tools: Both are backed by a workflow. A play is a segment-driven automation — it reacts to data changes in a segment (records added, updated, removed). A tool is an on-demand workflow — triggered manually, via API, or on a cron schedule. Workflows don't have a name field; use play list or tool list to find names and extract the workflowUuid.

Retrieve in the UI: plays live at app.getcargo.io/workspaces/<WORKSPACE_UUID>/plays/<PLAY_UUID> and tools at app.getcargo.io/workspaces/<WORKSPACE_UUID>/tools/<TOOL_UUID>. Get <WORKSPACE_UUID> from cargo-ai whoami under workspace.uuid.

Designing a new tool or play? Check templates first — they are pre-built node graphs for common automation patterns (enrichment pipelines, CRM syncs, lead scoring) and are an excellent starting point. List templates with cargo-ai orchestration template list and inspect a specific one with cargo-ai orchestration template get <slug>. Templates are tagged by kind so you can find ones suited for tools ("kind":"tool") or plays ("kind":"play") right away. See references/examples/templates.md for the full guide.

Compatibility rules:

• run create — only works with tool workflows (or no workflowUuid). Play workflows return playNotCompatible.
• batch create — allowed data kinds depend on the workflow type:
  • Play workflows: segment, change, filter, recordIds
  • Tool workflows (or no workflowUuid): file, records

Quick reference

# Single actions
cargo-ai orchestration action execute --action '{"kind":"tool","toolUuid":"<uuid>","config":{}}' --data '{"domain":"acme.com"}'
cargo-ai orchestration action execute-batch --action '{"kind":"connector","integrationSlug":"clearbit","actionSlug":"enrichCompany","config":{}}' --records '[{...},{...}]'
cargo-ai orchestration action get-output-schema --action '{"kind":"connector","integrationSlug":"clearbit","actionSlug":"enrichCompany","config":{}}' # → {"schema": <JSON Schema>} without executing

# Workflows (chain multiple actions)
cargo-ai orchestration run create --workflow-uuid <uuid> --data '{"company":"Acme","domain":"acme.com"}'
cargo-ai orchestration run create --data '{"domain":"acme.com"}' --nodes '[...]'
cargo-ai orchestration batch create --workflow-uuid <uuid> --data '{"kind":"segment","segmentUuid":"..."}'

# AI agents
cargo-ai ai message create --chat-uuid <uuid> --parts '[{"type":"text","text":"..."}]'

# Data
cargo-ai orchestration query execute "SELECT count() FROM runs WHERE status='error'" # ClickHouse: spans, runs, batches, records
cargo-ai segmentation segment fetch --model-uuid <uuid> --filter '{"conjonction":"and","groups":[]}' --fetching-limit 100
# For SQL against workspace storage (Companies, Contacts, …), see the cargo-storage skill: `storage query execute`

Polling async operations

All operations are asynchronous. Either poll until terminal state, or pass --wait-until-finished to block.

action execute returns a run. action execute-batch returns a batch. They poll the same way:

Result type	Poll command	Interval	Done when
Run	run get <uuid>	2s	status is success, error, or cancelled
Batch	batch get <uuid>	5s	status is success, error, or cancelled
Agent message	message get <uuid>	2s	status is success or error

For long-running batches (1000+ records), increase the interval to 10-15s after the first minute.

Execute actions

Run a single action — no workflow or node graph needed.

# One action, one record → returns a run
cargo-ai orchestration action execute \
  --action '{"kind":"connector","integrationSlug":"clearbit","actionSlug":"enrichCompany","config":{}}' \
  --data '{"domain":"acme.com"}' \
  --wait-until-finished

# One action, many records → returns a batch
cargo-ai orchestration action execute-batch \
  --action '{"kind":"tool","toolUuid":"<tool-uuid>","config":{}}' \
  --records '[{"domain":"acme.com"},{"domain":"globex.com"}]' \
  --wait-until-finished

Action kinds: tool, connector, agent, native. See references/examples/actions.md for all action kinds, parameters, retry config, response shapes, and end-to-end examples.

Resolve an action's output schema (without executing)

Never guess what an action outputs. Two free sources — no run, no credits:

1. Connector actions: the integration catalog carries the output schema inline — integration get <slug> (and integration list) return actions.<actionSlug>.output.schema next to the input config.jsonSchema. Not every action declares one.
2. Any action kind (tool / connector / agent / native) — resolve it with the same --action object as action execute:

cargo-ai orchestration action get-output-schema \
  --action '{"kind":"connector","integrationSlug":"clearbit","actionSlug":"enrichCompany","config":{}}'
# → {"schema": {"type": "object", "properties": {...}}}  — the JSON Schema is under the top-level "schema" key

Actions that declare no output schema fail with "Action has no output schema." (non-zero exit, status 404) — that's the signal to fall back to inspecting runContext from a real run. Use these to:

• Know which fields a downstream node can read ({{nodes.<slug>.<field>}}) before wiring the graph.
• See an agent action's real output envelope — a default free-text agent resolves to {"schema":{"type":"object","properties":{"answer":{"type":"string"}}}}, which is why downstream references need {{nodes.<slug>.answer...}}.
• Map an action's output onto storage columns without a throwaway run.

See references/examples/actions.md ("Resolve an action's output schema") for verified per-kind examples and the response/error shapes.

Create a run

A run processes a single record through a workflow. Use run create when you need to chain multiple actions together via a node graph, or when running an existing tool workflow.

Runs only work with tool workflows. Play workflows return playNotCompatible — use batch create instead.

cargo-ai orchestration run create \
  --workflow-uuid <tool.workflowUuid> \
  --data '{"company":"Acme","domain":"acme.com"}'
# → Poll with: cargo-ai orchestration run get <run-uuid>

# Or wait synchronously — blocks until the run reaches a terminal state and returns the final result
cargo-ai orchestration run create \
  --workflow-uuid <tool.workflowUuid> \
  --data '{"company":"Acme","domain":"acme.com"}' \
  --wait-until-finished

Also supports --release-uuid to pin a specific release.

Cancelling runs:

cargo-ai orchestration run cancel --workflow-uuid <uuid> --uuids run-uuid-1,run-uuid-2

See references/examples/tools.md for file uploads, monitoring, and cancellation. See references/nodes.md for custom node graphs.

Create a batch

Batches process multiple records at once. Allowed data kinds depend on the workflow type:

• Play workflows: segment, change, filter, recordIds
• Tool workflows (or no workflowUuid): file, records

# Play workflow — run on a segment
cargo-ai orchestration batch create \
  --workflow-uuid <play.workflowUuid> \
  --data '{"kind":"segment","segmentUuid":"..."}'

# Tool workflow — run on a file
cargo-ai orchestration batch create \
  --workflow-uuid <tool.workflowUuid> \
  --data '{"kind":"file","s3Filename":"..."}'
# → Poll with: cargo-ai orchestration batch get <batch-uuid>

# Or wait synchronously — blocks until the batch reaches a terminal state and returns the final result
cargo-ai orchestration batch create \
  --workflow-uuid <play.workflowUuid> \
  --data '{"kind":"segment","segmentUuid":"..."}' \
  --wait-until-finished

Downloading results: get the releaseUuid from batch get, then cargo-ai orchestration release get <release-uuid> to find nodes[].slug, then cargo-ai orchestration batch download --uuid <batch-uuid> --output-node-slug <slug>.

Cancelling a batch:

cargo-ai orchestration batch cancel <batch-uuid>

See references/examples/plays.md and references/examples/tools.md for filtering, record IDs, file uploads, monitoring, and cancellation.

Send a message to an AI agent

cargo-ai ai agent list                                    # 1. Find the agent
cargo-ai ai chat create \                                 # 2. Create a chat
  --trigger '{"type":"draft"}' \
  --agent-uuid <agent-uuid> --name "Research session"
cargo-ai ai message create \                              # 3. Send a message
  --chat-uuid <chat-uuid> \
  --parts '[{"type":"text","text":"Find the VP of Sales at Acme Corp"}]'
# → Extract assistantMessage.uuid, poll with: cargo-ai ai message get <uuid>
#   Done when .message.status is "success" (read .parts) or "error" (read .errorMessage)

Also supports --actions, --resources, --language-model-slug, --temperature, --max-steps, and --wait-until-finished (blocks until the assistant message reaches a terminal status). See references/examples/agents.md for multi-turn conversations, action/resource injection, and model selection.

Inspect records

Records are individual items processed by a workflow. Use these commands to list, count, download, or cancel records within a workflow.

# List records for a workflow
cargo-ai orchestration record list --workflow-uuid <uuid> --limit 50

# Filter by batch or status
cargo-ai orchestration record list --workflow-uuid <uuid> --batch-uuid <uuid> --statuses error

# Count records
cargo-ai orchestration record count --workflow-uuid <uuid>

# Download records as a file
cargo-ai orchestration record download --workflow-uuid <uuid>

# Get per-node execution metrics
cargo-ai orchestration record get-metrics --workflow-uuid <uuid>

# Cancel records
cargo-ai orchestration record cancel --workflow-uuid <uuid> --ids record-id-1,record-id-2

Query orchestration history (orchestration query)

Run SQL against orchestration runtime tables — spans, runs, batches, records — with orchestration query execute. Use this for ad-hoc analytics on workflow execution (error rates, throughput, slowest nodes) without the workflow-scoped filters of run get-metrics / run count.

cargo-ai orchestration query execute "SELECT count() FROM runs WHERE status = 'error'"
cargo-ai orchestration query execute "SELECT status, count() FROM batches GROUP BY status"
cargo-ai orchestration query execute "SELECT * FROM spans ORDER BY execution_started_at DESC LIMIT 10"

Tables are referenced without a schema prefix — just spans, runs, batches, or records. Workspace scoping is applied automatically. The query is read-only; DDL, table functions, dictionary accessors, and introspection are denied. See references/examples/queries.md for the schemas, example queries, and limits.

Fetch segment data

Retrieve live records from a segment. IMPORTANT: requires --model-uuid (not --segment-uuid). Get the modelUuid from segment list. Filter JSON uses conjonction (not conjunction) — this is intentional.

cargo-ai segmentation segment fetch \
  --model-uuid <uuid> \
  --filter '{"conjonction":"and","groups":[]}' \
  --fetching-limit 100 --fetching-offset 0

Supports --sort, --enrich, and --sync. See references/filter-syntax.md for the full filter syntax and references/examples/segments.md for filtering, pagination, sorting, enrollment filters, and enrichment.

Managing segments:

# Update a segment's name or filter
cargo-ai segmentation segment update --uuid <segment-uuid> --name "Updated Name"
cargo-ai segmentation segment update --uuid <segment-uuid> --filter '{"conjonction":"and","groups":[...]}'

# Remove a segment (fails if linked to a workflow)
cargo-ai segmentation segment remove <segment-uuid>

Use a workflow template

Templates are pre-built node graphs for common automation patterns (enrichment pipelines, CRM syncs, lead scoring). Browse with template list, inspect with template get <slug>, fill in placeholders, validate, and run.

cargo-ai orchestration template list              # list available templates
cargo-ai orchestration template get <slug>        # get template nodes + config

See references/examples/templates.md for the full guide including placeholder conventions and end-to-end examples.

Validate and test nodes

Always validate custom node graphs before running them.

cargo-ai orchestration node validate --nodes '[...]'
# → { "outcome": "valid" } or { "outcome": "notValid", "invalidNodes": [...] }

For debugging, use node compute (dry-run expressions) or node execute (live test, costs credits). For runs that complete with status: success but produce wrong output (wrong branch taken, empty downstream values), use run.executions[].title from run get only as a quick summary — it may be truncated — and read runContext.<nodeSlug> (returned at the top level of the same run get <run-uuid> response) to verify field-level data. See references/troubleshooting.md → "Debugging a workflow run" and references/nodes.md for the full node creation guide, validation error codes, and examples.

Help

Every command supports --help:

cargo-ai orchestration run create --help
cargo-ai orchestration template list --help
cargo-ai orchestration node validate --help
cargo-ai ai message create --help
cargo-ai orchestration query execute --help