cargo-orchestration

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
└── Testing ONE node of a
    workflow you're building     → node execute (debug only — see below)

Fanning out across many records (action execute-batch, batch create)? Sample first. Run 10–20 records, report the observed cost and hit-rate, then ask the user to approve the full enrollment — quoting the record count and the credit estimate. See Create a batch → the sample gate.

action execute, not node execute, is the default for running something. node execute is a debug surface for a node that already lives in a workflow: it requires --workflow-uuid, --release-uuid, --node, --computed-config and --context (all five, enforced client-side), and it bills like any live call. If you just want an operation's output — enrich a domain, call a connector action, invoke a tool or agent — use action execute / action execute-batch with a small --action + --data payload. Only reach for node execute when verifying one node's behavior before running the full graph.

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.

Show the graph, don't describe it. Before deploying a draft, and whenever the user asks what a workflow or play does, draw it: cargo-ai orchestration node diagram --workflow-uuid <uuid> --format ascii --raw (free, runs nothing; --format needs CLI ≥ 1.0.56, the command itself ≥ 1.0.54). Routing, fallback edges, and which steps bill are what the user is actually approving, and prose flattens all three. Pick the format by where the output goes: ascii renders a picture a person can read in a terminal or a chat reply; mermaid (the default) is source code, correct only when you are pasting into a PR, a doc, or a page that renders it. Sources, the ASCII legend, cost marking, and the duplicate-slug footgun: references/node-diagram.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-diagram.md — draw a node graph as a Mermaid flowchart (node diagram): every source (workflow / draft / release / run / raw nodes), marking paid nodes, highlighting a failing node, and why diagrams key on uuid rather than slug 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.

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 [email protected]  # 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

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: filter, recordIds, segment, change. Trigger a play with filter; segment takes a standalone segment only, never the segmentUuid from play list.
  • 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":"filter","modelUuid":"...","filter":{"conjonction":"and","groups":[]}}'

# 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:

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.

execute-batch bills per record. Pass a 10–20 record slice of --records first, report the observed per-record cost and hit-rate, and get approval (with the full record count and credit estimate) before sending the rest — same gate as Create a batch.

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

Sample first, then ask before enrolling everything — blocking. A batch fans one workflow across every record in its data source, so a mistake and a full bill land together. Never enroll a full segment/file/model on the first attempt: run a 10–20 record sample, report what it cost and returned, then ask the user to approve the full enrollment with the record count and credit estimate in the question. Mechanics below; the spend rules behind it are ../cargo-gtm/references/cost-discipline.md.

The sample gate

1. Count the pool first (free). Never quote an estimate from a guess:

cargo-ai segmentation segment get <segment-uuid>          # → recordsCount (also on `segment list`)
cargo-ai storage query execute "SELECT count() FROM <dataset>.<model>"   # for a filter/model source
# For a file source: wc -l on the CSV, minus the header row.

2. Run 10–20 records through the exact workflow and config. Sample by data kind:

# Play workflow, segment source → reuse the segment's own filter, capped by `limit`
cargo-ai segmentation segment get <segment-uuid>          # → copy .filter and .modelUuid
cargo-ai orchestration batch create \
  --workflow-uuid <play.workflowUuid> \
  --data '{"kind":"filter","modelUuid":"<modelUuid>","filter":<segment.filter>,"limit":15}' \
  --wait-until-finished

# Play workflow, explicit records → pick 10–20 ids
cargo-ai orchestration batch create \
  --workflow-uuid <play.workflowUuid> \
  --data '{"kind":"recordIds","modelUuid":"<modelUuid>","ids":["id-1","…","id-15"]}'

# Tool workflow, inline records → slice the array
cargo-ai orchestration batch create \
  --workflow-uuid <tool.workflowUuid> \
  --data '{"kind":"records","records":[ /* first 15 only */ ]}'

# Tool workflow, file → upload a truncated CSV (header + 15 rows), not the full file
head -n 16 leads.csv > leads-sample.csv
cargo-ai workspaceManagement file upload --file ./leads-sample.csv

limit is the sampling lever for kind: "filter". kind: "segment" and kind: "change" have no limit — they always enroll the whole set, so sample via filter or recordIds and switch to segment only for the approved full run.

3. Report the sample, then ask. The confirmation must carry both numbers the user needs to decide:

Sample: 15 of 1,240 records · 6.2 credits (0.41/record) · 13/15 enriched (87%)
Full enrollment: 1,225 remaining records ≈ 502 credits (balance: 780)

Enroll all 1,225? Or:
  1. Enroll all 1,225 (≈502 cr, leaves ~278)
  2. Trim scope — e.g. the 610 records with a domain set (≈250 cr)
  3. Stop here and review the sample output first

Wait for an explicit answer. Do not enroll the full set on an unanswered question, and don't treat approval of the sample as approval of the full run. Skip the gate only when the batch is free (no paid nodes) and small, or when the user has already named the scope and approved the cost this session.

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

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

Use filter to trigger a play — it queries the model directly. segment only accepts a standalone segment from segmentation segment list; passing the segmentUuid that play list returns is rejected (segmentLinkedToPlay, or noRecords on older backends) because a play's generated segment never has a populated record count.

# Play workflow — run over the play's model (empty filter = all rows)
cargo-ai orchestration batch create \
  --workflow-uuid <play.workflowUuid> \
  --data '{"kind":"filter","modelUuid":"...","filter":{"conjonction":"and","groups":[]}}'

# 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":"filter","modelUuid":"...","filter":{"conjonction":"and","groups":[]}}' \
  --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": [...] }

Then show it before deploying it — validate proves the graph is well-formed, not that it does what the user asked for:

cargo-ai orchestration node diagram --nodes '[...]' --format ascii --raw   # free, runs nothing

Same command draws a deployed workflow (--workflow-uuid), a draft (--draft), a release (--release-uuid), or the graph a run executed (--run-uuid). See references/node-diagram.md.

For debugging, use node compute (dry-run expressions) or node execute (live test of one node of an existing workflow — needs --workflow-uuid + --release-uuid + --computed-config, and costs credits; for anything that isn't node-level debugging, use action execute instead). 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