cargo-ai login --email (emailed code, no browser), --oauth, or an API token\nhomepage: https://github.com/getcargohq/cargo-skills\nmetadata:\nauthor: getcargo\nopenclaw:\nrequires:\nbins:\n- cargo-ai\ninstall:\n- kind: node\npackage: \"@cargo-ai/cli@latest\"\nbins:\n- cargo-ai\nhomepage: https://github.com/getcargohq/cargo-skillsAgent resource management: creating and configuring agents, attaching knowledge for retrieval-augmented generation (RAG), connecting MCP servers, and managing agent memories.
\n\n\nFor using agents (sending messages, multi-turn chat, polling), use
\ncargo-orchestration.\nFor uploading knowledge files and building knowledge libraries (thecontentdomain), usecargo-content. This skill covers how that knowledge attaches to an agent.\nFor workspace administration — folders (used to organize agents and files), users, API tokens, roles, and submitting reports when the CLI fails — usecargo-workspace-management.
\n\nSee
\nreferences/response-shapes.mdfor full JSON response structures.\nSeereferences/troubleshooting.mdfor common errors and how to fix them.\nSeereferences/examples/agents.mdfor agent CRUD and configuration examples.\nSeereferences/examples/mcp-servers.mdfor MCP server creation and management examples.
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`\ncargo-ai login --email you@company.com # emailed code, no browser; creates the account on first use\n # alternatives: --oauth (browser) · --token <api-token> (CI)\ncargo-ai whoami # confirm the active workspace before any write\nEvery 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.
cargo-ai ai agent list # all agents (uuid, name, description)\ncargo-ai ai template list # all AI agent templates (slug, name)\ncargo-ai ai mcp-server list # all MCP servers (uuid, name)\ncargo-ai ai memory list --scope agent --agent-uuid <uuid> # agent memories\n# Knowledge files & libraries live in the content domain — see cargo-content:\n# cargo-ai content file list / cargo-ai content library list\nRetrieve in the UI: agents live at app.getcargo.io/workspaces/<WORKSPACE_UUID>/agents/<AGENT_UUID>. Get <WORKSPACE_UUID> from cargo-ai whoami under workspace.uuid.
cargo-ai ai agent list\ncargo-ai ai agent get <agent-uuid>\ncargo-ai ai agent create --name <name> --icon-color blue --icon-face \uD83E\uDD16\ncargo-ai ai agent update --uuid <agent-uuid> --name <name>\ncargo-ai ai agent remove <agent-uuid>\ncargo-ai ai release list --agent-uuid <uuid>\ncargo-ai ai release get <release-uuid>\ncargo-ai ai release get-draft --agent-uuid <uuid>\ncargo-ai ai release update-draft --agent-uuid <uuid> --language-model-slug gpt-4o\ncargo-ai ai release deploy-draft --agent-uuid <uuid>\ncargo-ai ai template list\ncargo-ai ai template get <slug>\ncargo-ai ai mcp-server list\ncargo-ai ai mcp-server create --name \"Internal Tools\"\ncargo-ai ai mcp-server update --uuid <mcp-server-uuid> --name \"Updated Name\"\ncargo-ai ai mcp-server remove <mcp-server-uuid>\ncargo-ai ai mcp-client connect --name \"My MCP\" --url https://mcp.example.com/sse\ncargo-ai mcp --server <mcp-server-uuid> # serve a workspace MCP server over stdio\ncargo-ai ai memory list --scope agent --agent-uuid <uuid>\ncargo-ai ai memory update --mem0-id <id> --scope agent --agent-uuid <uuid> --content \"Updated memory\"\ncargo-ai ai memory remove --mem0-id <id> --scope agent --agent-uuid <uuid>\nAgents are AI resources with configured instructions, a language model, actions, and optional resources.
\nBefore creating an agent from scratch, check existing templates — they capture proven patterns for common use cases (lead research, classification, email drafting) and give you a ready-made system prompt, model, and temperature to start from:
\ncargo-ai ai template list # browse available patterns\ncargo-ai ai template get <slug> # inspect system prompt, model, and actions\n# List all agents\ncargo-ai ai agent list\n\n# Get a single agent (includes deployed release details)\ncargo-ai ai agent get <agent-uuid>\n\n# Create an agent\ncargo-ai ai agent create \\\n --name \"Lead Researcher\" \\\n --icon-color blue --icon-face \uD83E\uDD16 \\\n --description \"Researches leads and enriches data\"\n\n# Update an agent\ncargo-ai ai agent update --uuid <agent-uuid> \\\n --name \"Senior Lead Researcher\" \\\n --description \"Updated description\"\n\n# Move to a folder (find folder UUIDs via cargo-workspace-management)\ncargo-ai ai agent update --uuid <agent-uuid> --folder-uuid <folder-uuid>\n\n# Remove an agent\ncargo-ai ai agent remove <agent-uuid>\nAgent icon: --icon-color must be one of: grey, green, purple, yellow, blue, red. --icon-face is an emoji string.
Folders: Folder creation, listing, and management lives in cargo-workspace-management (cargo-ai workspaceManagement folder list/create/...). Use that skill to discover or create the <folder-uuid> you pass to --folder-uuid here.
Releases are versioned snapshots of an agent's configuration (system prompt, actions, resources, model, temperature). Agents execute against their deployed release.
\n# List releases for an agent\ncargo-ai ai release list --agent-uuid <uuid>\n\n# Get a specific release\ncargo-ai ai release get <release-uuid>\n\n# Get the current draft release (editable)\ncargo-ai ai release get-draft --agent-uuid <uuid>\n\n# Update the draft release\ncargo-ai ai release update-draft --agent-uuid <uuid> \\\n --system-prompt \"You are a lead research assistant...\" \\\n --language-model-slug gpt-4o \\\n --temperature 0.3 \\\n --max-steps 10\n\n# Deploy the draft release (makes it live)\ncargo-ai ai release deploy-draft --agent-uuid <uuid> \\\n --integration-slug openai \\\n --language-model-slug gpt-4o \\\n --actions '[]' \\\n --mcp-clients '[]' \\\n --resources '[]' \\\n --capabilities '[]' \\\n --suggested-actions '[]' \\\n --description \"Added research actions\"\nThe release API payload (both draft/update and draft/deploy) accepts two fields that release update-draft / release deploy-draft do not surface as flags (verified against the CLI source — there is no --output / --output-schema or --heartbeat):
| Field | \nShape | \nPurpose | \n
|---|---|---|
output | \n{\"type\":\"text\"} or {\"type\":\"jsonSchema\",\"jsonSchema\": <standard JSON Schema object>} | \nForce the agent to return structured output matching a JSON Schema. | \n
heartbeat | \n{\"intervalMinutes\": number, \"maxMessages\": number, \"prompt\": string \\| null} | \nPeriodically re-wake the chat (intervalMinutes) until it reaches maxMessages; prompt is the wake message (null = generic \"continue\"). | \n
The generic --options flag does not carry these — the API's options only holds {connectorUuidsByIntegrationSlug, modelUuidsByIntegrationSlug}. Until the flags ship, set these with a direct API call against the same endpoints the CLI uses:
# Structured (JSON Schema) output on the draft release\ncurl -sS -X PUT \"$CARGO_API_BASE/v1/ai/releases/draft/update\" \\\n -H \"Authorization: Bearer $CARGO_TOKEN\" -H \"Content-Type: application/json\" \\\n -d '{\"agentUuid\":\"<uuid>\",\"output\":{\"type\":\"jsonSchema\",\"jsonSchema\":{\"type\":\"object\",\"properties\":{\"score\":{\"type\":\"number\"}},\"required\":[\"score\"]}}}'\n# Deploy carries the same fields — POST .../v1/ai/releases/draft/deploy\nSend these payloads alongside the other fields you're updating (the endpoint replaces the draft config). File a workspaceManagement report (see ../cargo-workspace-management/SKILL.md) to request first-class --output / --heartbeat flags — this is the documented feedback channel for CLI/UI parity gaps.
Agent configuration workflow:
\ncargo-ai ai template list — find a template close to your use case, then cargo-ai ai template get <slug> to see its system prompt, model, and temperaturecargo-ai ai agent create --name \"...\" --icon-color blue --icon-face \uD83E\uDD16cargo-ai ai release get-draft --agent-uuid <uuid>cargo-ai ai release update-draft --agent-uuid <uuid> ...cargo-ai ai release deploy-draft --agent-uuid <uuid> ...Templates are pre-built agent configurations that capture proven patterns for common use cases. Always check templates before designing an agent from scratch — they give you a ready-made system prompt, recommended language model, temperature, and tool configuration that you can adopt as-is or adapt.
\n# List available agent templates\ncargo-ai ai template list\n\n# Get a template by slug — inspect its system prompt, model, and settings\ncargo-ai ai template get <slug>\nTemplates include a system prompt, actions, resources, and recommended model settings. Use them as a starting point and customize via release update-draft. See references/examples/templates.md for the full guide including an end-to-end example of creating an agent from a template.
| Use case | \nRecommended model | \nTemperature | \n
|---|---|---|
| Classification, extraction, scoring | \ngpt-4o-mini or claude-3-5-haiku | \n0.0 – 0.2 | \n
| Research, summarization, analysis | \ngpt-4o or claude-3-5-sonnet | \n0.2 – 0.5 | \n
| Copywriting, personalization | \ngpt-4o or claude-3-5-sonnet | \n0.5 – 0.8 | \n
| Brainstorming, creative ideation | \ngpt-4o or claude-opus | \n0.7 – 1.0 | \n
Low temperature (0.0–0.2) = deterministic, consistent outputs. High temperature (0.7+) = creative, varied outputs. For production workflows processing thousands of records, prefer low temperature.
Knowledge that grounds agent responses (retrieval-augmented generation, RAG) comes from the content domain — see cargo-content:
native (workspace-managed) or connector-backed (synced from an external source via an unstructured-data extractor).\n\nFiles and libraries moved out of
\naiinto the top-levelcontentdomain in CLI ≥ 1.0.19 (cargo-ai content file …/cargo-ai content library …). The oldai file …commands are gone. Everything content-related now lives incargo-content.
A file or library is inert until attached to an agent via the draft release's resources array and deployed. Upload files / build libraries in cargo-content, then wire them in here with release update-draft --resources … followed by release deploy-draft. See ../cargo-content/references/examples/files.md for the full upload → attach → deploy sequence.
MCP (Model Context Protocol) runs both ways in Cargo, and the two surfaces are unrelated:
\n| \n | Publish — ai mcp-server | \nConsume — ai mcp-client | \n
|---|---|---|
| What it is | \nA server your workspace exposes: the tools, agents, and data you choose to make callable | \nA connection to someone else's MCP server | \n
| Who calls it | \nAny MCP client — Claude Code, Claude Desktop, Cursor, ChatGPT | \nYour Cargo agents, during a chat or a workflow run | \n
| Wired via | \ncargo-ai mcp (stdio bridge, below) | \nrelease update-draft --mcp-clients … | \n
There is no first-party \"Cargo MCP server\" to install. A workspace builds its own and decides what goes in it.
\ncargo-ai ai mcp-server list\ncargo-ai ai mcp-server create --name \"CRM tools\" \\\n --actions '[{\"slug\":\"<tool-uuid>\",\"kind\":\"tool\",\"name\":null,\"description\":null,\"isBulkAllowed\":false,\"config\":{}}]' \\\n --resources '[{\"kind\":\"model\",\"slug\":\"<slug>\",\"name\":\"Accounts\",\"description\":null,\"integrationSlug\":\"hubspot\",\"modelUuid\":null,\"filter\":null,\"selectedColumnSlugs\":null,\"limit\":null,\"prompt\":null,\"isReadOnly\":true}]'\ncargo-ai ai mcp-server update --uuid <mcp-server-uuid> --name \"Updated name\"\ncargo-ai ai mcp-server remove <mcp-server-uuid>\nkind: \"tool\" or kind: \"agent\" — an agent can be exposed as a callable MCP tool, not just a tool. waitUntilFinished controls whether the call blocks on the run.kind: \"model\" (a filtered, column-selected view of a model — keep isReadOnly: true unless the client is meant to write) or kind: \"file\" (workspace files by UUID, see ../cargo-content/SKILL.md).update replaces --actions / --resources wholesale rather than merging — read the current server with mcp-server list and pass the full array back.cargo-ai mcpThe published server reaches any stdio MCP client through the CLI, using the credentials already on the machine. No token is copied into client config.
\ncargo-ai ai mcp-server list # find the server UUID\nclaude mcp add cargo -- cargo-ai mcp --server <uuid> # Claude Code / Claude Desktop\n# Cursor, Windsurf, and other stdio clients: same command as the server entry\nWith no --server, the bridge uses CARGO_MCP_SERVER_UUID, or the workspace's only MCP server when there is exactly one. stdout carries the MCP protocol and all logs go to stderr, so never print anything to stdout around it.
When to reach for this instead of the skills: the skills give an agent the whole CLI; a published MCP server gives it a curated, safe subset with no shell. Use the bridge for the in-conversation lookups a workspace has already blessed, and the CLI for batches, workflows, schema changes, and anything with a cost gate. Full routing rule: ../cargo/SKILL.md → \"These skills vs a workspace MCP server\".
cargo-ai ai mcp-client connect --name \"My MCP\" --url https://mcp.example.com/sse\ncargo-ai ai mcp-client connect --name \"My MCP\" --url https://mcp.example.com/sse \\\n --disabled-tool-slugs \"dangerous_tool,other_tool\"\n--authentication takes {\"issuedAt\": \"...\", \"accessToken\": \"...\"} or \"null\". Connected clients are attached to an agent through its release: release update-draft --mcp-clients …, then release deploy-draft.
Memories are pieces of information an agent stores from conversations for future reference. They can be scoped to a workspace, user, or specific agent.
\n# List agent memories\ncargo-ai ai memory list --scope agent --agent-uuid <uuid>\n\n# List workspace-wide memories\ncargo-ai ai memory list --scope workspace\n\n# List user-scoped memories\ncargo-ai ai memory list --scope user\n\n# Update a memory\ncargo-ai ai memory update \\\n --mem0-id <id> \\\n --scope agent --agent-uuid <uuid> \\\n --content \"Updated memory content\"\n\n# Remove a memory\ncargo-ai ai memory remove \\\n --mem0-id <id> \\\n --scope agent --agent-uuid <uuid>\nEvery command supports --help:
cargo-ai ai agent create --help\ncargo-ai ai release update-draft --help\ncargo-ai ai mcp-server create --help\ncargo-ai ai memory list --help\n