cargo-ai

Cargo CLI — AI

Agent resource management: creating and configuring agents, attaching knowledge for retrieval-augmented generation (RAG), connecting MCP servers, and managing agent memories.

For using agents (sending messages, multi-turn chat, polling), use cargo-orchestration. For uploading knowledge files and building knowledge libraries (the content domain), use cargo-content. This skill covers how that knowledge attaches to an agent. For workspace administration — folders (used to organize agents and files), users, API tokens, roles, and submitting reports when the CLI fails — use cargo-workspace-management.

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/agents.md for agent CRUD and configuration examples. See references/examples/mcp-servers.md for MCP server creation and management examples.

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

cargo-ai ai agent list                     # all agents (uuid, name, description)
cargo-ai ai template list                  # all AI agent templates (slug, name)
cargo-ai ai mcp-server list                # all MCP servers (uuid, name)
cargo-ai ai memory list --scope agent --agent-uuid   # agent memories
# Knowledge files & libraries live in the content domain — see cargo-content:
#   cargo-ai content file list   /   cargo-ai content library list

Retrieve in the UI: agents live at app.getcargo.io/workspaces//agents/. Get `` from cargo-ai whoami under workspace.uuid.

Quick reference

cargo-ai ai agent list
cargo-ai ai agent get 
cargo-ai ai agent create --name  --icon-color blue --icon-face 🤖
cargo-ai ai agent update --uuid  --name 
cargo-ai ai agent remove 
cargo-ai ai release list --agent-uuid 
cargo-ai ai release get 
cargo-ai ai release get-draft --agent-uuid 
cargo-ai ai release update-draft --agent-uuid  --language-model-slug gpt-4o
cargo-ai ai release deploy-draft --agent-uuid 
cargo-ai ai template list
cargo-ai ai template get 
cargo-ai ai mcp-server list
cargo-ai ai mcp-server create --name "Internal Tools"
cargo-ai ai mcp-server update --uuid  --name "Updated Name"
cargo-ai ai mcp-server remove 
cargo-ai ai memory list --scope agent --agent-uuid 
cargo-ai ai memory update --mem0-id  --scope agent --agent-uuid  --content "Updated memory"
cargo-ai ai memory remove --mem0-id  --scope agent --agent-uuid 

Agents

Agents are AI resources with configured instructions, a language model, actions, and optional resources.

Before 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:

cargo-ai ai template list          # browse available patterns
cargo-ai ai template get     # inspect system prompt, model, and actions

# List all agents
cargo-ai ai agent list

# Get a single agent (includes deployed release details)
cargo-ai ai agent get 

# Create an agent
cargo-ai ai agent create \
  --name "Lead Researcher" \
  --icon-color blue --icon-face 🤖 \
  --description "Researches leads and enriches data"

# Update an agent
cargo-ai ai agent update --uuid  \
  --name "Senior Lead Researcher" \
  --description "Updated description"

# Move to a folder (find folder UUIDs via cargo-workspace-management)
cargo-ai ai agent update --uuid  --folder-uuid 

# Remove an agent
cargo-ai ai agent remove 

Agent 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 `` you pass to --folder-uuid here.

Releases

Releases are versioned snapshots of an agent's configuration (system prompt, actions, resources, model, temperature). Agents execute against their deployed release.

# List releases for an agent
cargo-ai ai release list --agent-uuid 

# Get a specific release
cargo-ai ai release get 

# Get the current draft release (editable)
cargo-ai ai release get-draft --agent-uuid 

# Update the draft release
cargo-ai ai release update-draft --agent-uuid  \
  --system-prompt "You are a lead research assistant..." \
  --language-model-slug gpt-4o \
  --temperature 0.3 \
  --max-steps 10

# Deploy the draft release (makes it live)
cargo-ai ai release deploy-draft --agent-uuid  \
  --integration-slug openai \
  --language-model-slug gpt-4o \
  --actions '[]' \
  --mcp-clients '[]' \
  --resources '[]' \
  --capabilities '[]' \
  --suggested-actions '[]' \
  --description "Added research actions"

Structured output & heartbeat — not yet exposed as CLI flags

The 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):

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
curl -sS -X PUT "$CARGO_API_BASE/v1/ai/releases/draft/update" \
  -H "Authorization: Bearer $CARGO_TOKEN" -H "Content-Type: application/json" \
  -d '{"agentUuid":"","output":{"type":"jsonSchema","jsonSchema":{"type":"object","properties":{"score":{"type":"number"}},"required":["score"]}}}'
# Deploy carries the same fields — POST .../v1/ai/releases/draft/deploy

Send 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:

1. Browse templates for inspiration: cargo-ai ai template list — find a template close to your use case, then cargo-ai ai template get to see its system prompt, model, and temperature
2. Create the agent: cargo-ai ai agent create --name "..." --icon-color blue --icon-face 🤖
3. Get the draft release: cargo-ai ai release get-draft --agent-uuid
4. Update the draft with configured actions, resources, prompt, model: cargo-ai ai release update-draft --agent-uuid ...
5. Deploy: cargo-ai ai release deploy-draft --agent-uuid ...

Templates

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.

# List available agent templates
cargo-ai ai template list

# Get a template by slug — inspect its system prompt, model, and settings
cargo-ai ai template get 

Templates 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.

Model and temperature guidance

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 for RAG (files & libraries)

Knowledge that grounds agent responses (retrieval-augmented generation, RAG) comes from the content domain — see cargo-content:

• Files — uploaded binaries (PDFs, CSVs, text).
• Libraries — collections that group files, either native (workspace-managed) or connector-backed (synced from an external source via an unstructured-data extractor).

Files and libraries moved out of ai into the top-level content domain in CLI ≥ 1.0.19 (cargo-ai content file … / cargo-ai content library …). The old ai file … commands are gone. Everything content-related now lives in cargo-content.

Attaching knowledge to an agent

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 servers

MCP (Model Context Protocol) servers expose additional actions to agents. Once connected, agents can call MCP actions automatically during conversations or workflow runs.

# List all MCP servers
cargo-ai ai mcp-server list

# Create an MCP server
cargo-ai ai mcp-server create --name "Internal Tools"

# Update an MCP server
cargo-ai ai mcp-server update --uuid  --name "Updated Tools"

# Remove an MCP server
cargo-ai ai mcp-server remove 

MCP clients (connections to MCP servers) are configured on agent releases. Use release update-draft to attach MCP clients to an agent.

Memories

Memories are pieces of information an agent stores from conversations for future reference. They can be scoped to a workspace, user, or specific agent.

# List agent memories
cargo-ai ai memory list --scope agent --agent-uuid 

# List workspace-wide memories
cargo-ai ai memory list --scope workspace

# List user-scoped memories
cargo-ai ai memory list --scope user

# Update a memory
cargo-ai ai memory update \
  --mem0-id  \
  --scope agent --agent-uuid  \
  --content "Updated memory content"

# Remove a memory
cargo-ai ai memory remove \
  --mem0-id  \
  --scope agent --agent-uuid 

Help

Every command supports --help:

cargo-ai ai agent create --help
cargo-ai ai release update-draft --help
cargo-ai ai mcp-server create --help
cargo-ai ai memory list --help