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cargo-ai

Build and configure AI agents inside Cargo — create an agent, choose its model and temperature, write its prompt, attach knowledge for retrieval (RAG), connect MCP tool servers, manage memories, and deploy releases. Triggers: "create an agent", "make an agent that", "give the agent our docs", "attach this knowledge base", "attach this library to the agent", "add resources to the agent release", "connect an MCP server", "expose our tools as an MCP server", "use Cargo from Claude Desktop or ChatGPT", "change the agent model", "what does the agent remember", "deploy the agent", "the agent is answering wrong". Skip when: uploading the knowledge files themselves — use cargo-content; sending the agent a message or running it over records — use cargo-orchestration.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.4k

Updated

Oct 10, 2026

Version

2.4.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.4K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.4K downloadsadoption · observed Oct 10, 2026
Latest release: 2.4.0release · observed Sep 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s178dcd9wkfn0a2fqrygmt3jzn87j9e1:cargo-ai

Install using `clawhub skill install s178dcd9wkfn0a2fqrygmt3jzn87j9e1:cargo-ai` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/cargo-ai/cargo-ai before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-cargo-ai-cargo-ai/snapshot"

Documentation

CLAWHUB

148,180 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: cargo-ai
description: "Build and configure AI agents inside Cargo — create an agent, choose its model and temperature, write its prompt, attach knowledge for retrieval (RAG), connect MCP tool servers, manage memories, and deploy releases. Triggers: \"create an agent\", \"make an agent that\", \"give the agent our docs\", \"attach this knowledge base\", \"attach this library to the agent\", \"add resources to the agent release\", \"connect an MCP server\", \"expose our tools as an MCP server\", \"use Cargo from Claude Desktop or ChatGPT\", \"change the agent model\", \"what does the agent remember\", \"deploy the agent\", \"the agent is answering wrong\". Skip when: uploading the knowledge files themselves — use cargo-content; sending the agent a message or running it over records — use cargo-orchestration."
version: "2.4.0"
compatibility: Requires @cargo-ai/cli (npm). Sign in or create an account with `cargo-ai login --email` (emailed code, no browser), `--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 — 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`](../cargo-content/SKILL.md). 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`](../cargo-workspace-management/SKILL.md).

> 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.

## Bootstrap

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

```bash
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

_meta.json

{
  "ownerId": "kn7by8t6yt9yghbxtxz6hv0bts87k6bq",
  "slug": "cargo-ai",
  "version": "2.4.0",
  "publishedAt": 1789543549483
}

references/examples/agents.md

# Agent examples

## List all agents

```bash
cargo-ai ai agent list
```

## Find an agent by name

```bash
cargo-ai ai agent list
# → Scan the "name" fields in the response to find the target agent UUID
```

## Create an agent

```bash
cargo-ai ai agent create \
  --name "Lead Researcher" \
  --icon-color purple --icon-face 🔍 \
  --description "Researches and qualifies leads using web data"
```

## Create an agent in a folder

Folders are managed by the [`cargo-workspace-management`](../../../cargo-workspace-management/SKILL.md) skill — see its `references/examples/folders.md` for create/list/update.

```bash
cargo-ai workspaceManagement folder list
# → Find the folder UUID (kind: "agent")

cargo-ai ai agent create \
  --name "Lead Researcher" \
  --icon-color purple --icon-face 🔍 \
  --folder-uuid <folder-uuid>
```

## Configure and deploy an agent (full workflow)

```bash
# 1. Create the agent
cargo-ai ai agent create \
  --name "Company Scorer" \
  --icon-color green --icon-face 📊
# → agent.uuid

# 2. Get the draft release
cargo-ai ai release get-draft --agent-uuid <agent-uuid>
# → release.uuid

# 3. Configure the draft: set model, temperature, prompt
cargo-ai ai release update-draft --agent-uuid <agent-uuid> \
  --language-model-slug gpt-4o-mini \
  --temperature 0.0 \
  --max-steps 5 \
  --system-prompt "You are a company scoring assistant. Given a company record, score it from 1-10 based on fit criteria."

# 4. Deploy the draft
cargo-ai ai release deploy-draft --agent-uuid <agent-uuid> \
  --integration-slug openai \
  --language-model-slug gpt-4o-mini \
  --actions '[]' \
  --mcp-clients '[]' \
  --resources '[]' \
  --capabilities '[]' \
  --suggested-actions '[]' \
  --description "Initial deployment with scoring prompt"
```

## Update an agent's name and description

```bash
cargo-ai ai agent update --uuid <agent-uuid> \
  --name "Senior Lead Researcher" \
  --description "Advanced lead research with enrichment capabilities"
```

## Move an agent to a different folder

```bash
cargo-ai ai agent update --uuid <agent-uuid> --folder-uuid <folder-uuid>
```

## Remove an agent

```bash
cargo-ai ai agent remove <agent-uuid>
```

## Create an agent from a template

```bash
# 1. Browse templates — the response is complete, not a summary
cargo-ai ai template list

# 2. Pick one out of that same response (there is no `template get`)
cargo-ai ai template list | jq '.templates[] | select(.slug == "<template-slug>")'
# → Copy the systemPrompt, actions, resources, model settings

# 3. Create the agent
cargo-ai ai agent create \
  --name "My Custom Agent" \
  --icon-color blue --icon-face 🤖

# 4. Apply template settings to the draft
cargo-ai ai release update-draft --agent-uuid <agent-uuid> \
  --system-prompt "<from template>" \
  --language-model-slug <from template> \
  --temperature <from template>

# 5. Deploy
cargo-ai ai release deploy-draft --agent-uuid <agent-uuid> \
  --integration-slug <from template> \
  --language-model-slug <from tem

references/examples/mcp-servers.md

# MCP server examples

## List all MCP servers

```bash
cargo-ai ai mcp-server list
```

## Create an MCP server

```bash
cargo-ai ai mcp-server create --name "Internal Tools"
```

## Connect an MCP server to an agent

MCP servers are connected to agents as MCP clients on the release:

```bash
# 1. Create or find the MCP server
cargo-ai ai mcp-server list
# → mcp-server-uuid

# 2. Add as an MCP client on the agent's draft release
cargo-ai ai release update-draft --agent-uuid <agent-uuid> \
  --mcp-clients '[{"kind":"custom","name":"Internal Tools","url":"https://mcp.example.com","authentication":null,"disabledToolSlugs":[]}]'

# 3. Deploy
cargo-ai ai release deploy-draft --agent-uuid <agent-uuid> \
  --language-model-slug gpt-4o \
  --integration-slug openai
```

**MCP client kinds:**

- `custom` — URL-based MCP server. Requires `name`, `url`, and optionally `authentication`.
- `connector` — Integration-backed MCP client. Requires `name`, `connectorUuid`, `integrationSlug`.

## Connect a connector-backed MCP client

```bash
# 1. Find the connector
cargo-ai connection connector list
# → connector-uuid, integrationSlug

# 2. Add as an MCP client
cargo-ai ai release update-draft --agent-uuid <agent-uuid> \
  --mcp-clients '[{"kind":"connector","name":"HubSpot Tools","connectorUuid":"<connector-uuid>","integrationSlug":"hubspot","disabledToolSlugs":[]}]'

# 3. Deploy
cargo-ai ai release deploy-draft --agent-uuid <agent-uuid> \
  --language-model-slug gpt-4o \
  --integration-slug openai
```

## Disable specific actions from an MCP server

Use `disabledToolSlugs` to prevent the agent from using specific MCP actions:

```bash
cargo-ai ai release update-draft --agent-uuid <agent-uuid> \
  --mcp-clients '[{"kind":"custom","name":"Internal Tools","url":"https://mcp.example.com","authentication":null,"disabledToolSlugs":["dangerous_tool","admin_tool"]}]'
```

## Update an MCP server name

```bash
cargo-ai ai mcp-server update --uuid <mcp-server-uuid> --name "Production Tools"
```

## Remove an MCP server

```bash
cargo-ai ai mcp-server remove <mcp-server-uuid>
```

references/examples/templates.md

# AI template examples

## What is an AI template?

An **AI template** is a pre-built agent configuration — a ready-to-use agent blueprint with instructions, model settings, and action configuration already defined. Templates capture common agent patterns (lead research, company classification, email drafting) so you don't have to configure an agent from scratch.

**Always check templates before creating an agent.** Even if no template is a perfect match, they provide:
- A proven system prompt structure for the use case
- A recommended language model and temperature setting
- A list of actions and resources to consider attaching

AI templates are read-only. You discover them by listing, then use their configuration as a starting point when creating or updating an agent.

## List all AI templates

```bash
cargo-ai ai template list
```

Response:

```json
{
  "templates": [
    {
      "slug": "lead-researcher",
      "name": "Lead Researcher",
      "description": "Researches a prospect's company, role, and contact details",
      "languageModelSlug": "gpt-4o",
      "temperature": 0.3
    },
    {
      "slug": "company-classifier",
      "name": "Company Classifier",
      "description": "Classifies a company by industry, size, and ICP fit",
      "languageModelSlug": "gpt-4.1-mini",
      "temperature": 0.1
    },
    {
      "slug": "email-drafter",
      "name": "Email Drafter",
      "description": "Drafts personalised outbound emails based on enrichment data",
      "languageModelSlug": "gpt-4o",
      "temperature": 0.7
    }
  ]
}
```

Key fields:

- **`slug`** — identifier for reference
- **`name`** — human-readable name
- **`description`** — what the agent does
- **`languageModelSlug`** — recommended model for this use case
- **`temperature`** — recommended temperature setting

## Use a template to create an agent

The standard pattern:

1. List templates to find the right one
2. Create a new agent using the template's recommended settings
3. Attach any files or MCP servers the agent needs
4. Start chatting or embed the agent in a workflow

```bash
# 1. Find the right template
cargo-ai ai template list
# → Find "lead-researcher"

# 2. Create an agent
cargo-ai ai agent create \
  --name "Lead Researcher" \
  --icon-color purple --icon-face 🔍
# → Extract agent.uuid

# 3. Configure the draft release with template settings
cargo-ai ai release update-draft --agent-uuid <agent-uuid> \
  --system-prompt "You are a research assistant. Given a company domain and a contact name, find their role, LinkedIn profile, and email address. Be concise and structured." \
  --language-model-slug gpt-4o \
  --temperature 0.3

# 4. Attach a knowledge file (optional)
cargo-ai content file upload --file ./icp-criteria.pdf
# → Extract file.uuid — attach to agent via release update-draft --resources

# 5. Test with a message
cargo-ai ai chat create \
  --trigger '{"type":"draft"}' \
  --agent-uuid <agent-uuid> \
  --name "Test session"
# → Extract chat.uuid

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