> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/rowboatlabs/rowboat/llms.txt
> Use this file to discover all available pages before exploring further.

# CLI Overview

> Introduction to the Rowboat CLI tool for managing AI agents and workflows

Rowboat CLI (`rowboatx`) is a command-line tool for building, running, and managing AI agents and workflows. It provides a local-first approach to AI automation with support for multiple LLM providers and Model Context Protocol (MCP) servers.

## Key Features

* **Agent Runtime**: Run AI agents locally with interactive or non-interactive modes
* **Workflow Management**: Import, export, and share workflow configurations
* **MCP Integration**: Connect to Model Context Protocol servers for extended capabilities
* **Multi-Provider Support**: Works with OpenAI, Anthropic, Google, Ollama, and more
* **Interactive UI**: Optional terminal-based dashboard for managing runs
* **Local Configuration**: All data stored in `~/.rowboat` directory

## Quick Start

**1. Install the CLI**

Install rowboatx globally via npm:

```bash theme={null}
npm install -g @rowboatlabs/rowboatx
```

**2. Configure your model**

Set up your preferred LLM provider:

```bash theme={null}
rowboatx model-config
```

This interactive wizard will guide you through selecting a provider (OpenAI, Anthropic, Google, etc.) and configuring API keys.

**3. Run your first agent**

Start the default copilot agent:

```bash theme={null}
rowboatx
```

Or specify a custom agent:

```bash theme={null}
rowboatx --agent my-agent
```

## Architecture

### Configuration Directory

All Rowboat CLI data is stored in `~/.rowboat/`:

```
~/.rowboat/
├── agents/          # Agent definitions (JSON)
├── config/
│   ├── models.json  # LLM provider configuration
│   └── mcp.json     # MCP server definitions
└── runs/            # Run logs (JSONL)
```

### Core Components

> **Agents**
>
> JSON-based agent definitions with instructions, tools, and workflows

> **MCP Servers**
>
> External tool providers following the Model Context Protocol

> **Run Engine**
>
> Execute agents with state management and event streaming

> **API Server**
>
> Optional HTTP server for programmatic access

## Use Cases

* **Development Automation**: Create agents that help with coding tasks
* **Data Processing**: Build workflows for ETL and data transformation
* **Content Generation**: Automate writing and content creation tasks
* **Integration Testing**: Run agents in CI/CD pipelines with `--no-interactive` mode
* **Personal Assistant**: Local AI assistant with access to your tools and data

## Next Steps

> **[Installation](/platform/cli/installation)**
>
> Detailed installation and setup instructions

> **[Commands](/platform/cli/commands)**
>
> Complete command reference


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