Roo Commander is an advanced framework for VS Code that orchestrates specialized AI agents to manage complex software projects. It targets developers seeking to improve project structure, context management, and task delegation by providing a multi-agent system with defined roles, structured communication, and persistent project context.

How It Works

Roo Commander operates as a multi-agent system within VS Code, leveraging the Roo Code extension. It assigns specialized AI agents (modes) to tasks based on expertise (e.g., React, API design, Git). Communication and task delegation are structured using a defined system, with project history and context maintained through standardized TOML+Markdown files in hidden directories like .ruru/tasks/ and .ruru/decisions/. This approach mitigates LLM context limitations and ensures traceability.

Quick Start & Requirements

• Install: Download the latest roo-commander-vX.Y.Z-Codename.zip from releases and extract it to your VS Code project's root directory.
• Prerequisites: Roo Code VS Code extension must be installed and configured. For optimal performance, a large context window LLM (e.g., Gemini 2.5 Pro via Vertex AI API) and MCP servers (e.g., Vertex AI MCP Server) are recommended.
• Setup: Reload VS Code after extraction.
• Links: Roo Commander Releases, Roo Code Discord, Vertex AI Provider Setup

Highlighted Details

• Orchestrates specialized AI agents (modes) for specific development tasks.
• Utilizes a Markdown Task Management (MDTM) system with TOML+Markdown files for structured task tracking.
• Maintains project context and history via dedicated agents and structured artifacts.
• Supports a wide range of specialist modes for frameworks, cloud platforms, databases, and more.
• Implements Architectural Decision Records (ADRs) for logging significant project decisions.

Maintenance & Community

• Active community via Discord.
• Project appears to be actively developed with recent releases.

Licensing & Compatibility

• MIT License. Permissive for commercial use and closed-source linking.

Limitations & Caveats

The framework is opinionated, prescribing specific structures and processes. Optimal performance is heavily dependent on configuring external LLM APIs with large context windows and potentially incurring costs.