Devin AI vs Roo Code: What to Use Now That Roo Code Has Shut Down

Running an autonomous software agent directly inside your local development environment sounds convenient until the agent executes a destructive shell command that wipes your local directory. The choice between a cloud sandboxed virtual machine and a local editor extension is not about interface preference, it is a critical security and infrastructure tradeoff.

In a Devin AI vs Roo Code comparison, the primary difference lies in where the code runs and how the system isolates its operations. Devin AI runs every session within its own container in the cloud. This environment acts as a remote virtual machine, completely separate from your physical computer. When the agent installs a library, runs a test suite, or executes a shell script, it does so within this isolated container. If the agent makes a mistake, the damage is restricted to that container. Your local files, configuration keys, and system directories remain untouched. This isolation allows Devin to operate autonomously for hours without constant human monitoring.

Roo Code, which began as a fork of the Cline extension, takes a local first approach. It runs as an extension inside Visual Studio Code on your local machine. Because of this local design, the agent reads and writes files directly in your active project folder. It runs terminal commands using your local shell and user permissions. While this local architecture provides fast file access and removes network delay, it also exposes your host system to risks. If a model generates a command that modifies system configurations or deletes files, the local terminal executes it immediately. Roo Code prompts you to approve terminal commands, but inspecting hundreds of lines of code during a multi-step task can be tedious.

To prevent local damage, developers often run Roo Code inside local Docker containers or connect it to remote SSH virtual machines. This setup mimics Devin's isolation but requires you to configure and manage the virtual environments yourself.

The two platforms also differ in how they connect to large language models and manage API costs. Understanding the distinction between a managed subscription and a customizable key configuration is essential when comparing Roo Code vs Devin.

Devin AI uses a managed model structure. Cognition handles model hosting, rate limits, and API endpoints, abstracting these details behind standard pricing tiers. Individual developers can access the platform through a Pro plan priced at $20/month, which includes a set weekly usage quota and access to frontier models. For teams, the plan starts with a base fee and additional seat charges. While this subscription model provides cost predictability, it limits your choice of models. You must use the models selected and hosted by Cognition, and you cannot connect to custom fine-tuned models or run local models on your own servers.

Roo Code and the upstream Devin AI vs Roo Cline comparison highlight a different approach. Roo Code uses a Bring Your Own Key configuration. The extension supports all major model providers, including Anthropic, OpenAI, and Google Gemini, as well as local open-source models running via Ollama. You provide your own API credentials and pay the model hosts directly for the tokens your agent consumes. If you are doing simple coding tasks, you can use cheaper models to save money. If you are planning complex system integrations, you can switch to a larger model.

For power users, this key configuration provides flexibility but requires active management. You must monitor your own API usage, configure rate limits, and select models. Roo Code helps you manage this complexity by supporting custom modes, which allow you to configure separate instructions for different roles like coding, auditing, or planning.

The way coding agents integrate with developer workflows is changing. Initially, most agents lived inside the code editor as extensions. However, running long-running agent tasks inside a local editor can disrupt active coding.

The development of Roo Code shows this tension. As an open-source VS Code extension, Roo Code grew to approximately 3,000,000 installs. Despite this popularity, running an agent locally has limitations. It consumes local system resources, occupies the developer's terminal, and lacks a shared cloud context for team collaboration. These limitations led to a major change. The Roo Code team announced they were sunsetting the extension in May 2026 to focus on Roomote, a cloud-first autonomous agent, stating: "We made the call not because Roo Code failed, but because the world it served would cease to exist."

While the archived VS Code extension remains available and community forks like Zoo Code and Kilo Code continue to receive updates, the shift toward Roomote highlights a broader industry trend. Instead of live coding help in an editor, developers are moving toward cloud-hosted agents that work asynchronously. Roomote runs in isolated cloud sandboxes, clones your repository, writes code, runs tests, and creates pull requests. You assign a task from tools like Slack and review the pull request when the agent finishes.

This asynchronous workflow is similar to Devin AI. Devin operates in its own web dashboard, showing you its plan, editor, terminal, and browser. You do not need to keep your editor open or monitor its work line-by-line. While Cognition provides terminal and editor tools to connect to Devin, the actual code execution runs in the cloud, allowing developers to focus on other tasks while the agent runs.

Whether you choose a local-first editor extension or a cloud-hosted agent, coordinating these tools across a team is difficult. When multiple agents and humans work in the same repository, they need a shared, persistent space to store inputs, outputs, and build reports. Storing these files on local hard drives limits collaboration, while traditional cloud storage platforms are not designed for active developer agents.

Traditional options fall short. Local folders are isolated on individual laptops. Object storage buckets like Amazon S3 require you to build search tools to find files. General storage solutions like Google Drive, Dropbox, or Box are built for human file sync and struggle with the API rate limits and file formats common in agentic workflows.

Fast.io offers a different approach. It is an intelligent workspace platform designed for teams of humans and agents. Instead of treating storage as a simple file repository, Fast.io indexes files automatically. Once Intelligence Mode is enabled on a workspace, all uploaded files are indexed for semantic search and are queryable through chat.

Developers can connect local-first tools or cloud platforms like Devin AI to Fast.io using the remote Model Context Protocol (MCP) server. You configure the agent's connection in your settings file, pointing it to the remote MCP endpoint at https://mcp.fast.io/mcp and following the agent integration guidelines.

Here is a configuration example for a local developer extension:

{
  "mcpServers": {
    "fastio": {
      "url": "https://mcp.fast.io/mcp"
    }
  }
}

If the agent configuration requires token authentication, you can point it to the authenticated endpoint:

{
  "mcpServers": {
    "fastio": {
      "url": "https://mcp.fast.io/mcp/key"
    }
  }
}

Once connected, the agent can read and write files directly in the workspace using action-based tools. Humans use the web interface to view notes, monitor files, and track progress. Fast.io includes several features that support human-agent collaboration:

• Per-file version history. Every document keeps a full version history, allowing you to trace edits made by different agents and restore prior versions if needed.
• Coordination Rooms. These are shared spaces where agents and humans post messages, show presence and hand off files. An agent waiting on a room holds a long poll open and wakes when a peer posts.
• Workspace activity feed. There is no file-event webhook to register. Agents watch the workspace with the activity long poll, GET /current/activity/poll/{entity_id}?wait=95&lastactivity={timestamp}, which holds the connection open and returns as soon as something changes.
• Cloud Import. Files come in from Google Drive, OneDrive, Dropbox, or Box over OAuth, cloud to cloud, so nothing routes through a developer's local bandwidth. A source can be read only or set up with two-way write-back.
• Ownership transfer. An agent can set up a workspace, build the files, and transfer ownership of the organization to a human client while retaining admin access.

Every organization starts with a 14-day free trial, which requires a credit card. Paid plans include Starter, Business, and Growth, and users can review options on the pricing page, offering teams a secure, shared substrate for agentic development.

Coordinate cloud and local agent outputs in one workspace

Get a shared, versioned workspace for your development files, with indexing for semantic search and multi-agent coordination, whether the work comes from Devin, Roomote, Cline or your own scripts. Every organization starts with a 14-day free trial, which requires a credit card.

Start 14-Day Trial

Roo Code itself is no longer an option. The extension was shut down on 15 May 2026 and the repository archived, so the real decision is between an autonomous cloud agent like Devin AI and a live local-first extension: Roomote from the same team, Cline, which Roo Code was originally forked from, or the community forks Kilo Code and Zoo Code. Which side you land on depends on your team's working style, security policy, and budget.

Devin AI is best suited for engineering teams that want an autonomous, cloud-hosted teammate. It excels at handling background tasks that span multiple files and tools, such as running migrations, triaging issues, and refactoring large directories. The managed model approach and sandboxed VM environment reduce configuration overhead and protect your local system, making it an excellent choice for businesses that want a structured, hands-off platform.

The successors that carry Roo Code's design are ideal for developers who want to maintain close control over their workspace. If you prefer to write code alongside the AI, using it for fast edits, boilerplate generation, and debugging inside your local VS Code editor, the local-first approach is highly effective. The BYOK design gives you complete control over costs and model choices, allowing you to use local models or apply existing API contracts.

Many engineering teams find that a hybrid model works best. They use local extensions for active coding sessions and cloud agents for autonomous backlog tasks, linking both environments to a shared intelligent workspace like Fast.io. By keeping the agent's inputs and outputs inside a centralized workspace with version history and activity monitoring, you maintain visibility and control over your automated workflows.