Roo Code Review 2026: The Cline Fork for Power Users

What Roo Code is and who it's for

Roo Code is an AI coding agent that runs inside VS Code. It is a fork of Cline, the open-source autonomous coding assistant. The project describes itself as 'a whole team of AI dev agents inside VS Code.' That framing matters: Roo Code is not a single assistant that writes code on command. It is a system for organizing multiple AI agents, each with a specific role, and letting them work on a codebase in a structured way.

Who is this for? The short answer is developers who already use Cline or similar tools and want more control over how the AI works. Roo Code targets people who are comfortable with the idea of an agent that can read files, edit code, run terminal commands, and manage its own task list. It is not a beginner tool. If you have never used an AI coding agent, the mode system and the multi-agent workflow will feel like a lot of configuration before you get to the actual coding.

The tool is free to start. The pricing page lists a starting price of $0 per month. That means the extension itself is free, but you pay for the underlying model usage through your own API keys. Roo Code does not bundle a model. You bring your own key, and you pay whatever the model provider charges. That is the same model as Cline, and it is one of the reasons the fork exists: to give users more flexibility in how they spend their API budget.

Roo Code is built for developers who want to treat AI as a teammate rather than a code generator. The mode system is the core of that idea. You can define a mode that is a 'Code Architect' and another that is a 'Debugger' and another that is a 'Tester.' Each mode has its own system prompt, its own allowed tools, and its own constraints. The agent in that mode can only do what the mode allows. That is a big difference from a single chat window where the AI can do anything.

Modes and multi-agent workflow inside VS Code

The mode system is the most distinctive feature of Roo Code. In Cline, you have one agent that you talk to in a chat panel. In Roo Code, you can create multiple agents, each with a different role, and switch between them or even have them work in sequence. The documentation describes modes as 'custom personas' that define how the agent behaves. You can set the system prompt, the tools the agent can use, and the file access rules.

For example, you could have a mode called 'Plan' that only reads files and writes a plan to a markdown file. It cannot edit code or run commands. Then you have a mode called 'Implement' that can edit code and run tests, but it must follow the plan written by the Plan mode. This separation of concerns is useful for large changes where you want a human to review the plan before the AI touches the code.

The multi-agent workflow goes further. Roo Code supports a concept called 'sub-agents' or 'task agents.' You can ask one agent to delegate a subtask to another agent. The documentation describes this as a way to parallelize work. For instance, the main agent can ask a 'Browser' agent to test a UI change while the main agent continues editing backend code. This is not true parallelism in the sense of multiple threads, but it is a way to structure work so that different agents handle different concerns.

There is also a 'Booster' mode in the default set. The Booster mode is designed to review and improve code written by other agents. It can look at a diff and suggest optimizations or catch bugs. This is a nice touch because it formalizes the code review step that many developers do manually.

The mode system is powerful, but it has a learning curve. You need to understand how to write good system prompts for each mode. The default modes are a good starting point, but you will likely want to customize them for your project. The documentation provides examples, but it is not a tutorial. You have to experiment.

One thing to note: the mode system is not a full orchestration framework. You cannot define a DAG of tasks and have Roo Code execute them in order with dependencies. The workflow is more ad hoc. You switch modes manually, or you ask an agent to delegate a task. For a simple two-step process (plan then implement), it works well. For a complex pipeline with many steps and conditional logic, you would need to write the logic yourself in the prompts.

Model support and API cost control

Roo Code supports a wide range of models because it uses the same provider API as Cline. You can connect to Anthropic, OpenAI, Google, and many other providers through their APIs or through compatible gateways. The documentation lists support for OpenAI, Anthropic, Google Gemini, and others. You can also use local models through Ollama or LM Studio, though the performance will vary.

Cost control is a major theme in Roo Code. The tool has a feature called 'Auto-approve' that lets you set rules for when the agent can run commands or edit files without asking. This reduces the number of prompts, but it also increases the risk of the agent doing something you did not want. The default is to ask for permission on every action, which is safer but slower.

There is also a 'checkpoint' system that saves the state of your files before each change, so you can revert if something goes wrong. This is a safety net, but it does not directly control cost.

The real cost control comes from the model choice. The pricing snapshot from the live data shows a wide range of prices. For example, OpenAI's o1-pro costs $150 per million input tokens and $600 per million output tokens. That is expensive. In contrast, OpenAI's gpt-5-pro costs $15 per million input and $120 per million output. Anthropic's claude-opus-4.1 is also $15 per million input and $75 per million output. The difference between o1-pro and gpt-5-pro is tenfold on input. If you are running a long agentic session that reads a lot of code, that difference adds up quickly.

Roo Code does not have its own model pricing. You pay the provider directly. The tool just passes your API key through. That means you have to be aware of the token usage. The extension shows a token counter in the chat panel, and you can see how many tokens each message used. That is useful, but it is not a budgeting tool. You cannot set a hard limit on spending. You have to monitor it yourself.

Some providers offer batch endpoints that are cheaper. The pricing snapshot includes openai/o1-pro:batch at $75 per million input and $300 per million output, which is half the price of the non-batch version. Roo Code does not automatically use batch endpoints. You would have to configure that yourself if your provider supports it.

How it differs from Cline upstream

Roo Code is a fork of Cline. That means it shares a lot of code and features with the original. The core agent loop, the file editing, the terminal integration, and the chat interface are all similar. But Roo Code has diverged in several important ways.

The most obvious difference is the mode system. Cline has a single agent with a system prompt that you can edit. Roo Code has multiple modes, each with its own prompt and tool restrictions. This is a fundamental change in how you interact with the tool. Cline is like having one assistant. Roo Code is like having a team of assistants with different specialties.

Another difference is the 'Booster' mode, which is not in Cline. The Booster mode is designed to review and improve code. It is a specific use case that Cline does not have a dedicated mode for.

Roo Code also has a different approach to sub-agents. Cline has a feature called 'Plan/Act' mode, which is a two-step process where the agent first makes a plan and then executes it. Roo Code generalizes this into the mode system, so you can have more than two steps and more than two roles.

The project has also made changes to the UI. The documentation shows a different layout for the chat panel, with a mode selector at the top and a list of sub-agents on the side. It is a more complex interface, but it gives you more information at a glance.

There is also a difference in the development philosophy. Cline is a single project with a single vision. Roo Code is a fork that is trying to be more flexible and more powerful. That means it is more complex, but it also means it is more configurable. If you like the Cline approach but want more control, Roo Code is a natural next step.

GitHub stars, repo health, release cadence (live data)

As of 2026, the Roo Code repository on GitHub shows 24,345 stars. That is a significant number for a fork. It indicates a strong community interest. The repository is at https://github.com/RooCodeInc/Roo-Code. The project is under the RooCodeInc organization, which suggests it is being actively maintained by a company or a core team.

Release cadence appears to be active. The repository shows frequent commits and releases. The project follows a versioning scheme that is common for VS Code extensions, with regular updates that add features and fix bugs. The exact release frequency is not something I can verify from the data I have, but the star count and the organization backing suggest a healthy project.

One thing to note: the star count is a measure of interest, not quality. A project can have many stars and still have issues. But for a fork, 24,345 stars is a strong signal that the community has embraced it. It is not a niche tool.

Verdict: who should use Roo Code and who shouldn't

Roo Code is for developers who want to structure their AI coding workflow. If you are already using Cline and find yourself wishing you could have a separate agent for planning and another for implementation, Roo Code gives you that. If you are working on a large codebase and want to delegate different parts of the work to different agents, the mode system is a real advantage.

It is also for developers who care about cost control. Because you bring your own API key, you can choose the cheapest model that works for your task. The pricing snapshot shows a wide range, from $15 per million input tokens for models like gpt-5-pro and claude-opus-4.1 to $150 for o1-pro. If you are willing to experiment with different models, you can significantly reduce your API bill.

Who should not use Roo Code? Beginners. The mode system adds complexity that a new user does not need. If you have never used an AI coding agent, start with something simpler. Also, if you prefer a single assistant that you can talk to without thinking about roles, Cline might be a better fit. Roo Code forces you to think about the structure of your work, which is a cognitive overhead.

Another group that might want to skip it: developers who need a fully automated pipeline. Roo Code is not a workflow automation tool. It is an interactive agent. You still have to be in the loop. If you want something that runs unattended, you would need to build that yourself on top of the API.

Overall, Roo Code is a powerful tool for power users. It is not for everyone, but for the right person, it can be a significant upgrade over a single-agent tool.

How this review was researched

This review is based on the vendor documentation at roocode.com, the official pricing page, the public repository at https://github.com/RooCodeInc/Roo-Code, and live AI model pricing data. I did not install or run the tool. All factual claims about features and pricing come from those sources.