Orca Boom, AI Coding Goes Multi-Agent

● AI Worktree Boom

Complete Guide to Installing and Using Orca: An AI Coding IDE Guide for Running Codex, Claude Code, and Grok Simultaneously with Git Worktree

Key Takeaways at a Glance

The most important point about Orca is that it is not a new AI model, but a desktop IDE that lets you handle multiple AI coding agents on one screen at the same time.
In other words, it reduces the hassle of opening tools like Codex, Claude Code, and Grok separately, and it makes parallel development and result comparison easier by separating workspaces based on Git worktree.
In this article, we summarize the actual usage flow in a news-style format, covering installation, permission settings, repository addition, worktree creation, the first request, Diff review, testing, and commit.
As core points that are not often covered well elsewhere, we also separately highlight that “Orca is not a security tool but a task management tool,” that “the app is free but AI usage fees are separate,” and that “worktree and Agent Permissions are completely different concepts.”
If you are a frontend developer, you should definitely look at the built-in browser and Design Mode, and if you already use AI coding tools, the way Orca compares multiple results under the same criteria is quite practical.

Why Orca Is Suddenly Getting Attention

Orca is getting attention because it reduces one of the biggest problems created by the rise of AI coding agents: window management and task confusion.
When you use Codex, Claude Code, and Grok at the same time, terminals, branches, folders, and browsers can quickly become tangled, but Orca brings this complexity together into a single screen.
The key takeaway is that it is not an “app that writes code for you,” but closer to a “workspace for managing and comparing results created by multiple AI agents.”
That is why it is better suited for developers who care more about the operational efficiency of parallel work than about AI coding performance itself.

The Structure You Must Understand to Use Orca Properly

To understand Orca properly, you need to separate AI models, CLI agents, worktree, and IDE.

AI model: The actual reasoning engine, such as GPT, Claude, or Grok.
CLI agent: An execution tool that reads projects and performs commands, such as Codex, Claude Code, or Grok CLI.
Git worktree: A Git feature that reduces conflicts by splitting the same repository into separate folders for each task.
Orca: A desktop IDE that manages all of these in one screen.

The important point is that Orca does not directly provide AI models.
In other words, Orca’s performance ultimately depends on the connected agents and models, prompt quality, and repository state.
If you misunderstand this part, it is easy to assume that “Orca is not good,” but in reality, its strength lies in the way it manages work.

Why Git Worktree Matters in Orca

Git worktree is a method that reduces file conflicts by opening the same project separately for each task.
For example, even if you work on a login feature fix, a bug fix, and test reinforcement at the same time, each task is performed in a different folder, greatly reducing the chance that files will get mixed together.
Orca connects these worktrees by task, allowing each agent to work in an independent space.
Thanks to this structure, multi-agent collaboration becomes much more practical.

Pricing and Cost Structure, Where Many People Get Confused

The Orca app itself is free to use because it is based on the MIT license.
However, subscription fees or API costs for using agents such as Codex, Claude Code, and Grok are separate.
In other words, you should not think, “Since I installed Orca, AI usage fees must be included.”
If you run multiple agents in parallel, usage may be incurred separately for each service.
Therefore, actual costs should be checked based on the policies of the connected AI providers, not Orca itself.

What to Prepare Before Installation

The things you need to check before installing Orca are simple.

Check whether Git is installed on your PC.
Prepare one local repository for practice.
Make sure you have at least one CLI agent that is already logged in.

It is a good idea to check the Git version in the terminal first.
Then, you should also check whether the agent command you plan to use actually runs.
If you get stuck here, it is better to complete the installation and authentication of the relevant CLI before dealing with Orca.
If you skip this order, it becomes difficult to identify the cause inside Orca.

How to Install Orca on macOS

On macOS, the easiest way to install Orca is through Homebrew.
According to the official documentation, the installation command uses the Homebrew Cask method.

After installation is complete, you can launch Orca from the Applications folder or Spotlight.
If the Homebrew route does not work, you can download the dmg file directly from the official installation page or GitHub Releases.
The file name may differ between Apple Silicon and Intel Mac, so it is a good idea to check your CPU type first.

A macOS security confirmation window may appear on the first launch, and this is a normal process.
The important warning is not to bypass Gatekeeper using terminal commands.
Download it again through the official installation route, and if the message saying the file is damaged keeps appearing, first check the latest stable version and OS compatibility.

The First Settings You Should Change After Installation

After installing Orca, a checklist appears.
Among these items, the most important ones on the first day are the default agent and notification settings.

Select the default agent
Enable notifications
Add a repository
Create one worktree

There is no need to turn on every feature from the beginning.
In particular, CLI automation, external integrations, and multiple repositories are easier to understand if you look at them slowly after completing your first task.
If you turn on too many things from the start, the structure may become unnecessarily complicated.

It Is Best to Keep Agent Permissions on Manual

This is a truly important point.
Many people feel safe when worktrees are separated, but that is only half true.

A worktree reduces file conflicts, but it does not block the execution permissions of the agent itself.
Therefore, when working with an important repository for the first time, it is safer to keep Agent Permissions set to Manual.
This lets a human check once more what the agent is trying to do.

In other words, worktree is a task folder separation feature, while Manual permission is an execution control feature.
These two have completely different roles.
If you do not understand this difference, it is easy to fall into the misconception that “it is safe because it is separated.”

Start Your First Repository Addition with a Practice Project

At first, it is much better to add a personal practice repository rather than a company repository.
In Orca, click Add Repo and add a local Git repository.
After that, Orca creates a workspace based on branches and worktrees.

When you add a repository, an automatic setup script may be suggested, but this must be reviewed carefully for each repository.
Dependency installation commands can be useful, but commands that are difficult to undo, such as deployment or deletion, should not be included in automatic execution.
In other words, a setup script is a convenience feature, not something you should blindly trust and run.

Creating a Worktree Starts with One Plus Button

After adding a repository, create your first worktree using the plus button.
It is best to name the task briefly and clearly so you can recognize it later.
Usually, you can start from a base branch such as origin/main.

The important principle at this stage is “one worktree per task.”
The more you run different tasks simultaneously in the same repository, the more valuable worktree separation becomes.
On the other hand, if multiple agents modify the same files in the same worktree, conflicts are more likely to occur.

For the First Request, It Is Better to Have the Repository Read Without Modifying Files

At first, instead of immediately requesting modifications, it is better to confirm project understanding with a read-only request.
For example, you can say, “Explain what kind of project this repository is without modifying any files.”
This lets you first confirm whether Orca is connected to the correct repository and agent.

Check whether the response matches the contents of the README and major configuration files.
If no files were modified, the Diff should normally be empty.
If there are no issues at this first stage, the basic connection has succeeded.

Status Dots Make the Agent Flow Easier to Understand

Orca shows task status through status dots.

Green: The agent is working.
Yellow: Waiting for user input.
Gray: Idle.
No dot: It may be a regular shell or an unrecognized CLI.

This indicator is especially useful when running multiple agents.
As the number of tasks increases, it is much faster to skim through the Agents feed or notifications than to open each worktree one by one.
In other words, status dots are not just decoration, but an operational tool.

Diff Review Is the Core Point of Orca Workflow

The real reason to use Orca is not to “just leave everything to AI,” but to “compare and choose results.”
When an agent finishes a task, you must check what changed in the Diff.

The following checkpoints must be reviewed.

Whether only the requested files were changed
Whether API keys, tokens, or personal paths were included
Whether unnecessary large-scale formatting changes were made
Whether the required tests were actually run

This part is surprisingly often glossed over in other articles, but it is actually the most important.
AI-generated code may look correct, but if you merge it without verification, the repair cost can become much bigger later.
That is why Diff review in Orca is not just a simple check, but final quality control.

Testing and Commit Are Steps Where Humans Take Final Responsibility

It is best to run the tests or inspection commands listed in the README yourself.
If the tests pass, stage only the intended changes and commit them.
You can continue to push and open a PR if needed, but on the first day, reaching a local commit is enough.

The important point here is that Orca does not automatically perform a force-push.
In other words, the final decision to apply changes must be made by a human.
The more convenient AI coding becomes, the more important this final review step becomes.

Situations Where Orca Is Especially Useful in Real Development

Orca is not essential for every developer, but it clearly improves efficiency in the following situations.

When you want to assign the same bug to multiple agents in parallel
When you want to separate feature modification and test reinforcement
When you want to compare Diffs side by side and choose the result
When you want to handle frontend screen modifications and browser checks in one flow

Especially when using only one AI agent, a terminal may be enough, but once you start running two or more agents at the same time, Orca’s strengths become much clearer.
Ultimately, the core point is not “parallel execution” itself, but “managing work in a structure that allows comparison.”

Frontend Developers Should Definitely Check the Browser and Design Mode

Orca’s built-in browser is quite practical.
You can open a browser for each worktree and check the screen while running a local development server.

With Design Mode, you can select elements on the screen and pass their context to the agent.
For example, you can give more specific instructions for UI changes such as button color, spacing, and hover state.
In other words, it is closer to “selecting the target on the actually rendered screen” than “guessing from the code.”

This is especially useful for frontend work with frameworks such as React, Next.js, and Vue.
Because the browser snapshot and element context are passed together, the modification target becomes much clearer.
In real-world work, this kind of feature can save more time than expected.

Orca CLI and the Mobile App Are Only Supporting Tools

Orca CLI is an advanced feature that automates repetitive tasks from the app through commands.
However, it is not required when you first install Orca.
It is better to first learn the basic flow of the app and add CLI automation later when you need it.

The mobile app can be seen as a supplementary remote control for the desktop.
It is useful for checking ongoing tasks or sending short replies, but the full editing process and final decisions should happen on the desktop.
In other words, Orca is not meant to be operated solely through mobile.

The Most Important Core Points That Are Not Widely Discussed

Here are only the core points that other YouTube videos or news articles often do not point out clearly.

First, Orca is not a security solution.
It only separates task folders, and agent permissions must be managed separately.

Second, using worktree does not mean the agent is safe.
Network access, command execution, and setup scripts still need to be checked by humans.

Third, saying that the app is free and saying that AI usage is free are completely different things.
Actual costs come from the connected model and subscription policy.

Fourth, instead of running multiple agents from the first day, it is much more important to complete the flow of read, modify, Diff, test, and commit once in a single worktree.
Only after learning this sequence can you properly see the benefits of parallel execution.

Fifth, the real value of Orca is not that it “makes AI smarter,” but that it “makes it easier for humans to compare and control AI results.”
From this perspective, Orca’s position becomes much clearer.

In Summary, This Is How You Should View Orca

Orca is an IDE that manages multiple AI coding agents in one place.
It separates workspaces with Git worktree, compares results based on Diff and testing, and applies final changes based on human judgment.
At first, it is enough to start with one practice repository, keep permissions on Manual, and successfully complete one read-only request and one small modification.
After that, you can expand into parallel tasks, browser integration, Design Mode, the mobile companion app, and CLI automation.

Summary

Orca is not an AI model, but an IDE for managing multiple AI coding agents.
The core points are Git worktree separation, Manual permission settings, Diff review, testing, and local commit.
The app is free, but AI usage fees are separate, and the built-in browser and Design Mode are especially useful for frontend development.
The most efficient approach is to first learn the single-worktree workflow, then expand into multi-agent parallel work.

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*Source: https://goddaehee.tistory.com/623