An ADE, Not an IDE: Orca, a Command Center for Multiple Agents

In one terminal window, Claude Code is refactoring the payment module. In another, Codex is writing tests for the feature deployed yesterday. The agent in the third tab has been quiet for ten minutes. You cannot tell whether it has finished, gotten stuck, or is waiting for your approval.

It is a scene many developers in 2026 have encountered. AI coding agents have improved rapidly, but the way we manage several of them still amounts to manually switching between terminal tabs. Orca, which has recently seen explosive growth on GitHub, targets exactly this problem. It calls itself a new category: an ADE (Agent Development Environment), rather than an IDE.

An ADE Instead of an IDE? Start with the Terminology

Orca was created by Stably AI (legal name: Lovecast Inc.). Founded by Jinjing Liang and Neil Parker in 2022, the San Francisco company went through Y Combinator's winter batch that same year. The team originally built QA automation products. Orca is effectively its next big bet.

The official site puts it this way: “IDEs were built for you. ADEs are built for you and your agents.” The opening page of the documentation makes an even more direct statement: “Not a model. Orca runs agents you already use.”

That sentence is the key to understanding Orca. Cursor, Windsurf, and GitHub Copilot are integrated products that bundle model access into a subscription. Orca, by contrast, does not sell models. It does not even have its own inline autocomplete. Instead, it runs and manages several agents you already subscribe to, such as Claude Code, Codex, and Cursor CLI, at the same time, serving only as their command center.

The Core Mechanism: Spread Agents Across git Worktrees

What happens when several agents are let loose in one repository at once? They edit the same files, overwrite each other's changes, and tangle the branches. That is the fundamental multi-agent problem.

Orca's solution is almost elegantly simple: give every task its own git worktree. A worktree is a native git feature that creates another working copy of the same repository on disk. Instead of repeatedly stashing changes and switching branches, you give each task a physically separate workspace from the start. As the official documentation puts it, “This is what makes parallel agents safe—they never step on each other's files.”

Each worktree also gets its own agent terminal and browser tab. Orca's full workflow can therefore be summarized as follows.

The most striking part is steps 4–6: fan-out and winner selection. Give the same prompt to 3–5 different agents simultaneously, let each solve the problem on its own branch, then compare the diffs side by side and merge only the best result. The official tutorial captures the experience well: “Three branches. Three diffs. Same prompt.”

A review video posted in July showed an interesting side effect. The reviewer said they gave five agents different tasks, but told only one about a bug. Three ended up finding it on their own. Running multiple perspectives in parallel lets one catch what another misses—an effect that may sound obvious, but is hard to appreciate until you actually try it.

Supported Agents and the Real Cost Structure

Orca's principle is clear: “If it runs in a terminal, it runs in Orca.” The official support list includes 25–30 or more agents, including Claude Code, Codex, Cursor CLI, Gemini, GitHub Copilot CLI, OpenCode, Grok, and Pi. You can also add custom CLI agents that are not listed.

Authentication is BYO—bring your own. Orca itself has no login and no per-seat fee. The app passes your existing subscription credentials to each agent CLI, which calls its provider's API directly from your computer. Orca takes no margin in between.

That produces the following pricing comparison.

How It Differs from Cursor and Windsurf: A Layer Above Them

You may see headlines calling Orca a “free Cursor killer.” That may attract traffic, but it does not accurately describe the product's structure. Orca is closer to a layer above Cursor than a replacement for it. In fact, Cursor CLI is one of Orca's supported agents.

Independent reviewers' conclusions support this picture. Their point is: “Orca wins when you want to use two or more agents. If you use only one agent and like its UI, stay there.”

In other words, if Cursor's inline autocomplete or chat panel is central to your work, Orca is not the answer. Even the official documentation explicitly says it is not a fit if all you need is simple autocomplete assistance. Orca targets “engineers running 10–100 coding agents at the same time.”

Competitors in the Same Space: Conductor, cmux, and Herdr

Interestingly, the “agent orchestrator” category itself has become crowded very quickly in 2026. In July alone, reviews comparing orchestrators appeared in such numbers that they seemed to form a genre of their own.

Emerging tools such as Herdr have joined the field too. A “Herdr vs Orca” comparison video posted in mid-July drew nearly 5,000 views. Its central question captures the category well: “How do you give developers visibility and control over a fleet of agents without turning them into full-time managers?”

Three features are often cited as Orca's differentiators. First, a mobile companion: receive a notification when an agent finishes and send follow-up instructions from your phone. A Japanese-language podcast singled out the fact that “the agents keep running even after you close your computer” as the most impressive part. Second, Design Mode: click a UI element in an actual Chromium window, and its HTML, CSS, and a cropped screenshot go straight into the agent's prompt. Third, remote worktrees over SSH let you run work on a remote machine.

Install It and Run Three Agents in Five Minutes

Installation is straightforward. However, there is one trap for Mac users.

# macOS - 반드시 tap 경로를 명시할 것
brew install --cask stablyai/orca/orca

# Arch Linux (AUR)
yay -S stably-orca-bin

After installation, the first guide in the official documentation is the “first three-agent session” tutorial. It says going from an empty app to three agents running in parallel takes less than five minutes. The flow is as follows.

1. Create a worktree. A terminal opens in the new worktree. Choose Claude Code, Codex, Cursor CLI, or another agent in the selector, and Orca launches the CLI in the correct working directory and passes it your subscription credentials.
2. Repeat the process twice to create three worktrees in total.
3. Paste the same prompt into all three agents.
4. Drag the worktree tabs to the edges of the screen to split the view and watch all three agents at once.
5. When they finish, open each worktree's diff view and compare. You can also add inline comments with Annotate AI Diff and send them back to the agent whose result is closest to what you want.
6. Commit and push directly from Orca, then clean up the other two worktrees and their branches with one click.

What the Community Says, and What to Watch Out For

The speed of growth is itself the project's biggest news. A review video from early July said it had “passed 11,100 stars” at recording time. By late July, the repository's live count was 32,135 stars—nearly three times as many in a month. Releases arrive almost daily, and open issues have passed 2,500. Reviewers praise the team for triaging feature requests posted on Discord within a day.

First impressions are generally positive too. Review quotes such as “I didn't expect it to be this good” and “Everything gets so much easier” stand out, while video comments include reactions like “That explanation was really concise. I'm going to try Orca.”

For balance, however, most signals currently available come from YouTube reviewers, personal blogs, and small Reddit threads. There is still little accumulated critical scrutiny of the kind found in major Hacker News discussions or large-scale usage reports. As always with a young tool, early reviewers' enthusiasm needs to be taken with a grain of salt.

Three limitations have been raised repeatedly so far.

• It is overkill for single-agent users. If you use only one agent, Orca offers little benefit.
• Costs scale with the number of parallel agents. That applies to token consumption and disk usage per worktree alike.
• Without agent subscriptions, it is an empty shell. Orca itself cannot generate anything.

One amusing footnote: a Reddit commenter noted the name's similarity to MacStadium's macOS virtualization product Orka and quipped, “I don't think that name is going to last.” Naming collisions are indeed a practical problem for search traffic in this category.

Conclusion: Is It the Right Tool for You?

Orca in one sentence: a free, open-source command center that sells no model, but lets you run the agents you already have simultaneously in isolated worktrees.

If the competitive focus in AI coding tools is shifting from “Which model is smarter?” to “How do we manage multiple agents?”, Orca is one of the clearest examples. If it interests you, I recommend spending five minutes on the official three-agent tutorial. That is enough to get a feel for it.