Stop Juggling Coding Agents In Terminal Tabs: One Open Source App Runs Them All Side By Side


Running one AI coding agent is a workflow decision. Running three at once is an operations problem, and it is the problem this site's coverage of the agent orchestration survey approached from the research side. Each of your coding agents has its own terminal, its own session state, its own way of reporting progress, and its own habit of finishing silently while you are looking at a different window. Add Git branch juggling to keep their changes from colliding, and the promise of parallel agents collides with the reality of parallel window management.

Orca, a free and open-source Agent Development Environment from Stably AI, is the most complete answer to that problem this site has seen, and the numbers behind it explain why the category it founded has a name now. The repository was created in March 2026; the project's own site counts more than 110,000 GitHub stars and describes downloads across macOS, Windows and Linux, plus iOS and Android companion apps, all under an MIT license. This article covers what an Agent Development Environment actually is, what Orca does with Git worktrees, how it handles review, and where the trade-offs sit, based on the project's site and repository, read in full.

What an Agent Development Environment is, and why the category exists

Integrated development environments were built around a person typing. Every feature, from autocomplete to diff views, assumes one human driving one editor. An Agent Development Environment, or ADE, inverts the assumption: the human supervises, and several autonomous agents do the driving, often at the same time, on the same codebase.

That inversion is what separates coding agents from developer tools, and it breaks IDEs in specific, predictable ways. An IDE has no concept of an agent needing its own isolated working directory. It has no notion of fanning one prompt across five models and comparing the results. Its terminal is one pane where a human works, not a fleet dashboard where five processes report status. Tooling that tries to retrofit agents onto an IDE usually stops at embedding a terminal, which solves window management and nothing else.

Orca's own framing is blunt about this: editors were built for one person typing, agent wrappers stop at a terminal, and an ADE is the whole environment, worktrees, review, browser and remote access in one application. Whether or not ADE becomes the standard term, the capability set it describes is real and distinct from what an IDE plugin provides.

The core mechanism: one worktree per agent

The feature that makes parallel agents safe is the least glamorous one. Orca gives every task its own Git worktree, a separate working directory and branch, so agents never edit the same files at the same time. Import a repository, create workspaces, and each agent works in isolation while Orca manages the worktree creation and cleanup.

The site's own description of the pattern is worth repeating: fan one prompt across five agents, compare the results and merge the winner. That is a genuinely new development workflow, and it is only possible when isolation is automatic. Anyone who has manually created Git worktrees for parallel experiments knows the overhead: create, branch, run, diff, clean up, repeat. Orca absorbs that lifecycle, which converts parallel experimentation with coding agents from a discipline into a default, and it is the same division-of-labour pattern this site documented in the multi-agent cost analysis: more agents help only when their work does not collide.

Worktrees also solve the merge-conflict problem in the same stroke. Because each agent works on its own branch off the same repository, the integration step is a Git operation rather than an archaeology project, and the app's built-in Git integration handles staging and commits from the same interface.

Review without leaving the workspace

The second pillar is review, and it is where tooling for coding agents most often falls short. Getting an agent to write code is the easy part; inspecting what it actually changed, line by line, with the ability to comment and send that feedback back, is the part that determines whether the output is shippable.

Orca ships a built-in code editor and diff viewer. You can see what an agent changed, highlight specific lines, drop markdown comments on any diff line, batch those comments, and send them back to the agent as feedback. Review, edit and commit happen without switching to another editor, and pull requests can be opened and approved in-app alongside GitHub issues and Linear project boards. The design principle is that review feedback is a message to the agent rather than a note to a human, which is the right model when the author is a machine.

The browser, the terminal, and the rest of the loop

Development is not only code and diffs. Orca includes a real embedded Chromium browser, one per worktree, so a web app an agent is building can be previewed without leaving the workspace. Its Design Mode goes further: click any UI element in that browser and Orca sends the element's HTML, CSS and a cropped screenshot into the agent's prompt, which turns visual feedback into an agent instruction with no copy-paste.

The terminal is treated as a first-class citizen rather than an afterthought: GPU-rendered with WebGL, unlimited splits, scrollback that survives restarts, and full search. SSH worktrees let agents run on a more powerful remote machine with auto-reconnect and port forwarding, which matters for anyone whose laptop is not the right host for the job. Files can be dragged into an agent prompt, images included. A companion app for iOS and Android shows live agent status, sends finish notifications, and lets you send follow-up instructions from a phone, which is what makes unattended runs practical rather than hopeful.

Two smaller features round out the operations picture for coding agents running all day. A usage tracker at the bottom of the interface shows Claude and Codex usage and rate-limit resets across all agents in one place, ending the per-agent limit checking that multi-agent workflows otherwise require. And the Orca CLI inverts the control relationship: agents themselves can drive the environment, creating worktrees, taking snapshots and interacting with the interface through commands such as orca worktree create.

Agent support: everything with a CLI

Orca is agnostic about which agent does the work. The repository documents support for 27 named agents, including Claude Code, Codex, Grok, Cursor, GitHub Copilot, Gemini, OpenCode, Amp, Antigravity, Pi, Devin, Cline, Kilocode, Kimi, Qwen Code and more, and states the general rule: if it runs in a terminal, it runs in Orca. Setup detects agents already installed on the machine and offers the supported ones it did not find.

Because Orca wraps rather than replaces, your existing subscriptions and logins stay intact. There is no new account to create, no model to re-choose, and no lock-in: the agents keep working exactly as they did, and Orca is the workspace around them. That design choice is why the tool survived its own category's hype cycle; it does not ask users to bet on one agent's future, and it complements the serving-side plumbing in the Together Link analysis rather than competing with it.

The trade-offs, honestly

Three caveats belong next to the enthusiasm. First, Orca is one more application to learn, and its workflow assumes Git; a developer who does not use branches will find the worktree model foreign before it becomes an advantage. Second, the project moves fast, with the maintainers describing daily ships and the changelog as the real feature list, which is excellent for capability and demanding for stability; teams that need a frozen tool should pin a release and read release notes rather than auto-update. Third, parallel agents on one machine consume real resources, model subscriptions and local compute alike, and the usage tracker makes that visible precisely because it adds up quickly.

None of these are disqualifying, and all three are the standard costs of a tool that is genuinely new rather than a re-skin. The MIT license and the self-hosted desktop model mean the exit path is always open, and for a tool that touches your repositories, your credentials and your daily workflow, an exit path you control is not a footnote; it is the requirement that makes adopting it reasonable.

A day in the workflow, concretely

Abstract feature lists undersell what changes in practice, so consider what a realistic parallel session looks like with Orca in place.

You import the repository and describe three tasks: a checkout-flow redesign, a backend performance fix, and a bug in email handling. Orca creates a worktree per task, and you assign one coding agent to each, choosing whichever model suits the work. The interface splits into panes, one per agent, each showing its terminal, its status and its last output. Status indicators tell you which agents are running, which finished and which are blocked on permission, so the glance replaces the tab-switching.

An agent finishes. You open its diff in the built-in viewer, see eight files changed, and leave comments on two lines where the error handling is wrong. The comments go back to the agent as a batch, it revises, and you re-review without ever opening a terminal. When all three tasks are done, each lives on its own branch, and the integration step is three merges you control, rather than three agents' edits tangled in one directory.

Meanwhile your phone buzzes: the second agent hit its rate limit. The usage tracker at the bottom of the window already showed it trending there, and you hot-swap to a different account without touching the running session. Nothing about that sequence is exotic, and that is the point. Every step maps to a thing developers already do, with the agent-management overhead removed, which is what a good environment contributes: it deletes chores rather than adding capabilities.

The comparison to a pre-Orca workflow is the honest measure. Three terminal windows, three branches managed by hand, a code editor open separately, a browser for previewing, a browser tab for the issue tracker, and a per-agent limit check every hour. That workflow works, and it costs attention on every step that is not the actual work. Orca's value proposition is precisely that deleted attention, and the reason the project grew as fast as it did is that the attention cost of parallel coding agents was real, widespread and previously unsolved.

Why this category matters beyond one app

The interesting signal in Orca's growth is what it says about how software development is reorganising. The unit of work is shifting from a file edited by a person to a task delegated to an agent, and the tooling layer is re-forming around that shift the way IDEs once formed around source files. Worktree-per-agent is the parallelism primitive, review-as-feedback-to-the-agent is the quality loop, and a phone companion is the operations console. Those are the bones of a professional workflow, not a demo.

For developers running more than one of these coding agents seriously, the question is not whether to adopt a workspace tool but which one, and Orca is currently the open-source benchmark the others are measured against. For teams still running one agent at a time, the practical entry point is simpler: next time a task is genuinely parallel, for example a frontend and a backend change, or three alternative implementations of the same function, try one prompt fanned across agents in isolated worktrees and compare the results. That single workflow demonstrates what the category is for, and it is the one Orca was built around.

Where the numbers stop

• The 110,000-plus GitHub star count and download figures come from Orca's own site at the time of writing; they indicate community traction and are not an independent measure of quality or production adoption.
• The 27-agent support count is the repository's documented list at publication; the project ships daily and the list changes frequently, so the current number should be checked in the repository.
• All workflow descriptions come from Orca's site and repository. This article is not a hands-on review; no claim here rests on personal testing, and the site's quoted user posts are public testimonials collected by the project, not independent assessments.
• The comparison table pitting Orca against IDEs and wrappers is the project's own marketing material, and its characterisations of other tools, such as macOS-only support for some rivals, should be verified against those tools before being relied on.
• Stably AI's funding and team background are stated by the project itself; no independent verification of company details was performed for this article.

FAQ

What is Orca?

Orca is a free, open-source Agent Development Environment from Stably AI, released under the MIT license for macOS, Windows and Linux, with iOS and Android companion apps. It runs multiple AI coding agents side by side in one workspace, gives each task its own isolated Git worktree, and adds a built-in editor, diff viewer with commentable feedback, terminals, an embedded browser, GitHub and Linear integration, SSH remote worktrees, and a cross-agent usage tracker. Its repository counts more than 110,000 GitHub stars since March 2026.

How is an Agent Development Environment different from an IDE?

An IDE is built around a person writing code, while an ADE is built around a person supervising several agents that write code. The practical differences are structural rather than cosmetic: worktree-per-agent isolation for parallel work, review feedback that flows back to the agent as instructions, an embedded browser for previewing what agents build, and a status dashboard for a fleet rather than a single session. IDEs can embed a terminal, but they have no native concept of parallel agent workspaces.

Does Orca replace my coding agent or my subscription?

No. Orca wraps the agents you already use, Claude Code, Codex, Cursor, GitHub Copilot, OpenCode, Gemini and any other CLI agent, and leaves your logins, subscriptions and settings untouched. Setup detects installed agents and connects them; nothing is migrated and nothing is locked in. It is a workspace around your agents rather than a competing agent, which is also why it works with any CLI agent the developers have not explicitly listed.

The short version

Orca is the open-source answer to a problem that arrived with parallel coding agents: they are powerful individually and chaotic collectively. It gives every task its own Git worktree automatically, so agents never collide, and it wraps the full development loop, review with commentable diffs, an embedded browser with click-to-prompt design mode, serious terminals, SSH remotes, GitHub and Linear, cross-agent usage tracking and a phone companion, in one MIT-licensed desktop app that supports 27 agents and any other CLI tool. It does not replace your agents or your subscriptions; it makes running several of them at once sane. For anyone whose workflow involves more than one coding agent, it is the first tool that treats the fleet as the unit of work rather than an afterthought.

Sources

• Orca, the project site covering features, supported agents, platform availability and the ADE concept: https://www.onorca.dev/
• Stably AI, the Orca GitHub repository, covering the feature list, supported agents, installation, licensing and the mobile companion: https://github.com/stablyai/orca