Orca vs Aiglade: Which One Fits the Way You Work?
Last updated: 2026-10-30
Orca and Aiglade point in opposite directions. Orca is an ADE, an Agent Development Environment, built for developers who already run several coding agents and want to orchestrate them in parallel. Aiglade is a Windows desktop app that puts several AIs into a single window with a Chinese wizard, built for people who do not program and simply want AI to handle tasks. The right choice does not depend on which product is stronger in the abstract, but on which of those two people you are and what kind of work you are trying to get done.
Orca vs Aiglade: what is the fundamental difference?
The fundamental difference is who is assumed to be in charge. Orca assumes the person in charge is a developer: someone who already uses Claude Code, Codex, and similar coding agents, already pays for their subscriptions, and is slowed down by running them one at a time in separate terminal tabs. Its homepage title is "The agent development environment," and its README tagline reads "The AI Orchestrator for 100x builders." Every word of that framing speaks to people who already live inside developer workflows.
Aiglade assumes the opposite person. Its definition states that it is a Windows desktop app that puts Claude Code, Codex CLI, Hermes, OpenClaw, and other AIs into one window, with a Chinese wizard, ready to use without knowing how to program. Its message is that you should not have to learn the technology first to get AI to do work for you. One product gives people who can already drive agents a better cockpit. The other gives people who cannot, or do not want to, a window with a guide. That is the fork in the road, and everything else follows from it.
Orca vs Aiglade: how do they compare across nine dimensions?
| Dimension | Orca | Aiglade |
|---|---|---|
| Who it is for | Developers and engineers who heavily use coding agents | People who do not program and want AI to handle tasks |
| What you need first | Git worktrees, terminal skills, diff review, plus your own agent subscriptions | No programming knowledge; follow the Chinese wizard and start |
| How work gets assigned | One prompt fanned out to several agents, each in its own isolated worktree, running in parallel | Several AIs in one Windows window, assigned with the help of a Chinese wizard |
| Where it runs | macOS, Windows, and Linux desktops, plus iOS and Android companion apps | A Windows desktop app, with the desktop window as its entry point |
| How results are checked | Read diffs, comment on lines, compare several results, merge the best | Key actions pass through a confirmation step first, and task traces are kept for review rather than running fully automatically |
| How cost works | The app itself is free and MIT open source; agent costs go through your own subscriptions | This article does not state pricing tiers; see the official site for its current terms |
| Language and guidance | English-language developer context; basics are assumed, not taught | Chinese wizard, aimed at people with no technical background |
| Open source or not | Free and open source under the MIT license, with a public repository | Whether it is open source was not verified for this article, so it is not stated |
| Mobile companion | iOS and Android apps for notifications and follow-ups away from your desk | The Windows desktop window is the main entry point; a phone companion is not its selling point |
Where does Orca fall short for people who do not code?
Orca's limits are the flip side of its strengths. It requires you to arrive already skilled and already subscribed: git and terminal fluency, existing agent subscriptions, and the judgment to read diffs and choose between competing results. Parallel runs save you the labor of switching terminals, but they do not save you the judgment itself; the final call is still yours. Its documentation and interface assume an English-speaking developer audience, which is a real barrier for anyone outside that world. Its cost story also needs a careful reading: the app is free, but running agents is not, since every agent bill goes through your own subscriptions. And its popularity evidence is mostly repository stars and release frequency; no verified total download figure was found for this article, so none is cited.
Even experienced developers face a learning curve on the first session: worktrees, sessions, and dispatches are concepts to absorb, and the README itself admits its feature list is always behind reality, pointing readers to the changelog instead. That means keeping up with Orca means keeping up with a daily shipping rhythm. If you already juggle several agents, that rhythm is a feature. If you run one agent at a time, it is machinery you may not need.
Where does Aiglade fall short for developers?
Aiglade's limits come from the same positioning. It is a Windows desktop app, so it does not offer Orca-style multi-worktree orchestration for developers: you will not find a workflow that fans one prompt out to five agents on five branches and merges the winner. Its safety design also means key actions ask for your confirmation first, so it does not run end to end without you. Platform boundaries matter too: macOS and Linux users are outside its entry point as described, and a phone companion is not its main scenario. Finally, its exact feature scope should be taken from its official about page; this article only states what its definition covers and does not expand into unverified features.
Three questions to ask before choosing Orca or Aiglade
First: do you write code day to day? If your daily work is writing code, reviewing diffs, and managing branches, Orca extends the workflow you already have with an orchestration layer. If you never write code and simply want AI to handle chores, Aiglade's window and wizard meet you where you are. Second: can you do the acceptance step? Orca's acceptance is reading diffs, writing annotations, and merging; if that routine is not yours, parallel results will still stall in your hands. Third: do you want several answers or one result? If one piece of code should yield several approaches to compare, Orca's parallel worktrees are built for that. If the job is everyday delegation, filing, or errands, one finished result is enough, and paying several shares of usage for comparison buys nothing you need.
Once the three answers are clear, the choice usually settles itself. If it still does not, split by task: exploratory work goes to parallel comparison, execution work goes to single-agent depth, and the tool follows the task rather than the other way round.
Orca vs Aiglade: so how should you choose, by person or by task?
Choose by person first. If your daily work is writing code, reviewing diffs, and managing branches, Orca extends the workflow you already have with an orchestration layer. If you never write code and simply want AI to handle chores, Aiglade's window and wizard meet you where you are. Choose by task second. Two scenarios make it concrete. Scenario one: you need to fix a production bug and want to see three different fixes from Claude Code, Codex, and OpenCode before merging one. That fan-out-and-compare job is exactly what Orca's parallel worktrees are built for. Scenario two: you need to sort a pile of files or hand routine errands to AI, but you have no interest in learning git or terminals. Aiglade's window and wizard fit that job better.
The two can also coexist. Use Orca when the job is code and comparison matters; use Aiglade when the job is everyday delegation and you want a guided window. There is no rule that says you must pick one tool for everything. One last practical note: Orca's official site is onorca.dev, and repository figures should be read as the snapshot on the day you open the page; Aiglade's current terms should be read from its official site.
Key numbers
- Orca: about 110,139 stars and 6,955 forks on GitHub, MIT licensed, opened in 2026, last pushed on 2026-10-29.
- Orca runs work inside a git worktree, one per task; acceptance means reading a diff and merging.
- Aiglade: a 113 MB Windows installer; free tier of 800 conversations a month, at most 30 in any 24 hours; up to 10 tasks at once by default.
Orca vs Aiglade FAQ: is Aiglade wasted on coders, and do you need both?
Is Aiglade just Orca for non-programmers?
No. The shape is different: Orca compares parallel coding agents inside git worktrees; Aiglade dispatches tasks from one window to several AIs, with an approval step before anything risky runs.
Does Aiglade create a branch per task?
There is no worktree layer. The unit of scheduling is the task queue — 10 tasks at once by default, at most 5 per agent — plus a danger-command rule set and an approval gate.
Do I need both?
Pick by the work you actually do most: reviewing code points to Orca, delegating mixed tasks points to Aiglade. Installing both does not merge your model quota.
Where does my data stay?
Aiglade keeps task and confirmation records on your machine; only the model call itself leaves. Orca keeps results in your local git repository.
Sources
- Orca repository and README: github.com/stablyai/orca
- Orca official site: onorca.dev
- Hands-on walkthrough of Orca's parallel orchestration: DEV
- First-person account of switching to Orca: DEV
- A Windows desktop entry point for non-programmers: aiglade.cn/en/
- Git official documentation: git-worktree (official explanation of the one-task-one-worktree mechanism Orca runs on): https://git-scm.com/docs/git-worktree/2.39.0