How to Use Orca AI for Beginners (Free Guide 2026)

If you have ever watched two AI coding agents fight over the same file, you already know why this guide exists. You ask Claude Code to fix a bug, open a second terminal to ask Codex the same question, and ten minutes later both agents have edited the same file in different ways. The result is a mess of conflicting changes, and you spend more time untangling it than the bug ever cost you. Learning how to use Orca AI solves that exact problem, and this beginner-friendly guide shows you how.

Orca AI is a free, open-source desktop app from Stably AI that runs your coding agents side by side — Claude Code, Codex, Cursor CLI, GitHub Copilot, OpenCode, Pi, and around 30 more — with each agent working in its own isolated git worktree. You write one prompt, send it to several agents at once, compare what each one built, and merge only the winner. As of November 12, 2026, the project has passed 123,000 GitHub stars and ranks as the second-fastest-growing open-source AI tool of the week, so the timing could not be better to learn it.

In this guide, you will learn how to use Orca AI from a cold install to your first real fleet run: what it is, what you need before you start, how to install it on Mac, Windows, or Linux, how to add your first repository, how to fan one prompt across several agents, how to compare the results and merge the best one, and how to steer the whole fleet from your phone. You do not need to be a senior developer, and you do not need to pay anything for Orca itself — it is free and MIT-licensed. You only pay for the agent subscriptions you already use.

Step 1: Understand What Orca AI Actually Is

The first thing to understand about how to use Orca AI is what the letters on the box mean. Orca calls itself an ADE, short for agent development environment — a deliberate claim that supervising a team of AI agents is a different job from writing code, and deserves a different tool than a code editor.

Here is the problem Orca was built to fix. A coding agent is powerful, but it is also single-minded: it edits whatever files are in front of it. Run two agents on the same checkout and they step on each other, each building on the other's half-finished edits. The fix the industry has settled on is one git worktree per task, so each agent gets its own clean copy of the code. Orca makes that the default, then builds the terminal, the diff, and the browser preview around it.

How Orca AI for Beginners Changes the Workflow

Three ideas make Orca different from five terminal tabs:

• One prompt, many agents. Write a task once, fan it out to several agents at once — each in its own worktree, so they can never corrupt each other's files.
• Compare, then merge the winner. Every agent produces its own diff. Read them side by side and merge only the best one.
• Everything in one window. Each task gets its own worktree, terminal, diff view, and browser preview — instead of six terminals and a note.

Orca is not a model, and it is not a replacement for your editor or for git. You bring your own Claude, Codex, or OpenCode subscription, and every worktree is a real git worktree. What Orca adds is supervision: a clean way to run a fleet without losing track of who did what.

Step 2: Check What You Need Before You Start

Orca is a light, focused tool, so the checklist for how to use Orca AI is short — but two items catch beginners out, so check all five before you install anything.

1. A computer running macOS, Windows, or Linux. Orca ships desktop builds for Mac (Apple Silicon and Intel), Windows, and Linux (AppImage). There is also a Homebrew cask for Mac users.
2. A git repository to work on. Orca organizes everything around repositories and worktrees. A small practice project is ideal for your first run — a personal website, a to-do app, or any repo you can afford to experiment with.
3. At least one coding agent account. Orca itself is free, but the agents are not. Claude Code, Codex, Cursor CLI, Copilot, OpenCode, Pi, Grok, Amp, Goose, Devin, Cline, Continue, Droid, Kimi, Kiro, Qwen Code, and Antigravity are all supported — around 35 named CLI agents in total — and anything else that runs in a terminal works too. Start with whichever agent you already pay for.
4. Git installed and configured. Because every task becomes a real git worktree, git needs to work on your machine, and your repository needs at least one commit so worktrees can branch from it.
5. A phone, optional but useful. Orca has a mobile companion for iOS and Android, so you can watch your agents and send follow-ups from anywhere. Set it up later; it is not required for your first run.

One honest warning before you start: five agents on one prompt cost roughly five agents' worth of usage. If those agents come from different vendors, you are paying several subscriptions or API bills at once. That is the real cost of a fleet, and it is why your first runs should be small, cheap tasks — not a full rewrite of your app.

Step 3: Download and Install Orca AI Free

Installation takes about five minutes, and it is the easiest part of how to use Orca AI: there is no account, no license key, and no credit card involved — Orca is free and open source, so the download is the whole purchase.

1. Go to the Orca website at onorca.dev, or open the repository at github.com/stablyai/orca. The download links and the README live there, and the README is the most reliable source for the latest release.
2. Pick the build for your system: macOS (Apple Silicon or Intel), Windows, or Linux (AppImage). Download it and install it like any normal desktop app.
3. On a Mac, you can install from the terminal instead: brew install --cask stablyai/orca/orca. That is the fastest route if you already use Homebrew.
4. Open Orca. The first-session guide takes about five minutes and walks you through adding a repository and creating your first task.
5. If you care about privacy, know this up front: packaged builds collect anonymous usage telemetry, but they never send your prompts, code, or file paths, and you can turn it off with DO_NOT_TRACK=1 or ORCA_TELEMETRY_DISABLED=1.

One more detail worth knowing: Orca ships new releases almost every other day, so the changelog is effectively the real feature list. If a button in this guide looks slightly different on your screen, check the version in the app — you are probably on a newer build than the one this guide was written against, and the core flow has not changed.

Step 4: Add Your First Repository

With Orca installed, the next step is pointing it at code. This is the part that feels new if you have only ever used AI in a chat window, so take it slowly.

1. In Orca, add a repository. Pick the practice project from Step 2 — the one you can afford to experiment with.
2. Make sure the repository has at least one commit. Orca creates worktrees by branching from your current code, and an empty repository with no commits has nothing to branch from.
3. Check that your agent CLIs are signed in on this machine. Orca launches the agent's own CLI with your existing login — it does not ask for your API keys or your passwords — so Claude Code, Codex, or whichever agent you use must already work in your terminal.
4. Look at the status bar. Orca reads the usage state each agent already keeps on disk, under folders like ~/.claude and ~/.codex, and shows your remaining quota and reset windows there. That is how you learn about a rate limit before an agent stalls on it halfway through a task.

If you work with a digital agency or a small team, this is also the moment to agree on a simple rule: Orca worktrees are experiments until someone merges one. Nobody merges a fleet result straight into the main branch without reading the diff first — that review step is the whole point of the tool.

Step 5: Create Your First Task and Pick an Agent

Now the hands-on part of how to use Orca AI begins. Creating a task takes less than a minute, and the app asks you only three questions.

1. Click the plus icon to create a task and give it a clear name, such as "Add a contact form with validation" or "Fix the login redirect bug."
2. Answer the three questions: which project, which machine to run on, and which agent. Local is the default machine — the agent runs on your own computer — and your first task should use the agent you know best.
3. Write the task as a single clear prompt. Be specific about the outcome, not the clicks: what should exist when the agent is done, which files it may touch, and what "done" looks like. A good first prompt is something like: "Add a contact form to the homepage with name, email, and message fields, validate the email format, and show a success message after submit. Add tests."
4. Let Orca do the setup. It runs git worktree add, opens a terminal in the new directory, and launches the agent's CLI with your existing login. You can watch the agent work in its own tab.

Keep your first task small on purpose. A single form or a single bug fix teaches you the flow faster than a whole feature, and you can reuse the same steps for the bigger job later. A good first test is a task you have already done by hand, because you will instantly know whether the agent's result is good — and you will learn how much detail your prompts need.

Step 6: Fan One Prompt Across Several Agents

This is the step that makes Orca worth learning, and it is the heart of how to use Orca AI as a fleet instead of a single assistant. Instead of asking one agent and hoping for the best, you ask several and compare.

1. Create the same task two or three more times, each with a different agent — for example Claude Code, Codex, and Cursor CLI. Orca creates a separate isolated worktree for each one, so the three agents work on the same task in three checkouts that cannot touch each other.
2. Drag the tabs into a split view. Now you have three agents racing the same bug in three separate checkouts, and you can watch all of them at once.
3. Wait for them to finish, then read each diff. One agent may write cleaner tests, another may handle an edge case the others missed, and a third may take a simpler approach that is easier to maintain.
4. Pick the winner and merge only that worktree into your main branch. Discard the others — their worktrees are cheap, disposable copies, and throwing two of them away is the price of knowing you picked the best of three.

Why does this matter now? Because agents are uneven in a way that is hard to predict: the best agent for a bug fix is not always the best agent for a UI change. Fanning one prompt across a fleet turns that uncertainty into a comparison you can actually read, instead of a gamble on a single answer. If you follow agentic AI news, you will recognize the pattern — the new wave is not better chatbots, it is tools that give AI agents better skills, longer memory, and supervision that a human can actually follow.

Step 7: Compare the Results and Merge the Winner

When the agents finish, the review habit starts: do not just look at the result — read the diff. This is the step that separates Orca from a plain terminal, and it is where beginners should spend their time.

1. Open each worktree's diff in Orca. You should see proper, focused changes — the files the task needed, and nothing else. An agent that edited ten unrelated files is telling you its prompt was too vague.
2. Run the tests in each worktree. Orca gives every task its own terminal and its own browser tab showing the result, so you can check the real behavior, not just the code.
3. Check the small things: does the code follow your project's style, are the tests meaningful, and did the agent update the documentation it touched?
4. Merge the winner with git, from the worktree you chose. The other worktrees can be deleted from inside Orca once you are done with them.
5. If none of the results is good enough, say so — and refine the prompt instead of the code. A clearer task description beats three rounds of hand-editing an agent's output, and it is a skill that gets better fast.

One rule to keep from day one: the human merges, never the agent. Orca keeps the merge decision with you on purpose, because five agents produce five diffs and someone has to own the result that lands in the main branch.

Step 8: Use Design Mode to Fix UI With a Click

Orca has a feature that feels like magic the first time you use it, and it is built for the most annoying part of front-end work: describing a visual problem in words.

1. Open the task's browser tab in Orca. It shows your app running in a real embedded Chromium window, from the worktree that agent is editing.
2. Turn on Design Mode and click any element on the page — a button that sits too low, a card with the wrong spacing, a headline that wraps badly.
3. Orca sends the element's HTML and CSS, plus a cropped screenshot of exactly what you clicked, straight into the agent's prompt. You add one sentence in plain words, such as "Center this button and give it more space above it."
4. The agent edits the code, and you see the change in the browser tab without leaving Orca.

This is the fastest loop in the whole app, and it is worth learning early: point at the problem instead of describing it. It also shows what an ADE is for — the browser, the terminal, and the diff all hang off the same worktree, so a visual fix never gets lost between three different tools.

Step 9: Run Agents on a Remote Machine With SSH Worktrees

Sooner or later your laptop will feel slow — a big repository, a heavy test suite, or five agents running at once. The remote-machine step of how to use Orca AI has an answer that does not involve buying a new computer: run the agents somewhere else.

1. Pick an SSH host in the "Run on" menu when you create a task. Orca creates the worktree on the remote machine and runs the agent there, while the editor, the diff, and the browser stay on your laptop.
2. Everything still works the same way: the agent gets full file editing, git, and terminals on the remote box, with automatic reconnection and port forwarding included.
3. For an always-on setup, you can run a remote Orca server on a spare Mac mini or a VPS over Tailscale, headless if you want, using the orca serve command. Your phone app then pairs with that server, so the fleet keeps working while your laptop is closed.

The honest trade-off: remote worktrees need a machine you control and an SSH setup you trust. Start local, and move to SSH only when a real task is slow enough to justify it. Orca does not sell hosting, so there is no cloud bill hiding behind this feature — the remote machine is yours.

Step 10: Steer Your Fleet From Your Phone

The last piece of how to use Orca AI is the one that makes a fleet feel real: you do not have to sit at your desk while the agents work.

1. Install the Orca mobile companion from the iOS App Store or as an Android APK, and pair it with your desktop app. The pairing uses a relay that lives in the same open-source repository, and the app walks you through it.
2. When an agent finishes a task, your phone gets a push notification. You can read the summary, check the diff, and send a follow-up from anywhere — "looks good, now add the error state" — without opening your laptop.
3. Use the phone for steering, not for deep review. A quick follow-up is perfect on mobile; the final merge decision still belongs on a big screen where you can read the whole diff.

That is the full loop: one prompt, several agents, each in its own worktree, compared in one window, merged by you, and steerable from your pocket. It is a simple idea, and it is the reason Orca went from its first commit in March 2026 to more than 123,000 GitHub stars in under eight months.

Orca AI vs the Alternatives

Before you commit to a tool, it helps to see where it sits. The table below compares Orca with the two most common ways beginners run coding agents today, plus two close rivals — the short version of how to use Orca AI against the alternatives.

The honest takeaway: if you already have one agent subscription and you want to see what two or three agents would do with the same task, Orca is the most direct route for a beginner in November 2026, because parallel worktrees are its whole job — and the price is zero. If you need cloud sandboxes or a Slack bot, look at Conductor or Superset instead. Prices and free tiers change often, so check each tool's own pricing page before you commit.

Frequently Asked Questions About Orca AI

These are the questions beginners ask most about how to use Orca AI, answered straight.

Is Orca AI really free?

Yes. Orca is free to download and MIT-licensed, with no per-seat price and no subscription for the app itself. What costs money are the agents you run inside it — Claude Code, Codex, Cursor CLI, and the rest each have their own subscriptions or usage bills. Five agents on one prompt cost roughly five agents' worth of usage, so keep your first fleet runs small.

Do I need to know git to use Orca AI?

You need the basics: a repository with at least one commit, and comfort with the idea of branches. Orca handles the worktree commands for you — it runs git worktree add behind the scenes — but you should understand what a branch and a merge are before you let a fleet loose on your code. If you can commit and merge by hand, you know enough.

Which coding agents work with Orca AI?

Around 35 named CLI agents are listed in the README, including Claude Code, Codex, Cursor CLI, GitHub Copilot, OpenCode, Pi, Grok, Amp, Goose, Devin, Cline, Continue, Droid, Kimi, Kiro, Qwen Code, and Antigravity. The project's own rule is simple: if it runs in a terminal, it runs in Orca. You sign in with your existing agent login; Orca never asks for your API keys.

Can Orca AI run agents on my phone?

Not quite — the agents run on your computer or on a remote machine you control. The mobile companion for iOS and Android lets you monitor the fleet, get notified when a task finishes, and send follow-ups from anywhere. The heavy work stays on the desktop or the server, and the phone is for steering.

Is my code sent to Orca's servers?

No. Orca does not sell hosting and has no cloud where your code would live. Agents run locally, on your SSH host, or on your own Orca server. Packaged builds collect anonymous usage telemetry — never your prompts, code, or file paths — and you can disable it with DO_NOT_TRACK=1 or ORCA_TELEMETRY_DISABLED=1.

What happens if two agents disagree?

That is the point of the tool. Each agent's result lives in its own worktree with its own diff, so disagreement is visible instead of destructive. Read both diffs, test both results, merge the better one, and delete the other worktree. If neither is good enough, tighten the prompt and run the fleet again — a clearer brief beats hand-fixing a weak result.

Final Thoughts

Learning how to use Orca AI is less about learning a new app and more about learning a new habit: stop betting a task on a single agent, and start comparing. One prompt, several agents, each in its own worktree, one careful human merge — that loop is the whole product, and it is free, open source, and running on your own machine in about five minutes.

Start small. Pick a practice repository, run your best agent on one small task, then fan the same task across two more agents and read the three diffs side by side. The first time you merge the second-best-looking agent's work because its tests were actually better, you will understand why Orca passed 123,000 GitHub stars so fast. The fleet is only as good as the human supervising it — and now that human is you.

Explore More Free AI Tools

Orca AI is one of dozens of free AI tools that change how fast a beginner can build. If this guide helped you, try these next — they are all free to start:

• AI Title Generator — write headlines that earn the click: https://aitoolsteps.com/
• Meta Description Generator — craft search snippets that get opened: https://aitoolsteps.com/
• AI Rewriter — sharpen a rough draft into clear, publish-ready text: https://aitoolsteps.com/
• Keyword Density Checker — check your keyphrase balance before you publish: https://aitoolsteps.com/