# How to set up Canopy and run your first AI coding agent

> Install an agent CLI, open a project in Canopy, run the app, and review your first change. Includes a downloadable practice project.

Canonical HTML: https://canopyide.dev/guides/getting-started-with-ai-coding-agents
Article date: 2026-09-27

To set up Canopy, install the desktop app and a supported coding CLI, open a project, and run one small task you can inspect. You do not need to memorize terminal commands, but you do need Git and the runtime your project uses. The practice project on this page gives you a safe first run.

## 1. Download Canopy and prepare your CLI

Choose the current macOS, Windows, or Linux release from the Download Canopy button above. Install and open the desktop app. Canopy does not include a model subscription: install and sign in to a supported coding CLI with its own provider account before asking it to work. Have Git available, plus any runtime your project requires. The practice project has no package dependencies; its documented local server command uses Python 3.

## 2. Open a project

Create a project in Canopy and add the folder that contains your code. If you do not have a repository yet, download the practice project above, unzip it, and open its folder. It includes a simple task board and a first agent task. If your own product spans a website, API, and worker, add each as a labeled component. Canopy keeps their files, agents, and run commands under one project.

## 3. Choose an agent you already use

The launcher offers supported coding CLIs such as Claude Code, Codex CLI, Amp, and OpenCode, plus a plain shell. The CLI runs in a real terminal and uses its own account. If it is missing, the launcher shows an install route. You can keep separate CLI profiles for supported tools.

## 4. Give one testable request

For the practice project, use the brief in TASK.md to ask for an All / Open / Done filter. The checks are concrete: each view shows only matching tasks, and the done state survives a refresh. Ask the agent to report the files it changed, the tests it ran, and any decision it needs from you. Canopy's shared project context helps supported sessions see recent prompts and file activity.

## 5. Run and inspect the result

Configure your app's run commands once. The Servers panel can then start and stop the website, API, or background worker and show the ports and output. Open the preview, inspect the page, and send a screenshot or visual note back to the agent. Review the diff before accepting the change.

## 6. Pick up where you left off

When you return, Canopy lists restorable terminals and agent sessions. Resume the agent through its own CLI so its conversation history comes back, then restart the services you need. Treat every change like a teammate's contribution: check the result and the pull request before shipping.

## Practice resource

[Download the first agent practice project](https://canopyide.dev/first-agent-project.zip): A tiny local task board with no dependencies, a run command, a copyable agent task, and acceptance checks.

## Frequently asked questions

### Do I need to know how to code to use Canopy?

You can direct an agent without writing every line, but you still need a code project and someone must review the running result and changes before shipping. A developer should own production decisions you cannot verify.

### Does Canopy include an AI model subscription?

No. Canopy runs supported agent CLIs you install and sign into with their own provider accounts.

## Sources and further reading

- [Canopy app README: quick start and prerequisites](https://github.com/FluidWorksApp/canopy-ide/blob/main/README.md)
- [Canopy agent resume parity audit](https://github.com/FluidWorksApp/canopy-ide/blob/main/docs/agent-parity.md)

## Related Canopy pages

- [How do I find the command that runs an AI-built app?](https://canopyide.dev/guides/find-command-to-run-ai-built-app.md)
- [How do I start my website, API, and worker with one click?](https://canopyide.dev/use-cases/one-click-local-dev-stack.md)
- [AI-generated pull request review checklist and reviewer prompt](https://canopyide.dev/guides/ai-generated-pr-review-checklist.md)
- [Can a noncoder build software with AI coding agents?](https://canopyide.dev/guides/can-noncoders-build-software-with-ai-agents.md)
- [How do I ask a coding agent for a demo I can inspect?](https://canopyide.dev/guides/ask-coding-agent-for-inspectable-demo.md)

Canopy runs installed coding CLIs; CLI accounts, model selection, and provider billing remain separate. Check the installed release before relying on version-specific behavior.