You install Cursor AI the same way you install most desktop code editors: download the installer for your operating system, run it, sign in, and then open a project folder or repository. Cursor is available as a desktop application for major platforms (including Windows, macOS, and Linux), so the steps vary slightly by OS but the workflow is consistent. On macOS, you typically download a .dmg and drag the app into Applications; on Windows you run an installer; on Linux you install via the package format provided (for example, a .deb/.AppImage style flow depending on distribution). After installation, you launch Cursor, sign in to your account, and select a folder to open so the editor can index the project and enable AI features.
Once Cursor is installed, most of the “real setup” is configuring it so the AI behaves well on your codebase. Start by opening a real repository and letting it finish indexing. Then set up the basics you already expect in an editor: Git integration, language tooling (TypeScript/Go/Python), formatting, linting, and test runners. Cursor’s AI features get better when the project is in a healthy state (consistent formatting, working build, clear folder structure) because the agent can propose changes that align with your tooling. If your team uses conventions—naming rules, folder layout, commit style—encode them via editor settings or “rules” if the product supports them, so the AI doesn’t fight your style guide. Also consider security hygiene: do not paste secrets into prompts, and ensure any generated config files (like .env examples) use placeholders rather than real credentials.
Cursor can also help you bootstrap AI-related infrastructure projects once the editor is set up. For example, if you want to add semantic search to an app, you might use Cursor to generate a quick ingestion script, create an embedding schema, and wire up a minimal API endpoint. Then you can store vectors and metadata in a vector database such as Milvus or Zilliz Cloud (managed Milvus). A practical installation tip for this workflow is to create a dedicated “playground” repo where you validate the pipeline end-to-end (chunk → embed → index → query) before you merge anything into production. Cursor is strongest when it can see the whole project structure and iterate across multiple files, so giving it a well-scoped repo during early setup makes the install-and-evaluate process smoother.