If you have used Cursor as your primary editor for any significant period, there is a good chance it has created storage across every project you have opened — indexes, AI context, workspace state. Open enough projects and that storage adds up.
What Cursor stores locally
AI-powered editors like Cursor need to understand your codebase to give useful suggestions. That understanding comes from indexes — local databases that map your code structure, symbols, imports, and relationships. Building those indexes takes computation. Keeping them means storing them locally.
In addition to indexes, Cursor and similar tools may store:
- Per-project AI context and conversation history
- Workspace-specific settings and state
- Extension data and cached completions
Every project you open gets its own set of these files. Every project you stopped working on retains them.
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LLM Cleaner scans your Mac for local AI models, caches, indexes, and project memory — then shows what you can review, reveal, export, or safely move to Trash.
The old project problem
The storage that accumulates fastest is not from your current projects. It is from every project you have ever opened. Workspace indexes and AI context do not self-delete when you finish a project or archive a repository.
A developer who has used Cursor for a year might have workspace storage for dozens of projects — including freelance work that finished, codebases they were evaluating, sample projects from tutorials, and repositories they cloned once to look at something specific.
What is safe to delete versus what needs review
Workspace indexes are generally rebuildable — Cursor can regenerate them the next time you open the project. However:
- Rebuilding indexes for large codebases takes time
- Some AI context may not rebuild automatically
- Deleting storage for an active project is more disruptive than for a finished one
The key is separating old, inactive project storage from current project storage — not deleting everything at once.
Not sure what is safe to delete?
LLM Cleaner separates models, rebuildable caches, and project memory so you do not treat everything like junk.
Why this is harder than deleting regular files
Workspace storage folders are named with identifiers, not project names. You cannot look at the folder name and know it corresponds to the project you finished eight months ago. You have to cross-reference folder metadata, modification dates, and paths to figure out what each folder belongs to.
That is not impossible. But it is the kind of work most developers skip — until the storage is genuinely problematic.
Local AI storage is messy. Cleanup should not be.
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