Cross-platform desktop application for content-aware file organization and renaming. Supports local and remote LLMs, preview-based workflows, and fully user-con
AI File Sorter
AI File Sorter is a cross-platform desktop application that uses AI to organize files and suggest cleaner, more consistent names for images, documents, and supported audio/video files. It is designed to reduce clutter, improve consistency, and make files easier to find later, whether for review, archiving, or long-term storage.
The app can analyze picture files locally with built-in visual LLM backends and suggest meaningful, human-readable names. For example, a generic file like IMG_2048.jpg can be renamed to something descriptive such as clouds_over_lake.jpg. It can also analyze supported document files and propose clearer names based on their text content. AI File Sorter can also clean up messy audio and video filenames by using the metadata already stored inside supported media files. If tags such as year, artist, album, or title are available, the app can turn them into a clear suggestion like 2024_artist_album_title.mp3, which you can review, edit, or ignore before any change is applied.
AI File Sorter helps tidy up cluttered folders such as Downloads, external drives, or NAS storage by grouping files based on their names, file types, folder context, and past sorting results.
Instead of relying only on fixed rules, the app combines AI suggestions with optional whitelists, recent similar results, and your approved review decisions. This helps keep sorting more consistent over time while still letting you review and adjust everything before anything is changed.
Categories (and optional subcategories) are suggested for each file, and for supported file types, rename suggestions are provided as well. Once you confirm, the required folders are created automatically and files are sorted accordingly.
Privacy-first by design:
AI File Sorter can run entirely on your device. When you use a local model, your files, filenames, images, and metadata stay on your computer, and no telemetry is sent. An internet connection is only needed if you choose to use a remote model.
How It Works
- Point the app at a folder or drive
- Files (and image content, when applicable) are analyzed using the selected local or remote model
- Category and rename suggestions are generated
- You review and adjust if needed before anything is changed
Safe First Run
If you are trying AI File Sorter for the first time, start with a small test folder instead of a full archive or drive. Copy 20-50 files from Downloads, screenshots, photos, or documents into a temporary folder, run the analysis, and inspect the review table before applying anything.
This keeps the first run low risk: your files stay on your computer when you use local models, the AI only suggests categories and filenames, and no move or rename happens until you approve it. If you do apply changes and then want to reverse them, use Edit -> Undo last run.
Technical reference
The main README stays focused on installation, features, and normal everyday
use. For contributor-facing and integration-facing details that are too deep for
the main entry page, use these technical references:
Changelog
[1.9.2] - 2026-08-14
- Fixed a Windows startup crash in Qt GUI theme refresh handling.
[1.9.1] - 2026-08-06
- Fixed bundled Windows local-LLM runtime builds so the packaged GGML libraries run on generic SSE4.2-capable x64 CPUs instead of requiring AVX2.
- Fixed embedded CA bundle staging for packaged and Microsoft Store builds by writing the certificate bundle under writable app data instead of the read-only install directory.
[1.9.0] - 2026-07-03
- Added smart branching whitelists so categories can have their own allowed subcategories, with an improved whitelist editor that keeps global and category-specific subcategory modes mutually exclusive.
- Added structured project-folder protection for recursive scans, covering Unity, Unreal, Godot, Blender, Git repositories, and common source-code project layouts.
- Added file preview support in the Categorization Review dialog and improved accessibility labels/progress announcements for screen readers.
- Added custom visual model support, configurable local model storage, and improved reuse of already-downloaded Gemma 3 4B model files.
- Improved category and filename consistency by localizing suggested filenames, preserving UTF-8 metadata, stripping inline subcategory artifacts, and keeping date suffixes out of canonical cache labels.
- Improved remote LLM handling with rate-limit/backoff parsing and optional request pacing.
- Improved local runtime and release packaging reliability across Windows, Linux, and macOS, including safer backend probing, CUDA/Vulkan fallback handling, RPM packaging, and verified macOS release helpers.
[1.8.0] - 2026-05-10
- Added backend status indicator to the status bar.
- The app now runs as a single instance - opening it again brings the existing window to the front instead of starting a second copy.
- Restored the app launcher for the non-Microsoft Store versions of the app and improved GPU selection, now preferring CUDA over Vulkan when both are available.
- Improved local GPU startup and local visual model handling for better reliability and compatibility.
- Added Gemma 3 4B IT and set it as the default visual model.
- Added Gemma 3 4B IT and Gemma 1.1 7B as built-in local categorization model choices, replacing LLaMa 3B.
- Improved image categorization quality and consistency by preserving image descriptions, using richer prompt context, adding special handling for screenshots and UI captures, and reducing drift between category labels.
- Improved image analysis stability, fallback behavior, and model-download validation.
- Added options to clear categorization and app caches, including a deeper reset of stored categorization state.
- Added local learning from your review decisions to improve future suggestions.
- Added localized Quick Start help, an FAQ link, and additional interface languages including Hindi, Swedish, Icelandic, Norwegian, Finnish, Danish, and Simplified Chinese.
See CHANGELOG.md for the full history.
Features
- AI-powered categorization: Sort files using either local AI models on your computer or remote models with your own API key.
- Offline-Friendly: Use a local LLM to categorize files entirely - no internet or API key required.
- Robust categorization: Built-in rules and category matching help keep results more consistent across runs.
- Configurable categorization controls: Use whitelists, taxonomy normalization, consistency modes, and review-time edits to steer categories and subcategories.
- Two categorization modes: Pick More Refined for more specific labels with less pressure to stay in broad default categories, or More Consistent for steadier top-level categories across similar files.
- Category whitelists: Define named whitelists of allowed categories/subcategories, including smart branching lists where each main category has its own allowed subcategories. Manage them under Settings → Manage category whitelists…, then toggle/select them in the main window when you want to constrain model output for a session.
- Category and rename languages: Categories are chosen in English behind the scenes and then shown in your selected category language. Suggested filenames for images, documents, and supported audio/video files are localized the same way. The available languages depend on the selected local model.
- Custom local LLMs: Register your own local GGUF models directly from the Select LLM dialog. Add a matching MMProj file to make a custom model available for image analysis as well.
- Image content analysis (Visual LLM): Analyze supported picture files with built-in visual backends such as the default Gemma 3 4B IT and LLaVA 1.6 Mistral 7B, with special handling for screenshots and UI captures so categories describe on-screen content more accurately (rename-only mode supported).
- Image date-to-category suffix (optional): Append image creation date metadata to image category names when available.
- Document content analysis (Text LLM): Analyze supported document files to summarize content and suggest filenames; uses the same selected LLM (local or remote).
- Audio/video metadata filename suggestions: Turn embedded media tags into clean, library-style filenames for supported audio and video files, with full review before anything is renamed.
- Sortable review: Sort the Categorization Review table by file name, category, or subcategory to triage faster.
- Qt6 Interface: Lightweight and responsive UI with refreshed menus and icons.
- Interface languages: English, Danish, Dutch, Finnish, French, German, Hindi, Icelandic, Italian, Korean, Norwegian, Simplified Chinese, Spanish, Swedish, and Turkish.
- Cross-Platform Compatibility: Works on Windows, macOS, and Linux.
- Local Database Caching: Speeds up repeated categorization, preserves approved labels and rename suggestions, and provides recent-category hints for consistency.
- Local learning from approved reviews: Approved category decisions can be stored locally and reused as hints for future runs without modifying the underlying model.
- Cache maintenance tools: Use Settings → Clear cache… to inspect and clear categorization cache, image location cache, and logs, or Settings → Reset learned behavior… to remove the separate learned-review database.
- Sorting Preview: See how files will be organized before confirming changes.
- Dry run / preview-only mode to inspect planned moves without touching files.
- Persistent Undo ("Undo last run") even after closing the sort dialog.
- Project-folder protection: Recursive scans skip recog