Our take
Newless than six months old and changing fast- Activity
- 69
- Community
- 4
- Issues
- 65
- Pull requests
- 21
- Fewer contributors than 92% of the projects we track
DigUp is a promising, actively changing local search app, but its newness and dependence on a single contributor make it worth evaluating before relying on it for essential workflows.
Good fit if
- You want private, on-device semantic search across media and documents on macOS, Windows or Linux.
- You are comfortable trying a beta on Windows or Linux, or a fast-moving new project on any supported platform.
- You want optional code search alongside ordinary file search, with an MIT-licensed project you can inspect or build.
Look elsewhere if
- You need a mature project with a broader contributor base; the measured activity is concentrated entirely in one contributor.
- You need text recognition in screenshots on Windows or Linux, or need older Word documents, RAW photos or SVG files indexed on those platforms.
- You need a hosted, centrally managed search service rather than an index maintained on each computer.
All health signals
| Last commit | 2026-10-14 (today) |
|---|---|
| Commits, last 90 days | 100+ |
| Releases, last 12 months | 3 (latest v0.7.0-beta.1, 2026-10-14, pre-release) |
| Contributors | 1 |
| Issues closed, last 90 days | 8 (typically closed in 2 days) |
| Pull requests merged, last 90 days | 0 |
| Project age | 5 days |
Checked on 2026-10-14 with the GitHub API.
Overview
DigUp is a cross-platform desktop file search app built on Google DeepMind’s EmbeddingGemma 2 and llama.cpp. It lets you describe what you remember from a file, rather than relying only on its name or exact text, and searches the selected folders on macOS, Windows and Linux.
Its appeal is finding related content across file types: a query can match a photo, a passage in a PDF, a point in a recording or video, or code when code search is enabled. Indexing and search run on your computer, which suits people who want semantic search without sending their files to a hosted service.
Key Features
- Searches images, PDFs, documents, audio and video by their content, with results that can open at a relevant page or moment.
- Uses EmbeddingGemma 2’s shared text, image, audio and video embedding space to search across different kinds of files.
- Combines semantic matches with exact-word matching for terms such as names, invoice numbers and error codes.
- Supports cross-language search across the model’s supported languages.
- Offers optional, separate code search for selected project folders, with results that can open in an editor at the matching line.
- Keeps its index and model on-device; the README says files are not uploaded and there is no telemetry.
- Provides a global shortcut and menu-bar or system-tray access on macOS, Windows and Linux.
Use Cases
- Find a photo or screenshot by describing what it shows, even when its filename is unhelpful.
- Locate a statement in a lease or other long PDF and open the result at the relevant page.
- Jump to a particular topic or event in a podcast or video without manually searching through the recording.
- Search a local codebase by describing a function’s behavior, or by entering its name, when code search is enabled.
- Find files across languages or formats without uploading a work or personal archive to a search service.
What you need
Detected in the repository
- Swift (main language on GitHub)
- A test suite and automated checks on GitHub Actions
License in plain words
MITpermissive
- Commercial use: yes
- Modify and redistribute: yes
- You must keep: the copyright and license notice
- Share your changes: no
A summary, not legal advice: the LICENSE file is what applies.
Getting Started
On a Mac, install with Homebrew:
brew install --cask arahim3/tap/digup
Windows and Linux builds are marked beta in the README. See the README for downloads, platform-specific setup and build instructions.
Alternatives
- lilbee: lilbee searches files, code, and websites to provide cited answers through several interfaces, while DigUp focuses on semantic search across local files.
| Project | Language | License | Stars | Status |
|---|---|---|---|---|
| DigUp | Swift | MIT | 3.5k | New |
| lilbee | Python | MIT | 62 | Active |
Considerations
- The project is very new and changing quickly. Recent activity includes frequent commits and three releases in the past year, with the latest tagged release marked as a beta. Measured issue handling shows issues being closed, but no merged pull requests in the last 90 days.
- Development is concentrated in one contributor. That is a maintenance and continuity risk for teams depending on long-term support.
- The first setup downloads an 865 MB model, and initial indexing can take substantial time for large collections. The README notes that CPU-only indexing on Windows and Linux can be slow for large photo libraries; a GPU is used when available and found to be faster, but is not presented as a requirement.
- Platform support is not identical. Screenshot text recognition is currently limited to macOS, as are some older document and image formats. Windows and Linux are described as beta.
- The README says indexing is limited to selected folders and excludes cloud-only files, hidden or sensitive-looking folders, and several generated or dataset-like files. Check its file-handling rules against your needs before indexing a work collection.
- DigUp is MIT-licensed. The README also identifies third-party components with their own licenses, including EmbeddingGemma 2 under Apache 2.0 and FFmpeg under LGPL; review bundled license notices when redistributing packages.
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