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rclip is a semantic photo search tool for the command line, powered by OpenCLIP's top-performing ViT-B/32 AI model. Search a local photo library with natural-language queries, similar image search, or mixed text and image queries – entirely on your machine, with no cloud and no uploads. It builds on the CLIP architecture introduced by OpenAI.
- Natural-language search – find photos by describing them, e.g.
rclip "two parrots on a branch". - Reverse / image-to-image search – search by an example image from a local path or a URL.
- Combined & arithmetic queries – mix and weight text and image queries, e.g.
rclip "2:golden retriever" + "./pool.jpg" - fruit. - Local & private – works fully offline; your photos never leave your computer.
- Wide format support –
jpg,png,webp,heic,tiff,gif, and more, plus experimental RAW (arw,cr2,dng). - Fast incremental indexing – only new and changed images are reprocessed on subsequent runs.
- Terminal previews – view images inline in iTerm2, Konsole, wezterm, Mintty, and mlterm.
- Cross-platform – Linux, macOS (Apple Silicon), and Windows.
sudo snap install rclipAlternative options (AppImage and pip)
If your Linux distribution doesn't support snap, you can use one of the alternative installation options:
On Linux x86_64, you can install rclip as a self-contained executable.
-
Download the AppImage from the latest release.
-
Execute the following commands:
chmod +x <downloaded AppImage filename>
sudo mv <downloaded AppImage filename> /usr/local/bin/rclippip install --extra-index-url https://download.pytorch.org/whl/cpu rclipbrew install yurijmikhalevich/tap/rclipNote: We only support Apple Silicon (arm64) on macOS.
Alternative option (pip)
pip install rclip- Download the "*.msi" from the latest release.
- Install rclip by running the installer.
Alternative option (pip)
pip install rclipSearch the current directory with a natural-language query:
cd photos && rclip "search query"Example output
score filepath
0.297 "/photos/sunrise-beach.jpg"
0.286 "/photos/dawn-walk.png"
0.274 "/photos/morning-hike.heic"
The first time you run rclip in a directory, it extracts features from your images to build the search index. How long this takes depends on your CPU and the number of images you search. On my hardware, it took 15 hours to process 84,725 photos on a NAS with an old Intel Celeron J3455, 7 minutes to index 50,000 images on a MacBook with an M1 Max, and 3 hours to process 1.28 million images on the same MacBook.
For a detailed demonstration, watch the video: https://www.youtube.com/watch?v=tAJHXOkHidw.
You can also use an image as the query by passing a file path or image URL. rclip will return the images most similar to that query image. If you use a relative path to a local image, you must prefix it with ./. For example:
cd photos && rclip ./cat.jpg
# or use URL
cd photos && rclip https://raw.githubusercontent.com/yurijmikhalevich/rclip/main/tests/e2e/images/cat.jpgCheck this video out for the image-to-image search demo: https://www.youtube.com/watch?v=1YQZKeCBxWM.
You can combine and subtract image and text queries; here are a few examples:
cd photos && rclip horse + stripes
cd photos && rclip apple - fruit
cd photos && rclip "./new york city.jpg" + night
cd photos && rclip "2:golden retriever" + "./swimming pool.jpg"
cd photos && rclip "./racing car.jpg" - "2:sports car" + "2:snow"If you want to see how these queries perform when executed on the 1.28 million images ImageNet-1k dataset, check out the demo on YouTube: https://www.youtube.com/watch?v=MsTgYdOpgcQ.
rclip always indexes the following image formats: jpg, jpeg, png, webp, heic, tiff, tif, bmp, gif, jp2, pnm, pbm, pgm, and ppm.
RAW formats (arw, cr2, and dng) are supported when you pass the --experimental-raw-support flag:
rclip --experimental-raw-support catWhen this flag is enabled, a RAW file is skipped if a processed image (e.g., a JPEG) with the same name sits alongside it, so previews and exported variants are indexed instead of the RAW original.
If you are using either iTerm2, Konsole (version 22.04 and higher), wezterm, Mintty, or mlterm, all you need to do is pass the --preview (or -p) flag to rclip:
rclip -p kittyUsing a different terminal or viewer
If you use another terminal or want to open the results in a viewer of your choice, you can pipe rclip's output into it. For example, on Linux, the command below will open the top 5 results for "kitty" in your default image viewer:
rclip -f -t 5 kitty | xargs -d '\n' -n 1 xdg-openThe -f or --filepath-only flag makes rclip print only file paths, without scores or the header, which makes it ideal for use with a custom viewer as in the example.
I prefer to use feh's thumbnail mode to preview multiple results:
rclip -f -t 5 kitty | feh -f - -tYes. You can pipe rclip's output to another tool to copy matching images. For example, to copy the top 3 images matching "search query" to /path/to/destination on macOS, Linux, or WSL:
rclip -f -t 3 "search query" | xargs -I {} cp {} /path/to/destinationWhen you run rclip in a directory that has already been processed, it indexes only the new images added since the last run and removes deleted images from its index. This makes consecutive runs much faster.
If you know no images have been added or deleted since the last run, you can use
the --no-indexing (or -n) flag to skip indexing entirely and speed up the
search even more.
rclip -n cathttps://github.com/yurijmikhalevich/rclip/discussions/new/choose
This repository follows the Conventional Commits standard.
To run rclip locally from source, you must have Python and uv installed.
Then run:
# clone the source code repository
git clone git@github.com:yurijmikhalevich/rclip.git
# install dependencies and rclip
cd rclip
uv syncThen use uv run rclip, as described in the Usage section above.
Thanks go to these wonderful people and organizations (emoji key):
ramayer 💻 |
Caphyon 🚇 |
AbId KhAn 💻 |
Ben 💻 |
Tanmay Chaudhari 💻 |
Leo Auri 💻 |
Thanks to Caphyon and the Advanced Installer team for generously supplying the rclip project with the Professional Advanced Installer license for creating the Windows installer.
This project follows the all-contributors specification. Contributions of any kind are welcome!
MIT

