Photo Tagger

Photo Tagger asks a vision-language model to analyze your photos and writes Lightroom-compatible metadata. Use it as a command-line tool (photo-tagger) or as an optional desktop app (photo-tagger gui); both run the same pipeline and read the same config.

By default it keeps your originals untouched by creating XMP sidecars, but you can embed the updates directly into each photo with --embed-in-photo.

Full documentation: https://jbsilva.github.io/photo-tagger/

Highlights

• Works with RAW and standard image formats (CR3, CR2, NEF, JPG, PNG, and more)
• Generates a title, a concise description, and hierarchical keywords, in any language you ask for (--output-language)
• Merges with existing metadata unless you opt-in to overwrite
• Snaps keywords onto your own keyword list, so a run cannot fill your catalog with near-duplicates of terms you already curate, and builds that list for you from the photos you already have (photo-tagger vocabulary)
• Harmonizes each shoot: every frame of the same subject gets the same keyword and the same hierarchy, instead of "Osprey" here and "Ospreys" there
• Works with Ollama, LM Studio, llama.cpp, and any hosted OpenAI-compatible API
• Ships a doctor command that pre-flights ExifTool and your model provider
• Records what each run writes, so photo-tagger undo puts a bad batch back
• Watches a folder and tags photos as they arrive (photo-tagger watch)
• Optional desktop GUI (photo-tagger gui) for a point-and-click workflow, with the same vocabulary, shoot-harmonization, watch, and undo features, translated into your own language (English and Brazilian Portuguese ship today)
• Converts images to compact JPEG bytes to minimize token usage
• Reports what it did: a JSON run summary, NDJSON on stdout, or a per-photo CSV (--csv-file)
• Generates detailed log files for easy debugging and auditing
• Highly configurable via CLI flags and environment variables

Requirements

• Python 3.14+
• ExifTool available on PATH
• A running Ollama, LM Studio, or llama.cpp server exposing a vision-language model (for example Qwen-VL)
• libraw support for rawpy (install via Homebrew on macOS: brew install libraw)

Installation

For end-users, the recommended installation method is via uv:

uv tool install photo-tagger

To include the optional desktop GUI, install the gui extra:

uv tool install 'photo-tagger[gui]'

Or install from conda-forge (works with conda, mamba, or pixi). The GUI is a separate package that bundles PySide6:

conda install -c conda-forge photo-tagger        # CLI only
conda install -c conda-forge photo-tagger-gui     # CLI + desktop GUI

For development (tests, linting):

uv sync --group dev --group test

Configuration

Environment variables provide defaults so you can keep the CLI concise:

• OLLAMA_BASE_URL – override the Ollama HTTP endpoint (default http://localhost:11434/v1)
• OLLAMA_API_KEY – optional API key passed to Ollama requests
• LM_STUDIO_BASE_URL – override the LM Studio endpoint (default http://localhost:1234/v1)
• LM_STUDIO_API_KEY / OPENAI_API_KEY – API key for LM Studio’s OpenAI-compatible server
• LLAMA_CPP_BASE_URL – override the llama.cpp llama-server endpoint (default http://localhost:8080/v1)
• LLAMA_CPP_API_KEY – API key for llama.cpp, only needed when llama-server was started with --api-key
• OPENAI_BASE_URL – endpoint for the openai provider (default https://api.openai.com/v1)
• OPENAI_API_KEY – API key for the openai provider (required for that provider)
• MODEL_NAME – default model name (default qwen/qwen3-vl-30b)
• JPEG_DIMENSIONS, JPEG_QUALITY, TEMPERATURE, MAX_TOKENS, RETRIES, TIMEOUT_SECONDS, FREQUENCY_PENALTY – fine-tune runtime
• PHOTO_TAGGER_EXIFTOOL – path to the ExifTool binary, for installs not on PATH (overrides the config file's exiftool_path)
• PHOTO_TAGGER_LANG – language of the desktop app's own interface (en, pt_BR, or auto); the language of the generated metadata is --output-language, a separate setting

Any CLI flag takes precedence over the environment.

Config file

You can persist CLI defaults in a TOML file so they apply automatically. Search order:

1. $PHOTO_TAGGER_CONFIG environment variable (explicit path)
2. .photo-tagger.toml in the current working directory (project-local)
3. ~/.config/photo-tagger/config.toml (user-wide)

CLI flags override config file values, and the config file overrides built-in defaults.

Example .photo-tagger.toml:

extensions = "cr3,jpg,dng"
recursive = true
workers = 2

[provider]
model_name = "qwen/qwen3-vl-30b"
provider_name = "lmstudio"
api_base_url = "http://localhost:1234/v1"

[inference]
temperature = 0.2
max_tokens = 32768

[output]
preserve_keywords = true
max_keywords = 15

[artifacts]
cache_file = ".photo-tagger-cache.db"

[telemetry]
enabled = true

The section names match the internal option groups: provider, inference, output, log, display, filter, artifacts, and telemetry. Top-level keys cover extensions, recursive, workers, and exiftool_path (set this to your ExifTool binary if it lives somewhere unusual). Unknown keys are silently ignored, so the file stays forward-compatible.

Usage

The CLI is exposed as photo-tagger once installed, or you can invoke it directly:

photo-tagger -i ./photos --ext cr3,jpg -r

Key options:

• -i/--input PATH – repeatable; mix files and directories
• --ext – comma-separated extension list used when scanning directories (default cr3,jpg)
• -r/--recursive – recurse into subdirectories while scanning inputs
• -m/--model – model identifier understood by your provider
• --provider – ollama, lmstudio, llamacpp, or openai (defaults to lmstudio)
• --url / --api-key – override provider endpoint and credentials
• --output-language / --lang – language of the generated title, description, and keywords (any language name the model understands, for example German or "Brazilian Portuguese")
• --hint – a note about every photo in the run that the model must trust over its own reading of the image, for example "The animal in these photos is a deer"
• --overwrite-keywords – replace instead of merge existing keyword metadata
• --no-write-title / --no-write-description / --no-write-keywords – skip writing those fields
• --no-backup-xmp – avoid creating *_original snapshot before writing
• --embed-in-photo – write metadata directly into the image instead of creating an XMP sidecar
• --dry-run – run the model and log the proposed metadata without writing XMP
• -w/--workers N – process N photos concurrently using a thread pool (default 1)
• --no-progress – hide the live rich progress bar (auto-disabled on non-interactive stdouts)
• --max-keywords N – cap how many AI-generated keywords are kept per photo before merging
• --vocabulary PATH – restrict generated keywords to the terms in a Lightroom keyword-list export (either the .txt or the .csv shape of Metadata > Export Keywords, or a plain list of terms and Animal|Bird|Osprey paths). Matching ignores case and punctuation, and every match is rewritten to the file's own spelling and hierarchy. Works in any language: write the file in the language you generate in (see --output-language), and use the {synonym} syntax for inflected forms. Add --vocabulary-strict to drop keywords the file does not cover; the run summary then lists every dropped term and how often it came up
• --session-gap MINUTES – group photos into shoots separated by this much idle time (by capture time, falling back to mtime) and make each shoot's keywords agree with itself: the spelling and hierarchy most of the session used win for all of it. Nothing in a session is written until every photo in it has been analyzed
• --prompt-file PATH – override the default user prompt with the contents of PATH
• --summary-file PATH – write a JSON run summary (token usage, success/failure counts) to PATH on completion
• --csv-file PATH – write a spreadsheet-friendly report with one row per photo: what was generated, the keywords already on the file, the camera and location EXIF read as context, and per-photo tokens and timing. Rows stream as photos finish, so a stopped run still leaves a valid file
• --cache-file PATH – persistent SQLite cache of model outputs keyed by an image-data hash (ExifTool's ImageDataHash, which ignores metadata so it survives --embed-in-photo) plus model+prompt+settings. Reruns skip the model call when nothing relevant has changed
• --lock-file PATH – acquire an exclusive file lock on PATH before running and refuse to start if another photo-tagger already holds it (prevents two runs racing on the same folder). Works on Linux, macOS, and Windows
• --json – emit one NDJSON line per processed photo to stdout (file, status, title, description, keywords, token usage, cache flag); logs and progress stay on stderr so you can pipe straight into jq or your own tools
• --newer-than DATE / --older-than DATE – filter the input batch by file mtime. Accepts ISO 8601 like 2024-01-01 or 2024-01-01T14:30; naive timestamps use local time
• --no-undo-log – do not record this run's writes (recording is on by default, and is what photo-tagger undo puts back)
• --no-telemetry – turn off anonymous usage telemetry for this run (see Telemetry)
• --jpeg-dimensions, --jpeg-quality, --temperature, --max-tokens, --retries, --timeout-seconds, --frequency-penalty – control inference behavior

Skipping and resuming

Three flags work together so you can re-run on a folder without redoing finished work:

• --skip-from FILE – skip filenames listed in FILE (one per line; # lines are comments).
• --append-to-skip-file FILE – append each successfully tagged filename to FILE as the run progresses. The file is created if missing, so the same path can be passed to both flags from the very first run.
• --skip-tagged – skip files that already have keywords, a description, or a title in either the image or its XMP sidecar. Catches photos tagged in Lightroom or by hand without needing a skip list at all.

Resume-on-failure pattern: pass the same path to both flags so a killed run can be restarted with a single command.

photo-tagger -i ~/Pictures/Shoot -r \
  --skip-from processed.txt \
  --append-to-skip-file processed.txt

To process a folder mixing already-tagged and untagged photos:

photo-tagger -i ~/Pictures/Mixed --skip-tagged

A successful run creates or updates an .xmp sidecar for every processed image (unless you embed the metadata). Existing metadata is merged so Lightroom keeps hierarchical keywords such as Animal|Bird|Osprey intact.

Examples

Process a folder of RAW and JPEG files recursively:

photo-tagger -i ~/Pictures/Portfolio --ext cr3,jpg -r

Tag a few explicit files and overwrite existing keywords:

photo-tagger \
  -i IMG_0001.CR3 \
  -i IMG_0002.CR3 \
  --overwrite-keywords

Embed metadata directly into a set of JPEGs:

photo-tagger -i ./exports --ext jpg --embed-in-photo

Build a keyword list from the photos you already have, then tag against it:

photo-tagger vocabulary -i ~/Pictures -r -o vocabulary.txt --report dropped.csv
photo-tagger -i ~/Pictures/Shoot --vocabulary vocabulary.txt --vocabulary-strict

The keywords are read from the photos themselves with ExifTool, so it works whatever wrote them: Lightroom, digiKam, darktable, Immich, PhotoPrism, Synology Photos, and anything else that writes XMP or IPTC. Photos and catalog are left untouched; the output is a text file to review and edit, and --report names every keyword it dropped and why.

Add --organize to let the model fold synonyms together (Golden Light becomes a {synonym} of Golden Hour, so it still matches) and file the keywords under categories. It never decides what to keep, and never invents a keyword: everything it returns is matched back to one your library already uses.

Tag photos as they land in an import folder, until you stop it with Ctrl-C:

photo-tagger watch -i ~/Pictures/Inbox -r --skip-tagged

Put back what the last run wrote (a bad prompt across 500 photos is one command to revert):

photo-tagger undo            # or: photo-tagger undo --list / --dry-run

Check your setup before a big run (verifies ExifTool and that the provider serves the model):

photo-tagger doctor --provider lmstudio --model qwen/qwen3-vl-30b

Send requests to a remote Ollama host with a custom model:

photo-tagger -i ./shoot --provider ollama --model llava:34b --url http://ollama-box:11434/v1

Use a hosted OpenAI-compatible API (key read from OPENAI_API_KEY):

photo-tagger -i ./shoot --provider openai --model gpt-4o-mini

Preview proposed metadata without writing anything (useful when iterating on prompts):

photo-tagger -i ./sample --dry-run

Process a large folder concurrently with a live progress bar and a JSON summary:

photo-tagger -i ~/Pictures/Trip -r --workers 4 --summary-file ~/Pictures/Trip/run.json

Keep generated keywords inside the vocabulary your Lightroom catalog already uses:

photo-tagger -i ./shoot --vocabulary ~/lightroom-keywords.txt --vocabulary-strict

Tag a trip so every frame of the same subject agrees, grouping shoots an hour apart:

photo-tagger -i ~/Pictures/Trip -r --session-gap 60

Use a custom prompt tuned for wildlife photography:

photo-tagger -i ./shoot --prompt-file prompts/wildlife.txt --max-keywords 12

Cache model outputs so reruns on the same folder skip the inference cost:

photo-tagger -i ~/Pictures/Shoot -r --cache-file ~/.cache/photo-tagger.db

Tag only photos from a specific trip and stream NDJSON for downstream tools:

photo-tagger -i ~/Pictures/Camera -r \
  --newer-than 2026-04-01 --older-than 2026-05-01 \
  --json --no-progress | jq -c 'select(.status == "ok") | {file, title}'

Refuse to start if another run is already in flight on this folder:

photo-tagger -i ~/Pictures/Camera --lock-file /tmp/photo-tagger.lock

Desktop GUI

Prefer a point-and-click workflow? Install the optional gui extra (or the photo-tagger-gui conda-forge package) and launch the desktop app:

uv tool install 'photo-tagger[gui]'        # or: conda install -c conda-forge photo-tagger-gui
photo-tagger gui

The GUI is a review-before-write frontend over the same building blocks as the CLI. Drag in photos or folders, pick what to process from a checkable tree (with per-file type, status, and already-tagged columns), choose a provider and model (Connection... holds the URL, API key, and the same checks as doctor), then Generate Selected to run the model on a background thread with a progress bar; results are cached so re-runs are free. Each photo's proposed title, description, and keywords appear next to the existing values in a side-by-side detail pane, where you can edit any field before you Save. A photo that fails turns red, shows why, and can be retried from its right-click menu, and Help > Open Logs opens the run log folder. It reads the same config file and environment variables as the CLI, and Settings > Save Settings as Defaults writes them back. PySide6 is only pulled in by the gui extra, so the plain CLI install stays lightweight.

The window covers the same keyword workflow as the CLI. Settings > Keyword Rules... picks the controlled vocabulary generated keywords are snapped onto (with a strict toggle) and the session gap that harmonizes each shoot, applied to the proposals so you still review before saving. Tools holds the jobs that act on a whole library: Build Vocabulary... writes a keyword file out of the keywords your photos already carry (optionally organized by the model), Watch Folder... picks up and generates photos as they land, and Undo Writes... puts back what a recorded run wrote, whether it came from the window or the command line.

Build a standalone macOS app

For a double-clickable Photo Tagger.app that needs no Python install to run:

./packaging/build_macos_app.sh

This bundles Python, PySide6, and everything else into dist/Photo Tagger.app with PyInstaller (its source and spec live in packaging/). Two things to know:

• It is unsigned, so the first launch needs a right-click > Open (Gatekeeper asks once); it opens normally after that.
• ExifTool is not bundled - install it however you like (brew install exiftool, Nix, MacPorts, ...). A Finder launch gives apps a minimal PATH, so on startup the app inherits the PATH your login shell would set, finding exiftool wherever your package manager put it. Test Connection reports a clear error if it is still missing.

Telemetry

photo-tagger sends a single anonymous beacon at the end of each run so development can be guided by how the tool is actually used (which models, platforms, and hardware are common, typical batch sizes), and one when the app crashes so breakage is visible without waiting for bug reports. It is opt-out: on by default, with a one-time notice on the first run, and easy to disable.

What is collected, and nothing else:

• app version and interface (cli or gui)
• provider and model name
• batch size (photo count), run duration, and outcome counts (successes, failures, cache hits, retry recoveries, worker count, token totals, model time, dry-run flag), plus coarse failure buckets (fixed labels like timeout or metadata-write, never error text)
• CPU architecture, OS, OS release, and Python version
• CPU and GPU model names (for example "Apple M3 Pro" or "NVIDIA GeForce RTX 4070"), logical core count, and RAM size in whole gigabytes: generic values shared by millions of machines
• on a crash: the exception type and its code location inside photo-tagger (module:function:line), never the error message (messages can embed paths)
• a random install id (a UUID generated once, not derived from any hardware identifier)

What is never collected: file paths, filenames, photo contents, generated tags/titles/ descriptions, prompts, API keys, IP addresses, error messages, or anything else that identifies you. The exact, closed payloads are the build_payload and build_crash_payload functions; there is nothing else to leak.

Where it goes: our own Cloudflare Worker at telemetry.tagger.photo. No third-party analytics service is involved, and the collector's full source (and the queries run against it) lives in telemetry/. Sending happens on a background thread with a short timeout and is wrapped so it can never crash or slow down a run.

How to disable it (any one of these):

• pass --no-telemetry on the command line
• set PHOTO_TAGGER_NO_TELEMETRY=1 (or the cross-tool DO_NOT_TRACK=1) in your environment
• put enabled = false under [telemetry] in your config file
• in the desktop app, uncheck Settings > Send Anonymous Telemetry (the first-run dialog also offers a one-click "Turn It Off"); the choice is remembered for next launch

The environment variables win over everything, so exporting PHOTO_TAGGER_NO_TELEMETRY=1 once disables telemetry everywhere, including the GUI.

Logging

Logs are written to stderr and to a timestamped file (for example 20260101...-photo_tagger.log). Adjust levels with --console-log-level and --file-log-level, or disable either by setting the value to OFF.

Testing

Run the unit tests with:

uv run pytest

The GUI tests are skipped automatically unless the gui extra is installed. To exercise them, sync the extra and run headless via Qt's offscreen platform:

uv sync --extra gui --group test
QT_QPA_PLATFORM=offscreen uv run pytest tests/test_gui.py

If you plan to contribute, also run uv run ruff check for linting before opening a PR.