Fewer subscriptions.
More vibe-coding.

Open-source tools & agent skills to build the things you’re paying for.

LibrePhotos

Project8.1K

Self-host a searchable photo and video library with machine-learning-assisted organization.

Added 2026-10-07
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GitHub

What you can build

  • Extend metadata search with project-specific fields so researchers can find scanned images by site, specimen, or visit.
  • Build a review step where collection managers confirm face classifications before labels are used in shared browsing.
  • Add configurable rules that group photos into event albums using available date and location metadata.
  • Let team members save and reuse semantic searches for recurring topics in a fieldwork photo collection.
  • Adapt the multiuser workflow so different groups can browse and curate their own photo collections.

Try this prompt

Adapt LibrePhotos for a small internal [research or team] photo archive. Implement one end-to-end workflow for [specific need], using documented scanning, metadata, album, multiuser, or search capabilities where relevant. Make the smallest changes needed across the interface and backend, add focused tests, and document configuration and deployment requirements. Preserve original media; do not overwrite or delete user files. Do not assume integrations or paid services that are not documented.

Get started

  1. Check requirements

    Use a Docker-compatible operating system. The README lists 4 GB RAM and 10 GB storage as minimums, with 8 GB or more of RAM recommended for machine-learning features.

  2. Install the service

    Follow the official standard installation guide at https://docs.librephotos.com/docs/installation/standard-install. The repository includes Dockerfiles, Compose configurations, and Kubernetes manifests.

  3. Prepare for development

    For code changes, consult CONTRIBUTING.md and https://docs.librephotos.com/docs/development/dev-install. The Docker Compose development environment is in deploy/compose/.

License & notes

MITMIT-licensed; retain the license notice when redistributing. Self-hosting requires compute, storage, and hosting, with costs depending on your setup. The README recommends at least 8 GB RAM for smoother machine-learning use. The listed ML stack runs on ONNX Runtime; no paid API requirement or prices are documented.

  • Self-hosting requires maintaining deployment and storage. Machine-learning features are memory-intensive; the README recommends 8 GB or more of RAM for smoother use. Hosting and compute costs vary by setup. The supplied evidence does not document managed hosting.
  • The README documents photo and video library management, not a general-purpose asset distribution service.

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Added 2026-10-07
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Immich

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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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