# apprenticeharper/DeDRM_tools

DeDRM tools for ebooks

Repository: https://github.com/apprenticeharper/DeDRM_tools
Canonical: https://ross.abutalabs.com/products/dedrm_tools
Language: Python
License Family: other
Last push: 2024-08-20T20:39:46+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4195, "days_push": 743, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 15313, forks 1670 (observed 2026-08-28T04:11:09.582307+00:00)

## What it is
A collection of Python scripts packaged as calibre plugins (DeDRM and Obok) that remove DRM from ebooks, covering Amazon, Adobe Digital Editions, Barnes & Noble, and Kobo formats. The original project is no longer maintained; users are directed to noDRM's fork for updates.

## Use cases
- remove DRM from Kindle ebooks
- decrypt Adobe Digital Editions books for calibre
- strip Kobo DRM with the Obok plugin
- convert purchased ebooks to DRM-free files
- unlock Barnes & Noble epub books

## When to choose
- you use calibre 5.x+ and need DRM removal for Amazon, Adobe, B&N, or Kobo books
- you want a well-known, historically reliable set of DRM removal plugins

## When to avoid
- you need support for the latest Amazon KFX DRM changes - use the actively maintained noDRM fork instead
- you use Apple iBooks (Requiem) or Microsoft .lit formats, which are excluded
- you want a maintained project with ongoing bug fixes

## Facets
- artifact type: plugin
- maturity: abandoned
- function: security, cryptography, reverse-engineering, file-system
- domain: files, privacy, developer-tools
- platform: python, cross-platform
- tags: drm-removal, ebooks, calibre-plugin, kindle, adobe-digital-editions, kobo, obok, desktop

## Member repositories
- apprenticeharper/DeDRM_tools (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:09.582307+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:06:22.894640+00:00, confidence not recorded.
  - readme: https://github.com/apprenticeharper/DeDRM_tools (fetched 2026-08-28T04:11:09.582307+00:00, sha b6e91fe20939)
- Data as of 2026-08-30T08:39:29.467469+00:00.