Engram
Engram is a modern disc ripping and media organization tool with a reactive web dashboard.
Platform availability
Available on 1 of 9 platforms
- Unraid
- TrueNAS (not listed)
- Umbrel (not listed)
- ZimaOS (not listed)
- Proxmox (not listed)
- Helm (not listed)
- Mac (not listed)
- Windows (not listed)
- Linux (not listed)
Health score?A 0-100 score computed nightly from four factors: maintained (40%: recent commits and steady activity), popular (25%: GitHub stars, log scale), easy to install (20%: platforms, install notes, setup guides, Docker image) and light to run (15%: minimum RAM, ARM support). When the source code has been checked, a fifth factor, security check (20%: automated scan for committed secrets, unsafe desktop-app settings, known-vulnerable dependencies and project hygiene; any serious finding caps it at 20), is added. Factors without data are left out and the rest are rescaled.
62/100 Good
- Maintained
- 100last commit 0 days ago
- Popular
- 3556 GitHub stars
- Easy to install
- 201 install platform, Docker image
- Light to run
- No data
Based on 3 of 4 factors.
Availability: platform listings confirmed today
- GitHub stars
- 56
- Open issues
- 17
- Last commit
- 2026-10-23
- Latest release
- v0.43.1
- License
- AGPL-3.0
- Activity
- rising
Checked today - source: GitHub (jsakkos/engram)
Resources & Compatibility
Requirements not published yet.
Checked today - source: Docker Hub image tags and published docs
First-install notes
No first-install notes yet.
Variants
Alternatives
No alternatives collected yet.
Common questions
What must be set in the Engram web UI on first run?
A TMDB Read Access Token must be set in the Engram web UI on first run.
Is a MakeMKV license required to rip discs?
No; a MakeMKV license key is optional.
Does the acoustic fingerprint feature upload my media?
No audio or video leaves your machine; the shared catalog uses chromaprint audio fingerprints to identify episodes without needing subtitles.
What does Engram do when episode matching is ambiguous?
It puts the item in a human-in-the-loop review queue with a pre-filled correction modal. This covers low-confidence matches, unreadable disc labels, and ambiguous content.
Answers sourced from github.com
Community
No community discussions collected yet.
