kometa-team/kometa

Python script to update metadata information for items in plex as well as automatically build collections and playlists. The Wiki Documentation is linked below.

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 20th, 2026
Created on November 30th, 2020
Open Issues & Pull Requests: 23 (+1)
GitHub issues: Enabled
Number of forks: 368
Total Stargazers: 3,416 (+0)
Total Subscribers: 31 (+0)

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Issue Activity (beta)

Open issues: 9
New in 7 days: 7
Closed in 7 days: 14
Avg open age: 202 days
Stale 30+ days: 4
Stale 90+ days: 2

Recent activity

Opened in 7 days: 4
Closed in 7 days: 13
Comments in 7 days: 0
Events in 7 days: 0

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  • priority:low (3)
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  • status:added-to-nightly (3)
  • bug:upstream (1)

Detailed Description

Kometa is a Python automation tool that updates metadata and builds collections and playlists for Plex media servers.

The tool solves the problem of manually managing media library metadata and organization at scale. It works by connecting to Plex and integrating with third-party services like TMDb, Trakt, and IMDb to automatically update item metadata such as artwork, titles, and summaries. Users define collections and overlays through configuration, which Kometa then applies to their library. The tool can also integrate with Sonarr and Radarr to automate library growth alongside metadata management.

Kometa suits users who want granular control over how their media libraries look and are organized without manual effort. It works well for those already using external services like Trakt or TMDb and who want to leverage that data to create themed collections or visual overlays. The project provides pre-made modular collections and overlays to reduce setup time, allowing users to apply common patterns rather than building everything from scratch. Anyone managing a large Plex library who finds the default interface limiting will find value in the customization depth Kometa offers.

The project maintains active development across multiple branches with ongoing commits to both develop and nightly tracks. The tool is distributed via Docker in addition to direct Python execution, making deployment flexible. Community engagement is supported through Discord and Reddit channels, and the project maintains translated documentation across multiple languages. A feature request system is in place for users to propose enhancements.