SuggestArr is an open-source automation platform designed to recommend and automatically request movies, TV shows, and anime based on a user’s viewing history in self-hosted media servers. The project integrates with popular media management systems such as Jellyfin, Plex, and Emby, allowing it to analyze recently watched content and identify similar titles using metadata from the TMDb database. Once potential recommendations are identified, SuggestArr can automatically send download or request instructions to services like Jellyseer or Overseerr, which then coordinate with media download tools and libraries. The application includes a web interface that allows users to configure integrations, schedule automated recommendation jobs, and monitor system logs in real time. More recent versions also introduce optional large language model integration, enabling AI-driven personalized recommendations and natural language search for discovering content.

Features

  • Multi-media server integration supporting Jellyfin, Plex, and Emby for retrieving watch history
  • TMDb API integration for discovering movies and TV shows similar to previously watched content
  • Automated media request system that sends recommendations directly to Jellyseer or Overseerr
  • Optional AI-powered recommendation engine using OpenAI-compatible large language model providers
  • Natural language AI search that finds content based on descriptive prompts and viewing history
  • Web-based interface with real-time logs, configuration management, and cron job scheduling

Project Samples

SuggestArr Screenshot 1

Project Activity

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Categories

Large Language Models (LLM)

License

MIT License

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SuggestArr Web Site

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Additional Project Details

Programming Language

Python

Related Categories

Python Large Language Models (LLM)

Registered

2026-03-09
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