SuggestArr automates media recommendation and download requests for users of Jellyfin, Plex, or Emby. It targets self-hosted media enthusiasts seeking to keep their libraries updated with relevant content by integrating with recommendation engines and download managers. The primary benefit is the automated discovery and acquisition of new media based on existing watch history.

How It Works

SuggestArr ingests watch history from supported media servers (Jellyfin, Plex, Emby), queries the TMDb API for similar content, and optionally leverages AI (via OpenAI-compatible APIs like Ollama) for hyper-personalized suggestions with reasoning. It then automatically sends download requests for these recommendations to Jellyseer or Overseer, streamlining library expansion.

Quick Start & Requirements

• Primary Install: Docker Compose is the recommended method.

  docker-compose up
  

• Prerequisites: Python 3.x or Docker, TMDb API Key, configured Jellyfin/Plex/Emby server. Jellyseer or Overseer are optional but recommended for automated requests. External databases (PostgreSQL, MySQL) are supported for scalability.
• Access: Web interface available at http://localhost:5000 (or custom port).
• Links: Documentation, Discord Community.

Highlighted Details

• Multi-media server support (Jellyfin, Plex, Emby).
• AI-powered recommendations using OpenAI-compatible LLMs (Ollama, OpenRouter, LiteLLM).
• Automated request submission to Jellyseer/Overseer.
• User-specific request management and configurable cron job scheduling via web UI.
• Content filtering based on regional streaming availability.
• Support for external databases (PostgreSQL, MySQL) and real-time log viewing.

Maintenance & Community

The project is actively maintained, with recent commits and releases indicated by GitHub badges. A Discord community is available for support and discussion. Contributions are welcomed via issues and pull requests.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source applications.

Limitations & Caveats

AI recommendations are currently in beta. Specific Jellyseer user integration for requests currently only supports local Jellyseer users. If the AI engine is unavailable or fails, SuggestArr transparently falls back to the standard TMDb recommendation algorithm.