Instead of manually hunting for what to watch next after finishing a series on Plex, Jellyfin, or Emby, let SuggestArr turn your household's recent viewing history into automated media discovery and download requests. The platform analyzes recently watched movies, shows, and anime, queries TMDb or any OpenAI-compatible LLM (including Ollama, OpenRouter, and LiteLLM) for tailored recommendations with plain-language explanations, and dispatches requests straight to Seer, Jellyseerr, or Overseerr. Built-in AI Search lets users describe a mood or plot in natural language and request matching titles in a single click. Administrators can schedule recurring discovery jobs, import public watchlists from Trakt and MDBList, and enforce granular filters for minimum ratings, release years, genres, and regional streaming service availability so you never download content you already pay to stream. Smart request controls pause jobs when Seer queues are full, require optional human approval before submission, auto-decline stale requests, and clean up unwatched suggestions automatically. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

To uncover deep viewing insights and AI-powered content recommendations across your self-hosted Jellyfin media server, Streamystats delivers a comprehensive analytics and watch-history platform. Once connected to one or more Jellyfin instances using native MediaBrowser authentication, the dashboard tracks live playback sessions, per-user watch durations, device and client breakdowns, and library-wide growth metrics without requiring third-party reporting plugins. You can import existing historical logs from Jellystat or the Playback Reporting plugin so years of viewing habits carry over immediately, then explore interactive watch-time graphs, genre radar charts, actor and director leaderboards, and a yearly Wrapped summary. When you enable vector embeddings through OpenAI, OpenRouter, Google Gemini, Voyage AI, or a local Ollama model, the recommendation engine calculates cosine similarity against your watch history and explains exactly which watched movies or series inspired each suggestion. An integrated AI chat assistant equipped with thirteen specialized function-calling tools lets you search your catalog semantically, ask natural-language questions about your viewing statistics, and generate shared movie recommendations for multiple household members at once. Administrators can also mark titles as watched or unwatched with automatic watch-time inference, manage watchlists over an external API, and connect Janitorr for automated media cleanup. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.