Summary

AutoBangumi is an automated, RSS-based tool for anime fans, simplifying series tracking, downloading, and organization. It automatically renames files for seamless integration with media servers like Plex and Jellyfin, eliminating manual metadata scraping and collection management effort. The project targets users seeking a hands-off approach to building and maintaining their anime library.

How It Works

The tool parses RSS feeds from anime sites (e.g., Mikan Project) to automatically generate download rules. It then downloads episodes and meticulously organizes/renames files into media-library-friendly formats (e.g., SeriesName S01E01.mp4). Recent updates enhance this with multi-vendor LLM parsers for improved metadata handling and first-class aria2 integration for robust download management.

Quick Start & Requirements

A 7-step setup wizard guides initial configuration. While specific commands aren't detailed, the project implies straightforward setup. It supports multiple RSS sources and downloaders (qBittorrent, aria2). Links to "Official Website" and "Quick Start" are mentioned for documentation. No specific hardware or OS prerequisites beyond standard Python execution are listed.

Highlighted Details

• Fully automated RSS-based anime tracking, downloading, and organization.
• Automatic file renaming ensures seamless media library integration (Plex, Jellyfin).
• Supports multiple RSS sources and aggregation.
• v3.3 features: in-program updates with signature verification, multi-vendor LLM parsers (OpenAI, Claude, Gemini), advanced aria2 integration, support for Movies/OVAs/Specials, and SSE-driven WebUI.
• Built-in TMDB parser for complete metadata generation.

Maintenance & Community

Contributions are welcomed via Issues and Pull Requests, with guidelines in CONTRIBUTING.md. A "TG 群组" (Telegram Group) facilitates community interaction. Specific maintainer details or sponsorships are not provided.

Licensing & Compatibility

Released under the permissive MIT license, allowing for commercial use and integration into closed-source projects, subject to license terms.

Limitations & Caveats

No explicit limitations are detailed. Effectiveness relies on consistent RSS feed availability and format. Advanced features like LLM parsing may incur external API costs.