AutoBangumi is a fully automated anime download and organization tool. It subscribes to RSS feeds (Mikanani, etc.), downloads torrents via qBittorrent or Aria2, parses release titles to extract episode/metadata, renames and organizes files for media libraries (Plex, Jellyfin), and provides a web UI for management. The backend is a FastAPI application with async SQLite storage, background schedulers, and a multi-provider notification system. The frontend is a Vue 3 + TypeScript single-page app with a custom component library. The architecture employs clean abstraction layers for downloaders, notification providers, and LLM-based title parsing, with a state machine for durable rename operations and a secure online update mechanism. It is a well-engineered application with extensive test coverage (unit, integration, E2E with Playwright) and active development reflected in frequent releases and a robust CI pipeline.
PaperQA2 is an agentic retrieval-augmented generation (RAG) system for scientific literature. It answers questions by searching, indexing, and analyzing PDFs, text documents, and clinical trials using a multi-step agent workflow. The system integrates with multiple scholarly metadata providers (Crossref, Semantic Scholar, OpenAlex, Unpaywall) for DOI resolution, citation counts, retraction detection, and journal quality scoring. It supports multiple LLM backends via LiteLLM, pluggable PDF readers (pypdf, PyMuPDF, Docling, Nemotron), and vector stores (Numpy, Qdrant). The architecture is a Python library with a CLI (`pqa`), featuring a hierarchical Pydantic settings system, an agent framework with tool selection and memory, and a plugin-based document parsing pipeline. While primarily an application, it is research-backed with published benchmarks (LitQA2, LFRQA) and includes config presets for different quality/performance trade-offs.