AnythingLLM is a full-stack, self-hosted AI chat platform designed for Retrieval-Augmented Generation (RAG) over private documents. It provides a web interface where users create workspaces, upload documents (PDF, DOCX, images with OCR, audio, etc.), which are then chunked, embedded, and stored in a vector database (default LanceDB, with support for PGVector, Pinecone, Qdrant, and others). Chat queries retrieve relevant document chunks and feed them to an LLM of choice (supporting 30+ providers including OpenAI, Anthropic, Ollama, and local models via KoboldCPP). The platform also features an extensible agent system (aibitat) with plugins for file system operations, SQL databases, Gmail, Google Calendar, web scraping, image generation, and scheduled jobs. A separate 'collector' microservice handles document ingestion and format conversion, while the 'open-computer' subproject provides a QEMU-based virtual machine for autonomous browser automation. The frontend is a React SPA with multi-user support, role-based access, internationalization, embeddable chat widgets, and a community hub for sharing agent skills, system prompts, and slash commands. The architecture is a monolith (Express server) with the collector as an isolated service, and the open-computer as a standalone CLI/service. It relies on external AI APIs, vector databases, and local storage, making it suitable for both cloud and on-premises deployment.