DeepTutor is a full-stack AI-powered learning companion that provides chat, problem-solving, research, visualization, and structured learning capabilities. The system features a modular architecture with a Python/FastAPI backend, Next.js/React frontend, and a CLI interface. It implements an agent-based system where capabilities like deep solve, deep question, and deep research are implemented as loop capabilities with access to tools such as web search, code execution, and knowledge base retrieval. The platform includes a three-layer memory system for contextual awareness, supports multiple RAG backends (LlamaIndex, PageIndex, GraphRAG, LightRAG, IMA), and enables integration with external knowledge bases (Obsidian, MarginNote 4) and partner agents (Claude Code, Codex). The application employs a plugin architecture for extensibility, allowing new capabilities, tools, and subagents to be added via entry points. Configuration is managed through JSON/YAML files with runtime overrides, and services are decoupled via provider abstractions for LLMs, embeddings, search, and media generation. Real-time communication is facilitated by WebSockets and an internal event bus (StreamBus). The system supports multi-user deployments with role-based access control, isolated workspaces, and grant-based resource sharing for skills, knowledge bases, and partners. Deployment options include local installation, Docker containers, and CLI-only mode. The codebase demonstrates strong engineering practices with extensive unit testing, clear separation of concerns, and reusable patterns such as the capability protocol, tool composition, and atomic file operations. While the system's feature richness introduces complexity, its modular design allows for selective adoption of components.