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  1. DeepTutor connects tutoring, research, quizzes, and shared memory

    A Juejin AI article describes DeepTutor as an open-source, agent-native tutoring workspace that links Chat, Quiz, Research, and Visualize through one runtime. It reports that the system shares session context and memory across workflows, supports several RAG engines, and uses a file-backed three-layer memory system for inspectable traces, summaries, and synthesis. The article also identifies Next.js and FastAPI as its frontend and backend technologies and says Codex and Claude Code can operate as subagents.

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DeepTutor: Lifelong personalized tutoring with multi-agent collaboration and retrieval-augmented generation.

What problem it solves Learners need tutoring that can adapt across long-term study, use their own learning materials, and connect answers to sources. They also need a flexible environment for reading, practice, research, visualization, and other agent-assisted workflows without separating each activity from their learning history. Key features Personalized agent collaboration — The tutoring envir…

github.comhttps://github.com/HKUDS/DeepTutor

ragflow: An open-source Retrieval-Augmented Generation engine that combines RAG with Agent capabilities.

What problem it solves Teams building retrieval-augmented AI systems need to extract useful knowledge from complex, heterogeneous documents and ground generated answers in traceable evidence. They also need an orchestrated workflow that can combine document understanding, chunking, retrieval, reasoning, and citations. Key features Deep document understanding — Extract knowledge from unstructured d…

github.comhttps://github.com/infiniflow/ragflow