RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
MS-Agent: a lightweight framework to empower agentic execution of complex tasks
Your thinking deserves a map: an infinite canvas where LLM conversations grow into an editable thought graph. Wires are the context.
Scholar All-In-One: A research infrastructure for AI agents
Official code for the paper "A New Role for Relevance: Guiding Corpus Interaction in Agentic Search"