LeanRAG is a Retrieval-Augmented Generation (RAG) framework that leverages knowledge graph structures with semantic aggregation and hierarchical retrieval to generate context-aware, concise, and high-fidelity responses. The system processes documents through a pipeline: chunking, LLM-based triple extraction, hierarchical graph construction with clustering, and structure-guided retrieval from fine-grained entities upward through the graph to reduce redundancy and improve evidence quality. It targets question-answering tasks across domains (general, CS, legal, agriculture) and uses LLMs (OpenAI, Qwen, DeepSeek) for extraction and generation, with Milvus for vector search and MySQL for storing graph entities, relations, and communities. Evaluation is performed via an external LLM API scoring answers on comprehensiveness, empowerment, diversity, and overall quality.