A high-performance Rust + Python engine for graph-based RAG, unifying vector search, graph traversal, and LLM reasoning in a single system. Query → Embedding → HNSW retrieval → Graph expansion (BFS) → Ranking → LLM answer LinkingMem combines vector search and graph traversal in one tightly integrated pipeline, enabling fast multi-hop reasoning, efficient memory usage, and production-ready scalability.

LinkingMem is a Graph-native RAG engine combining Rust performance with Python AI plugins. It unifies vector search (HNSW), graph traversal (BFS), and LLM reasoning in a single pipeline for fast multi-hop retrieval. Key differentiators include tight graph+vector integration, embedding-based entity resolution, pluggable LLM/embedding backends, mmap-based low-latency storage, and production-ready scalability for large knowledge graphs.