LinkingMem — Graph-native RAG Engine

LinkingMem is a cutting-edge, graph-native Retrieval-Augmented Generation (RAG) engine designed to unify multiple AI retrieval techniques into a single, high-performance pipeline. Built with Rust for speed and reliability, it seamlessly integrates vector search via HNSW, graph traversal with BFS, and large language model (LLM) reasoning, making it highly effective for complex multi-hop retrieval tasks. Its architecture emphasizes tight integration between graph structures and vector embeddings, enabling precise entity resolution and rapid information retrieval. The system also supports pluggable backends for LLMs and embeddings, offering flexibility for various AI stacks, while mmap-based storage ensures low-latency performance suitable for large-scale knowledge graphs. Whether for enterprise knowledge management, AI-powered search, or data integration, LinkingMem offers a scalable, production-ready solution that caters to demanding AI applications.