LinkingMem - v0.3.0
LinkingMem — Graph-native RAG Engine
LinkingMem - v0.3.0 launched on July 4, 2026 with 10 votes, #1,657 of 24,623 launches that month and more than 90% of that year's launches.
What it does
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.
Graph-native RAG engine — Rust + Python, HNSW vector search + graph traversal + LLM reasoning
Rust + Python engine unifying vector search, graph traversal, and LLM reasoning in a single pipeline — Query → Embed → HNSW → BFS → Score → LLM answer. CSR graph and HNSW vector index share one pipeline — no separate vector database, no Neo4j + Qdrant split. Text and image nodes share the same vector space. Query by text, by image URL, or both — caption or CLIP embedding backends. The LLM can request additional graph expansion iteratively via /query/multihop before producing a final answer. OpenAI-compatible client works with OpenAI, Ollama, Gemini compat, Groq, LM Studio, vLLM — just point OPENAI_BASE_URL. Delta store + WAL crash recovery, hot-swap graph merge, Prometheus metrics, per-key…from linking-mem.vercel.app
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