MemoryOS
Hybrid AI Memory: Vector RAG meets Knowledge Graphs
What it does
Most AI memory relies on Vector RAG, which lacks structural context. MemoryOS is a hybrid knowledge base that runs Vector Search and Graph Traversal in parallel. Using recursive SQL CTEs, it finds relational links that standard RAG misses—like discovering "HNSW" when you only query "indexing." It features a real-time Retrieval Inspector, UMAP semantic cluster maps, and an MCP server to connect your local knowledge memory directly to any AI assistant like Claude or ChatGPT.
Does the same job
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- IRI replaced vector databases with Git for AI memory (PoC)2025 · github.com · ▲198
Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
MCP Memory – Fast Agent Memory Using Google's OKF and SQLite FTS524d ago · github.com · ▲70An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents. - fellowgeek/mcp-memory
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