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MemoryOS
Hybrid AI Memory: Vector RAG meets Knowledge Graphs
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.
Hey hunters! 👋
I built MemoryOS because standard AI memory (RAG) often feels "flat." It understands what your notes sound like (semantic similarity), but it doesn't understand how they relate (structural connectivity).
MemoryOS creates a high-fidelity knowledge graph of your data with typed, weighted relations. Every query walks the graph and the vector space simultaneously, using recursive logic to bridge the gap between "what I said" and "what I meant."
Key Features:
Dual-Path Retrieval: Parallel pgvector search + Recursive BFS graph traversal.
Visual X-Ray: A "Retrieval Inspector" that shows you the exact path the AI took through your nodes.
AI Bridge: Built-in MCP server so you can use your MemoryOS as the brain for your coding assistants.
It's open-source and we'd love to hear your thoughts on the hybrid approach! 🧠🕸️
About MemoryOS on Product Hunt
“Hybrid AI Memory: Vector RAG meets Knowledge Graphs”
MemoryOS was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #160 on the daily leaderboard. 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.
On the analytics side, MemoryOS competes within Productivity, Open Source, Artificial Intelligence and GitHub — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how MemoryOS performed against the three products that launched closest to it on the same day.
Who hunted MemoryOS?
MemoryOS was hunted by Nursan Omarov. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of MemoryOS including community comment highlights and product details, visit the product overview.
Nursan Omarov