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CortexDB
Single-file AI memory and knowledge graph for agents
Pure-Go AI memory and knowledge graph library backed by SQLite. It combines vector search, RAG storage, agent memory workflows, RDF/SPARQL, and MCP-style tool APIs in one local-first database file.
Top comment
I built CortexDB to make agent memory simpler: one local-first Go library for vectors, RAG, graph knowledge, and tool APIs instead of a stack of separate services.
About CortexDB on Product Hunt
“Single-file AI memory and knowledge graph for agents”
CortexDB was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #71 on the daily leaderboard. Pure-Go AI memory and knowledge graph library backed by SQLite. It combines vector search, RAG storage, agent memory workflows, RDF/SPARQL, and MCP-style tool APIs in one local-first database file.
On the analytics side, CortexDB competes within Developer Tools and GitHub — topics that collectively have 555.3k followers on Product Hunt. The dashboard above tracks how CortexDB performed against the three products that launched closest to it on the same day.
Who hunted CortexDB?
CortexDB was hunted by Liang Li. 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 CortexDB including community comment highlights and product details, visit the product overview.