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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.
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.
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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.
CortexDB was featured in Developer Tools (514k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 95k products, making this a competitive space to launch in.
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.
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Liang Li