Hi HN, I'm CTO at TypeDB. We shipped an agent mode in our web Studio that lets you describe what you want in plain English and get executable database queries back. The blog calls it "vibe querying," which is a little tongue-in-cheek, but the workflow is what you expect: ask a question, get TypeQL, run it, iterate.
TypeDB's data model has a strict schema, higher-level abstractions (role-based interfaces, subtyping & inheritance), and hypergraph structures (n-ary relations & relations in relations) built in. This combination enables better AI query generation.
It's like giving the LLM a strongly typed language instead of a loosely typed one. With a relational db, the model has to infer relationships from foreign keys and naming conventions. With graph databases, there's no enforced schema. In TypeDB, the schema says "friendship is a relation with two friends, each played by a person" — the LLM gets a constraint space to navigate rather than guess at. It still gets things wrong — the post shows a syntax error it had to recover from — but the error surface is smaller and more correctable.
Would love to hear your thoughts. Happy to answer anything about TypeDB as well!
Hi HN, I'm CTO at TypeDB. We shipped an agent mode in our web Studio that lets you describe what you want in plain English and get executable database queries back. The blog calls it "vibe querying," which is a little tongue-in-cheek, but the workflow is what you expect: ask a question, get TypeQL, run it, iterate.
TypeDB's data model has a strict schema, higher-level abstractions (role-based interfaces, subtyping & inheritance), and hypergraph structures (n-ary relations & relations in relations) built in. This combination enables better AI query generation.
It's like giving the LLM a strongly typed language instead of a loosely typed one. With a relational db, the model has to infer relationships from foreign keys and naming conventions. With graph databases, there's no enforced schema. In TypeDB, the schema says "friendship is a relation with two friends, each played by a person" — the LLM gets a constraint space to navigate rather than guess at. It still gets things wrong — the post shows a syntax error it had to recover from — but the error surface is smaller and more correctable.
Would love to hear your thoughts. Happy to answer anything about TypeDB as well!