[banditelol]

Sounds cool, I want to try this kind of things out, but do you have or planned to have a sandboxing environment, where the agent can try running the query in let say duckdb first to confirm its validity/result before sending it over to bq? Or use something like tablesample when developing the query to reduce cost? One more thing, how do you compare with nao ( https://github.com/getnao/nao ), it's something I've followed for a while and seem to answer similar issue as what ktx build

[andreybavt]

Thanks! ktx maintainer here, related to the queries sandbox question, we're attacking this problem from a different angle: instead of catching bad queries at runtime we try to make it impossible to generate a bad query. ktx allows agents to generate semantic queries (declarative json) and takes care of translating it to the correct SQL with the help of the join graph that it builds during the data source ingestion (dijkstra path finding + fan-out handling etc.). So the agent doesn't need to think "how" it needs to query the data, it just says "what" it wants and ktx builds queries. Also the join graph allows you combining raw tables and high level ktx metrics.

On nao: it's solving an adjacent but different problem, it's a full analytics app, whereas we believe that the general purpose agents (e.g. Claude Desktop) are getting better at the representation layer generating widgets and that's where people work anyway. What these agents need is a solid foundation - this is what we're focusing on. ktx isn't an agent or UI, it's an executable institutional knowledge that keeps any analyst (nao, Claude, Codex, your own agent) from guessing which table is canonical or which join is safe.

[lucamrtl]

Just to complete the answer if that's where your question was going - we actually built a prepackaged project that users can use to experiment with ktx before running any kind of ingestion. In our docs we also have a link to a demo postgres, dbt, metabase, and notion that users can access to try out ktx

And better comparisons from the semantic layer space are tools like Wren, Cube, dbt MetricFlow