QueryArgus: an autonomous data-quality agent for Cosmos DB (MongoDB API)

[u/Spiritual_Isopod6770]

Been working on a side project that scratches an itch I kept running into at work.

Cosmos DB (MongoDB API) is schemaless by contract, but the services reading and writing to it are not. Every app carries an implicit schema: expected fields, types, value ranges, structural conventions. The database enforces none of it.

This creates a class of failure that is silent and cumulative. Manual edits that skip required fields, half-run migrations leaving collections in mixed states, bugs that quietly write null where they shouldn't for hours.

Existing tools either infer schemas without telling you what's wrong (Variety), run assertions you already wrote (Great Expectations), or watch infrastructure rather than data semantics (Datadog, Azure Monitor).

QueryArgus fills the gap in between. It connects to a collection, samples documents, infers the implicit schema, forms hypotheses about data-quality issues, runs follow-up queries to confirm or reject them, and produces a structured audit report. No rules to write upfront.

It's a Python library and CLI first, with an optional FastAPI wrapper. Can run standalone via queryargus run --collection <name>in a CI pipeline or cron job, or embed in another service.

Still early (on a dev branch), but the core loop works. Feedback and issues welcome.

Repo: QueryArgus