Qdrant

Qdrant ranks 8th overall in the AI category with a composite score of 71 out of 100. The organization describes itself as Creating advanced vector search technology, which frames how developers first encounter the brand on GitHub, in package registries and in search results.

Its documentation and learning material and AI and agent readiness come across as excellent, and are what most developers will notice first when they land on the organization. By contrast, its developer experience and developer reach look minimal today, which is the clearest area where a small amount of focused work would visibly move the needle. Taken together, the picture is of a company whose public developer surface is neither uniformly polished nor uniformly weak, and where different audiences (contributors, integrators, evaluators) will likely form very different first impressions depending on which door they walk through.

Qdrant maintains 133 public repositories on GitHub, built primarily in Rust, Python and TypeScript, which together have collected 45,951 stars and drawn contributions from 119 developers outside the core team, 88 of those repositories have seen commits in the last 90 days, a useful proxy for how much of the codebase is genuinely alive rather than archived.

For anyone evaluating Qdrant as a technology choice, weighing a contribution, integrating the APIs, or comparing it against similar companies in the space, the sections below break down each of these signals in detail and link straight through to the underlying repositories, documentation and community threads that inform the score.