Do coding agents recommend Qdrant?
Qdrant was chosen in 15% of 158 judged vector search sessions, ranking third. Measured with Claude Code, Codex and Cursor.
Qdrant was chosen in 15% of 158 judged vector search sessions, ranking third. It was also raised as a candidate in 62 further sessions without being chosen.
This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in vector search inside a realistic codebase. Not what a chat assistant says about Qdrant. What an agent actually installed.
The numbers
| Category | Vector search |
| Sessions in the category | 158 |
| Sessions where Qdrant was chosen | 23 |
| Install share | 15% |
| Rank in category | 3 of 15 |
| Codebases it won in | 3 |
| Raised as a candidate, not chosen | 62 |
| Chosen when considered | 27% |
| Site | qdrant.tech |
By agent
The three agents land within 9 points of each other on Qdrant, which is closer than most products in this experiment manage.
| Agent | Sessions | Chose Qdrant | Share |
|---|---|---|---|
| Claude Code | 58 | 7 | 12% |
| Codex | 66 | 9 | 14% |
| Cursor | 34 | 7 | 21% |
By who was asking
Qdrant does much better with one kind of buyer than another. It won 63% of sessions asked as enterprise team and 0% of those asked as senior engineer.
| Who is asking | Sessions | Chose Qdrant | Share |
|---|---|---|---|
| Vibe coder | 45 | 3 | 7% |
| Junior developer | 60 | 3 | 5% |
| Senior engineer | 26 | 0 | 0% |
| Enterprise team | 27 | 17 | 63% |
What Qdrant was up against
The full ranking in vector search, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Neon | 54 | 34% |
| 2 | OpenAI Vector Stores | 24 | 15% |
| 3 | Qdrant (this page) | 23 | 15% |
| 4 | Typesense | 20 | 13% |
| 5 | Pinecone | 19 | 12% |
| 6 | Meilisearch | 7 | 4% |
| 7 | Elasticsearch | 2 | 1% |
| 8 | turbopuffer | 2 | 1% |
What this means
A solid second or third position in a category means the agent is genuinely choosing rather than reaching automatically. That is a winnable position, because the inputs it uses can be changed.
The fastest gains are usually in the sessions that were nearly won: read them, find the step where the agent moved on, and fix that step.
Where these numbers come from
The 158 sessions in vector search are part of a published set of 5,915, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every vector search run can be replayed on the board.
If you work on Qdrant: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
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Common questions
Do coding agents recommend Qdrant?
Yes. Qdrant was chosen in 23 of the 158 judged sessions in vector search, a 15% install share, ranking third in its category.
Does Claude Code recommend Qdrant?
In 7 of the 58 sessions in vector search run with Claude Code, which is 12%.
Do different coding agents treat Qdrant differently?
Not much. The three agents chose it at similar rates, between 12% and 21% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 56 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.
How often is Qdrant considered but not chosen?
It was raised as a candidate in 62 sessions without being chosen, and chosen in 23. That is a 27% conversion from considered to chosen.
Where this comes from
Armature ran 5,915 judged sessions with Claude Code, Codex and Cursor inside 56 realistic codebases, and published every run. The numbers on this page come from that work.