# 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.

Source: https://armature.tech/library/do-coding-agents-recommend-qdrant
Published: 2026-09-08
Publisher: Armature, Inc. (https://armature.tech)

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> 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](/library/how-we-measured-this).

Every vector search run can be replayed on [the board](/leaderboards/vector-search).

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.

## Read next

- [How to get picked for vector search by coding agents](https://armature.tech/library/vector-search-coding-agents-playbook) (Markdown: https://armature.tech/library/vector-search-coding-agents-playbook.md)
- [Do coding agents recommend Neon?](https://armature.tech/library/do-coding-agents-recommend-neon) (Markdown: https://armature.tech/library/do-coding-agents-recommend-neon.md)
- [Do coding agents recommend OpenAI Vector Stores?](https://armature.tech/library/do-coding-agents-recommend-openai-vector-stores) (Markdown: https://armature.tech/library/do-coding-agents-recommend-openai-vector-stores.md)
- [Do Claude Code, Codex and Cursor pick the same tools?](https://armature.tech/library/do-claude-code-and-codex-agree) (Markdown: https://armature.tech/library/do-claude-code-and-codex-agree.md)

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