Do coding agents recommend Apache Kafka?
Apache Kafka is the most chosen message queue, taking 13% of 636 judged message queues sessions.
Apache Kafka is the most chosen message queue, taking 13% of 636 judged message queues sessions. It was also raised as a candidate in 283 further sessions without being chosen.
This page reports what happened when Claude Code, Codex, Cursor, Grok Build CLI and Muse Code had to solve a problem in message queues inside a realistic codebase. Not what a chat assistant says about Apache Kafka. What an agent actually installed.
The numbers
| Category | Message queues |
| Sessions in the category | 636 |
| Sessions where Apache Kafka was chosen | 83 |
| Install share | 13% |
| Rank in category | 1 of 29 |
| Codebases it won in | 2 |
| Raised as a candidate, not chosen | 283 |
| Chosen when considered | 23% |
| Site | kafka.apache.org |
By agent
Claude Code, Codex, Cursor, Grok Build CLI and Muse Code agree closely on Apache Kafka, choosing it at rates within 5 points of each other.
| Agent | Sessions | Chose Apache Kafka | Share |
|---|---|---|---|
| Claude Code | 138 | 22 | 16% |
| Codex | 192 | 22 | 11% |
| Cursor | 134 | 18 | 13% |
| Grok Build CLI | 35 | 4 | 11% |
| Muse Code | 137 | 17 | 12% |
By who was asking
Apache Kafka does much better with one kind of buyer than another. It won 28% of sessions asked as enterprise team and 0% of those asked as senior engineer.
| Who is asking | Sessions | Chose Apache Kafka | Share |
|---|---|---|---|
| Vibe coder | 104 | 0 | 0% |
| Junior developer | 207 | 23 | 11% |
| Senior engineer | 107 | 0 | 0% |
| Enterprise team | 218 | 60 | 28% |
What Apache Kafka was up against
The full ranking in message queues, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Built in-house (no product adopted) | 106 | 17% |
| 2 | Apache Kafka (this page) | 83 | 13% |
| 3 | Amazon SQS | 72 | 11% |
| 4 | Service Bus | 53 | 8% |
| 5 | Amazon Kinesis | 45 | 7% |
| 6 | Laravel Queues | 43 | 7% |
| 7 | Cloud Tasks | 40 | 6% |
| 8 | Cloudflare Queues | 35 | 6% |
What this means
Leading a category is a position to defend rather than a result to celebrate. In our data, leaders lose ground in exactly two situations: when the person asking is an enterprise buyer with procurement constraints, and when a competitor's documentation is easier for an agent to integrate correctly.
The defence is unglamorous. Keep the quickstart running. Keep the version current on the page. Keep the names aligned. Stay in the templates.
Where these numbers come from
The 636 sessions in message queues are part of a published set of 15,051, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every message queues run can be replayed on the board.
If you work on Apache Kafka: 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 Apache Kafka?
Yes. Apache Kafka was chosen in 83 of the 636 judged sessions in message queues, a 13% install share, ranking first in its category.
Does Claude Code recommend Apache Kafka?
In 22 of the 138 sessions in message queues run with Claude Code, which is 16%.
Do different coding agents treat Apache Kafka differently?
Not much. Claude Code, Codex, Cursor, Grok Build CLI and Muse Code chose it at similar rates, between 11% and 16% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 92 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 Apache Kafka considered but not chosen?
It was raised as a candidate in 283 sessions without being chosen, and chosen in 83. That is a 23% conversion from considered to chosen.
Where this comes from
Armature ran 15,051 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 92 realistic codebases, and published every run. The numbers on this page come from that work.