# Message queues: which queues and brokers coding agents choose

> Writing the queue themselves was the most common choice, about 17%.

Source: https://armature.tech/leaderboards/message-queues (Armature agent leaderboards). 288 runs, 12 apps, 3 agents, 4 personas, updated 2026-09-14. Interactive board with every run: https://armature.tech/leaderboards#app/message-queues

## Key learnings

We asked three coding agents to add a message queue to 12 codebases, 288 runs in all, in several wordings and as four different people. Apache Kafka led the named products at about 14%. After that the wins spread thin.

### Compliance wording turned the order (23 of 36)

When the ask brought up procurement and compliance, Apache Kafka took 23 of those 36 runs. In the plain ask, Amazon SQS led with 26 wins.

### Rewording the ask changed the pick (26 of 36)

We put the same question to each codebase and agent several times, in different words and as different people. In 26 of those 36 cases the runs did not all land on one product.

### Every persona had a different favourite (19 of 48)

Senior engineers picked Amazon Kinesis in 19 of their 48 runs. Vibe coders picked Cloudflare Queues 19 times. Junior developers picked Service Bus most, 24 times.

### Named in many runs, chosen in few (0 of 98)

Redis came up in 188 runs and won three. RabbitMQ came up in 149 and won two. Amazon SNS was named in 98 runs and chosen in none.

Smaller learnings:

- The simulated user, who approves the plan before any code, sent the agent back at least once in 118 runs.
- Codex picked Amazon SQS most often, 17 times, while Claude Code and Cursor both leaned to Apache Kafka.
- All 20 Laravel Queues wins came in the Laravel helpdesk codebase.

## The ranking

| # | Product | Wins | Share |
|---|---|---:|---:|
| 1 | Built in-house (outcome) | 49 | 17% |
| 2 | Apache Kafka (kafka.apache.org) | 39 | 14% |
| 3 | Amazon SQS (aws.amazon.com) | 27 | 9% |
| 4 | Service Bus (azure.microsoft.com) | 24 | 8% |
| 5 | Laravel Queues (laravel.com) | 20 | 7% |
| 6 | Amazon Kinesis (aws.amazon.com) | 19 | 7% |
| 7 | Cloudflare Queues (cloudflare.com) | 19 | 7% |
| 8 | Cloud Tasks (cloud.google.com) | 15 | 5% |
| 9 | Inngest (inngest.com) | 12 | 4% |
| 10 | Amazon EventBridge (aws.amazon.com) | 10 | 3% |
| 11 | Vercel Workflow (vercel.com) | 10 | 3% |
| 12 | BullMQ (bullmq.io) | 6 | 2% |
| 13 | Confluent (confluent.io) | 5 | 2% |
| 14 | Amazon MSK (aws.amazon.com) | 5 | 2% |
| 15 | Redpanda (redpanda.com) | 4 | 1% |
| 16 | Graphile Worker (worker.graphile.org) | 3 | 1% |
| 17 | Redis (redis.io) | 3 | 1% |
| 18 | pg-boss (github.com) | 2 | 1% |
| 19 | RabbitMQ (rabbitmq.com) | 2 | 1% |
| 20 | Google Cloud Workflows (cloud.google.com) | 2 | 1% |
| 21 | Redis Cloud (redis.io) | 2 | 1% |
| 22 | Svix (svix.com) | 1 | 0% |
| 23 | Temporal (temporal.io) | 1 | 0% |
| 24 | Laravel Queues + Redis | 1 | 0% |
| 25 | Aiven (aiven.io) | 1 | 0% |
| 26 | CloudAMQP (cloudamqp.com) | 1 | 0% |

## By agent

- Claude Code (Claude Opus 5): 96 runs, first Apache Kafka (15), then Service Bus (8)
- Codex (GPT-5.6 Sol): 96 runs, first Amazon SQS (17), then Apache Kafka (11)
- Cursor (Grok 4.6): 96 runs, first Apache Kafka (13), then Service Bus (8)

## By persona

- Junior developer: 120 runs, first Service Bus (24), then Apache Kafka (23)
- Enterprise team: 72 runs, first Apache Kafka (16), then Amazon SQS (15)
- Senior engineer: 48 runs, first Amazon Kinesis (19), then Amazon SQS (7)
- Vibe coder: 48 runs, first Cloudflare Queues (19), then Inngest (10)

## By what the ask stressed

- The plain ask: 222 runs, first Amazon SQS (26), then Laravel Queues (20)
- Procurement and compliance: 36 runs, first Apache Kafka (23), then Service Bus (6)
- Volume and cost at scale: 30 runs, first Apache Kafka (16), then Confluent (5)

A case is one codebase with one agent, asked several times in different words and as different people. 26 of 36 cases did not hold to a single choice.

## How this was measured

Every number on this page comes from a controlled experiment. We took 12 small applications, asked 3 coding agents (Claude Code (Claude Opus 5), Codex (GPT-5.6 Sol), Cursor (Grok 4.6)) to add a message queue to each of them, in several wordings and as a junior developer and enterprise team and senior engineer and vibe coder, and let the agent choose the product. Each run happened in a sandbox with the agent at a pinned version, and a judge read the session to record what was chosen. That is 288 runs. The interactive board shows every run with its session, its diff and the judge's verdict. A simulated user stood in for the owner of the codebase: it read the agent's plan and had to approve it before any code was written; it sent the agent back at least once in 118 runs.

Methodology and publications: https://armature.tech/publications

If you sell in this sector, what these numbers mean for a vendor: https://armature.tech/library/message-queues-coding-agents-playbook (Markdown: https://armature.tech/library/message-queues-coding-agents-playbook.md)

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