# Do coding agents recommend NATS? > Coding agents raised NATS in 83 of 288 judged message queues sessions and chose it in none of them. Measured with Claude Code, Codex and Cursor. Source: https://armature.tech/library/do-coding-agents-recommend-nats Published: 2026-09-15 Publisher: Armature, Inc. (https://armature.tech) --- > Coding agents raised NATS in 83 of 288 judged message queues sessions and chose it in none of them. This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in message queues inside a realistic codebase. Not what a chat assistant says about NATS. What an agent actually installed. ## The numbers | | | | --- | --- | | Category | Message queues | | Sessions in the category | 288 | | Sessions where NATS was chosen | 0 | | Install share | 0% | | Raised as a candidate, not chosen | 83 | | Chosen when considered | 0% | | Site | nats.io | ## Which agents raised NATS Every agent considered it and none adopted it. Cursor raised it most often, in 39% of its message queues sessions. | Agent | Sessions | Raised NATS | Chose it | | --- | --- | --- | --- | | Claude Code | 96 | 25 (26%) | 0 | | Codex | 96 | 21 (22%) | 0 | | Cursor | 96 | 37 (39%) | 0 | ## Which buyers it came up for It surfaced most for requests written as senior engineer, in 52% of those sessions. Knowing which buyer already has the product in mind tells you which pages to fix first. | Who is asking | Sessions | Raised it | | --- | --- | --- | | Vibe coder | 48 | 7 (15%) | | Junior developer | 120 | 23 (19%) | | Senior engineer | 48 | 25 (52%) | | Enterprise team | 72 | 28 (39%) | ## What NATS was up against The full ranking in message queues, from the same sessions: | # | Product | Runs won | Share | | --- | --- | --- | --- | | 1 | Built in-house (no product adopted) | 49 | 17% | | 2 | Apache Kafka | 39 | 14% | | 3 | Amazon SQS | 27 | 9% | | 4 | Service Bus | 24 | 8% | | 5 | Laravel Queues | 20 | 7% | | 6 | Amazon Kinesis | 19 | 7% | | 7 | Cloudflare Queues | 19 | 7% | | 8 | Cloud Tasks | 15 | 5% | ## What this means Raised 83 times and chosen none is a specific, diagnosable result, and a better starting position than being unknown. The agent has the product in mind. It reached the shortlist 83 times. Something then lost every one of those sessions. In our data the causes, in the order they occur: - The quickstart does not run when pasted, so the agent abandoned it mid-integration. - The documentation describes an interface that changed, so the generated code failed. - The product name and the package name differ, so the install step went wrong. - The fit was genuinely wrong for the repository, which is fine and worth knowing. All but the last are fixable in days, and the reason is written down in the session transcript. ## Where these numbers come from The 288 sessions in message queues are part of a published set of 7,852, 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 message queues run can be replayed on [the board](/leaderboards/message-queues). If you work on NATS: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts. ## Common questions ### Do coding agents recommend NATS? They raise it but do not choose it. Across 83 sessions where NATS came up as a candidate, agents chose something else every time. ### Does Claude Code recommend NATS? In 0 of the 96 sessions in message queues run with Claude Code, which is 0%. ### Do different coding agents treat NATS differently? Not much. The three agents chose it at similar rates, between 0% and 0% of their runs. ### How was this measured? Real coding agents at pinned versions were run in sandboxes inside 90 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. ## Read next - [How to get picked for message queues by coding agents](https://armature.tech/library/message-queues-coding-agents-playbook) (Markdown: https://armature.tech/library/message-queues-coding-agents-playbook.md) - [Do coding agents recommend Apache Kafka?](https://armature.tech/library/do-coding-agents-recommend-apache-kafka) (Markdown: https://armature.tech/library/do-coding-agents-recommend-apache-kafka.md) - [Do coding agents recommend Amazon SQS?](https://armature.tech/library/do-coding-agents-recommend-amazon-sqs) (Markdown: https://armature.tech/library/do-coding-agents-recommend-amazon-sqs.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) --- Armature helps software products get discovered and used by coding agents. 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