Do coding agents recommend django-axes?
django-axes was chosen in 7% of 160 judged bot protection sessions, ranking fourth. Measured with Claude Code, Codex and Cursor.
django-axes was chosen in 7% of 160 judged bot protection sessions, ranking fourth. It was also raised as a candidate in 15 further sessions without being chosen.
This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in bot protection inside a realistic codebase. Not what a chat assistant says about django-axes. What an agent actually installed.
One thing to read first: every one of those wins came from a single codebase. That is a result about one repository rather than about bot protection in general, and the splits below cannot separate the two. Treat them as a description of that repository.
The numbers
| Category | Bot protection |
| Sessions in the category | 160 |
| Sessions where django-axes was chosen | 11 |
| Install share | 7% |
| Rank in category | 4 of 6 |
| Codebases it won in | 1 |
| Raised as a candidate, not chosen | 15 |
| Chosen when considered | 42% |
| Site | github.com |
By agent
With 11 wins spread across three agents, the rates below are small numbers and a difference between them is not yet a finding. They are here because the direction is worth knowing, not because the gap is established.
| Agent | Sessions | Chose django-axes | Share |
|---|---|---|---|
| Claude Code | 54 | 3 | 6% |
| Codex | 54 | 2 | 4% |
| Cursor | 52 | 6 | 12% |
By who was asking
django-axes does much better with one kind of buyer than another. It won 31% of sessions asked as enterprise team and 0% of those asked as junior developer.
| Who is asking | Sessions | Chose django-axes | Share |
|---|---|---|---|
| Vibe coder | 61 | 0 | 0% |
| Junior developer | 63 | 0 | 0% |
| Enterprise team | 36 | 11 | 31% |
What django-axes was up against
The full ranking in bot protection, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Cloudflare Turnstile | 91 | 57% |
| 2 | Vercel BotID | 18 | 11% |
| 3 | Built in-house (no product adopted) | 18 | 11% |
| 4 | ALTCHA | 12 | 8% |
| 5 | django-axes (this page) | 11 | 7% |
| 6 | Google reCAPTCHA | 6 | 4% |
| 7 | Laravel RateLimiter | 2 | 1% |
What this means
django-axes was raised in 15 sessions and chosen in 11. That ratio is balanced enough that the ceiling is presence rather than integration: the product converts reasonably when it is on the table, and it is not on the table often enough.
Where these numbers come from
The 160 sessions in bot protection are part of a published set of 5,292, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every bot protection run can be replayed on the board.
If you work on django-axes: 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 django-axes?
Yes. django-axes was chosen in 11 of the 160 judged sessions in bot protection, a 7% install share, ranking fourth in its category.
Does Claude Code recommend django-axes?
In 3 of the 54 sessions in bot protection run with Claude Code, which is 6%.
Do different coding agents treat django-axes differently?
Not much. The three agents chose it at similar rates, between 4% and 12% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 51 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 django-axes considered but not chosen?
It was raised as a candidate in 15 sessions without being chosen, and chosen in 11. That is a 42% conversion from considered to chosen.
Where this comes from
Armature ran 5,292 judged sessions with Claude Code, Codex and Cursor inside 51 realistic codebases, and published every run. The numbers on this page come from that work.