From the experiment

Do coding agents recommend Apify?

Apify was chosen in 2% of 591 judged AI search sessions, ranking number 13. Measured with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code.

Published September 24, 2026 Read as Markdown

Apify was chosen in 2% of 591 judged AI search sessions, ranking number 13. It was also raised as a candidate in 33 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 AI search inside a realistic codebase. Not what a chat assistant says about Apify. What an agent actually installed.

The numbers

CategoryAI search
Sessions in the category591
Sessions where Apify was chosen10
Install share2%
Rank in category13 of 42
Codebases it won in2
Raised as a candidate, not chosen33
Chosen when considered23%
Siteapify.com

By agent

With 10 wins spread across 5 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.

AgentSessionsChose ApifyShare
Claude Code14000%
Codex14443%
Cursor13611%
Grok Build CLI3500%
Muse Code13654%

By who was asking

Apify performs similarly across the four kinds of buyer, from 0% to 6%. That is unusual: the category leader changed with the persona in 22 of the 29 categories we measured.

Who is askingSessionsChose ApifyShare
Vibe coder9800%
Junior developer19621%
Senior engineer14586%
Enterprise team15200%

What Apify was up against

The full ranking in AI search, from the same sessions:

#ProductRuns wonShare
1Anthropic web search8615%
2Brave Search API8314%
3Exa7513%
4Tavily7312%
5Built in-house (no product adopted)325%
6OpenAI web search295%
7Perplexity Sonar264%
8Firecrawl254%

What this means

This is an integration problem, not a presence problem. Agents raised Apify in 33 sessions and chose it in 10, so it reaches the shortlist and then loses. Something at the last step is costing the session, and in our data that is usually a quickstart that does not run when pasted, documentation describing an interface that changed, or a package name that does not match the product name.

That is the cheaper of the two problems to have. The reason is written down in each losing transcript.

Where these numbers come from

The 591 sessions in AI search are part of a published set of 13,497, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.

Every AI search run can be replayed on the board.

If you work on Apify: 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 Apify?

Yes. Apify was chosen in 10 of the 591 judged sessions in AI search, a 2% install share, ranking number 13 in its category.

Does Claude Code recommend Apify?

In 0 of the 140 sessions in AI search run with Claude Code, which is 0%.

Do different coding agents treat Apify differently?

Not much. Claude Code, Codex, Cursor, Grok Build CLI and Muse Code chose it at similar rates, between 0% and 4% of their runs.

How was this measured?

Real coding agents at pinned versions were run in sandboxes inside 91 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 Apify considered but not chosen?

It was raised as a candidate in 33 sessions without being chosen, and chosen in 10. That is a 23% conversion from considered to chosen.

Where this comes from

Armature ran 13,497 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 91 realistic codebases, and published every run. The numbers on this page come from that work.

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