What coding agents choose in a Python project

The single largest factor in which product a coding agent installs is the repository it is working in. Language is the biggest part of that. This page is what the numbers look like inside Python codebases.

What coding agents choose in Python

Each row is at least 25 judged sessions inside a Python codebase. Categories with fewer are left out rather than reported thin.

Read the single-codebase rows as one repository, not as Python. 8 of these 15 categories ran on one Python codebase, because the panel only had one with that seam open. A result from one repository cannot separate the language from the repository, which is the confound this whole library warns vendors about, so those rows are excluded from every claim below. They are: evals, deployment, bot protection, product analytics, cloud, performance testing, serverless compute, search.

The same leader, a different grip

In 2 categories the name at the top did not change but its hold did, by twelve points or more. That is still a different market.

A leader that is stronger here has the Python templates and the Python documentation right. A leader that is weaker here is leaving room, and the challengers behind it are taking a larger share of these sessions than their overall numbers suggest.

Where Python disagrees with the overall result

4 categories. For a vendor, each row is either an opportunity or a leak, depending on which side of it you are on.

Agent frameworks. In Python codebases the agents chose Temporal most often, in 15% of 132 sessions. Across all languages the leader is Vercel AI SDK at 14%. See the Agent frameworks playbook.

Code sandboxes. In Python codebases the agents chose Modal most often, in 51% of 108 sessions. Across all languages the leader is E2B at 42%. See the Code sandboxes playbook.

Observability. In Python codebases the agents chose Amazon CloudWatch most often, in 35% of 72 sessions. Across all languages the leader is Sentry at 37%. See the Observability playbook.

File storage. In Python codebases the agents chose Google Cloud Storage most often, in 67% of 27 sessions. Across all languages the leader is Amazon S3 at 46%. See the File storage playbook.

Where agents write it themselves in Python

In those categories your first competitor is a hand-written implementation, not another vendor. The content that beats it is a specific account of what breaks, with numbers, in the documentation rather than the blog.

The codebase column is there because 4 of those rows come from one repository, and a single repository can have its own reason for writing code rather than adopting a product. Weigh them accordingly.

Why the split matters

4 leaders swapped and 2 held their place with a different grip, out of the 7 categories measured across more than one codebase. Nobody changed a product or rewrote a page between those sessions.

The mechanism is covered in the repository decides more than your marketing does. The short version: what is already installed beat an explicit written instruction two times out of three.

If you sell into Python

Split your own install share by language before you read it. A vendor in ai gateways looking at 21% overall is looking at 42% here, across 3 codebases. Then ship an official Python template for the stacks your users run, and check your package name in the Python registry against your product name.

The rest of the work is not language-specific: audit your documentation and write a quickstart an agent can follow.

How this was measured

The 983 Python sessions are part of a published set of 5,292, run with real coding agents inside realistic codebases and judged blind. Full method: how we measured this. Every run is on the sector boards.

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