Python Tools That Turn Web Pages Into AI Text Grew 11.5 Times Faster Than Python SEO Tools Since Early 2023

Summary

Picture an accountant who used to worry about two kinds of reader besides clients: the search engine crawler and the SEO tools people pointed at her site to check rankings. Today a third kind of reader shows up far more often, software that grabs her pages and turns them into text for an AI model.

Both groups of tools are open source and installed from the Python Package Index. Python SEO packages handle tasks such as Google Trends lookups, site audits and Search Console exports. Web-to-text packages strip pages down to clean text or Markdown for language models. Comparing their downloads shows which kind of web reading is growing faster among people who write code.

Indexed to early 2023, web-to-text downloads grew 61.9x by Q3 2026, while SEO package downloads grew 5.4x. The AI side grew 11.5 times faster, and SEO tools did not even keep pace with the 8.3x growth of PyPI itself.

What we measured

We queried the public PyPI download log in Google BigQuery (bigquery-public-data.pypi.file_downloads), which records one row for every file downloaded from the Python Package Index. To keep the query affordable we sampled the first seven days of every month from January 2022 through September 2026, 399 days in total, and summed downloads by month, package, country, CI flag, installer and weekday versus weekend into one working table. A quarter in this study therefore means 21 sampled days (days 1 to 7 of each of its three months), not a full quarter. We removed downloads made by mirroring and proxy tools (bandersnatch, Nexus, Artifactory, devpi), which copy packages in bulk rather than install them. To control for the growth of PyPI itself we also pulled total daily download counts for the whole index from the table's partition metadata for the same sampled days.

The SEO group has four packages: pytrends, advertools, pyseoanalyzer, searchconsole. The two other SEO packages we checked, python-seo-analyzer and google-searchconsole, had no downloads under those names. The web-to-text group has 14 packages that turn web pages into text or Markdown. Both series are indexed to their Q1 2023 total (21 sampled days) set to 100.

Web-to-text tools grew 11.5 times faster

Index: each group's Q1 2023 sampled downloads = 100. PyPI overall is shown for reference.

SEO packages stayed below their Q1 2023 level through Q1 2024 and hovered between 114 and 155 through 2025, then rose in 2026 to an index of 539. Web-to-text packages passed an index of 1,000 in Q2 2025 and reached 6,194 in Q3 2026.

The size gap went from 8.8x to 101.4x

Raw download totals for the sampled days of each quarter.

In Q1 2023, for every download of a Python SEO package there were 8.8 downloads of a web-to-text tool. By Q3 2026 there were 101.4.

Python SEO work is concentrated in one data source

Download share by SEO package, Q3 2026 sample days.

Most Python SEO work in this data is pulling Google Trends data. Broader SEO tooling mostly lives in paid web apps rather than open-source Python, so this comparison covers the code-writing slice of SEO work, not the SEO industry as a whole. Even so, it shows how much more often developers now build tools that read web pages for AI.

What this means

Most SEO effort still targets search engine crawlers and ranking tools, and that work still matters. But a fast-growing group of machines reading your site turns pages into text for AI models, and those tools care about different things: whether your main facts sit in plain text, whether headings describe the content, and whether a page makes sense when stripped of design. AI search optimization adds those checks to the SEO work you already do.

A practical step is to read your key pages as plain text, with no styling, and see whether they still answer a customer's main questions. For a fuller picture, request a free AI visibility audit of how AI assistants describe your business; it shows which pages need LLM SEO work and which are fine.

Limits

A download is not a user. One developer, one Docker build or one CI job can download the same package many times, and a single busy pipeline can move a package's numbers sharply from month to month.

Figures cover the first seven days of each month only (21 days per quarter), so they are sample totals, not full-month or full-quarter totals. Growth rates compare like-for-like sample periods.

PyPI as a whole grew from 12.1 billion downloads in the Q3 2022 sample days to 115.0 billion in the Q3 2026 sample days (9.5x), so part of every package's growth is the index growing. We report share of all PyPI downloads where it matters.

PyPI-wide downloads in the September 2026 sample week were 38.1% lower than in the August 2026 sample week (28.7 billion vs 46.4 billion), so September figures for individual packages should be read with that drop in mind.

The CI flag is only populated in this dataset from March 2024, and it only catches environments that announce themselves as CI. Automated downloads that do not set a CI variable are counted as non-CI.

Country codes come from the downloading IP address. Cloud servers sit mostly in the United States, so country splits describe where machines run, not where developers live.

Python packages capture only the code-writing side of SEO. Most SEO work runs on commercial web tools that do not show up in PyPI logs.

The SEO group is small and dominated by one package, so its index can move sharply with that package's downloads.

Some web-to-text packages are used outside AI work.

Frequently asked questions

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