12 huge Python projects on GitHub depend on a single person — I scored 200 of them

Bus factor = 1: hugely popular projects, single point of failure

A repo's bus factor is the smallest number of top contributors who together hold ≥50% of all contributions. A bus factor of 1 means: one person is the project. If they stop, momentum stalls.

Out of 200 popular Python projects, 41 have bus factor = 1. The most exposed:

Awesome-lists owning their bus factor is fine. But FastAPI, ComfyUI, Whisper, open-webui are critical infrastructure for a lot of people. Worth keeping in mind when you ship them in production.

"Active-looking" but actually dormant

These are not archived, they look maintained on the surface, yet nobody pushed a commit in the last 4 weeks:

• donnemartin/system-design-primer — 348k stars, last push 57d ago
• TheAlgorithms/Python — 221k stars, no commits in 4w
• AUTOMATIC1111/stable-diffusion-webui — 163k stars, last push 75d ago
• ytdl-org/youtube-dl — 140k stars, last push 85d ago
• openai/whisper — 99k stars, last push 30d ago
• 3b1b/manim — 86k stars, last push 27d ago

Some are by design (curated lists, finished tools). Others are slowly going stale.

Release cadence extremes

Shipping fastest among popular projects:

Slowest cadence at 5k+ stars:

A long gap is not automatically bad — finished tools, security playbooks, and content collections do not need weekly releases.

What 5000+ star Python projects are about

Top topics across the 200 repos:

python              92
ai                  36
llm                 34
deep-learning       21
machine-learning    20
pytorch             18
hacktoberfest       17
openai              17
chatgpt             17
rag                 15
agents              14
ai-agents           13
cli                 11
data-science        11
gpt                 10

The Python OSS leaderboard in 2026 is overwhelmingly an AI agents / RAG / LLM-app leaderboard.

Aggregates

• Total stars across the 200 repos: 12,498,901
• Total forks: 1,981,076
• Median Health Score: 91 / 100
• With explicit OSS license: 188 / 200 (12 repos still have no license — risky to depend on those)

How to reproduce / explore your own list

I packaged the analysis as a one-shot Apify Actor — you give it a search query (or a list of owner/repo) and a free GitHub token, you get one row per repo with all the metrics above plus a transparent breakdown of how the Health Score is computed.

GitHub Repository Insights on Apify Store

Free trial, then $0.005 per analyzed repo. You can also clone the input from this post (language: Python, minStars: 5000, pushedSince: 2025-12-01, maxResults: 200) and re-run it against any other language or topic.

If you find more interesting patterns, drop them in the comments — I am curious what your stack looks like.