I scored 14 popular AI frameworks on behavioral commitment — here's the data

When you're choosing an AI framework, what do you actually look at? Usually: stars, documentation quality, whether the README looks maintained. All of that is stated signal. Easy to manufacture, doesn't tell you if the project will exist in 18 months. I built a tool that scores repos on behavioral commitment — signals that cost real time and money to fake. Here's what I found when I ran 14 of the most popular AI frameworks through it. The methodology Five behavioral signals, weighted by how hard they are to fake: Signal Weight Logic Longevity 30% Years of consistent operation Recent activity 25% Commits in the last 30 days Community 20% Number of contributors Release cadence 15% Stable versioned releases Social proof 10% Stars (real people starring costs attention) Archived repos or projects with no pushes in 2+ years are penalized 50%. The results Framework Score Age 30d Commits Stars 🥇 openai/openai-python 95/100 5.4 yr 28 30k 🥇 deepset-ai/haystack 95/100 6.4 yr 100 25k 🥈 langchain-a