Decide which AI dev library
to adopt — axis by axis.
A GitHub star count can’t tell you whether a library will lock you in or ship a breaking change next month. EverythingRated scores 24 AI dev libraries on 5 separate axes — maintenance, community, license, API stability, footprint, and AI portability — so the adoption trade-off is visible before you commit.
Your turn
Rate a tool yourself — takes 30 seconds
Open the AI dev tools board
Every tool sits on one board with per-axis averages side by side.
Open a tool
See the per-aspect averages and how many people have rated it.
Rate the axes
Give each aspect a 1–5. Your scores update the averages instantly.
Reading the numbers
How confident is a rating?
An average score is only as trustworthy as what backs it. Two raters who disagree is not the same as twenty raters who agree. Each aspect shows its rating count next to the bar — use it to weight the number.
A 3.0 from two people who disagreed is mostly noise. The next rating could move it half a point in either direction. Treat aspects with fewer than ~5 ratings as a starting opinion, not a verdict.
A 4.3 from two dozen raters who broadly agreed is a real signal. One new rating barely moves the average, so the number you see is close to what you would get tomorrow.
- Rating count. Shown next to every aspect — under ~5 is thin, 10+ is solid.
- Spread. A 3.0 built from 4s and 2s is different from a 3.0 built from a stack of 3s.
- Per axis, not per item. A library can have a confident maintenance score and a thin stability score — read them separately.
- Re-rating counts. Visitors can update their score, so the average tracks current opinion, not first impressions.
Shareable lists
Build a ranked list people can actually use
Rate a few tools, save your ranking, and share the final list. Instead of saying “best AI tool,” show exactly why one wins.
Make your list →Best AI dev libraries
Ranked by stability, maintenance, and AI portability
Vercel AI SDK
Best provider portability
LangChain
Largest ecosystem
LiteLLM
No model lock-in
Why multi-axis
Axes that decide adoption
AI dev libraries are scored on maintenance, community, license, API stability, footprint, and AI portability — the six dimensions the adopt/skip call actually turns on.
One number per axis
Every aspect gets its own average, plus an overall score across aspects. The detail stays visible instead of collapsing into one star.
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