Ranking AI agent tools by star growth speed, not total stars
Most 'top AI agent tool' lists just reflect what was popular months ago rather than what's actually gaining traction right now. An alternative ranking method scores projects by star growth velocity instead of total star count: it looks at 24-hour and 7-day GitHub star growth, ranks projects by percentile within their own source category, applies time decay so old spikes fade, and cross-checks GitHub activity against the . The cross-check matters because it downweights projects that spike on one source but show no activity elsewhere — a signal often tied to fake-star campaigns (the StarScout paper documented millions of fraudulent ).
This week's fastest-rising tool is codex-model-routing-team, just one day old with 107 stars. It routes parallel Codex tasks to ers, letting each worker use a different, explicitly chosen model. Normally, subagents inherit the same model as their parent session, so this gives much finer control over cost and model choice per task.
Also rising fast: pilotfish (+359 stars), a multi-model for Claude Code, and motion-anything (+354 stars). Notably, the fastest-growing projects aren't about spawning more agents — they focus elsewhere.
Key points
- Ranks projects by star growth velocity (24h/7d growth, time decay, percentile within source) instead of total stars
- Cross-checks GitHub against the to filter out fake-star-inflated projects
- codex-model-routing-team: 107 stars in 1 day, routes parallel Codex tasks to workers each using a different explicit model for finer cost/model control
- Subagents normally inherit the parent session's model — this tool works around that limitation
- pilotfish (+359★, multi-model for Claude Code) and motion-anything (+354★) are also rising fast