 | I built AgentTape because none of the existing model leaderboards quite cover all the things that I was interested in: benchmark performance is one part, but so is who's actually using a model, who's talking about it, and how it compared on cost and speed. It pulls hourly data from GitHub, Hugging Face, OpenRouter, MCP registries, npm, PyPI, arXiv, Hacker News, and more - to score and compare each public AI agent and foundation model. I'm still tweaking the scoring methodology (it's early days), so I'd love to hear your thoughts, if it's helpful, or anything you think I've got wrong! [link] [comments] |
I built a live ranking of every AI agent and foundation model (open source)
Reddit r/artificial / 5/20/2026
💬 OpinionDeveloper Stack & InfrastructureSignals & Early TrendsTools & Practical Usage
Key Points
- The article introduces AgentTape, a live, open-source ranking of AI agents and foundation models aimed at covering more than benchmark performance.
- AgentTape aggregates hourly data from sources including GitHub, Hugging Face, OpenRouter, MCP registries, npm, PyPI, arXiv, and Hacker News to score models and agents.
- The scoring is designed to reflect not only performance, but also real-world adoption signals such as usage and discussion.
- It also attempts to compare cost and speed alongside other factors, though the author notes the methodology is still being tuned.
- The creator invites feedback on the early scoring approach and potential inaccuracies.
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