Top AI Product

I keep an eye on [GitHub Trending via trendshift.io](https://trendshift.io), and every once in a while something pops up that makes me stop scrolling. [Agency-Agents](https://github.com/msitarzewski/agency-agents) is one of those projects. It hit 7k stars with over 1.1k forks, and after spending some time with it, I get why.

The idea is straightforward but surprisingly well-executed. Agency-Agents is an open-source collection of AI agent personas, each defined as a markdown file with a distinct personality, a specific workflow, and clear deliverables. Think of it like a casting directory for AI roles — you’ve got a Product Trend Researcher, a Backend Architect, a Brand Guardian, a Growth Hacker, a UX Researcher, and plenty more. There are even fun ones like a “Whimsy Injector” and a “Reddit Community Ninja.” Each agent isn’t just a name and a job title — they come with detailed process descriptions and expected outputs you can actually verify.

What got the community excited is the multi-agent collaboration angle. You can deploy several of these agents together to evaluate a software opportunity and produce a unified product plan. The [Nexus Spatial Discovery Exercise](https://github.com/msitarzewski/agency-agents) in the repo demonstrates eight agents working in concert, which is pretty cool to see in action.

The backstory adds to the appeal. It started as a Reddit post, and within the first 12 hours, over 50 Redditors were asking for it. The creator, [msitarzewski](https://github.com/msitarzewski), iterated on it for months based on community feedback before it blew up on GitHub Trending. That organic growth from a real community need — rather than some VC-backed launch — gives it credibility.

It’s MIT licensed, so you can use it however you want, commercially or otherwise. If you’ve been experimenting with AI agents and felt like most of them are too generic, this repo is worth bookmarking. The agents here feel like they were designed by someone who actually runs projects, not someone writing hypothetical specs. And honestly, that makes all the difference.