Do not read 548 READMEs. I am serious. Pick a shortlist, build one small thing, and let the weekend teach you what documentation never will. Docs tell you what a framework can do. Building tells you what it feels like to live inside it, which is the thing that actually matters.
Filter one is maintenance, and be strict about it. An agent framework is infrastructure, and infrastructure needs a pulse: recent commits, issues getting real answers, a release in the last few months. Our database is basically an open source AI agent GitHub activity tracker tool, scoring all of this across 548 projects, which is why I check it before I check anything else. Most of the list is experiments. Experiments are for weekends, not foundations.
Then pick one framework per philosophy and try the one that matches your gut. Like control? Go graph-based. Like teams of specialists? Go role-based. You will learn more in one weekend of building than a month of comparing, and your gut is smarter about this than you think. And here is a small contradiction: if code is not your thing, start with a no-code AI agent builder app instead. Plenty are free to try. There is no shame in it. Building first in any tool teaches you what to demand from a framework later.
Before you get attached, check the license and the model layer. Some frameworks quietly assume one provider, and swapping vendors later is the kind of pain you remember for years. A pluggable model layer keeps the door open. People usually get this wrong by falling in love with the demo and skipping the boring checks.
Last: read the examples and feel the community. Good examples mean good design instincts. An active community means the thing survives. Our open source AI agent frameworks database covers 548 projects, all open source, ranked on activity instead of hype, and honestly it is the best open source AI agents shortlist machine I know. Use it at localaiagents.fyi. Or skip the learning curve and I will build custom AI agents scoped to your actual workflow.