That’s a fair point — ecosystems naturally evolve with competing frameworks, and sometimes that diversity helps the best ideas emerge. But in those examples (PHP vs. .NET vs. Django, React vs. Angular), we usually see clear differences in philosophy, performance, developer experience, or use case fit.

What I find puzzling in the current wave of AI agent libraries is that many seem to be doing almost exactly the same thing — wrapping LLMs, defining tools, planning steps — just with slightly different APIs or names. It’s hard to see a fundamental divergence in capability or vision, at least so far.

So my question is less about why variety exists — and more about whether this current fragmentation is actually productive, or just noise driven by branding and platform lock-in strategies.

Would love to be wrong here if others are seeing more substantial innovation beneath the surface.

Also, I’d love to see a shared protocol or framework emerge — something like the Model Context Protocol (MCP) or an open standard — that different tools and platforms can build on and improve together. That kind of foundation could push the whole ecosystem forward instead of scattering innovation across isolated silos.