[maxbond]

It seems pretty rare that communities get "stuck with" a framework. Frameworks are pretty fluid. Eg, Python didn't get "stuck on Django," and they didn't get "stuck on flask," and they're not "stuck on FastAPI" now - the ecosystem continued to evolve, and none of these projects even had to die for a different vision of how a framework should be organized to capture the zeitgeist. They've each got dedicated communities which are continuing to improve them.

Similarly, I expect creative hackers to pursue new approaches in the space of LLM frameworks and for some of those to catch on, and that they don't need to uproot langchain to do so.

[startupsfail]

The difference is, lots of data is being generated. And, open models in particular, are trained on it. So there is a certain level of stickiness.

An analogy of a file format is a close one. Imagine someone invents something nasty and bulky, like a twisted form of XML or something. And, simply because it is early, it catches on. It could be buggy and unstable and ugly, but it still wins, because it is early.

The call here is to try to examine LangChain design a bit closer. And maybe consider that a start from scratch is a good idea.