Do you want a banana? You should first create the universe and the jungle and use dependency injection to provide every tree one at a time, then create the monkey that will grab and eat the banana.
https://www.johndcook.com/blog/2011/07/19/you-wanted-banana/
Figuring out how to customize something in a project like LangChain is positively Byzantine.
What you're alluding to is people coming from Java to Python in 2010+ and having a use-classes-for-everything approach.
Idiomatic and maintainable TypeScipt is no worse than vanilla JavaScript.
Second reason - to fail fast. No sense in sculpting novel ideas in C++ while you can muddle with Python 3x faster, that's code intended to be used just a few times, on a single computer or cluster. That was an era dominated by research, not deployments.
Llama.cpp was only possible after the neural architecture stabilized and they could focus on a narrow subset of basic functions needed by LLMs for inference.
I still find LC really useful if you stick to the core abstractions. That tends to minimize the dependency issues.
My point is to follow a dogmatic OOP approach (think all the nouns like Agent, Prompt, etc.) to model something rather sequential.
Simula 67, like Java, may fit into some newer OOP definitions that have come about over the years, but it was not considered OOP at the time of its arrival. The term OOP hadn't even been invented yet. And when OOP finally did get used for the first time, it most definitely did not refer to Simula 67. Where on earth did you get the idea that it did? Whatever gave you that idea was, frankly, nonsense.
Furthermore, message passing was the defining feature of OOP. Of course it was. That is the distinction OOP was calling attention to – what made OOP different from the other object-based languages that had been around for decades beforehand. Nobody was going to randomly come up with OOP out of the blue to describe a programming model from the long ago past. Method calling may be superficially similar, but OOP was coined to call attention to what difference there is.
You are, of course, quite free to come up with your own redefinition of OOP that includes Simula, C++, Java, whatever you wish. You would not be the first. However, as we are talking about the original definition, whatever you want to define it as does not apply. It is not your definition that is under discussion.
I'm guessing only Smalltalk rivals Java in OOP-ness, as in Smalltalk literally everything is an object, while in Java only most things are objects.
I know Pythonista's regard themselves more as artists than engineers, but the rest of us needs reliable and deterministically running applications with observability, authorization, and accessible documentation. I don't want to drop into a notebook to understand what the current throughput is, I don't want to deploy huge pickle and CSV files alongside my source to do something interesting.
LangChain might not be the answer, but having no standard tools at all isn't either.
Langchain is, when you boil it down, an abstraction over text concatenation, staged calls to open ai, and calls to vector search libraries.
Even without standard tooling, an experienced programmer should be able to write an understandable system that does those things.
That's the central idea here. Most guys available to hire aren't. Hence why they get constrained into a framework that limits the damage they can cause. In other areas of software development the frameworks are quite mature at this point so it works well enough.
This AI/LLM/whatever you want to call it area of development, however, hadn't garnered much interest until recently, and thus there isn't much in the way of frameworks to lean on. But business is trying to ramp up around it, thus needing to hire those who aren't good to fill seats. Like the parent says, LangChain may not be the framework we want, but it is the one we have, which beats letting the not-very-good developers create some unconstrained mess.
If you win the lottery by snagging one of the small few good developers out there, then certainly you can let them run wild engineering a much better solution. But not everyone is so fortunate.
Sounds like your hiring team just isn’t very good.
There are plenty of skilled people working in LLM land
"More artists than engineers": yes and no. I've been working with Pandas and Scikit-learn since 2012, and I haven't even put any "LLM/AI" keywords on my LinkedIn/CV, although I've worked on relevant projects.
I remember collaborating back then with PhD in ML, and at the end of the day, we'd both end up using sklearn or NLTK, and I'd usually be "faster and better" because I could write software faster and better.
The problem is that the only "LLM guy." I could trust with such a description, someone who has co-authored a substantial paper or has hands-on training experience in real big shops.
Everyone else should stand somewhere between artist and engineer: i.e., the LLM work is still greatly artisanal. We'll need something like scikit-learn, but I doubt it will be LangChain or any other tools I see now. You can see their source code and literally watch in the commit history when they discover things an experienced software engineer would do in the first pass. I'm not belittling their business model! I'm focusing solely on the software. I don't think they their investors are naive or anything. And I bet that in 1-2 years, there'll be many "migration projects" being commissioned to move things away from LangChain, and people would have a hard time explaining to management why that 6-month project ended up reducing 5K LOC to 500 LOC.
For the foreseeable future though, I think most projects will have to rely on great software engineers with experience with different LLMs and a solid understanding of how these models work.
It's like the various "databricks certifications" I see around. They may help for some job opportunities but I've never met a great engineer who had one. They're mostly junior ones or experienced code-monkeys (to continue the analogy)