I think that’s the wrong goal.
Langchain, with its kitchen-sink approach is great for newbies playing around with language models, to get a feel for the different things possible. The uniform interfaces make it very easy to snap together pieces and try things out (iff you don’t care to understand what’s happening under the hood).
Once you know exactly what you need (which might be 10% of Langchain’s capabilities), it might make sense to avoid all the cruft and build your own simple wrappers suited to the task at hand — especially if you need a robust and transparent/debuggable system. Langchain has a little too much indirection, and clumsy abstractions, to be fit for this purpose.
Where I do find it useful is the many tools it saves me from having to build from scratch. For example I use a page scraper and an embedding service with retries are two things I use it for with the bot I built for my companies slack and discord. In theory I could see using it more in my project eventually. https://github.com/ShelbyJenkins/shelby-as-a-service
Langchain has a lot of surface area and it is still on 0.0.x, so also much advised to pin the versions hard.
My feeling is that with the agentic/AutoGPT hype fading and OpenAI adding the functions API there's a lot less value in LangChain's abstractions, at least for production use cases. They're still cool for hacking/toys.
They used to have a "concepts" page that links to relevant papers but it seems that the page is gone from the doc for some reason :-(
"Don't be snarky."
Class incomprehension is the defining feature of LangChain!
