[KevinBenSmith]

As someone who has created several LLM-based applications running in production, my personal experience with langchain has been that it is too high of an abstraction for steps that in the end are actually fairly simple.

And as soon as you want to slightly modify something to better accomodate your use-case, you are trapped in layers & layers of Python boiler plate code and unnecessary abstractions.

Maybe our llm applications haven’t been complex enough to warrent the use of langchain, but if that’s the case, then I wonder how many of such complex applications actually exist today.

-> Anyways, I came away feeling quite let down by the hype.

For my own personal workflow, a more “hackable” architecture would be much more valuable. Totally fine if that means it’s less “general”. As a comparison, I remember the early days of HugginfaceTransformers where they did not try to create a 100% high-level general abstraction on top of every conceivable Neural Network architecture. Instead, each model architecture was somewhat separate from one another, making it much easier to “hack” it.

[dpbrinkm]

>someone who has created several LLM-based applications running in production

Come and talk about what you are doing and challenges of it at our LLM in production virtual conference? https://home.mlops.community/home/events/llm-in-prod-part-ii...

[19h]

Comparing Langchain to Hugging Face Transformers is apples and oranges. One is for research, one is for production. Production ML requires more abstraction, not less.

[crazyedgar]

I disagree. Production systems don't need to be full of AbstractSingletonProxyFactoryBeans which is basically what LangChain is. For example, Linux certainly isn't.