I have developed multiple multi-step LLM workflows, expressible as both conditional and parallel DAGs, using mostly plain Python, and I still don't understand why these langchain-type libraries feel the need to exist. Plain Python is quite sufficient for advanced LLM workflows if you know how to use it.
LLMs are innately unreliable, and they require a lot of hand-holding and prompt-tuning to get them to work well. Getting into the low-level details of the prompts is too essential. I don't want any libraries to come in the way because I have to be able to find and cleverly prevent the failure cases that happen just 1 in 500 times.
These libraries seem to mainly just advertise each other. If I am missing something, I don't know what it is.
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