LLM that can call multiple tool APIs with one request

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.

If you wanted to compare OpenAI models against Anthropic or Google, wouldn't the framework help a lot? Breaking APIs is more about bad framework development than frameworks in general.

I think frameworks tend to provide an escape hatch. LlamaIndex comes to mind. It seems to me that by not learning and using an existing framework, you're building your own, which is a calculated tradeoff.

That is a good use case and it's a good problem to have, certainly the kind I wanted to hear, but it's not a problem I have had yet.

Moreover, I absolutely expect to have to update my prompts if I have to support a different model, even if its a different model by the same provider. For example, there is a difference in the behavior of gpt4-turbo vs gpt4-o even though both are by OpenAI.

Specific LLMs have specific tendencies and preferences which one has to work with. What I'm saying is that the framework will help, but it's not as simple as switching the model class.