Building Effective AI Agents

> These frameworks make it easy to get started by simplifying standard low-level tasks like calling LLMs, defining and parsing tools, and chaining calls together. However, they often create extra layers of abstraction that can obscure the underlying prompts and responses, making them harder to debug. They can also make it tempting to add complexity when a simpler setup would suffice.

> We suggest that developers start by using LLM APIs directly

Best advice of the whole article by far.

It's insane that people use whole frameworks to send what is essentially an array of strings to a webservice.

We've removed LangChain and LangGraph from our project at work because they are literally worthless, just adding complexity and making you write MORE code than if you didn't use them because you have to deal with their whole boilerplate.

I suppose langflow also falls into this bucket.

I still think it has a definite use case in regularising all of your various flows into a common format.

Sure, I could write some code to get SD to do all the steps to generate an image, or write some shader code. But it's so much more organised to use comfy-UI, or a shader graph, especially if I have n>1 flows/tasks, and definitely while experimenting with what I'm building.