My understanding is that the patterns are similar (in that you're enabling an LLM to use external tools/information), and all those patterns would fall under the "agents" pattern.

But, I think the difference is that WebGPT was actually fine-tuned / retrained for its specific use case, while the agents in these libraries just use the generic model without fine-tuning. My guess (and I'm not an expert here) is that fine-tuning these models for specific agent uses cases would probably result in better outcomes... Though as the models get more powerful, they might just perform well enough out of the box. (Also, some of the most recent OpenAI models don't support fine-tuning, and even for the ones that do, you'd need to generate the data to fine tune).

I think the ReAct paper also popularized this approach: https://arxiv.org/abs/2210.03629

Perhaps these papers are also just coincidence. This field is so new and this type of reasoned completion chaining seems like it was an inevitability. I imagine many other active gpt products that got started early hand rolled similar systems.

Haystack's agent is indeed using the approach suggested in the ReAct paper