Cool stuff. I think agents are the most promising and potentially revolutionary part of the current LLM developments. Moreover, they're quite easy to write your

Cool stuff. I think agents are the most promising and potentially revolutionary part of the current LLM developments. Moreover, they're quite easy to write yourself, you don't need something like Langchain, the prompt structure is simple and it's a bit of text templating (it's all based on the ReAct paper[0]). That said, the bit about "The trick to avoid hallucination" in the attached blog post is a very neat idea. Obvious in hindsight, as it often is: > So the trick is that we send a stop pattern which in this case is when we see Observation: in it’s output, because then it has created a Thought, an Action and used a tool and hallucinates the Observation: itself :D > This stop parameter is a normal parameter of the OpenAI API by the way, so nothing special to implement there. In general, it's magical when you put these things in a feedback loop. The way they can automatically respond to errors and adjust their actions is really cool - take a look at the last gif here[1]. [0]: https://arxiv.org/abs/2210.03629 https://arxiv.org/abs/2210.03629 [1]: https://github.com/cube2222/duckgpt https://github.com/cube2222/duckgpt