AutoGPT VS Auto-Vicuna

Define agency. Does AutoGPT or BabyAGI fit the definition?

This leap is evident in projects like BabyAGI and AutoGPT, showcasing how such agents can prioritize and execute tasks based on a pre-defined objective and the results of previous actions, such as sales prospecting or ordering pizza.

A system and a user message are constructed from the task given by the user in code and passed to the LLM as input.

A: How much do you know? If you can easily read code (in this example Python, but this will still benefit anyone who can read code), you should check out Auto-GPT. If you are looking to explore different options, check out this doc on AI Agents.

Also I think most agents I have seen have implemented some form of long-short term memory. Why does it say autogpt doesnt support it? https://github.com/Significant-Gravitas/Auto-GPT/tree/master/autogpts/autogpt/autogpt/memory

In my newest experiment, I try out MetaGPT, which is supposed to be better than AutoGPT according to MetaGPT's paper.

In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376

Check out the new Re-Arch README and ARCHITECTURE_NOTES.

Which works, but produces nonsense 90% of the time for me. There's also that one notebook/colab for testing langchain but is above my abilities to use. And there's Auto-Vicuna of course, whose development however seems stalled