babyagi VS AutoGPT

Define agency. Does AutoGPT or BabyAGI fit the definition?

BabyAGI: github.com/yoheinakajima/babyagi

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

People also have been trying to build multi-agent and task-planning systems. MS research in Asia seems to produce decent results with Task Matrix and HuggingGPT. Similar things have been tried in the form of Auto-GPT and BabyAGI , but both projects are setting their goal so high that they may not achieve the at all, and they are likely to see a complete rework when multi-modal solutions become widespread.

Joke's on you, /u/ILoveThisPlace is actually just a bot responding using the BabyAGI script, we've all been had!

BabyAGI AI-Powered Task Management for OpenAI + Pinecone or Llama.cpp

Yes, we haven't seen anything like that yet. But we do see the people trying to build these things (see AutoGPT, babyagi, ChaosGPT, etc) today, and with the last few years of advancement in LLMs they now have the fundamental building blocks to succeed in the near term (say the next 5 years) rather than in some imaginary far future.

You should check out some of the projects that combine LangChain with LLMs to automate this process like BabyAGI (https://github.com/yoheinakajima/babyagi) and AutoGPT (https://github.com/Significant-Gravitas/Auto-GPT). They were originally designed around ChatGPT models but have expanded to include llamacpp as an alternative. These provide your language models with the ability to save long term memory, a goal-oriented task list and extra functionality like surfing the web and, in some cases, creating and modifying files on disk.

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.