openai-python VS AutoGPT

The ongoing model anchoring/grounding issue likely affects all GPT-4 checkpoints/variants, but is most prominent with the latest "gpt-4-turbo-2024-04-09" variant due to its most recent cutoff date, might imply deeper issues with the current model architecture, or at least how it's been updated:

https://github.com/openai/openai-python/issues/1310

See also the original thread on OpenAI's developer forums (linked on the GitHub issue) with confirmations from others.

A test code snippet is included in the GitHub issue to A/B test the problem yourself with your own questions if need be.

- Iterate on large codebases quickly: we use Rust for maximum performance

GritQL has already been used on thousands of repositories for complex migrations[1] but we're excited to collaborate more with the open source community.

[1] Ex. https://github.com/openai/openai-python/discussions/742

Explore OpenAI and learn how to easily integrate it into your existing application with the OpenAI Python module.

You tried to access openai.Completion, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742

Relatedly, I checked and OpenAI deleted all references to their ChatML spec from their GitHub repo.

This is what it said in an earlier commit: https://github.com/openai/openai-python/blob/2942bf4bb635b1e...

Make sure to install the OpenAI library first. Since I'm using Python, I need

Source: https://github.com/openai/openai-python/discussions/742

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.