openai-quickstart-node VS AutoGPT

Github repo link: https://github.com/openai/openai-quickstart-node

Building with OpenAI API

The next step is to pass this context to an LLM. We'll use SOTA gpt-3.5-turbo model. You'll need an OpenAI API key for this, which you can get here.

You can check out OpenAI's docs, including their quick start example here: https://platform.openai.com/docs/quickstart

For these prompts, I used the quickstart tutorial from Open AI: https://beta.openai.com/docs/quickstart

While the concept of versatile AI coding agents that can automate any programming task is exciting, the reality often falls short. Tools like Auto-GPT and other general-purpose coding agents tend to go off the rails, compounding errors and leading to unexpected results.

AutoGPT is a framework that seems nice. It has a cool CLI and a flutter UI to create agents from the browser. Its main purpose is to work with your local stuff (documents, audio, videos, etc)

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.