BabyCatAGI: Fast and Feline

Abstract:

In this paper, we present BabyCatAGI, a modified version of the BabyBeeAGI code base, designed to improve research workflow through intelligent task automation and creation using GPT-4. BabyCatAGI builds upon the foundations of BabyBeeAGI, offering faster execution and increased stability by employing a task creation agent that runs once at the beginning and utilizes JSON to manage task completion. We describe the key differences between BabyBeeAGI and BabyCatAGI, highlighting the significance of these changes for future AI models and applications.

1. Introduction

The BabyAGI code aimed to create a simple framework for managing tasks using AI, which was later improved upon by BabyBeeAGI, expanding its capabilities for more comprehensive task management. In this paper, we introduce BabyCatAGI, a modified version of BabyBeeAGI that further enhances research workflow optimization through intelligent task automation and creation.

2. High-Level Description of BabyCatAGI

BabyCatAGI is built on top of the GPT-4 architecture, resulting in a more advanced version of the BabyBeeAGI code. By introducing a task creation agent that runs once at the beginning and using JSON to manage task completion, BabyCatAGI significantly speeds up the execution of the full task list, making it faster and more stable than BabyBeeAGI.

3. Key Differences Between BabyBeeAGI and BabyCatAGI

3.1. Task Creation Agent

One significant difference between BabyBeeAGI and BabyCatAGI is the introduction of a task creation agent that runs once at the beginning, replacing the task manager that worked between each task in BabyBeeAGI. This change results in a significant speed-up in the execution of the full task list.

3.2. Intelligent Agent Tool

BabyCatAGI introduces the first “agent” as a tool, a complex combination of tools that includes a web search, followed by looping through results, scraping each, and looping through chunks to extract only relevant information in a condensed form to pass to other tasks. This agent allows for more efficient and accurate information retrieval.

3.3. GPT-4 Integration and Stability

BabyCatAGI relies on the GPT-4 architecture for improved performance and stability compared to BabyBeeAGI. While the gpt-3.5-turbo model sometimes works with BabyCatAGI, it often errors out due to token limit, making GPT-4 the preferred choice.

4. Conclusion

BabyCatAGI offers a significant improvement over BabyBeeAGI, enhancing research workflow through intelligent task automation and creation using GPT-4. The introduction of a task creation agent and intelligent agent tool, combined with the integration of GPT-4, showcases the potential of AI in streamlining and optimizing the research process. BabyCatAGI paves the way for future AI models and applications that can further advance research workflow efficiency and effectiveness.

Publishing Notes

While we are continuing to work on expanding functionality for our Core BabyAGI code on Github, it’s sometimes easier to play around with the framework from the simple OG BabyAGI code (on Replit). These mods are designed to facilitate discussion and we will discuss what to pull into core BabyAGI.

⭐ Core BabyAGI GitHub Repo: https://github.com/yoheinakajima/babyagi