AI Agents: AutoGPT Architecture & Breakdown
AutoGPT has gained significant popularity lately, and many people have been experimenting with it as a black box. However, as an agentic AI system, it is essential to understand how it works under the hood to be able to customize and extend it to fulfill different requirements. Here is a breakdown of the AutoGPT Architecture, which can serve as a valuable reference design for those who are looking to develop their agentic AI systems.
AutoGPT v0.2.1 is analyzed below, which is the version available at the time of writing this post (2023/04/22). However, AutoGPT v0.2.2 has been released, which reflects the incredible progress made by the community.
Commands in AutoGPT
One of the most powerful features of AutoGPT is its ability to issue and execute commands. AutoGPT's LLM system (ChatGPT) knows all the available commands and their functions through the text in the prompt, making use of OpenAI's ChatCompletion. Each command has a brief description, and ChatGPT selects the command based on the current context. There is a unique executor in AutoGPT for each command. The ability to expand the set of available commands opens up numerous possibilities. One can extend commands to physical-world applications such as smart home controls and prioritize safety, considering that these LLM-based autonomous agents are still in their early stages of development.
For instance, having an AutoGPT command to add online retailer products to a shopping cart could lead to various objectives, such as finding the best tennis strings suitable for a topspin baseline player and adding them to the user's cart.
Chat History Representation
AutoGPT utilizes OpenAI's ChatCompletion, which expects a list of dicts to represent the chat history. To provide better clarity, the prompts that go into ChatGPT are output as strings. For instance, the prompt below:
George Sung in Udacity Inc
Will be printed as:
----1 https://www.linkedin.com/in/georgesung/ George Sung in Udacity Inc--35 George Sung in Udacity Inc--35--35 Leonie Monigatti in Towards Data Science--12 LucianoSphere in Towards AI--14 Arslan Mirza in Level Up Coding--6 ascendancedevin Rustaceans Thrive-- Leonie Monigatti -- How2Market -- 1 HelpStatusWritersBlogCareersPrivacyTermsAbout
The above prompt is a representation of the chat history that goes into the model to predict the subsequent response. This representation plays a significant role in the quality of the responses generated by AutoGPT.
To sum up, AutoGPT's ability to carry out commands makes it a powerful agentic AI system. The breakdown of its architecture can be useful for those looking to build their AI systems. It is still in the early stages of development, so it is crucial to prioritize safety while expanding the system's capabilities.
