Code,  LangChain,  Prompt Design

Helpful AI GPT Agents for automation

Today, I will be exploring the different aspects of the Auto-GPT project, a Python and LangChain based software that leverages the capabilities of GPT for automation. This analysis covers a detailed code review, the creation of Gherkin Syntax Features and Scenario Outlines, and the visualization of the code flow through Sequence Diagrams… and all of these tasks were executed autonomously by an Auto-GPT agent.

docker compose run --rm auto-gpt -C ai_settings_software_engineer.yaml
Auto-GPT Analysis Mind Map

Section 1: Code Review

Structure and Logic Analysis

The first step in this journey was to watch Auto-GPT conducting a comprehensive code review of the file. Auto-GPT found a well-structured code, adhering to the principles of the FastAPI framework.

Potential Improvements AI generated by Auto-GPT

The helpful Autonomous Software Engineer Agent thinks that despite its strengths, there are areas where the code can be fortified:

  1. Security Enhancements: The username and password are hardcoded, posing a security risk. A more secure approach would be to store these credentials in a database and hash them for added security.
  2. Configurable Secret Key: The secret key for JWT is hardcoded, which should ideally be stored in a secure and configurable manner, such as in environment variables.
  3. Documentation: The code lacks comments and docstrings, which are vital for enhancing readability and maintainability.
  4. Testing: Incorporating tests would ensure the correctness of the code and prevent future regressions.

Section 2: Gherkin Syntax Features and Scenario Outlines

In this section, Auto-GPT created Gherkin Syntax Features and Scenario Outlines. These outlines serve as a blueprint, detailing both the expected outcomes and the non-happy paths that might be encountered in the code.

Section 3: Sequence Diagrams

To visualize the flow of the code, Auto-GPT created a set of Sequence Diagrams using the Mermaid Diagram Specification.

These diagrams illustrate both the expected outcomes and the non-happy paths, providing a graphical representation of the interactions between the user and the server. The diagrams are documented in the file.


Through this analysis, I have uncovered the various layers of the Auto-GPT project, providing insights into its structure and potential areas for automating the work of a seasoned Software Engineer Agent.

As I wrap up, I hope that this comprehensive analysis serves as a stepping stone for further enhancements to the project.

In our next session, I will introduce a method for validating business ideas utilizing the expertise of a Business Idea Researcher GPT Agent.

Thank you for joining me on this analytical journey. Stay tuned for more insights into the world of AI Autonomous Agents!