Abstract
LLM Workflow Engine is an open-source AI & Machine Learning project. Power CLI and Workflow manager for LLMs (core package). LLM Workflow Engine (LWE) is a Power CLI and Workflow manager for LLMs. It is built using Python, OpenAI API. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
LLM Workflow Engine (LWE) is a Power CLI and Workflow manager for LLMs.
ChatGPT Wrapper was an amazing tool for its time, thank you to its original creator mmabrouk for all your hard work, it lives on in a new form :)
Runs in Shell. You can call and interact with ChatGPT/GPT4 in the terminal.
2. Objective
Power CLI and Workflow manager for LLMs (core package)
This project demonstrates how Python, OpenAI API can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
- bookast: ChatGPT Podcast Generator For Books
- ChatGPT.el: ChatGPT in Emacs
- ChatGPT Reddit Bot
- Smarty GPT
- ChatGPTify
- selection-to-chatgpt
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/llm-workflow-engine/llm-workflow-engine.git
cd llm-workflow-engineFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by llm-workflow-engine and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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