Contributor Guide

Welcome to the AdalFlow community! We’re building the most user-friendly, modular, and powerful library for building and auto-optimizing LLM applications, rom Chatbots and RAGs to Agents. Think of AdalFlow for LLM applications and prompt engineering as the PyTorch/TensorFlow/JAX equivalent for AI modeling. The goal is to provide basic and fundamental building blocks to create advanced applications with auto-optimization out of the box. As we mature, we anticipate that more RAG, memory-based chatbots, or agent frameworks will be built on top of AdalFlow’s building blocks, such as retriever and generator. We highly suggest you read our design principle before you start contributing.

We only accept high quality contributions. We appreciate contributors, but we have to hold our libary responsible for our users. Once you decide to contribute, we hope it’s not just to list your name on the repo. More importantly, we want you to learn and improve your own skills, support your favorite projects, and contribute to the community!

It took us three months to set up this contributor guide, as we first tested the process with early contributors. Our goal is to design the best process for maintaining the quality of our library while enabling the community to make meaningful contributions. We are determined to make AdalFlow as great and legendary as PyTorch. This guide covers the overall contributing process, along with development essentials for environment setup, coding, testing, and documentation.

Here’s to the future of LLM applications!

By Li Yin.

Contributor Guide

• Contributing Process
• Development Essentials