RDAgent aims to automate the most critical and valuable aspects of the industrial R&D process, and we begins with focusing on the data-driven scenarios to streamline the development of models and data.
Methodologically, we have identified a framework with two key components: 'R' for proposing new ideas and 'D' for implementing them.
We believe that the automatic evolution of R&D will lead to solutions of significant industrial value.
You can click the [🎥link]() above to view the demo. More methods and scenarios are being added to the project to empower your R&D processes and boost productivity.
In this project, we are aiming to build a Agent to automate Data-Driven R\&D that can
+ 📄Read real-world material (reports, papers, etc.) and **extract** key formulas, descriptions of interested **features** and **models**, which are the key components of data-driven R&D .
+ 🛠️**Implement** the extracted formulas, features, factors and models in runnable codes.
+ Due the limited ability for LLM in implementing in once, evolving the agent to be able to extend abilities by learn from feedback and knowledge and improve the agent's ability to implement more complex models.
+ 💡Propose **new ideas** based on current knowledge and observations.
This project welcomes contributions and suggestions.
You can find issues in the issues list or simply running `grep -r "TODO:"`.
Making contributions is not a hard thing. Solving an issue(maybe just answering a question raised in issues list ), fixing/issuing a bug, improving the documents and even fixing a typo are important contributions to RDAgent.
