
kai
Konveyor AI - static code analysis driven migration to new targets via Generative AI
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Kai is an AI-enabled tool that simplifies the process of modernizing application source code to a new platform. It uses Large Language Models (LLMs) guided by static code analysis, along with data from Konveyor. This data provides insights into how the organization solved similar problems in the past, helping streamline and automate the code modernization process. Kai assists developers by providing suggestions and solutions to common problems through Retrieval Augmented Generation (RAG), working with LLMs using Konveyor analysis reports about the codebase and generating solutions based on previously solved examples.
README:
Kai (/kaɪ/, rhymes with pie) - An AI-enabled tool that simplifies the process of modernizing application source code to a new platform. It uses Large Language Models (LLMs) guided by static code analysis, along with data from Konveyor. This data provides insights into how the organization solved similar problems in the past, helping streamline and automate the code modernization process.
Kai is an AI-enabled tool that assists with modernizing applications. Kai is designed to help developers write code more efficiently by providing suggestions and solutions to common problems. It does this by performing Retrieval Augmented Generation (RAG), working with LLMs by using Konveyor analysis reports about the codebase and generating solutions based on previously solved examples.
Now, you may be thinking: How is Kai different than other generative AI tools?
Konveyor generates analysis reports via Kantra throughout a migration. This history of reports tells you what’s wrong with your codebase, where the issues are, and when they happened. This functionality exists today, and developers are already using this data to make decisions. And because of our RAG approach, this is all possible without additional fine-tuning.
As you migrate more pieces of your codebase with Kai, it can learn from the data available, and get better recommendations for the next application, and the next, and so on. This shapes the code suggestions to be similar to how your organization has solved problems in the past.
LLMs are very powerful tools, but without explicit guidance, they can generate a lot of garbage. Using Konveyor’s analysis reports allows us to focus Kai’s generative power on the specific problems that need to be solved. This pointed, specific data is the key to unlocking the full potential of large language models.
[!NOTE]
Kai is in early development. We are actively working on improving the tool and adding new features. If you are interested in contributing to the project, please see our Contributor Guide.
- See ROADMAP.md to learn about the project's goals and milestones
- Technical background for our approach
- Initial presentation slides introducing Kai
- See other technical design related information at docs/design
- 2025 April 01: Project Lightning Talk: Revolutionizing Legacy Migrations with Konveyor AI - Jonah Sussman
- 2024 November 22: Konveyor AI: supporting application modernization
- 2024 August 29: Incident Storage in Kai - A Deep Dive
- 2024 August 26: Modernization 101: A Beginner's Guide to Application Modernization and Methodology - DevConf.US 2024
- 2024 July 23: Embracing the Future of Application Modernization with KAI
- 2024 May 07: Apply generative AI to app modernization with Konveyor AI
- 2024 May 07: Deep Dive: Kai - Generative AI Applied to Application Modernization
Check out our 15 minute guided demo video to see Kai in action!
We recommend new users download a release of
Kai and then walk
through a guided scenario to get a feel of Kai's potential. We've streamlined
the install experience so you just need to download a .vsix
file and install
it in your VSCode IDE.
- Please follow the steps here to proceed with getting started: docs/getting_started.md
- After you have Kai installed we encourage you to run through one of our guided scenarios at: docs/scenarios
Please see docs/README.md for an overview of our documentation
Our project welcomes contributions from any member of our community. To get started contributing, please see our Contributor Guide.
Refer to Konveyor's Code of Conduct here.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
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