GPT-Migrate

What is the project about?

GPT-Migrate is a tool designed to automatically migrate codebases from one programming language or framework to another.

What problem does it solve?

It addresses the challenges of codebase migration, which is often a costly, time-consuming, and complex process. It aims to automate a significant portion of this process, reducing manual effort and potential errors.

What are the features of the project?

Automated dependency conversion: Identifies and translates third-party dependencies from the source to the target language/framework.
Recursive code migration: Migrates code starting from a specified entry point, handling file structure and dependencies.
Docker environment setup: Creates a Dockerized environment for the target language, ensuring a consistent and isolated execution context.
Automated unit test generation: Generates unit tests for the migrated code.
Optional testing against source: Can validate generated unit tests against the original application if exposed.
Iterative debugging: Uses an LLM to debug the migrated code, identifying and fixing errors with context from logs and code structure. It can propose and execute file modifications (with user approval).
Customizable migration: Offers options to specify source/target languages, directories, entry points, ports, and stylistic guidelines.
Step-by-step execution: Allows running specific migration steps (setup, migrate, test) individually.
Prompt Design: Uses a hierarchical prompt design to guide the LLM.

What are the technologies used in the project?

Python: The primary language used for the migration tool itself.
Poetry: Dependency management.
Docker: Used for creating isolated environments for the target language.
Large Language Models (LLMs): Primarily GPT-4 (including GPT-4-32k) via OpenAI and OpenRouter APIs.
unittest: Python's unit testing framework is used for generating tests.

What are the benefits of the project?

Reduced migration time and cost: Automates a significant portion of the migration process.
Minimized manual effort: Reduces the need for developers to manually rewrite code.
Improved consistency: Uses a standardized process, reducing the risk of human error.
Iterative debugging assistance: Helps resolve issues that arise during migration.
Facilitates framework/language upgrades: Makes it easier to adopt new technologies.

What are the use cases of the project?

Migrating legacy codebases to modern frameworks or languages.
Upgrading applications to newer versions of a framework.
Consolidating codebases written in different languages.
Experimenting with different technologies by quickly porting existing code.
Refactoring large projects by migrating parts of the codebase.