MigrateLib automates Python library switching with LLM-powered code transformation

Swapping one software library for another is a tedious, error-prone task that forces developers to learn two different APIs, find equivalent functions, and rewrite code by hand. Researchers have now built an end-to-end automation solution called MigrateLib to handle this burden, leveraging large language models as the core engine for code migration across arbitrary pairs of Python libraries.

How the tool works#

Rather than limiting automation to API mapping alone—the traditional stopping point for migration tools—MigrateLib goes the full distance. It combines large language models with static and dynamic analysis to identify and transform all the code changes needed to swap libraries. The system works as a command-line application that developers can run against their projects.

What the research found#

The team first benchmarked LLM capabilities on 321 real-world library migrations to understand what works and where post-processing could help. They then tested MigrateLib on 717 additional Python applications not in the benchmark set. Results showed that the tool successfully completed 32% of migrations with full correctness. For projects where migration remained incomplete, only 14% of the required changes still needed manual developer intervention in more than half of the cases.

Why it matters#

Library migration happens regularly in practice—dependencies become outdated, performance needs shift, or projects require different specialized tools. Automating this process could save significant development time and reduce the chance of subtle bugs introduced during manual code rewrites. By applying LLM-driven transformation with verification steps, MigrateLib moves beyond experimental prototypes toward practical, deployable automation.