This research introduces a novel approach leveraging large language models to automatically resolve Python dependency conflicts, eliminating hours of manual debugging work.

• Achieves 89.4% accuracy in resolving dependency issues without requiring extensive knowledge bases
• Outperforms traditional approaches by handling a greater variety of dependency error types
• Provides actionable solutions that directly fix environment configuration files
• Works across diverse Python environments and package combinations

For engineering teams, this innovation significantly reduces development downtime and maintenance costs by automating a common but complex troubleshooting task that typically requires expert knowledge.

Raiders of the Lost Dependency: Fixing Dependency Conflicts in Python using LLMs