AMRerank: A Framework for Library Migration Recommendations Using Multi‐Agent Analysis and Data‐Driven Reranking

Open‐source libraries are indispensable for modern software development but can create substantial maintenance burdens when they become deprecated or unmaintained. Selecting an appropriate replacement among many candidates remains challenging, since methods relying only on historical mining or similarity metrics often miss subtle differences in meaning. We propose AMRerank, a novel framework that integrates multi‐agent qualitative analysis with a data‐driven, interpretable reranking model. AMRerank first deploys specialized agents to examine and classify semantic relationships between libraries, generating evidence‐backed labels and concise summaries. An interpretable reranking framework then fuses these qualitative signals with heuristic and semantic features to produce a fine‐grained, explainable ranking. Evaluated on the GT2014 benchmark against competitive baselines (LMG, MMR, MMRLC), AMRerank achieves Precision@1 of 0.899 and mean reciprocal rank (MRR) of 0.928. As our case studies show, the system provides actionable, human‐readable evidence that helps developers make more reliable migration choices.