The objective of a general-purpose content-based image retrieval system is to find images in a database that match an external measure of relevance. Since users follow different and inconsistent relevance measures, processing queries in a task-specific manner has shown to be an effective approach. Viewing specialized image retrieval algorithms as agents, we propose a general-purpose image retrieval system that uses a new multi-agent meta-learning framework. The framework adapts a distance function defined over both image distance weights and image queries to identify clusters of algorithms that produce similar solutions to similar problems. Experiments compare our approach with a traditional information retrieval algorithm; results show that our framework provides better average relevance scores.