Electoral debates are influential moments in public discourse, providing candidates with a high-visibility platform to present their proposals, contrast their positions, and engage in exchanges that shape voter decisions. In Brazil, these debates reach a broad and diverse audience, reflecting regional, social, and ideological variations that affect linguistic choices and thematic content. This paper presents CoDEl-BR (Corpus de Debates Eleitorais, in Portuguese), a corpus of transcripts from 22 second-round mayoral debates held in 13 Brazilian state capitals during the 2024 municipal elections. It comprises 2,943 transcript segments totaling approximately 32 hours. Exploratory analyses reveal differences in thematic priorities between candidates and voters’ questions, as well as variations by race and party affiliation. The corpus aims to enable research in discourse and argumentation analysis, stance and sentiment detection, polarization modeling, and other related NLP tasks. We demonstrate that this initial release provides a curated, high-quality subset of debates with significant potential for expansion.