The pull request (PR)-based software development mechanism is a crucial practice in open-source software development. Effective code reviewers play a vital role in helping contributors identify potential errors in PRs through code reviews, thereby ensuring quality assurance for the continuous development and integration process. However, the complexity of code changes and the inherent diversity of review behaviors significantly increase the difficulty of recommending appropriate reviewers. Existing methods primarily focus on extracting semantic information from PRs or constructing reviewer profiles based on review history, subsequently recommending reviewers through various static strategy combinations. These approaches are limited by the richness of model training corpora and the complexity of interaction types, resulting in suboptimal recommendation performance. In response to these limitations, this paper proposes a novel code reviewer recommendation method based on inter-agent collaboration. This method leverages advanced large language models to accurately capture the rich textual semantics of PRs and reviewers. Furthermore, the robust planning, collaboration, and decision-making capabilities of AI agents enable the integration of diverse interaction types of information, providing high flexibility and adaptability. Experimental analysis based on real-world datasets demonstrates that the proposed method outperforms baseline reviewer recommendation approaches, with performance improvements ranging from 4.45% to 26.04%. Additionally, case studies highlight the exceptional interpretability of the proposed method, further validating its effectiveness and reliability in practical applications.