High-quality code documentation is crucial for software development especially in the era of AI. However, generating it automatically using Large Language Models (LLMs) remains challenging, as existing approaches often produce incomplete, unhelpful, or factually incorrect outputs. We introduce DocAgent, a novel multi-agent collaborative system using topological code processing for incremental context building. Specialized agents (Reader, Searcher, Writer, Verifier, Orchestrator) then collaboratively generate documentation. We also propose a multi-faceted evaluation framework assessing Completeness, Helpfulness, and Truthfulness. Comprehensive experiments show DocAgent significantly outperforms baselines consistently. Our ablation study confirms the vital role of the topological processing order. DocAgent offers a robust approach for reliable code documentation generation in complex and proprietary repositories.

本文解决了现有自动生成代码文档方法存在的不完整、无用或事实错误的问题。提出的DocAgent系统通过多智能体协作和拓扑代码处理来构建增量上下文，并显著提高了文档生成的质量和可靠性。实验结果表明，DocAgent在文档生成任务中优于传统基线，同时验证了拓扑处理顺序的重要性。

DocAgent：一种用于自动化代码文档生成的多智能体系统