Code generation agents based on large language models (LLMs) are profoundly revolutionizing the software development paradigm. Compared with previous code generation techniques, code generation agents have the following three core features. The first feature is autonomy. The agents can independently execute the entire workflow from task decomposition to coding and debugging. The second is expanded task scope. The agents’ capabilities have extended from generating code snippets to encompassing the full software development life cycle (SDLC). The third is the enhancement of engineering practicality. The research focus has shifted from model algorithmic innovation toward engineering challenges such as process management, system reliability, and tool integration. In recent years, this technical domain has witnessed rapid development and demonstrated tremendous application potential, with explosive growth in related research. To this end, this study presents a systematic review of the field of LLM-based code generation agents. The technology’s developmental trajectory since its inception is traced, and its core techniques including both single-agent and multi-agent systems are sorted out and categorized. Furthermore, this study summarizes both various applications of code generation agents in the full SDLC and the mainstream evaluation benchmarks and metrics, and reviews representative tools. Finally, by analyzing the key challenges, the long-term core research directions in the future for this field are pointed out.