Project building is pivotal to support various program analysis tasks, such as generating intermediate represen- tation code for static analysis and preparing binary code for vulnerability reproduction. However, automating the building process for C/C++ projects is a highly complex endeavor, involving tremendous technical chal- lenges, such as intricate dependency management, diverse build systems, varied toolchains, and multifaceted error handling mechanisms. Consequently, building C/C++ projects often proves difficult in practice, hindering the progress of crucial downstream applications. Unfortunately, research on facilitating the building of C/C++ projects remains insufficient. The emergence of Large Language Models (LLMs) offers promising solutions to the above-mentioned challenges. Trained on extensive corpora, LLMs can help unify diverse build systems through their comprehension capabilities and address complex errors by leveraging tacit knowledge storage. Moreover, LLM-based systems, such as agents, can be systematically designed to dynamically interact with the environment, effectively managing dynamic building issues. Motivated by these opportunities, we conduct an empirical study to systematically analyze the current challenges in the C/C++ project building process. Particularly, we observe that most popular C/C++ projects encounter an average of five errors when relying solely on the build systems. Based on our study, we develop an automated build system called CXXCrafter to specifically address the above-mentioned challenges. Our evaluation on open-source software demonstrates that CXXCrafter achieves a success rate of 78% in project building. Specifically, among the Top100 dataset, 72 projects are built successfully by both CXXCrafter and manual efforts, 3 by CXXCrafter only, and 14 manually only. Despite the slightly lower performance, CXXCrafter can save tremendous manual efforts and can also be easily applied to a wider range of applications automatically.