Most vulnerability detection studies focus on datasets of vulnerabilities in C/C++ code, offering limited language diversity. Thus, the effectiveness of deep learning methods, including large language models (LLMs), in detecting software vulnerabilities beyond these languages is still largely unexplored. In this paper, we evaluate the effectiveness of LLMs in detecting and classifying Common Weakness Enumerations (CWE) using different prompt and role strategies. Our experimental study targets six state-of-the-art pre-trained LLMs (GPT-3.5- Turbo, GPT-4 Turbo, GPT-4o, CodeLLama-7B, CodeLLama- 13B, and Gemini 1.5 Pro) and five programming languages: Python, C, C++, Java, and JavaScript. We compiled a multi-language vulnerability dataset from different sources, to ensure representativeness. Our results showed that GPT-4o achieves the highest vulnerability detection and CWE classification scores using a few-shot setting. Aside from the quantitative results of our study, we developed a library called CODEGUARDIAN integrated with VSCode which enables developers to perform LLM-assisted real-time vulnerability analysis in real-world security scenarios. We have evaluated CODEGUARDIAN with a user study involving 22 developers from the industry. Our study showed that, by using CODEGUARDIAN, developers are more accurate and faster at detecting vulnerabilities.