Imagine an AI coding assistant that not only understands your current task but also seamlessly integrates the entire project's codebase to generate smarter, contextually relevant code. This isn't science fiction—it's the promise of CatCoder, a novel framework designed to elevate code generation to a whole new level. Traditional AI code generation often struggles with larger projects. Existing methods might retrieve similar code snippets, but they often lack the broader understanding necessary to truly integrate within a complex codebase. This can lead to errors, inconsistencies, or code that simply doesn't fit within the project's architecture. CatCoder tackles this challenge by enhancing repository-level code generation through the innovative integration of contextual information. For statically-typed languages like Java and Rust, CatCoder leverages static analyzers to extract type dependencies—crucial information about how different parts of the code interact. This 'type context,' when combined with retrieved code snippets, forms a richer, more comprehensive prompt for the AI model. Essentially, CatCoder gives the AI a much deeper understanding of the project's structure and logic. The results? In benchmarks using real-world Java and Rust projects, CatCoder outperforms existing methods by a significant margin, demonstrating its effectiveness in generating high-quality, contextually-aware code. This translates to fewer bugs, faster development cycles, and more robust software overall. What sets CatCoder apart is its generalizability. It doesn’t rely on training a new model; it enhances existing, off-the-shelf large language models (LLMs). This means developers can integrate CatCoder with their preferred LLM, regardless of its size or specialization. The implications of CatCoder are far-reaching. By unlocking AI's potential to understand and leverage the rich context of entire repositories, CatCoder paves the way for a future where AI coding assistants are not just tools, but true collaborators in the software development process. However, challenges remain. The initial implementation primarily focuses on Java and Rust, and there's still room to improve efficiency for larger repositories. Further research could explore extending CatCoder to more languages and streamlining the integration process. Despite these challenges, CatCoder represents a significant step forward in AI-powered code generation, offering a glimpse into a future where coding becomes a more intuitive, efficient, and collaborative endeavor.