The performance of repository-level code completion depends upon the effective leverage of both \emph{general} and \emph{repository-specific} knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs’ general code knowledge and the repository-specific knowledge via a \emph{graph-based} \emph{retrieval-generation} process. In particular, GraphCoder captures the context of completion target more accurately through \emph{code context graph} (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a \emph{coarse-to-fine} retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of $+6.06$ in code match and $+6.23$ in identifier match, while using less time and space.