Application Programming Interfaces (APIs) are crucial in modern software development. Large Language Models (LLMs) assist in automated code generation but often struggle with API hallucination, including invoking non-existent APIs and misusing existing ones in practical development scenarios. Existing studies resort to Retrieval-Augmented Generation (RAG) methods for mitigating the hallucination issue, but tend to fail since they generally ignore the structural dependencies in practical projects and do not indeed validate whether the generated APIs are available or not. To address these limitations, we propose {\tool}, a framework for mitigating API hallucination in code generated by LLMs with hierarchical dependency aware. {\tool} consists of two phases: {\phaseone}, which analyzes local and global dependencies of the current function, aiming to supplement comprehensive project context in LLMs’ input, and {\phasetwo}, which utilizes mined dependencies to adaptively constrain the generation process, aiming to ensure the generated APIs align with the project’s specifications. To facilitate the evaluation of the degree of API hallucination, we introduce a new benchmark {\dataset} and two new metrics including Micro Hallucination Number (MiHN) and Macro Hallucination Rate (MaHR). Experiments on six state-of-the-art LLMs demonstrate that {\tool} effectively reduces API hallucinations, achieving an average decrease of 67.52% in MiHN and 73.56% in MaHR compared to the RAG approach. Applied to Huawei’s internal projects and two proprietary LLMs, {\tool} achieves average decreases of 57.33% in MiHN and 59.41% in MaHR.