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Graph-structured data is ubiquitous among a plethora of real-world applications. However, as graph learning algorithms have been increasingly deployed to help decision-making, there has been rising societal concern in the bias these algorithms may exhibit. In certain high-stake decision-making scenarios, the decisions made may be life-changing for the involved individuals. Accordingly, abundant explorations have been made to mitigate the bias for graph learning algorithms in recent years. However, there still lacks a library to collectively consolidate existing debiasing techniques and help practitioners to easily perform bias mitigation for graph learning algorithms. In this paper, we present PyGDebias, an open-source Python library for bias mitigation in graph learning algorithms. As the first comprehensive library of its kind, PyGDebias covers 13 popular debiasing methods under common fairness notions together with 26 commonly used graph datasets. In addition, PyGDebias also comes with comprehensive performance benchmarks and well-documented API designs for both researchers and practitioners. To foster convenient accessibility, PyGDebias is released under a permissive BSD-license together with performance benchmarks, API documentation, and use examples at https://github.com/yushundong/PyGDebias.