Flaky tests obstruct software development, and studying and proposing mitigations against them has therefore become an important focus of software engineering research. To conduct sound investigations on test flakiness, it is crucial to have large, diverse, and unbiased datasets of flaky tests. A common method to build such datasets is by rerunning the test suites of selected projects multiple times and checking for tests that produce different outcomes. While using this technique on a single project is mostly straightforward, applying it to a large and diverse set of projects raises several implementation challenges such as (1) isolating the test executions, (2) supporting multiple build mechanisms, (3) achieving feasible run times on large data sets, and (4) analyzing and presenting the test outcomes. To address these challenges we introduce FlaPy, a framework for researchers to mine flaky tests in a given or automatically sampled set of Python projects by rerunning their test suites. FlaPy isolates the test executions using containerization and fresh execution environments to simulate real-world CI conditions and to achieve accurate results. By supporting multiple dependency installation strategies, it promotes diversity among the studied projects. FlaPy supports parallelizing the test executions using SLURM, making it feasible to scan thousands of projects for test flakiness. Finally, FlaPy analyzes the test outcomes to determine which tests are flaky and depicts the results in a concise table. A demo video of FlaPy is available at https://youtu.be/ejy-be-FvDY