FLAML is a lightweight Python library from Microsoft Research that provides efficient and economical machine learning model selection using cutting-edge algorithms designed to be resource-efficient and easily parallelizable. FLAML can utilize Ray Tune for distributed hyperparameter tuning, scaling up its AutoML methods across a cluster. The library addresses the need for economical AutoML methods by leveraging insights about the structure of the search space to choose search orders optimized for both good performance and low cost. This allows for efficient exploration of the search space while minimizing computational resources. FLAML's CFO and BlendSearch methods demonstrate this approach, offering advantages in finding good configurations quickly and navigating ones with good performance while concentrating on low evaluation time. The library is integrated with Ray Tune, enabling parallelization of hyperparameter search and leveraging cutting-edge optimization algorithms at scale. FLAML has been shown to achieve comparable or better performance than state-of-the-art AutoML solutions using significantly fewer computational resources, demonstrating its potential for real-world applications.