DeepRetrieval is a novel reinforcement learning framework that trains LLMs for query generation to enhance information retrieval. Unlike supervised approaches, the system learns through direct trial and error using retrieval metrics as rewards. The LLM generates reasoning steps in a think section followed by the final augmented query, enabling explicit chain-of-thought reasoning before query formulation. The project includes model weights and easy-to-use APIs for query rewriting.