ReCall trains language models to agentically use and combine arbitrary tools via reinforcement learning, requiring no supervised data on tool call trajectories or reasoning steps. The framework enables LLMs to develop sophisticated tool-based reasoning capabilities by generating synthetic data with diverse environments and multi-step tasks. It serves as a successor to ReSearch, extending beyond search to support reasoning with any user-defined tools as a drop-in replacement.