Recently, Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities through reinforcement learning (RL). However, enabling LLM-based agents to effectively orchestrate multiple tools remains an open challenge. In this paper, we introduce Tool-Star, an end-to-end agentic post-training framework that empowers LLM-based web agents to strategically interact with external multi-tool environments. Tool-Star begins with a general tool-integrated data synthesis pipeline that combines two complementary sampling strategies to generate tool-use trajectories, followed by quality normalization and difficulty-aware curriculum construction to filter noisy samples and organize training data from easy to hard. We then introduce a two-stage training paradigm for multi-tool collaborative reasoning: (1) cold-start supervised fine-tuning with tool feedback to bootstrap long-horizon tool-augmented reasoning, and (2) a multi-tool self-critic RL algorithm with hierarchical rewards to reinforce effective tool coordination. Experiments across 13 benchmarks demonstrate Tool-Star's effectiveness. Further analyses provide practical insights for optimizing strategic tool use in web agents. The code is available at https://github.com/RUC-NLPIR/Tool-Star.