The paper introduces the Data Interpreter, a large language model (LLM)-based agent designed to enhance problem-solving capabilities in data science. The Data Interpreter addresses the challenges of real-time data adjustment, expertise in optimization, and logical error identification through three key techniques: dynamic planning with hierarchical graph structures, tool integration and evolution, and automated confidence-based verification. The agent is evaluated on various data science and real-world tasks, demonstrating superior performance compared to open-source baselines. Specifically, it shows a 10.3% improvement in machine learning tasks, a 26% increase on the MATH dataset, and a 112% improvement in open-ended tasks. The Data Interpreter's effectiveness is attributed to its ability to dynamically adapt to data changes, integrate and generate tools, and verify logical consistency, making it a robust solution for complex data science problems. The paper also includes a detailed methodology, experimental setup, and ablation studies to validate the contributions of each component of the Data Interpreter.The paper introduces the Data Interpreter, a large language model (LLM)-based agent designed to enhance problem-solving capabilities in data science. The Data Interpreter addresses the challenges of real-time data adjustment, expertise in optimization, and logical error identification through three key techniques: dynamic planning with hierarchical graph structures, tool integration and evolution, and automated confidence-based verification. The agent is evaluated on various data science and real-world tasks, demonstrating superior performance compared to open-source baselines. Specifically, it shows a 10.3% improvement in machine learning tasks, a 26% increase on the MATH dataset, and a 112% improvement in open-ended tasks. The Data Interpreter's effectiveness is attributed to its ability to dynamically adapt to data changes, integrate and generate tools, and verify logical consistency, making it a robust solution for complex data science problems. The paper also includes a detailed methodology, experimental setup, and ablation studies to validate the contributions of each component of the Data Interpreter.