  Large Language Model (LLM)-based agents have demonstrated remarkable
effectiveness. However, their performance can be compromised in data science
scenarios that require real-time data adjustment, expertise in optimization due
to complex dependencies among various tasks, and the ability to identify
logical errors for precise reasoning. In this study, we introduce the Data
Interpreter, a solution designed to solve with code that emphasizes three
pivotal techniques to augment problem-solving in data science: 1) dynamic
planning with hierarchical graph structures for real-time data adaptability;2)
tool integration dynamically to enhance code proficiency during execution,
enriching the requisite expertise;3) logical inconsistency identification in
feedback, and efficiency enhancement through experience recording. We evaluate
the Data Interpreter on various data science and real-world tasks. Compared to
open-source baselines, it demonstrated superior performance, exhibiting
significant improvements in machine learning tasks, increasing from 0.86 to
0.95. Additionally, it showed a 26% increase in the MATH dataset and a
remarkable 112% improvement in open-ended tasks. The solution will be released
at https://github.com/geekan/MetaGPT.
