Imagine effortlessly analyzing complex geospatial data with the help of an AI agent. No more wrestling with complicated code or specialized libraries. Researchers have developed GeoAgent, a groundbreaking framework that empowers Large Language Models (LLMs) to tackle geospatial data analysis automatically. GeoAgent cleverly combines a code interpreter, static analysis, and Retrieval-Augmented Generation (RAG) techniques within a Monte Carlo Tree Search (MCTS) algorithm. This allows it to navigate the intricacies of geospatial data processing, including complex data structures and spatial constraints, with remarkable efficiency. Historically, LLMs have struggled with the nuances of geospatial analysis due to the need to integrate various data sources and function calls seamlessly. GeoAgent overcomes these hurdles by leveraging external knowledge through RAG and refining its approach through MCTS, dynamically adjusting to feedback and ensuring logical consistency in multi-step processes. To rigorously test GeoAgent, the researchers also created GeoCode, a new benchmark specifically designed for geospatial tasks. This benchmark utilizes a wide range of Python libraries and tests both single-turn and multi-turn tasks like data acquisition, analysis, and visualization. The results are impressive. GeoAgent demonstrates superior performance compared to baseline LLMs, showing significant improvements in handling complex function calls and completing geospatial tasks. This innovative framework opens exciting new possibilities for automating complex geospatial analysis, allowing researchers and analysts to focus on extracting insights rather than grappling with code. The development of GeoAgent represents a significant leap forward in making geospatial data analysis more accessible and efficient, ultimately contributing to a deeper understanding of our world.