AIDE ML is an open-source research framework designed to explore automated machine learning development through agent-based search and code optimization. The project implements the AIDE algorithm, which uses a tree-search strategy guided by large language models to iteratively generate, evaluate, and refine code. Instead of relying on manual experimentation, the agent autonomously drafts machine learning pipelines, debugs errors, and benchmarks performance against user-defined evaluation metrics. The system repeatedly improves its generated code by exploring different implementation paths and selecting the best-performing solutions. AIDE ML is packaged as a Python toolkit with built-in utilities such as command-line tools, configuration presets, and visualization interfaces that allow researchers to observe how the search process evolves. The framework is designed for experimentation and academic research into automated programming and machine learning optimization.
Features
- LLM-guided tree search algorithm for automated code optimization
- Autonomous generation and debugging of machine learning pipelines
- Benchmark evaluation against user-defined performance metrics
- Python toolkit with CLI utilities and configuration presets
- Visualization tools for analyzing the search and optimization process
- Framework designed for research in autonomous AI coding systems