MLE agent

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Basic Information

MLE-Agent is an LLM-powered assistant designed to pair with machine learning engineers and researchers to accelerate end-to-end AI engineering and research workflows. It automates the creation of ML baselines, proposes state-of-the-art solutions, organizes project file structure, executes and debugs code locally, and can participate in Kaggle competitions autonomously. The project exposes CLIs to create and start projects, run interactive terminal chat sessions, generate weekly work reports from local or GitHub repositories, and launch an Auto-Kaggle mode that can run tasks with minimal human intervention. It integrates literature and implementation search to recommend best practices, provides local retrieval-augmented generation support for personal coding assistance, and ships with both CLI and web UI components for interactive human-in-the-loop usage.

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App Details

Features
Major features described in the README include an autonomous baseline generator that drafts ML solutions from user requirements, end-to-end task execution including an Auto-Kaggle mode, integration with ArXiv and Papers With Code for literature and SOTA search, and a smart debugging loop that runs and fixes code. It provides file system and local code execution integration, a suite of CLI commands such as mle new, mle start, mle chat, mle report and mle kaggle, and a web application mode to generate reports. The project supports local RAG, multiple LLM backends including OpenAI, Anthropic and Ollama, periodic report generation, and a roadmap for more integrations and cloud deployment tools.
Use Cases
MLE-Agent reduces manual overhead across the ML development lifecycle by automating repetitive tasks, providing recommended SOTA approaches based on web and literature search, and scaffolding project structure so engineers can prototype baselines faster. Its ability to run code, detect errors and iteratively debug saves developer time during experimentation. The Auto-Kaggle and Kaggle CLI modes let practitioners run competition workflows with less manual coordination. Report and summary generation automates weekly updates and documentation. Local RAG and tool integrations make it a contextual coding assistant for private projects. Combined CLI and web interfaces support both interactive human-in-the-loop workflows and largely autonomous execution when desired.

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