RAT: RunAnyThing via Fully Automated Environment Configuration

2026-06-05 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

RAT (RunAnyThing) is a novel, language-agnostic framework designed to automate environment configuration for arbitrary software repositories, addressing a critical bottleneck for autonomous code agents. It features a multi-stage pipeline that includes semantic initialization via ImageRetriever, a dual-mode planning mechanism (Standard and Automated), a specialized toolset, and a robust sandbox. To enable rigorous evaluation, the authors also introduce RATBench, a large-scale multilingual benchmark comprising over 2,000 GitHub repositories, curated through stratified sampling to reflect real-world diversity. Extensive experiments demonstrate that RAT achieves state-of-the-art performance, improving the Environment Setup Success Rate (ESSR) by an average of 29.6% over strong baselines and even surpassing human engineers in success rate. The framework also shows performance scaling with increased execution steps.

Key takeaway

For AI Engineers developing autonomous code agents, the challenge of environment configuration can be significantly mitigated by adopting frameworks like RAT. You should consider integrating language-agnostic, LLM-driven pipelines that incorporate semantic initialization and specialized toolsets. This approach not only boosts Environment Setup Success Rate (ESSR) by nearly 30% compared to baselines but also enables scalable, reproducible deployments, freeing your teams from labor-intensive manual setups and accelerating LLM training data synthesis.

Key insights

Fully automated, language-agnostic environment configuration for diverse repositories is achievable and outperforms manual efforts.

Principles

• Semantic analysis optimizes base image selection.
• Dual-mode planning adapts to repository complexity.
• Specialized tools enhance LLM context management.

Method

RAT employs an LLM-driven multi-stage pipeline: ImageRetriever for base image selection, dual-mode planning, a specialized toolset for execution, and a robust sandbox.

In practice

• Use ImageRetriever for optimal base image inference.
• Implement a dual-mode planning for varied repository complexity.
• Integrate specialized tools for precise environment interaction.

Topics

• Automated Environment Configuration
• Large Language Models
• Autonomous Code Agents
• Software Engineering Agents
• Docker Containerization
• RATBench Benchmark

Code references

• bndr/pipreqs

Best for: Research Scientist, AI Scientist, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.