RAT: RunAnyThing via Fully Automated Environment Configuration

Technical Summary: RAT (RunAnyThing)

RAT (RunAnyThing) is a language-agnostic, modular framework designed to automate the complex task of environment configuration for repository-level software engineering. It addresses a critical bottleneck in the development of autonomous code agents: the transition from generating code snippets to managing entire, executable software repositories.

1. The Problem: The Environment Configuration Bottleneck

While Large Language Models (LLMs) have become proficient at writing code, they struggle with repository-level tasks because these tasks require strict adherence to intricate inter-dependencies and specific runtime environments.

• Current Limitations: Existing approaches often rely on pre-defined artifacts (like existing Dockerfiles or CI logs) or are restricted to specific programming languages.
• Consequences: Without automated configuration, code agents cannot verify their work, hindering scalable data synthesis, execution-based reinforcement learning (RL), and system reliability.

2. Methodology: The RAT Framework

RAT employs an LLM-driven, multi-stage pipeline operating within a robust, containerized sandbox. Its architecture consists of four primary pillars:

• Language-Agnostic Abstraction: Instead of hard-coded rules, RAT identifies the primary language and encapsulates language-specific heuristics (dependency patterns, package managers, and test runners) into a unified interface.
• ImageRetriever (Semantic Initialization): To reduce configuration latency, this module performs semantic analysis of the repository's documentation and artifacts. It selects an optimal base image (e.g., python:3.10) or queries Docker Hub for specialized images (e.g., those containing PyTorch) to provide a high-quality starting point.
• Dual-Mode Planning Mechanism:
  • Standard Plan Mode: Uses a fixed exploratory workflow for simpler, language-specific tasks.
  • Automated Plan Mode: For high-complexity repositories, the agent autonomously generates a structured plan.md file. This acts as "external memory," allowing the agent to track progress and maintain context across iterative installation phases.
• Specialized Agent Toolset: RAT provides a suite of high-level tools that go beyond raw terminal commands, including:
  • Repository Analysis: view-outline (AST-based parsing) and ls-structure.
  • Knowledge Retrieval: issue-retrieval (searching internal issue pools) and search-web.
  • Environment Management: change-version (dynamic switching of language versions via Docker commits) and error-recovery.
  • Validation: test-synthesis (generating smoke tests for undocumented repos) and test-runners.

3. Key Contributions

• RAT Framework: A modular, language-agnostic system that handles arbitrary repositories by reasoning through complex dependencies.
• RATBench: A large-scale, multilingual benchmark comprising over 2,000 GitHub repositories. Unlike previous benchmarks, it uses stratified sampling to ensure diversity in project size, popularity, and programming languages (Python, Java, Rust, JS/TS), and validates success through actual execution rather than static analysis.
• Long-term Expertise Accumulation: A mechanism for agents to synthesize expertise from historical execution trajectories into a structured schema to improve future performance.

4. Experimental Results

The framework was evaluated on RATBench using the DeepSeek-V3 backbone:

• State-of-the-Art Performance: RAT achieved an average Environment Setup Success Rate (ESSR) improvement of 29.6% over strong baselines like SWE-agent.
• Language Versatility: It demonstrated exceptional performance across diverse ecosystems, notably achieving a 98.7% ESSR in Rust and 68.7% in JS/TS.
• Human-Level Capability: In comparative tests, RAT achieved a 91.52% ESSR, slightly surpassing senior human engineers (89.41%) in success rate, albeit with higher latency.
• Scaling Laws: Experiments showed that increasing the computational budget (number of execution steps) consistently improves the ESSR, suggesting a scaling law for environment configuration.

5. Significance

RAT represents a fundamental shift in autonomous programming. By solving the environment configuration problem, it enables:

1. Scalable Data Synthesis: Transforming static code into verifiable, runnable datasets for training.
2. True Reinforcement Learning: Allowing reward models to move from heuristic-based signals to functional, execution-based feedback.
3. Autonomous Reliability: Moving code agents from "symbolic generators" (which write code that might work) to "reliable autonomous systems" (which provide code that is proven to work).