MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering

• URL: http://arxiv.org/abs/2601.22859v2
• Date: Mon, 02 Feb 2026 03:17:52 GMT
• Title: MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
• Authors: Chuanzhe Guo, Jingjing Wu, Sijun He, Yang Chen, Zhaoqi Kuang, Shilong Fan, Bingjin Chen, Siqi Bao, Jing Liu, Hua Wu, Qingfu Zhu, Wanxiang Che, Haifeng Wang,
• Abstract summary: We introduce MEnvAgent, a framework for automated Environment construction.<n>MEnvAgent employs a multi-agent Planning-Execution-Verification architecture to autonomously resolve construction failures.<n>MEnvData-SWE is the largest open-source polyglot dataset of realistic verifiable Docker environments to date.
• Score: 54.236614097082395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages. To address this, we introduce MEnvAgent, a Multi-language framework for automated Environment construction that facilitates scalable generation of verifiable task instances. MEnvAgent employs a multi-agent Planning-Execution-Verification architecture to autonomously resolve construction failures and integrates a novel Environment Reuse Mechanism that reduces computational overhead by incrementally patching historical environments. Evaluations on MEnvBench, a new benchmark comprising 1,000 tasks across 10 languages, demonstrate that MEnvAgent outperforms baselines, improving Fail-to-Pass (F2P) rates by 8.6% while reducing time costs by 43%. Additionally, we demonstrate the utility of MEnvAgent by constructing MEnvData-SWE, the largest open-source polyglot dataset of realistic verifiable Docker environments to date, alongside solution trajectories that enable consistent performance gains on SWE tasks across a wide range of models. Our code, benchmark, and dataset are available at https://github.com/ernie-research/MEnvAgent.

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