MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
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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Chuanzhe Guo
claimed_verified
2026-02-05T10:57:02.035Z
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chuanzheguo
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czguo
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Jingjing Wu
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Sijun He
claimed_verified
2026-02-05T10:57:03.932Z
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Sijun He
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user
sijunhe
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Yang Chen
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Zhaoqi Kuang
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Shilong Fan
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Bingjin Chen
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Siqi Bao
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Jing Liu
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Hua Wu
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Qingfu Zhu
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Wanxiang Che
69815cd4ce18b18628095f61
Haifeng Wang
MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
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.
MEnvAgent is a multi-language framework that automates environment construction for software engineering tasks using a planning-execution-verification architecture and environment reuse mechanism, achieving improved performance on a new benchmark and creating the largest open-source polyglot dataset of verifiable Docker environments.
Large Language Model agents software engineering verifiable datasets multi-agent Planning-Execution-Verification architecture Environment Reuse Mechanism Docker environments MEnvBench MEnvData-SWE