EnvX：以智能体AI赋能万物

摘要

开源代码库的广泛普及催生了大量可复用的软件组件，然而其使用方式仍停留在手动、易出错且孤立的状态。开发者需要查阅文档、理解API并编写集成代码，这为高效的软件复用设置了显著障碍。为解决这一问题，我们提出了EnvX框架，该框架利用智能代理AI将GitHub代码库转化为智能自主代理，使其具备自然语言交互和代理间协作的能力。与将代码库视为静态代码资源的现有方法不同，EnvX通过三阶段流程重新构想其为活跃代理：(1) TODO引导的环境初始化，设置必要的依赖项、数据和验证数据集；(2) 人机对齐的代理自动化，使特定代码库的代理能够自主执行现实任务；(3) 代理间(A2A)协议，支持多个代理协作。通过将大语言模型能力与结构化工具集成相结合，EnvX不仅自动化了代码生成，还实现了理解、初始化和操作代码库功能的完整流程自动化。我们在GitTaskBench基准上评估了EnvX，使用了涵盖图像处理、语音识别、文档分析和视频处理等领域的18个代码库。结果显示，EnvX实现了74.07%的执行完成率和51.85%的任务通过率，优于现有框架。案例研究进一步展示了EnvX通过A2A协议实现多代码库协作的能力。这项工作标志着从将代码库视为被动代码资源到智能交互代理的转变，促进了开源生态系统内更大的可访问性和协作性。

English

The widespread availability of open-source repositories has led to a vast collection of reusable software components, yet their utilization remains manual, error-prone, and disconnected. Developers must navigate documentation, understand APIs, and write integration code, creating significant barriers to efficient software reuse. To address this, we present EnvX, a framework that leverages Agentic AI to agentize GitHub repositories, transforming them into intelligent, autonomous agents capable of natural language interaction and inter-agent collaboration. Unlike existing approaches that treat repositories as static code resources, EnvX reimagines them as active agents through a three-phase process: (1) TODO-guided environment initialization, which sets up the necessary dependencies, data, and validation datasets; (2) human-aligned agentic automation, allowing repository-specific agents to autonomously perform real-world tasks; and (3) Agent-to-Agent (A2A) protocol, enabling multiple agents to collaborate. By combining large language model capabilities with structured tool integration, EnvX automates not just code generation, but the entire process of understanding, initializing, and operationalizing repository functionality. We evaluate EnvX on the GitTaskBench benchmark, using 18 repositories across domains such as image processing, speech recognition, document analysis, and video manipulation. Our results show that EnvX achieves a 74.07% execution completion rate and 51.85% task pass rate, outperforming existing frameworks. Case studies further demonstrate EnvX's ability to enable multi-repository collaboration via the A2A protocol. This work marks a shift from treating repositories as passive code resources to intelligent, interactive agents, fostering greater accessibility and collaboration within the open-source ecosystem.

EnvX：以智能体AI赋能万物

EnvX: Agentize Everything with Agentic AI

摘要

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