EnvX: Agentize Everything with Agentic AI
Linyao Chen, Zimian Peng, Yingxuan Yang, Yikun Wang, Wenzheng Tom Tang, Hiroki H. Kobayashi, Weinan Zhang
2025-09-11
Summary
This paper introduces EnvX, a new system that uses artificial intelligence to make it much easier to use existing code from online repositories like GitHub.
What's the problem?
Currently, finding and using code from open-source repositories is difficult and time-consuming. Developers have to manually search through code, figure out how it works, and then adapt it to their own projects. This process is prone to errors and doesn't take full advantage of the vast amount of reusable code available.
What's the solution?
EnvX tackles this problem by turning each code repository into an 'agent' powered by AI. This agent can understand natural language requests, set itself up with the necessary tools and data, and even work with other agents from different repositories. It does this in three steps: first, it prepares the repository, then it lets the agent perform tasks automatically, and finally, it allows multiple agents to collaborate on bigger projects. Essentially, EnvX automates the whole process of understanding, setting up, and using code from these repositories.
Why it matters?
This work is important because it shifts how we think about open-source code. Instead of just being a collection of files, code repositories become intelligent, interactive tools that can help developers build software more efficiently and collaboratively. It makes it easier for anyone to leverage the power of open-source, even without being an expert programmer.
Abstract
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.