EnvX: Agentize Everything with Agentic AI
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.
68c2337029b8ec9932cd0929
Linyao Chen
68c2337029b8ec9932cd092a
Zimian Peng
68c2337029b8ec9932cd092b
Yingxuan Yang
68c2337029b8ec9932cd092c
Yikun Wang
68c2337029b8ec9932cd092d
Wenzheng Tom Tang
68c2337029b8ec9932cd092e
Hiroki H. Kobayashi
68c2337029b8ec9932cd092f
Weinan Zhang
EnvX: Agentize Everything with Agentic AI
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 leverages Agentic AI to transform GitHub repositories into intelligent agents capable of natural language interaction and collaboration, automating the entire process of understanding, initializing, and operationalizing repository functionality.
Agentic AI agentization natural language interaction inter-agent collaboration TODO-guided environment initialization human-aligned agentic automation Agent-to-Agent (A2A) protocol large language model structured tool integration GitTaskBench benchmark