MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era
Lei Zhang, Mouxiang Chen, Ruisheng Cao, Jiawei Chen, Fan Zhou, Yiheng Xu, Jiaxi Yang, Liang Chen, Changwei Luo, Kai Zhang, Fan Yan, KaShun Shum, Jiajun Zhang, Zeyu Cui, Hu Feng, Junyang Lin, Binyuan Hui, Min Yang
2026-01-13
Summary
This paper introduces MegaFlow, a new system designed to help researchers and developers train and test complex AI agents that can perform tasks like coding or using computers.
What's the problem?
As AI agents become more sophisticated and capable of handling complicated tasks, it's becoming really difficult to actually train and test them effectively. Existing tools just aren't built to manage the huge number of interactions these agents need with their environment, and there aren't any good open-source options available to handle this large scale.
What's the solution?
The creators of this paper built MegaFlow, which works by breaking down the agent training process into three separate parts: the 'brain' of the agent (Model Service), the agent itself (Agent Service), and the simulated world it interacts with (Environment Service). These parts all communicate in a standardized way, allowing them to be scaled up or down independently and used in different combinations. MegaFlow can manage tens of thousands of agents working at the same time, keeping the system stable and using resources efficiently.
Why it matters?
MegaFlow is important because it fills a critical gap in the tools available for developing advanced AI agents. By making it easier to train these agents on a large scale, it helps accelerate progress in the field of 'agentic AI,' where AI systems are becoming more autonomous and capable of taking initiative.
Abstract
The rapid development of interactive and autonomous AI systems signals our entry into the agentic era. Training and evaluating agents on complex agentic tasks such as software engineering and computer use requires not only efficient model computation but also sophisticated infrastructure capable of coordinating vast agent-environment interactions. However, no open-source infrastructure can effectively support large-scale training and evaluation on such complex agentic tasks. To address this challenge, we present MegaFlow, a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads. MegaFlow abstracts agent training infrastructure into three independent services (Model Service, Agent Service, and Environment Service) that interact through unified interfaces, enabling independent scaling and flexible resource allocation across diverse agent-environment configurations. In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization. By enabling such large-scale agent training, MegaFlow addresses a critical infrastructure gap in the emerging agentic AI landscape.