MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866918285531938816 |
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| author | Zhang, Lei Chen, Mouxiang Cao, Ruisheng Chen, Jiawei Zhou, Fan Xu, Yiheng Yang, Jiaxi Ma, Zeyao Chen, Liang Luo, Changwei Zhang, Kai Yan, Fan Shum, KaShun Zhang, Jiajun Cui, Zeyu Hu, Feng Lin, Junyang Hui, Binyuan Yang, Min |
| author_facet | Zhang, Lei Chen, Mouxiang Cao, Ruisheng Chen, Jiawei Zhou, Fan Xu, Yiheng Yang, Jiaxi Ma, Zeyao Chen, Liang Luo, Changwei Zhang, Kai Yan, Fan Shum, KaShun Zhang, Jiajun Cui, Zeyu Hu, Feng Lin, Junyang Hui, Binyuan Yang, Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07526 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era Zhang, Lei Chen, Mouxiang Cao, Ruisheng Chen, Jiawei Zhou, Fan Xu, Yiheng Yang, Jiaxi Ma, Zeyao Chen, Liang Luo, Changwei Zhang, Kai Yan, Fan Shum, KaShun Zhang, Jiajun Cui, Zeyu Hu, Feng Lin, Junyang Hui, Binyuan Yang, Min Distributed, Parallel, and Cluster Computing Software Engineering 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. |
| title | MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era |
| topic | Distributed, Parallel, and Cluster Computing Software Engineering |
| url | https://arxiv.org/abs/2601.07526 |