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MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks

arXiv:2602.22808v1 Announce Type: new Abstract: Despite the remarkable progress of large language models (LLMs), the capabilities of standalone LLMs have begun to plateau when tackling real-world, complex tasks that require interaction with external tools and dynamic environments. Although recent agent frameworks aim to enhance model autonomy through tool integration and external interaction, they still suffer from naive workflows, unstable performance, limited support across diverse benchmarks and tasks, and heavy reliance on costly commercial APIs. In this work, we propose a high-performance and robust open-source agent framework, termed MiroFlow, which incorporates an agent graph for flexible orchestration, an optional deep reasoning mode to enhance performance, and a robust workflow execution to ensure stable and reproducible performance. Extensive experiments demonstrate that MiroFlow consistently achieves state-of-the-art performance across multiple agent benchmarks, including G

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Shiqian Su, Sen Xing, Xuan Dong, Muyan Zhong, Bin Wang, Xizhou Zhu, Yuntao Chen, Wenhai Wang, Yue Deng, Pengxiang Zhu, Ziyuan Liu, Tiantong Li, Jiaheng Yu, Zhe Chen, Lidong Bing, Jifeng Dai · · 1 min read · 3 views
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arXiv:2602.22808v1 Announce Type: new Abstract: Despite the remarkable progress of large language models (LLMs), the capabilities of standalone LLMs have begun to plateau when tackling real-world, complex tasks that require interaction with external tools and dynamic environments. Although recent agent frameworks aim to enhance model autonomy through tool integration and external interaction, they still suffer from naive workflows, unstable performance, limited support across diverse benchmarks and tasks, and heavy reliance on costly commercial APIs. In this work, we propose a high-performance and robust open-source agent framework, termed MiroFlow, which incorporates an agent graph for flexible orchestration, an optional deep reasoning mode to enhance performance, and a robust workflow execution to ensure stable and reproducible performance. Extensive experiments demonstrate that MiroFlow consistently achieves state-of-the-art performance across multiple agent benchmarks, including GAIA, BrowseComp-EN/ZH, HLE, xBench-DeepSearch, and notably FutureX. We hope it could serve as an easily accessible, reproducible, and comparable baseline for the deep research community.

Executive Summary

The article proposes MiroFlow, a high-performance and robust open-source agent framework designed to tackle real-world, complex tasks that require interaction with external tools and dynamic environments. MiroFlow incorporates an agent graph for flexible orchestration, an optional deep reasoning mode, and a robust workflow execution to ensure stable and reproducible performance. Extensive experiments demonstrate that MiroFlow achieves state-of-the-art performance across multiple agent benchmarks. The authors aim to provide a reproducible and comparable baseline for the deep research community. While MiroFlow addresses the limitations of existing agent frameworks, its performance and generalizability across diverse tasks and environments remain to be thoroughly assessed.

Key Points

  • ▸ MiroFlow is an open-source agent framework designed for high-performance and robustness
  • ▸ The framework incorporates an agent graph for flexible orchestration and an optional deep reasoning mode
  • ▸ MiroFlow achieves state-of-the-art performance across multiple agent benchmarks

Merits

Strength in Design

MiroFlow's modular design, incorporating an agent graph and deep reasoning mode, allows for flexible orchestration and enhanced performance.

Improved Performance

MiroFlow's robust workflow execution and optional deep reasoning mode enable consistent and state-of-the-art performance across diverse tasks and environments.

Accessibility and Reproducibility

MiroFlow's open-source nature and reproducible experiments provide a valuable resource for the deep research community, allowing for easy comparison and benchmarking.

Demerits

Limited Generalizability

While MiroFlow demonstrates state-of-the-art performance across multiple agent benchmarks, its performance and generalizability across diverse tasks and environments remain to be thoroughly assessed.

Dependence on Commercial APIs

MiroFlow's performance may still be influenced by the use of commercial APIs, which could impact its robustness and reproducibility.

Expert Commentary

MiroFlow represents a significant advancement in the field of agent frameworks, offering a high-performance and robust solution for tackling complex tasks. While its performance and generalizability remain to be thoroughly assessed, the framework's design and reproducibility make it a valuable resource for the deep research community. As the field continues to evolve, MiroFlow's impact on the development and application of AI and machine learning will be worth monitoring.

Recommendations

  • ✓ Further experimentation and evaluation of MiroFlow's performance across diverse tasks and environments are necessary to fully assess its capabilities.
  • ✓ The development of tools and resources to support the adoption and customization of MiroFlow would facilitate its widespread use and application.

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