MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks
#MiroFlow #Agent Framework #Large Language Models #Deep Research #Open Source AI #AI Benchmarking #External Tools Integration #Dynamic Environments
📌 Key Takeaways
- MiroFlow addresses limitations of current LLMs and agent frameworks in handling complex real-world tasks
- The framework features an agent graph, deep reasoning mode, and robust workflow execution
- MiroFlow achieved state-of-the-art performance across multiple benchmarks including GAIA, BrowseComp-EN/ZH, HLE, xBench-DeepSearch, and FutureX
- The framework aims to serve as an accessible, reproducible baseline for the research community
📖 Full Retelling
Researchers led by Shiqian Su and 15 collaborators introduced MiroFlow, a high-performance and robust open-source agent framework for general deep research tasks, in a paper submitted to arXiv on February 26, 2026, addressing the limitations of current language models and agent frameworks that struggle with complex real-world tasks requiring external tools and dynamic environments due to naive workflows, unstable performance, and heavy reliance on costly commercial APIs. The MiroFlow framework represents a significant advancement in AI agent technology by incorporating three key innovations: an agent graph for flexible orchestration, an optional deep reasoning mode to enhance performance, and robust workflow execution to ensure stable and reproducible results across diverse tasks. According to the research team, 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. The researchers hope this framework will serve as an easily accessible, reproducible, and comparable baseline for the deep research community, potentially accelerating development in AI agent systems while reducing dependency on proprietary solutions.
🏷️ Themes
Artificial Intelligence, Open Source, Research Frameworks
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22808 [Submitted on 26 Feb 2026] Title: MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks Authors: 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 View a PDF of the paper titled MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks, by Shiqian Su and 15 other authors View PDF HTML Abstract: Despite the remarkable progress of large language models , 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22808 [cs.AI] (or arXiv:2602.22808v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22808 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission hi...
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