MiroFlow achieved a pass@1 score rate of 72.2% (average of three runs) on the GAIA validation set using Claude Sonnet 3.7 as the primary large language model. This performance is at the forefront of open-source intelligent body frameworks, demonstrating its ability to handle complex multi-tool tasks.
The significance of this achievement lies in the following: first, it verifies the stability and reproducibility of the framework, which is lacking in many open source projects; second, the official provision of fully open evaluation scripts and configuration files, and the release of data from multiple independent runs on HuggingFace ensures the transparency of the results; and lastly, this benchmark provides developers with objective performance references to choose a framework.
This answer comes from the articleMiroFlow: a framework for building, managing and scaling AI intelligencesThe
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