OAgents: An Empirical Study of Building Effective Agents
Recently, Agentic AI has become an increasingly popular research field.
However, we argue that current agent research practices lack standardization
and scientific rigor, making it hard to conduct fair comparisons among methods.
As a result, it is still unclear how different design choices in agent
frameworks affect effectiveness, and measuring their progress remains
challenging. In this work, we conduct a systematic empirical study on GAIA
benchmark and BrowseComp to examine the impact of popular design choices in key
agent components in a fair and rigorous manner. We find that the lack of a
standard evaluation protocol makes previous works, even open-sourced ones,
non-reproducible, with significant variance between random runs. Therefore, we
introduce a more robust evaluation protocol to stabilize comparisons. Our study
reveals which components and designs are crucial for effective agents, while
others are redundant, despite seeming logical. Based on our findings, we build
and open-source OAgents, a new foundation agent framework that achieves
state-of-the-art performance among open-source projects. OAgents offers a
modular design for various agent components, promoting future research in
Agentic AI.
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OAgents: An Empirical Study of Building Effective Agents
Recently, Agentic AI has become an increasingly popular research field.
However, we argue that current agent research practices lack standardization
and scientific rigor, making it hard to conduct fair comparisons among methods.
As a result, it is still unclear how different design choices in agent
frameworks affect effectiveness, and measuring their progress remains
challenging. In this work, we conduct a systematic empirical study on GAIA
benchmark and BrowseComp to examine the impact of popular design choices in key
agent components in a fair and rigorous manner. We find that the lack of a
standard evaluation protocol makes previous works, even open-sourced ones,
non-reproducible, with significant variance between random runs. Therefore, we
introduce a more robust evaluation protocol to stabilize comparisons. Our study
reveals which components and designs are crucial for effective agents, while
others are redundant, despite seeming logical. Based on our findings, we build
and open-source OAgents, a new foundation agent framework that achieves
state-of-the-art performance among open-source projects. OAgents offers a
modular design for various agent components, promoting future research in
Agentic AI.