We introduce O-Researcher, a new family of research agents that natively perform end-to-end, multi-turn, multi-tool deep research problem solving—without external frameworks or manual intervention. Built on a Multi-Agent Data Synthesis paradigm, O-Researcher leverages collaborative AI agents to simulate complex tool-integrated reasoning and automatically generate diverse, research-grade instructional data. To train these models, we develop a two-stage strategy combining supervised fine-tuning with agentic reinforcement learning on verifiable multi-domain tasks, maximizing both alignment and research capability. O-Researcher sets new state-of-the-art results on major deep research benchmarks, and we release all model weights, inference code, and datasets to accelerate future research on agentic AI. For more details, please refer to our paper and GitHub.

If you find O-Researcher useful in your research or applications, we would appreciate it if you could cite our work: