💡 The paper presents Orchestra-o1, an omnimodal agent orchestration framework that enables efficient collaboration across multiple modalities such as text, image, audio, and video. The existing agent orchestration frameworks are limited to a narrow set of modalities and struggle to generalize to complex settings where heterogeneous modalities coexist and interact. To address this limitation, Orchestra-o1 introduces a unified orchestration mechanism that enables modality-aware task decomposition, online sub-agent specialization, and parallel sub-task execution. This allows agent systems to effectively tackle complex real-world tasks involving heterogeneous information sources. The framework is trained using decision-aligned group relative policy optimization, an efficient agentic reinforcement learning approach. The results show that Orchestra-o1 achieves superior performance on complex multimodal benchmarks, surpassing the second-best approach by 10.3 percent accuracy on the OmniGAIA benchmark. Additionally, the trained Orchestra-o1-8B model achieves state-of-the-art performance against all existing open-source omnimodal agents, demonstrating the effectiveness of the proposed framework. Overall, the paper contributes to the development of omnimodal agent orchestration frameworks that can efficiently collaborate across multiple modalities, enabling the creation of more complex and powerful agent systems.