Building Cost-Aware Hierarchical Multi-Agent Systems with DeepSeek Dual Models

This article presents a configuration-based approach to building hierarchical multi-agent systems using DeepSeek V4 dual models within the OpenCode framework. The key innovation is cost-aware scheduling, which dynamically allocates tasks between a powerful but expensive model and a lighter, cheaper one. By leveraging prompt engineering, the system achieves high reliability without additional dependencies. This method is particularly valuable for teams looking to optimize AI development costs while maintaining performance. The zero-dependency configuration makes it easy to adopt and scale, offering a blueprint for efficient multi-agent collaboration in real-world applications.