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Two-Layer Context Optimization for Multi-Agent Systems: A DeepSeek V4 Case Study

Score: 8/10 Topic: Optimizing context management in multi-agent systems with DeepSeek V4

Advanced context management optimization for multi-agent systems using DeepSeek V4, reducing token costs and improving performance.

Multi-agent frameworks like OpenCode can incur high token costs due to context bloat from multiple agents (orchestrator, planner, worker, reviewer). This post presents a two-layer optimization strategy using DeepSeek V4 to manage context more efficiently. The first layer focuses on reducing redundant information passed between agents, while the second layer optimizes token usage within each agent's context window. The result is significant cost reduction without sacrificing performance. This is highly relevant for teams deploying multi-agent systems in production, where token costs are a major concern.