Recent advanced LLM-powered agent systems have exhibited their remarkable capabilities in tackling complex, long-horizon tasks. Nevertheless, they still suffer from inherent limitations in resource efficiency, context management, and multimodal perception. Based on these observations, Lemon Agent is introduced, a multi-agent orchestrator-worker system built on a newly proposed AgentCortex framework, which formalizes the classic Planner-Executor-Memory paradigm through an adaptive task execution mechanism. Our system integrates a hierarchical self-adaptive scheduling mechanism that operates at both the overall orchestrator layer and workers layer. This mechanism can dynamically adjust computational intensity based on task complexity. It enables orchestrator to allocate one or more workers for parallel subtask execution, while workers can further improve operational efficiency by invoking tools concurrently. By virtue of this two-tier architecture, the system achieves synergistic balance between global task coordination and local task execution, thereby optimizing resource utilization and task processing efficiency in complex scenarios. To reduce context redundancy and increase information density during parallel steps, we adopt a three-tier progressive context management strategy. To make fuller use of historical information, we propose a self-evolving memory system, which can extract multi-dimensional valid information from all historical experiences to assist in completing similar tasks. Furthermore, we provide an enhanced MCP toolset. Empirical evaluations on authoritative benchmarks demonstrate that our Lemon Agent can achieve a state-of-the-art 91.36% overall accuracy on GAIA and secures the top position on the xbench-DeepSearch leaderboard with a score of 77+.

翻译：近期基于大型语言模型的高级智能体系统在处理复杂、长周期任务方面展现出卓越能力。然而，这些系统在资源效率、上下文管理和多模态感知方面仍存在固有局限。基于这些观察，我们提出了柠檬智能体——一个基于全新AgentCortex框架构建的多智能体协调-执行系统。该框架通过自适应任务执行机制，将经典的规划器-执行器-记忆范式形式化。我们的系统集成了在整体协调层与执行层同时运作的分层自适应调度机制，该机制可根据任务复杂度动态调整计算强度。这使得协调器能够分配一个或多个执行器进行并行子任务处理，而执行器可通过并发调用工具进一步提升运行效率。凭借这种双层架构，系统实现了全局任务协调与局部任务执行的协同平衡，从而优化了复杂场景下的资源利用与任务处理效率。为降低并行步骤中的上下文冗余并提升信息密度，我们采用三层渐进式上下文管理策略。为更充分利用历史信息，我们提出了自演进记忆系统，该系统能从所有历史经验中提取多维有效信息以辅助完成类似任务。此外，我们提供了增强型MCP工具集。在权威基准测试上的实证评估表明，我们的柠檬智能体在GAIA基准上实现了91.36%的整体准确率，达到当前最优水平，并在xbench-DeepSearch排行榜以77+的分数位列榜首。