用大模型动态调度多个智能体，提升任务成功率与执行效率。
Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration
- 基于大模型和ReAct机制动态分配任务，支持异步并行执行。
- 在GAIA基准上任务成功率更高，延迟降低且API成本减少。
- 提供实时可观测性，适合需要透明调度的复杂应用。
我们提出Gradientsys，一种新一代多智能体调度框架，通过类型化模型-上下文协议（MCP）和基于ReAct的动态规划循环，协调多样化的专业AI智能体。核心是基于大模型的调度器，实现智能的一对多任务分发，支持异步并行执行，涵盖PDF解析、网络搜索、GUI控制、网页构建等异构智能体。框架支持混合同步/异步执行，尊重智能体容量限制，并具备健壮的重试与重规划机制以优雅处理失败。为增强透明度与信任，系统集成可观测层，通过服务器推送事件（SSE）实时流式输出智能体活动与中间推理过程。我们从可扩展性、调度拓扑、工具复用性、并行度和可观测性等方面评估了Gradientsys，并在GAIA通用助手基准上验证其优于类似MinionS的基线，表现出更高的任务成功率、更低的延迟与更少的API开销，证明了其大模型驱动的多智能体编排优势。
原文摘要 · Abstract (English)
We present Gradientsys, a next-generation multi-agent scheduling framework that coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-based dynamic planning loop. At its core, Gradientsys employs an LLM-powered scheduler for intelligent one-to-many task dispatch, enabling parallel execution of heterogeneous agents such as PDF parsers, web search modules, GUI controllers, and web builders. The framework supports hybrid synchronous/asynchronous execution, respects agent capacity constraints, and incorporates a robust retry-and-replan mechanism to handle failures gracefully. To promote transparency and trust, Gradientsys includes an observability layer streaming real-time agent activity and intermediate reasoning via Server-Sent Events (SSE). We offer an architectural overview and evaluate Gradientsys against existing frameworks in terms of extensibility, scheduling topology, tool reusability, parallelism, and observability. Experiments on the GAIA general-assistant benchmark show that Gradientsys achieves higher task success rates with reduced latency and lower API costs compared to a MinionS-style baseline, demonstrating the strength of its LLM-driven multi-agent orchestration.
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