一位开发者使用 MCP 工具和 Dapr 展示了一个多智能体工作流，仅需大约 60 行 Python 代码。该工作流允许智能体按顺序执行，并在每一步记录结果，以确保即使在智能体之间出现进程失败也能保持持久性。该示例包括一个使用 MCP 工具的研究员智能体和一个总结研究结果的撰写者智能体，所有这些都由 Dapr 工作流进行编排。 AI
影响 能够实现更健壮、容错性更强的多智能体 AI 系统的执行。
排序理由 演示了用于构建 AI 智能体工作流的特定软件工具/框架。
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A Multi-Agent Workflow With MCP Tools in About 60 Lines An MCP agent demo is usually one agent calling one tool server. Add a second agent and a hand-off between them, and a boring question shows up fast: if the process dies between agent one and agent two, what runs again? Below…
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