A Managed Deep Agents project has a required agent entry and optional files that enable managed capabilities. It is a regular Python project. 托管深度代理目前处于私有测试阶段，仅在LangSmith Cloud的美国区域提供。加入等待列表以申请访问权限。

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Project layout

唯一的必需文件是代理入口：agent.py、agent.ts 或 agent.tsx。它必须导出一个使用 define_deep_agent 或 defineDeepAgent 创建的、名为 agent 的定义。tools/ 和 middleware/ 文件夹是常规做法，并非特殊的注册表：托管的智能代理将普通项目文件组织在内，因此代理导入的任何本地模块都能正常工作。当这些文件夹存在时，命令行工具会将剩余文件视为托管的系统提示（instructions.md）、身份标识（identity.*）、连接器（connectors/**）、消息通道（channels/**）、定时任务（schedules/**）、技能（skills/**）、沙箱配置（sandbox/）以及本地 Harbor 评测任务（evals/）。 The only required file is agent.py at the project root. It must export a named agent created with define_deep_agent. Use only one agent entry in a project. See Agent definition.

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How MDA treats project files

  • Managed context: instructions.md defines the system prompt. Each directory under skills/ contains task-specific instructions. MDA syncs both to Context Hub.
  • Application code: Files under tools/ and middleware/ are ordinary project modules. Import them from the agent entry. Other local modules work the same way.
  • Managed configuration: Root identity.py and memory.py, direct children of channels/ and schedules/, and sandbox/__init__.py enable their corresponding capabilities.
  • Dependencies and secrets: Declare dependencies in pyproject.toml. MDA loads .env locally and forwards eligible values as deployment secrets, but never includes .env files in the build archive.
  • Evals: Managed Deep Agents evals are Harbor evals. evals/tasks/ is the canonical Harbor task dataset. Author tasks there directly, or run mda evals init <name> to create an optional starter under evals/scaffold/. mda evals compile copies scaffolds into evals/tasks/ and packages the agent for Harbor. The evals/ directory is not included in the deployed agent build.
The layout above shows the common .py names.
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