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Python MCP workflow tools for host-controlled planning workflows

Project description

Ymcp

Trae MCP工具包 提供工作流以及mempalace的长记忆存储

记忆工具

Ymcp 依赖 mempalace 提供长期记忆能力,默认使用全局个人记忆空间:wing="personal"room="ymcp"

常用工具:

  • memory_store:保存一条长期记忆
  • memory_search:搜索长期记忆
  • memory_get / memory_update / memory_delete:读取、更新、删除指定记忆
  • memory_status / memory_list_wings / memory_list_rooms / memory_taxonomy:查看记忆空间状态
  • memory_graph_*memory_kg_*memory_*_tunnelmemory_diary_*:封装 MemPalace 的图谱、关系和日记能力

记忆写入是持久化副作用。请不要保存密钥、隐私或未经确认的敏感信息。

Trae 中常用记忆 prompt

调用 Ymcp 的 memory_search,搜索:“当前项目的发布流程”。请只总结与当前任务相关的记忆。
调用 Ymcp 的 memory_store,保存这条长期项目约定:“Ymcp 的文档和用户回复都使用中文,代码接口名保持英文。”

更多示例见 docs/trae-integration.md

Trae Workflow 最佳调用链

推荐链路:deep_interview → ralplan → ralph → memory_store

  • 需求不清晰:先调用 deep_interview,按 next_question 多轮澄清。
  • 需要共识规划:调用 ralplan,按 planner_draft → architect_review → critic_review 推进。
  • 执行验证:调用 ralph,根据 stop_continue_judgement 继续、修复或完成。
  • 完成沉淀:调用 memory_search 查重,再用 memory_store 保存稳定经验。

完整 prompt 模板见 docs/trae-integration.md

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