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_*_tunnel、memory_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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