memos-cloud-server by memtensor/memos-cloud-skill
npx skills add https://github.com/memtensor/memos-cloud-skill --skill memos-cloud-server此技能允许 Agent 与 MemOS 云端 API 进行交互,以实现记忆的搜索、添加、删除和反馈。
在执行任何 API 操作之前,你(Agent)必须确保已配置以下环境变量:
MEMOS_API_KEY(MemOS 云端服务 API 密钥)和 MEMOS_USER_ID(当前用户的唯一标识符)。~/.zshrc 或 ~/.bashrc 中)。你可以直接通过 memos_cloud.py 脚本执行操作。该脚本会自动读取 MEMOS_API_KEY 环境变量。所有操作请求和响应都以 JSON 格式输出。
/v1/search/memory)广告位招租
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搜索与用户查询相关的长期记忆。
用法:
python3 skills/memos-cloud-server/memos_cloud.py search <user_id> "<query>" [--conversation-id <id>]
示例:
python3 skills/memos-cloud-server/memos_cloud.py search "$MEMOS_USER_ID" "Python 相关项目经验"
/v1/add/message)用于将多轮对话中的高价值内容存储到云端。
conversation_id:必需。当前对话的 ID。messages:必需。必须是包含 role 和 content 字段列表的有效 JSON 字符串。用法:
python3 skills/memos-cloud-server/memos_cloud.py add_message <user_id> <conversation_id> '<messages_json_string>'
示例:
python3 skills/memos-cloud-server/memos_cloud.py add_message "$MEMOS_USER_ID" "topic-123" '[{"role":"user","content":"I like apples"},{"role":"assistant","content":"Okay, I noted that"}]'
/v1/delete/memory)删除云端存储的记忆。根据 API 规范,memory_ids 是严格必需的。
用法:
# 通过记忆 ID 删除(逗号分隔)
python3 skills/memos-cloud-server/memos_cloud.py delete "id1,id2,id3"
/v1/add/feedback)添加关于对话的反馈,以纠正或强化云端记忆。
用法:
python3 skills/memos-cloud-server/memos_cloud.py add_feedback <user_id> <conversation_id> "<feedback_content>" [--allow-knowledgebase-ids "kb1,kb2"]
示例:
python3 skills/memos-cloud-server/memos_cloud.py add_feedback "$MEMOS_USER_ID" "topic-123" "之前的回答不够详细"
每周安装数
124
代码仓库
GitHub 星标数
1
首次出现
2026年3月4日
安全审计
安装于
gemini-cli122
github-copilot122
codex122
amp122
cline122
kimi-cli122
This skill allows the Agent to interact with MemOS Cloud APIs for memory search, addition, deletion, and feedback.
Before executing any API operations, you (the Agent) must ensure the following environment variables are configured:
MEMOS_API_KEY (MemOS Cloud Service API Key) and MEMOS_USER_ID (Unique identifier for the current user) must be configured.~/.zshrc or ~/.bashrc).You can execute operations directly via the memos_cloud.py script. The script automatically reads the MEMOS_API_KEY environment variable. All operation requests and responses are output in JSON format.
/v1/search/memory)Search for long-term memories relevant to the user's query.
Usage:
python3 skills/memos-cloud-server/memos_cloud.py search <user_id> "<query>" [--conversation-id <id>]
Example:
python3 skills/memos-cloud-server/memos_cloud.py search "$MEMOS_USER_ID" "Python related project experience"
/v1/add/message)Used to store high-value content from multi-turn conversations to the cloud.
conversation_id: Required. The ID of the current conversation.messages: Required. Must be a valid JSON string containing a list with role and content fields.Usage:
python3 skills/memos-cloud-server/memos_cloud.py add_message <user_id> <conversation_id> '<messages_json_string>'
Example:
python3 skills/memos-cloud-server/memos_cloud.py add_message "$MEMOS_USER_ID" "topic-123" '[{"role":"user","content":"I like apples"},{"role":"assistant","content":"Okay, I noted that"}]'
/v1/delete/memory)Delete stored memories on the cloud. According to the API spec, memory_ids is strictly required.
Usage:
# Delete by Memory IDs (comma-separated)
python3 skills/memos-cloud-server/memos_cloud.py delete "id1,id2,id3"
/v1/add/feedback)Add feedback regarding a conversation to correct or reinforce memory in the cloud.
Usage:
python3 skills/memos-cloud-server/memos_cloud.py add_feedback <user_id> <conversation_id> "<feedback_content>" [--allow-knowledgebase-ids "kb1,kb2"]
Example:
python3 skills/memos-cloud-server/memos_cloud.py add_feedback "$MEMOS_USER_ID" "topic-123" "The previous answer was not detailed enough"
Weekly Installs
124
Repository
GitHub Stars
1
First Seen
Mar 4, 2026
Security Audits
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Installed on
gemini-cli122
github-copilot122
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amp122
cline122
kimi-cli122
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