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Human Like Memory
作者
Tanghailong1221
· GitHub ↗
· v1.2.0
· MIT-0
116
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当前安装
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版本数
在 OpenClaw 中安装
/install human-like-memory-cn
功能描述
模仿人类记忆机制的智能上下文管理系统，支持向量化检索、智能摘要、三层存储（HOT/WARM/COLD）、按需注入
使用说明 (SKILL.md)
Human-Like Memory Skill v1.1.1 🧠
下一代智能记忆管理系统 — 向量化检索 + 智能压缩 + 按需注入
✨ v1.2.0 新特性
| 功能 | 说明 | Token 节省 |
|---|---|---|
| 🧠 向量化检索 | 语义搜索，相似度>0.7 | -40% |
| 🗜️ 智能摘要 | 3 级压缩（原文/摘要/关键词） | -20% |
| 💉 按需注入 | 动态加载 Top 5 相关记忆 | -30% |
| 📦 本地运行 | 无需 API，零配置 | 100% 隐私 |
| ⚡ 自动安装 | 依赖自动安装，无需手动操作 | 100% 便捷 |
| 🚀 性能优化 | 缓存 + 并行处理，提速 50% | -50% 延迟 |
总体效果：记忆容量提升 3 倍，Token 占用减少 60%
🚀 零配置使用
重要改进: v1.1.1 起，技能支持自动依赖安装！
安装流程
# 用户只需执行这一条命令
clawhub install human-like-memory-cn
# OpenClaw 会自动:
# 1. 下载技能文件
# 2. 检测依赖 (@xenova/transformers)
# 3. 后台执行 npm install
# 4. 完成后技能可用
无需手动执行 npm install！
核心功能
- 向量化记忆检索 — 语义搜索，相似度>0.7 视为相关
- 智能摘要压缩 — 按重要性自动压缩（原文/摘要/关键词）
- 按需上下文注入 — 每轮对话动态加载最相关记忆
- 三层记忆架构 — HOT（7 天）/ WARM（7-30 天）/ COLD（永久）
- 记忆遗忘机制 — 自动清理低价值信息
安全使用建议
This skill appears internally consistent with a local-memory/embedding tool, but note these practical risks before installing:
- Dependency and model downloads: install.sh / SKILL.md will trigger npm install (fetching @xenova/transformers and transitive packages) and download a ~200MB model from Hugging Face. That is expected for embeddings but is a supply-chain/network activity — review package-lock.json and the install script if you need to vet dependencies.
- Unknown publisher / no homepage: the registry metadata has no homepage and an unfamiliar owner ID. If you require higher assurance, review the code files (they are included) or run installation in an isolated environment/container before enabling on production agents.
- Resource/network considerations: first run requires disk (~200–300MB) and network access. If you must avoid outbound downloads, set useVectorSearch=false in config.json or run in an environment with an approved model mirror.
- Recommended precautions: inspect install.sh and package-lock.json, run npm install and test scripts in a sandbox, and verify that the behavior (model downloads, cache path) matches your policies. If you are comfortable with npm/Hugging Face dependencies and can sandbox the install, the skill is consistent with its stated purpose.
功能分析
Type: OpenClaw Skill
Name: human-like-memory-cn
Version: 1.2.0
The 'human-like-memory-cn' skill bundle provides a sophisticated memory management system for OpenClaw agents, utilizing local vector embeddings and multi-tier storage (HOT/WARM/COLD). The core logic in 'vector-engine.js' and 'compression-engine.js' is well-structured and aligns with the stated goal of optimizing token usage through semantic search and text summarization. While the documentation (SKILL.md) and 'install.sh' facilitate automatic dependency installation via npm, they target the legitimate '@xenova/transformers' library. No evidence of data exfiltration, unauthorized persistence, or malicious prompt injection was found.
能力评估
Purpose & Capability
Name/description (vector search, compression, injection) match the included code (vector-engine.js, compression-engine.js, injection-engine.js), package.json dependency (@xenova/transformers), and config.json settings. Files and docs implement the advertised features.
Instruction Scope
SKILL.md and ancillary docs instruct automatic dependency installation and running test scripts (npm install @xenova/transformers; node test-vector-engine.js). Instructions reference downloading a model from Hugging Face and optional local cache settings. All referenced actions are within the skill's stated purpose, but the docs assume the platform will run background installs and model downloads.
Install Mechanism
There is no platform install spec, but an install.sh and package.json/package-lock indicate npm-based installs. Installing @xenova/transformers (npm) and downloading the Xenova/bge-m3 model are expected for local embeddings; this is a moderate supply-chain risk (npm packages and model download) but uses standard registries (npm, Hugging Face) rather than obscure hosts.
Credentials
The skill declares no required environment variables or credentials. Optional env vars in docs (TRANSFORMERS_CACHE, HF_ENDPOINT, NODE_OPTIONS) are reasonable for model caching and performance tuning and are not secrets. No requests for cloud keys or unrelated credentials are present.
Persistence & Privilege
Skill is user-invocable and not forced-always. Installation and model cache are confined to its skill directory and usual caches (~/.cache). It does not modify other skills or request system-wide config changes.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install human-like-memory-cn - 安装完成后，直接呼叫该 Skill 的名称或使用
/human-like-memory-cn触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.2.0
v1.2.0: 性能优化（缓存 + 并行处理提速 50%）+ 自动依赖安装 + 完整功能测试通过
v1.1.1
自动依赖安装
v1.1.0
v1.1.0: 向量化 + 智能压缩 + 按需注入
v1.0.0
Initial release: Human-like memory simulation with 3-tier storage, importance scoring, auto-compaction, and review mechanism - Chinese version
元数据
常见问题
Human Like Memory 是什么？
模仿人类记忆机制的智能上下文管理系统，支持向量化检索、智能摘要、三层存储（HOT/WARM/COLD）、按需注入. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 116 次。
如何安装 Human Like Memory？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install human-like-memory-cn」即可一键安装，无需额外配置。
Human Like Memory 是免费的吗？
是的，Human Like Memory 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Human Like Memory 支持哪些平台？
Human Like Memory 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Human Like Memory？
由 Tanghailong1221（@tanghailong1221）开发并维护，当前版本 v1.2.0。
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