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Hermes 记忆与技能系统
by
Clement Gu
· GitHub ↗
· v1.0.0
· MIT-0
75
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0
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1
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Install in OpenClaw
/install hermes-memory-system
Description
Hermes 智能体增强系统 — 自动记忆管理、技能生成、跨会话持久化
README (SKILL.md)
🧠 Hermes Agent Skill
概述
将 Hermes Agent 的 Skill 自动生成 + 持久化记忆系统移植到 OpenClaw,让 OpenClaw 拥有任务复盘提炼技能和跨会话记忆的能力。
核心能力
| 功能 | 描述 |
|---|---|
| 🧠 跨会话记忆 | 自动同步对话、预取相关上下文,实现连续对话体验 |
| 🔧 Skill 自动生成 | 从任务轨迹中复盘分析,自动提炼可复用的技能 |
| 🔗 无缝集成 | 与 OpenClaw 现有体系 100% 兼容,零入侵设计 |
| 📊 数据导出 | 将轨迹数据导出为标准化案例,支持模型优化 |
基准性能
| 指标 | 实测值 | 标准 |
|---|---|---|
| sync_turn | 0.1ms | \x3C 500ms |
| prefetch | 3.6ms | \x3C 500ms |
| 内存占用 | 17.8MB | \x3C 100MB |
| 并发处理 | 10线程 | 10+ |
安装
前提条件
- OpenClaw 2026.4.15+
- Python 3.9+
- PyYAML 6.0+
通过 ClawHub 安装
clawhub install hermes-agent-skill
手动安装
# 1. 克隆项目
git clone https://github.com/openclaw/hermes-agent-skill.git
cd hermes-agent-skill
# 2. 安装依赖
pip install pyyaml
# 3. 初始化系统
./scripts/deploy.sh --workspace ~/.openclaw/workspace --environment production
配置
主配置文件
hermes:
enabled: true
memory:
enabled: true
auto_sync: true
max_memory_size: 500
retention_days: 30
skill_generation:
enabled: true
require_approval: true
min_trajectories: 3
data_export:
enabled: true
export_dir: data/exports
环境配置
支持三种环境:
development— 调试模式,详细日志staging— 预发布验证production— 生产环境,性能优先
使用方法
初始化系统
hermes-init --workspace ~/.openclaw/workspace --config hermes.yaml --environment production
检查系统状态
hermes-status --health
在 OpenClaw 配置文件启用
# ~/.openclaw/config.yaml
skills:
hermes-agent-skill: true
hermes:
enabled: true
memory:
auto_sync: true
版本历史
详见 CHANGELOG.md
| 版本 | 日期 | 说明 |
|---|---|---|
| 1.0.0 | 2026-04-23 | 初始版本:记忆系统 + Skill 生成 + 兼容性层 |
开发
本地开发
# 安装开发依赖
pip install pyyaml psutil pytest
# 运行测试
python3 tests/test_memory.py
python3 tests/test_skill_generation.py
python3 tests/test_compatibility.py
python3 tests/test_performance.py
python3 tests/test_integration.py
发布新版本
# 1. 更新版本号
echo "1.1.0" > VERSION
# 2. 更新变更日志
vim CHANGELOG.md
# 3. 发布到 ClawHub
clawhub publish ./hermes-agent-skill --slug hermes-agent-skill \
--name "Hermes Agent Skill" \
--version $(cat VERSION) \
--changelog "$(head -3 CHANGELOG.md)"
许可
MIT License
由 OpenClaw 社区维护 | 报告问题
Usage Guidance
This package appears to implement the claimed memory and skill-generation features, but take these precautions before installing: 1) Verify the repository origin and release artifacts (confirm the GitHub organization/owner and GPG signatures); the manifest contains inconsistent/placeholder URLs. 2) Inspect any hermes-init / bin/* binaries that the deploy scripts will invoke — they are not included in the manifest you provided; do not run deploy/init scripts until you confirm what will execute. 3) Before enabling in production, disable network exports (webhooks, data_export.sync) and set use_embedding=false until you supply and vet any embedding/encryption keys. 4) Review and narrow 'record_file_operations' / 'record_code_cases' defaults — these can capture local file paths or fragments that may be sensitive. 5) Test installation in an isolated environment (VM or container) and back up ~/.openclaw before running. 6) Prefer an official release artifact from a trusted origin (signed release) or ask the author to resolve the repo/packaging inconsistencies; if you cannot verify origin and binaries, treat the deployment scripts as potentially risky.
Capability Analysis
Type: OpenClaw Skill
Name: hermes-memory-system
Version: 1.0.0
The Hermes Memory System bundle is a legitimate utility designed to provide cross-session memory and automated skill generation for OpenClaw agents. The deployment scripts (deploy.sh) and test scripts (start_acceptance_tests.sh) perform standard file operations, directory setup, and local environment validation within the user's workspace. While the configuration files (hermes.yaml) contain some hardcoded local paths, there is no evidence of data exfiltration, unauthorized network activity, or malicious prompt injection within the SKILL.md instructions.
Capability Tags
Capability Assessment
Purpose & Capability
Name/description (memory + skill generation) align with the included files (memory config, skill generation, deployment and test docs). However there are inconsistencies: SKILL.md points to openclaw/hermes-agent-skill.git while deployment docs reference releases under 'your-org/hermes-system' and other placeholders — this mismatch suggests packaging or origin ambiguity. The code/config expect to read/write the user's OpenClaw workspace and manage skills, which is reasonable for this purpose, but some declared features (data export, webhooks, embedding APIs) introduce integrations beyond simple local memory management.
Instruction Scope
Runtime instructions and shipped scripts instruct the agent/operator to clone the repo, run ./scripts/deploy.sh and hermes-init/hermes-status binaries, and to copy/link files into ~/.openclaw/workspace. The config defaults enable recording 'tool calls' and 'file operations' and persisting trajectories/code cases — behavior that can capture filesystem paths, file contents, and tool usage traces. SKILL.md and scripts also recommend enabling data export and webhooks (external endpoints configurable), which could transmit collected traces externally if enabled. The deploy/start scripts assume binaries (bin/hermes-*) exist but those files are not present in the manifest, so running the provided deploy steps as-is may either fail or cause unexpected behavior if different binaries are supplied later.
Install Mechanism
This is an instruction-only skill with no packaged install spec (lowest install risk). Shipped shell scripts will copy files into the user's workspace, create symlinks, and invoke init/status binaries. The docs also contain examples of downloading a release tarball and verifying a GPG signature from GitHub, but the URLs use placeholder organizations. There is no direct download from unknown personal servers in the provided manifest, but the mismatch and placeholders mean an operator could be instructed to fetch code from a different (possibly untrusted) URL.
Credentials
The skill declares no required environment variables or credentials (good), but its configuration surface includes optional embedding API keys, encryption keys, webhook secrets and API keys for an HTTP API. More importantly, defaults enable recording of file operations, tool calls, and code cases — collecting those artifacts is functionally related to 'skill generation' but broad in scope and could capture sensitive local data (file paths, snippets, or secrets) unless explicitly limited. The SKILL.md does not require credentials up front, but runtime configuration would need careful hardening before enabling exports or embedding providers.
Persistence & Privilege
The skill is not marked always:true and is user-invocable (normal). It intends to persist files into the user's OpenClaw workspace and create services/binaries in that workspace during install — expected for this kind of integration. Autonomous invocation (model-invocation not disabled) is allowed by default; combined with the recording/export features this increases potential blast radius if misconfigured or if the code were replaced with a malicious binary.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install hermes-memory-system - After installation, invoke the skill by name or use
/hermes-memory-system - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
初始发布:跨会话记忆 + 自动Skill生成 + OpenClaw兼容性层,验收测试15/15全部通过
Metadata
Frequently Asked Questions
What is Hermes 记忆与技能系统?
Hermes 智能体增强系统 — 自动记忆管理、技能生成、跨会话持久化. It is an AI Agent Skill for Claude Code / OpenClaw, with 75 downloads so far.
How do I install Hermes 记忆与技能系统?
Run "/install hermes-memory-system" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Hermes 记忆与技能系统 free?
Yes, Hermes 记忆与技能系统 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Hermes 记忆与技能系统 support?
Hermes 记忆与技能系统 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Hermes 记忆与技能系统?
It is built and maintained by Clement Gu (@clementgu); the current version is v1.0.0.
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