Hermes vs OpenClaw：基于源码的 Agent Loop 全面分析

作者：吴佳浩

撰稿时间：2026-6-7

最后更新：2026-6-10

声明：本文所有结论均来自俺对两个仓库源码的分析和测（转载请注明出处吴佳浩Alben）

Hermes: github.com/NousResearch/hermes-agent

OpenClaw: github.com/openclaw/openclaw

引言

Hermes和OpenClaw的对比文章视频很多，但大多数停留在功能层面：支持哪些模型、有哪些工具、是否支持多Agent。

真正决定一个Agent系统上限的，其实不是工具数量，而是Agent Loop本身。

从学术谱系来看，两者都源于姚顺雨等人在2022年提出的ReAct（Reason + Act）范式：让大模型在推理（Thought）与行动（Action）之间循环迭代，通过工具调用获取外部信息，再将Observation反馈回下一轮推理。

然而，当ReAct从论文走向工程落地之后，两条完全不同的演化路线出现了。

Hermes选择继续强化Agent本身------记忆、技能、学习、长期成长；OpenClaw则选择强化Agent运行环境------会话、调度、流式事件、生命周期与可靠性。

前者试图回答：

"Agent如何变得越来越聪明？"

后者试图回答：

"Agent如何在生产环境中稳定运行数千次甚至数万次任务？"

这也是为什么，同样是Agent Loop，两者最终演化出了完全不同的架构形态。

一、两个项目的真实起源

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text{fill:lightgray;}#mermaid-svg-1Om6nO0AbJ8rVVXf .disabled text{fill:#efefef;}#mermaid-svg-1Om6nO0AbJ8rVVXf .section-root rect,#mermaid-svg-1Om6nO0AbJ8rVVXf .section-root path,#mermaid-svg-1Om6nO0AbJ8rVVXf .section-root circle{fill:hsl(240, 100%, 46.2745098039%);}#mermaid-svg-1Om6nO0AbJ8rVVXf .section-root text{fill:#ffffff;}#mermaid-svg-1Om6nO0AbJ8rVVXf .icon-container{height:100%;display:flex;justify-content:center;align-items:center;}#mermaid-svg-1Om6nO0AbJ8rVVXf .edge{fill:none;}#mermaid-svg-1Om6nO0AbJ8rVVXf .eventWrapper{filter:brightness(120%);}#mermaid-svg-1Om6nO0AbJ8rVVXf :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 2025-11-24 OpenClaw 前身Clawdbot 发布作者 PeterSteinberger（奥地利，vibecoder） TypeScript，本地Gateway +消息平台集成 2026-01-27 Clawdbot → Moltbot 应 Anthropic商标投诉改名 2026-01-30 Moltbot →OpenClaw 开始病毒式传播 2026-02-14 Steinberger 加入OpenAI 成立非营利基金会接管项目 2026-02 Hermes Agent 发布 NousResearch出品，Python 重写定位「在 OpenClaw基础上填补记忆短板」内置 hermes clawmigrate 迁移工具两个项目的诞生时间线

关键背景：OpenClaw 的 VISION.md 明确写道：

"OpenClaw started as a personal playground to learn AI and build something genuinely useful: an assistant that can run real tasks on a real computer."

而 Hermes 的发布文章则直接写明：

"OpenClaw...Its limitation, as users noted publicly, is that memory between sessions requires manual setup. Hermes was built to solve that gap."

这不是两个竞争者，而是一个明确的继承与扩展关系。

二、技术栈对比：语言差异决定了执行模型

	Hermes	OpenClaw
主语言	Python 83%（主逻辑）TypeScript 13%（TUI / Web）	TypeScript（Node.js 22+）
并发模型	同步线程模型 `concurrent.futures` + `threading.Lock`	原生异步模型 `async/await` + Event Loop
包管理	uv + pip	pnpm monorepo
安装方式	`curl ...	bash`（Shell Installer）或 PowerShell
LLM 客户端层	`auxiliary_client.py`（264KB）--- 统一 LLM 调用抽象	pi-ai（Mario Zechner 作品）--- 多 Provider 抽象 + 流式事件归一化
Agent Loop 层	`AIAgent` 类（`run_agent.py`，6933 行）	pi-agent-core（Mario Zechner 作品）--- Agent Loop + 工具执行 + 事件系统
Agent Runtime 层	`agent/` 子包（prompt_builder、context_compressor、display 等）	pi-coding-agent --- 完整 Runtime（文件工具、JSONL session、压缩、扩展）
CLI / TUI 层	`hermes_cli/` + `hermes_tui/`	pi-tui --- Terminal UI
Gateway 层	Gateway（Python 实现）	OpenClaw Gateway --- 在 pi-* 层次之上构建的多渠道 + Hook 系统

OpenClaw 的架构层次

复制代码

pi-ai           → LLM 多 provider 抽象，流式事件归一化
pi-agent-core   → Agent Loop，工具执行，事件系统
pi-coding-agent → 完整 Agent Runtime（文件工具、JSONL session、压缩、扩展）
pi-tui          → Terminal UI
─────────────────────────
OpenClaw Gateway → 在以上层次之上构建的 Gateway + 多渠道 + Hook 系统

这不是简单的语言偏好问题。Python 的 GIL 和同步模型天然倾向于 while 循环 + ThreadPool；Node.js 的 Event Loop 天然倾向于事件驱动 + 异步流。两个系统的执行模型差异，根源在语言选择，不只是设计哲学。

三、仓库规模的真实对比

Hermes Agent 代码规模（agent/ 目录核心文件）

文件	大小	职责
`run_agent.py`	6,933 行 / 335KB	主入口，AIAgent 类（不是 15k+）
`agent/agent_runtime_helpers.py`	~110KB	Agent Runtime 辅助函数
`agent/agent_init.py`	~88KB	Agent 初始化
`agent/auxiliary_client.py`	~264KB	LLM 统一调用抽象
`agent/anthropic_adapter.py`	~102KB	Anthropic 原生 SDK 适配
`agent/prompt_builder.py`	~19KB	System Prompt 组装
`agent/context_compressor.py`	~22KB	上下文压缩引擎
`agent/tool_guardrails.py`	~18KB	工具循环检测护栏
`agent/background_review.py`	~31KB	后台自我回顾（Memory Nudge）
`agent/display.py`	~20KB	CLI 展示（spinner、diff 渲染、Tool Preview）
`agent/model_metadata.py`	~21KB	模型元数据、上下文长度探测
`agent/iteration_budget.py`	~3.4KB	迭代预算计数器
`agent/tool_dispatch_helpers.py`	~17KB	工具并发调度
`agent/bedrock_adapter.py`	~50KB	AWS Bedrock 适配
`tools/registry.py`	---	22 个工具模块的自注册注册表

OpenClaw 代码规模

层级	说明
`src/` 核心	TypeScript，数十个子系统
`packages/` 工作区	pnpm monorepo
`extensions/` 插件	渠道适配器等
58,209 commits	约为 Hermes 的 5.2 倍

Commit 数量差异（11,158 vs 58,209）是最直观的迭代深度指标。OpenClaw 的迭代密度远高于 Hermes，这符合「更早诞生、更大社区、更长演化时间」的事实。

四、共同骨架：两者都实现了同一个 ReAct 循环

#mermaid-svg-MNk9mlmGM4wZHpej{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-MNk9mlmGM4wZHpej .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-MNk9mlmGM4wZHpej .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-MNk9mlmGM4wZHpej .error-icon{fill:#552222;}#mermaid-svg-MNk9mlmGM4wZHpej .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-MNk9mlmGM4wZHpej .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-MNk9mlmGM4wZHpej .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-MNk9mlmGM4wZHpej .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-MNk9mlmGM4wZHpej .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-MNk9mlmGM4wZHpej .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-MNk9mlmGM4wZHpej .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-MNk9mlmGM4wZHpej .marker{fill:#333333;stroke:#333333;}#mermaid-svg-MNk9mlmGM4wZHpej .marker.cross{stroke:#333333;}#mermaid-svg-MNk9mlmGM4wZHpej svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-MNk9mlmGM4wZHpej p{margin:0;}#mermaid-svg-MNk9mlmGM4wZHpej .label{font-family:"trebuchet ms",verdana,arial,sans-serif;color:#333;}#mermaid-svg-MNk9mlmGM4wZHpej .cluster-label text{fill:#333;}#mermaid-svg-MNk9mlmGM4wZHpej .cluster-label span{color:#333;}#mermaid-svg-MNk9mlmGM4wZHpej .cluster-label span p{background-color:transparent;}#mermaid-svg-MNk9mlmGM4wZHpej .label text,#mermaid-svg-MNk9mlmGM4wZHpej span{fill:#333;color:#333;}#mermaid-svg-MNk9mlmGM4wZHpej .node rect,#mermaid-svg-MNk9mlmGM4wZHpej .node circle,#mermaid-svg-MNk9mlmGM4wZHpej .node ellipse,#mermaid-svg-MNk9mlmGM4wZHpej .node polygon,#mermaid-svg-MNk9mlmGM4wZHpej .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-MNk9mlmGM4wZHpej .rough-node .label text,#mermaid-svg-MNk9mlmGM4wZHpej .node .label text,#mermaid-svg-MNk9mlmGM4wZHpej .image-shape .label,#mermaid-svg-MNk9mlmGM4wZHpej .icon-shape .label{text-anchor:middle;}#mermaid-svg-MNk9mlmGM4wZHpej .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#mermaid-svg-MNk9mlmGM4wZHpej .rough-node .label,#mermaid-svg-MNk9mlmGM4wZHpej .node .label,#mermaid-svg-MNk9mlmGM4wZHpej .image-shape .label,#mermaid-svg-MNk9mlmGM4wZHpej .icon-shape .label{text-align:center;}#mermaid-svg-MNk9mlmGM4wZHpej .node.clickable{cursor:pointer;}#mermaid-svg-MNk9mlmGM4wZHpej .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#mermaid-svg-MNk9mlmGM4wZHpej .arrowheadPath{fill:#333333;}#mermaid-svg-MNk9mlmGM4wZHpej .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-MNk9mlmGM4wZHpej .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-MNk9mlmGM4wZHpej .edgeLabel{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-MNk9mlmGM4wZHpej .edgeLabel p{background-color:rgba(232,232,232, 0.8);}#mermaid-svg-MNk9mlmGM4wZHpej .edgeLabel rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-MNk9mlmGM4wZHpej .labelBkg{background-color:rgba(232, 232, 232, 0.5);}#mermaid-svg-MNk9mlmGM4wZHpej .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-MNk9mlmGM4wZHpej .cluster text{fill:#333;}#mermaid-svg-MNk9mlmGM4wZHpej .cluster span{color:#333;}#mermaid-svg-MNk9mlmGM4wZHpej div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-MNk9mlmGM4wZHpej .flowchartTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-MNk9mlmGM4wZHpej rect.text{fill:none;stroke-width:0;}#mermaid-svg-MNk9mlmGM4wZHpej .icon-shape,#mermaid-svg-MNk9mlmGM4wZHpej .image-shape{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-MNk9mlmGM4wZHpej .icon-shape p,#mermaid-svg-MNk9mlmGM4wZHpej .image-shape p{background-color:rgba(232,232,232, 0.8);padding:2px;}#mermaid-svg-MNk9mlmGM4wZHpej .icon-shape .label rect,#mermaid-svg-MNk9mlmGM4wZHpej .image-shape .label rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-MNk9mlmGM4wZHpej .label-icon{display:inline-block;height:1em;overflow:visible;vertical-align:-0.125em;}#mermaid-svg-MNk9mlmGM4wZHpej .node .label-icon path{fill:currentColor;stroke:revert;stroke-width:revert;}#mermaid-svg-MNk9mlmGM4wZHpej :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} tool_calls
text
用户输入
组装 Prompt

系统提示词 + 工具 Schema
调用 LLM
响应类型？
执行工具
追加工具结果到历史
生成最终回复
持久化 Session
返回用户

两者入口对称：

	Hermes	OpenClaw
主入口	`agent.chat()` / `agent.run_conversation()`	`runEmbeddedPiAgent()`
立即返回	阻塞等待最终回复	`{ runId, acceptedAt }` 立即返回
结果获取	函数返回值	`agent.wait` RPC 轮询 lifecycle end
核心位置	`run_agent.py: AIAgent`	`src/agents/pi-embedded-runner/run.ts`

五、执行模型：同步 while vs 异步事件流

Hermes：同步 while 循环 + 可中断 HTTP

工具后台线程（HTTP） AIAgent 用户工具后台线程（HTTP） AIAgent 用户 #mermaid-svg-SXv6eLAMk9QAAvup{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-SXv6eLAMk9QAAvup .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-SXv6eLAMk9QAAvup .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-SXv6eLAMk9QAAvup .error-icon{fill:#552222;}#mermaid-svg-SXv6eLAMk9QAAvup .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-SXv6eLAMk9QAAvup .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-SXv6eLAMk9QAAvup .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-SXv6eLAMk9QAAvup .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-SXv6eLAMk9QAAvup .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-SXv6eLAMk9QAAvup .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-SXv6eLAMk9QAAvup .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-SXv6eLAMk9QAAvup .marker{fill:#333333;stroke:#333333;}#mermaid-svg-SXv6eLAMk9QAAvup .marker.cross{stroke:#333333;}#mermaid-svg-SXv6eLAMk9QAAvup svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-SXv6eLAMk9QAAvup p{margin:0;}#mermaid-svg-SXv6eLAMk9QAAvup .actor{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-SXv6eLAMk9QAAvup text.actor>tspan{fill:black;stroke:none;}#mermaid-svg-SXv6eLAMk9QAAvup .actor-line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);}#mermaid-svg-SXv6eLAMk9QAAvup .innerArc{stroke-width:1.5;stroke-dasharray:none;}#mermaid-svg-SXv6eLAMk9QAAvup .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:#333;}#mermaid-svg-SXv6eLAMk9QAAvup .messageLine1{stroke-width:1.5;stroke-dasharray:2,2;stroke:#333;}#mermaid-svg-SXv6eLAMk9QAAvup #arrowhead path{fill:#333;stroke:#333;}#mermaid-svg-SXv6eLAMk9QAAvup .sequenceNumber{fill:white;}#mermaid-svg-SXv6eLAMk9QAAvup #sequencenumber{fill:#333;}#mermaid-svg-SXv6eLAMk9QAAvup #crosshead path{fill:#333;stroke:#333;}#mermaid-svg-SXv6eLAMk9QAAvup .messageText{fill:#333;stroke:none;}#mermaid-svg-SXv6eLAMk9QAAvup .labelBox{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-SXv6eLAMk9QAAvup .labelText,#mermaid-svg-SXv6eLAMk9QAAvup .labelText>tspan{fill:black;stroke:none;}#mermaid-svg-SXv6eLAMk9QAAvup .loopText,#mermaid-svg-SXv6eLAMk9QAAvup .loopText>tspan{fill:black;stroke:none;}#mermaid-svg-SXv6eLAMk9QAAvup .loopLine{stroke-width:2px;stroke-dasharray:2,2;stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);}#mermaid-svg-SXv6eLAMk9QAAvup .note{stroke:#aaaa33;fill:#fff5ad;}#mermaid-svg-SXv6eLAMk9QAAvup .noteText,#mermaid-svg-SXv6eLAMk9QAAvup .noteText>tspan{fill:black;stroke:none;}#mermaid-svg-SXv6eLAMk9QAAvup .activation0{fill:#f4f4f4;stroke:#666;}#mermaid-svg-SXv6eLAMk9QAAvup .activation1{fill:#f4f4f4;stroke:#666;}#mermaid-svg-SXv6eLAMk9QAAvup .activation2{fill:#f4f4f4;stroke:#666;}#mermaid-svg-SXv6eLAMk9QAAvup .actorPopupMenu{position:absolute;}#mermaid-svg-SXv6eLAMk9QAAvup .actorPopupMenuPanel{position:absolute;fill:#ECECFF;box-shadow:0px 8px 16px 0px rgba(0,0,0,0.2);filter:drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));}#mermaid-svg-SXv6eLAMk9QAAvup .actor-man line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-SXv6eLAMk9QAAvup .actor-man circle,#mermaid-svg-SXv6eLAMk9QAAvup line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;stroke-width:2px;}#mermaid-svg-SXv6eLAMk9QAAvup :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 主线程 wait：响应 / interrupt_event / 超时 alt $并发安全 \& 多工具$ $含 clarify 等交互工具$ alt $has tool_calls$ $纯文本$ loop $while True（同步）$ agent.chat("...")threading.Thread 发起 HTTPresponseThreadPoolExecutor（最多8线程）顺序执行结果（按原始顺序重排）追加 tool 消息，continue持久化 session返回 final_response

源码中有精细的并发判断逻辑（_should_parallelize_tool_batch()）：

python 复制代码

# 永不并发的工具（交互式）
_NEVER_PARALLEL_TOOLS = frozenset({"clarify"})

# 可安全并发的只读工具（来自 agent/tool_dispatch_helpers.py，已验证）
_PARALLEL_SAFE_TOOLS = frozenset({
    "ha_get_state", "ha_list_entities", "ha_list_services",
    "read_file", "search_files", "session_search",
    "skill_view", "skills_list", "vision_analyze",
    "web_extract", "web_search",
})

# 路径作用域工具：检查路径是否重叠才决定是否并发
_PATH_SCOPED_TOOLS = frozenset({"read_file", "write_file", "patch"})

# 注意：_MAX_TOOL_WORKERS（ThreadPoolExecutor max_workers）在 run_agent.py
# 中使用，不是模块级常量；MCP 工具并发通过 _is_mcp_tool_parallel_safe() 单独判断

还有专门的 _SafeWriter 类包装 stdout/stderr，防止 daemon 模式下 broken pipe 崩溃------这是生产化运行的细节。

OpenClaw：异步事件流 + 会话串行化

模型 pi-agent-core Pi-Agent Runtime 会话 Queue Gateway RPC 客户端（任意渠道）模型 pi-agent-core Pi-Agent Runtime 会话 Queue Gateway RPC 客户端（任意渠道） #mermaid-svg-62Cw1TtfxsOwIdlJ{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-62Cw1TtfxsOwIdlJ .error-icon{fill:#552222;}#mermaid-svg-62Cw1TtfxsOwIdlJ .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-62Cw1TtfxsOwIdlJ .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-62Cw1TtfxsOwIdlJ .marker{fill:#333333;stroke:#333333;}#mermaid-svg-62Cw1TtfxsOwIdlJ .marker.cross{stroke:#333333;}#mermaid-svg-62Cw1TtfxsOwIdlJ svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-62Cw1TtfxsOwIdlJ p{margin:0;}#mermaid-svg-62Cw1TtfxsOwIdlJ .actor{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-62Cw1TtfxsOwIdlJ text.actor>tspan{fill:black;stroke:none;}#mermaid-svg-62Cw1TtfxsOwIdlJ .actor-line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);}#mermaid-svg-62Cw1TtfxsOwIdlJ .innerArc{stroke-width:1.5;stroke-dasharray:none;}#mermaid-svg-62Cw1TtfxsOwIdlJ .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:#333;}#mermaid-svg-62Cw1TtfxsOwIdlJ .messageLine1{stroke-width:1.5;stroke-dasharray:2,2;stroke:#333;}#mermaid-svg-62Cw1TtfxsOwIdlJ #arrowhead path{fill:#333;stroke:#333;}#mermaid-svg-62Cw1TtfxsOwIdlJ .sequenceNumber{fill:white;}#mermaid-svg-62Cw1TtfxsOwIdlJ #sequencenumber{fill:#333;}#mermaid-svg-62Cw1TtfxsOwIdlJ #crosshead path{fill:#333;stroke:#333;}#mermaid-svg-62Cw1TtfxsOwIdlJ .messageText{fill:#333;stroke:none;}#mermaid-svg-62Cw1TtfxsOwIdlJ .labelBox{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-62Cw1TtfxsOwIdlJ .labelText,#mermaid-svg-62Cw1TtfxsOwIdlJ .labelText>tspan{fill:black;stroke:none;}#mermaid-svg-62Cw1TtfxsOwIdlJ .loopText,#mermaid-svg-62Cw1TtfxsOwIdlJ .loopText>tspan{fill:black;stroke:none;}#mermaid-svg-62Cw1TtfxsOwIdlJ .loopLine{stroke-width:2px;stroke-dasharray:2,2;stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);}#mermaid-svg-62Cw1TtfxsOwIdlJ .note{stroke:#aaaa33;fill:#fff5ad;}#mermaid-svg-62Cw1TtfxsOwIdlJ .noteText,#mermaid-svg-62Cw1TtfxsOwIdlJ .noteText>tspan{fill:black;stroke:none;}#mermaid-svg-62Cw1TtfxsOwIdlJ .activation0{fill:#f4f4f4;stroke:#666;}#mermaid-svg-62Cw1TtfxsOwIdlJ .activation1{fill:#f4f4f4;stroke:#666;}#mermaid-svg-62Cw1TtfxsOwIdlJ .activation2{fill:#f4f4f4;stroke:#666;}#mermaid-svg-62Cw1TtfxsOwIdlJ .actorPopupMenu{position:absolute;}#mermaid-svg-62Cw1TtfxsOwIdlJ .actorPopupMenuPanel{position:absolute;fill:#ECECFF;box-shadow:0px 8px 16px 0px rgba(0,0,0,0.2);filter:drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));}#mermaid-svg-62Cw1TtfxsOwIdlJ .actor-man line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-62Cw1TtfxsOwIdlJ .actor-man circle,#mermaid-svg-62Cw1TtfxsOwIdlJ line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;stroke-width:2px;}#mermaid-svg-62Cw1TtfxsOwIdlJ :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} opt $工具调用$ loop $Agent Loop（异步事件驱动）$ agent RPC（带 sessionKey）{ runId, acceptedAt }（立即）入队（全局 Queue + 会话 Channel 串行化）runEmbeddedPiAgent({ sessionId, sessionKey,provider, model, prompt, timeoutMs,bashElevated, abortSignal, ... })构建 Pi 会话，订阅事件流式请求token 流text_delta / thinking_delta 事件stream: "assistant"实时推送（各渠道适配）tool start/update/end 事件stream: "tool"工具进度实时可见lifecycle: { phase: "end", payload, usage }最终结果

runEmbeddedPiAgent 的真实参数签名（来自源码）：

typescript 复制代码

runEmbeddedPiAgent({
  sessionId: string,
  sessionKey: string,          // 会话串行化 key
  sessionFile: string,         // JSONL 转录文件路径
  workspaceDir: string,
  agentDir: string,
  config: OpenClawConfig,
  prompt: string,
  timeoutMs: number,           // 默认 600,000ms（600秒）
  runId: string,
  provider?: string,
  model?: string,
  bashElevated?: {
    enabled: boolean,
    allowed: string[],
    defaultLevel: string
  },
  abortSignal?: AbortSignal,   // 外部可取消
})

注意：timeoutMs 默认值是 600 秒（10 分钟），不是 48 小时。

六、工具注册机制：自注册 vs 集中注册

Hermes：自注册式 Registry + Agent 级拦截

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model_tools.py 启动时发现
todo/memory/delegate/session_search
普通工具
import tools.web_tools
import tools.terminal_tool
import tools.delegate_tool
...22个模块
tools/registry.py

registry.register() 触发
get_tool_definitions()

按 toolset 过滤 schema
传给 LLM API
handle_function_call()

registry.dispatch()
Agent 级工具？
AIAgent 直接拦截处理

修改内部状态
工具 Handler

执行并返回结果

Agent 级工具 ：todo、memory、delegate_task、session_search、skill_manage 等工具不走 registry dispatch，而是由 AIAgent 直接拦截、修改内部状态------这使它们能操作 Agent 的内存、技能、子任务等核心状态。

OpenClaw：Provider 插件化 + 工具事件化

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pi-agent-core 触发工具调用
before_tool_call Hook

{ block: true } = 终止（低优先级 handler 停止）
Exec Approval 检查

危险 bash 命令需确认
执行工具
tool start/update/end 事件

实时流出
after_tool_call Hook
tool_result_persist Hook

落盘前最后变换
写入 JSONL 转录

（会话写锁保护）

七、工具循环护栏（Tool Guardrails）------ Hermes 独有

这是 Hermes Agent Loop 中一个重要的保护层，源码位于 agent/tool_guardrails.py（~500 行）。OpenClaw 没有等价物------工具的重复调用完全由 LLM 自觉，没有护栏层。

工具分类体系

python 复制代码

# 只读工具（无副作用）
IDEMPOTENT_TOOL_NAMES = frozenset({
    "read_file", "search_files", "web_search", "web_extract",
    "session_search", "browser_snapshot", "browser_console",
    "browser_get_images", "mcp_filesystem_read_file",
    "mcp_filesystem_list_directory", ...
})

# 有副作用的工具
MUTATING_TOOL_NAMES = frozenset({
    "terminal", "execute_code", "write_file", "patch",
    "todo", "memory", "skill_manage",
    "browser_click", "browser_type", "browser_navigate",
    "send_message", "cronjob", "delegate_task", "process", ...
})

三种检测机制

检测类型	触发条件	默认警告阈值	默认阻断阈值
exact_failure	完全相同参数 + 相同错误结果	2 次	5 次
same_tool_failure	同工具不同参数均失败	3 次	8 次
no_progress	连续只使用只读工具无进展	2 次	5 次

护栏行为层级

复制代码

ALLOW  → 静默通过
WARN   → 注入 synthetic system message 提示 agent 改变策略
HALT   → 直接截断，强制 LLM 给出最终答案

Warnings 默认开启且不阻止工具执行；Hard Stops 需要显式在 config.yaml 中开启（hard_stop_enabled: true）。

针对不同工具的失败提示

护栏系统会根据失败的工具类型给出定制化的提示：

python 复制代码

# terminal 工具失败的专项提示
if tool_name == "terminal":
    return common + (
        "For terminal failures, run a small diagnostic such as "
        "`pwd && ls -la` in the same tool, then try an absolute path, "
        "a simpler command, a different working directory, "
        "or a different tool such as read_file/write_file/patch."
    )

# 通用工具失败提示
return common + (
    "Try different arguments, a narrower query/path, "
    "an absolute path when relevant, or a different tool "
    "that can make progress."
)

设计哲学

tool_guardrails.py 的 docstring 明确声明：

"The controller in this module is intentionally side-effect free: it tracks per-turn tool-call observations and returns decisions. Runtime code owns whether those decisions become warning guidance, synthetic tool results, or controlled turn halts."

护栏层是纯检测+决策，不主动干预执行------是否转为警告/阻断由 Runtime 决定。

八、迭代预算（Iteration Budget）------ Hermes 独有

Hermes 的 agent/iteration_budget.py（线程安全计数器，源码已验证）

python 复制代码

class IterationBudget:
    """Thread-safe iteration counter for an agent."""
    def __init__(self, max_total: int):
        # 父 Agent 默认 90，子 Agent 从 delegation.max_iterations 取（默认 50）
        self.max_total = max_total
        self._used = 0
        self._lock = threading.Lock()

    def consume(self) -> bool:  # 消耗 1 次，返回 True 表示在预算内
    def refund(self) -> None:   # 退还 1 次（仅 execute_code 成功后调用）

    @property
    def used(self) -> int:      # 已使用次数
    @property
    def remaining(self) -> int: # 剩余次数

关键设计：

父子关系 （代码内有两处矛盾的注释）：agent_init.py 注释说 "Shared --- consumed across parent + all subagents"，而 iteration_budget.py docstring 说 "Each subagent gets an independent budget"。实际行为取决于 delegate_task 调用时是否传入父 Agent 的 budget 对象。子 Agent 默认上限为 delegation.max_iterations（默认 50）
软限制：用完时注入 system message 提示尽快结束，而非直接截断
精准退还 ：只有 execute_code 成功时才 refund，防止 agent 仅依赖 LLM 推理而不实际执行

OpenClaw 靠 timeoutMs 硬超时，没有「濒临上限时注入提示」的软机制。

九、并发模型的根本差异

Hermes：为单/少量 Agent 设计

复制代码

用户请求 → AIAgent 实例（一般每用户一个进程）
                → ThreadPoolExecutor（仅用于工具并发，max 8 workers）
                        → 工具 A / 工具 B / 工具 N

OpenClaw：为多用户/多渠道/多 Agent 并发设计

复制代码

多渠道入站（WhatsApp / Telegram / Discord / ...）
        → Gateway RPC
            → 全局 Queue（可选，跨会话调度）
                → 会话 Channel A（本 session 所有 Run 串行）
                → 会话 Channel B
                → 会话 Channel N
                    → Session Write Lock（跨进程 + 进程感知，文件级锁，等待超时 60s）
                        → pi-embedded-runner A
                        → pi-embedded-runner B

Session Write Lock 是跨进程的文件锁（不只是进程内互斥），能捕获来自其他进程的写入者。这是 OpenClaw 为多实例部署设计的保护机制。

十、System Prompt 组装策略

Hermes：砖块式 Prompt 建筑

agent/prompt_builder.py 定义了多个独立的「指导砖块」，按需组合：

python 复制代码

# 基础身份
DEFAULT_AGENT_IDENTITY = "You are Hermes Agent, an intelligent AI assistant..."

# 如何使用 memory 工具
MEMORY_GUIDANCE = (
    "You have persistent memory across sessions. Save durable facts using the memory "
    "tool: user preferences, environment details, tool quirks, and stable conventions. "
    "Memory is injected into every turn, so keep it compact..."
    "Do NOT save task progress, session outcomes, completed-work logs..."
    "Write memories as declarative facts, not instructions to yourself..."
)

# 何时创建 skill
SKILLS_GUIDANCE = (
    "After completing a complex task (5+ tool calls), fixing a tricky error, "
    "or discovering a non-trivial workflow, save the approach as a "
    "skill with skill_manage so you can reuse it next time."
    "When using a skill and finding it outdated, incomplete, or wrong, "
    "patch it immediately with skill_manage(action='patch')..."
)

# 何时用 session_search
SESSION_SEARCH_GUIDANCE = (
    "When the user references something from a past conversation... "
    "use session_search to recall it before asking them to repeat themselves."
)

# 强制执行工具调用
TOOL_USE_ENFORCEMENT_GUIDANCE = (
    "# Tool-use enforcement\n"
    "You MUST use your tools to take action --- do not describe what you would do "
    "or plan to do without actually doing it..."
)

# 必须跑出真实结果
TASK_COMPLETION_GUIDANCE = (
    "# Finishing the job\n"
    "When the user asks you to build, run, or verify something, the deliverable is "
    "a working artifact backed by real tool output --- not a description of one."
    "NEVER substitute plausible-looking fabricated output..."
)

按模型品牌定制行为指导

Hermes 针对不同模型家族的已知缺陷注入专项指导：

python 复制代码

# 这些模型家族需要额外的「必须用工具，别只说不做」prompt
TOOL_USE_ENFORCEMENT_MODELS = (
    "gpt", "codex", "gemini", "gemma", "grok", "glm", "qwen", "deepseek"
)

OpenAI GPT/Codex 和 xAI Grok 还有额外的 OPENAI_MODEL_EXECUTION_GUIDANCE，解决这些模型「声称完成但不实际执行」「跳过前置检查」「幻觉替代工具结果」等已知失败模式。

看板模式

当 Agent 运行在 Kanban（看板）模式时，会注入完整的任务协议：

python 复制代码

KANBAN_GUIDANCE = (
    "# Kanban task execution protocol\n"
    "1. Orient → kanban_show()\n"
    "2. Work inside the workspace\n"
    "3. Heartbeat on long operations → kanban_heartbeat()\n"
    "4. Block on genuine ambiguity → kanban_block()\n"
    "5. Complete with structured handoff → kanban_complete()\n"
    "6. If follow-up work appears, create it; don't do it → kanban_create()\n"
)

OpenClaw：分层注入

复制代码

SOUL.md → AGENTS.md → Skills → TOOLS.md → runtime context

模板化，文件和 prompt 分开管理。没有按模型品牌定制的行为指导。

十一、上下文文件安全扫描 ------ Hermes 独有

agent/prompt_builder.py 中包含一个安全层------在加载 AGENTS.md / HERMES.md 等上下文文件前，扫描注入威胁：

python 复制代码

def _scan_context_content(content: str, filename: str) -> str:
    """Scan context file content for injection. Returns sanitized content.

    Uses the "context" scope from the shared threat-pattern library, which
    covers classic injection + promptware/C2 patterns + role-play hijack.
    Content matching is BLOCKED at this layer because the file would
    otherwise enter the system prompt verbatim and the user has no chance
    to intervene.
    """
    findings = _scan_for_threats(content, scope="context")
    if findings:
        return f"[BLOCKED: {filename} contained potential prompt injection "
               f"({', '.join(findings)}). Content not loaded.]"
    return content

Hermes 还会在项目目录中搜索 .hermes.md 和 HERMES.md（从 cwd 向上遍历到 git root），在注入 system prompt 前剥离 YAML frontmatter。OpenClaw 没有这层安全扫描。

十二、模型元数据与上下文长度探测 ------ Hermes 更深

agent/model_metadata.py（~21KB）实现了复杂的上下文长度探测系统：

Provider 推断

Hermes 能从 base_url 自动推断 provider：

python 复制代码

_URL_TO_PROVIDER = {
    "api.openai.com": "openai",
    "api.anthropic.com": "anthropic",
    "api.deepseek.com": "deepseek",
    "api.x.ai": "xai",
    "dashscope.aliyuncs.com": "alibaba",
    "integrate.api.nvidia.com": "nvidia",
    # ... 30+ 条目
}

上下文长度探测阶梯

当模型未知时，从高到低逐级探测：

python 复制代码

CONTEXT_PROBE_TIERS = [256_000, 128_000, 64_000, 32_000, 16_000, 8_000]
MINIMUM_CONTEXT_LENGTH = 64_000  # 低于此值的模型拒绝运行

本地端点识别

能识别 Tailscale CGNAT（100.64.0.0/10）、Docker 内部 DNS、RFC-1918 私有地址，自动调整超时策略。

OpenClaw 的模型元数据解析内嵌在 pi-ai 层，没有 Hermes 这么细粒度的探测和 provider 推断逻辑。

十三、OpenClaw 的 VISION.md 里有一条被忽略的关键声明

OpenClaw 的 VISION.md 明确写着不会合并的功能：

复制代码

What We Will Not Merge (For Now):
- Agent-hierarchy frameworks (manager-of-managers / nested planner trees)
  as a default architecture
- Heavy orchestration layers that duplicate existing agent and tool
  infrastructure

这不是「OpenClaw 架构天然支持 Agent Tree」，而是主动拒绝它 。这与 Hermes 内置 delegate_task() 的 Agent Tree 路线形成了清晰的哲学对立。

Hermes 的 delegate_task：

直接在 run_agent.py 内拦截，创建新的 AIAgent 实例
子 Agent 的迭代上限来自 delegation.max_iterations（默认 50），总额独立于父 Agent
⚠️ 注意：agent_init.py 注释称 budget "shared across parent + subagents"，但 iteration_budget.py docstring 称子 Agent 有 "independent budget"。实际行为取决于传入的 iteration_budget 参数

OpenClaw 的立场：明确不做这个。

十四、Provider 抽象与 Fallback

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getModel(provider, modelId)
api 字段路由
openai-completions

（兼容所有 OpenAI 格式端点）
anthropic

（原生 Anthropic SDK）
google / groq / bedrock...
统一 stream 事件

text_delta / thinking_delta / done
runWithModelFallback()

FailoverError 分类引擎
Hermes：深度 Provider 抽象
统一入口
API 模式判断

4级优先级
chat_completions

OpenAI / OpenRouter / 自定义
codex_responses

OpenAI Codex
anthropic_messages

原生 Anthropic SDK
统一 OpenAI 格式
Fallback 链

主 → 备1 → 备2

各辅助任务独立 Fallback 链

两边都有 Fallback，但实现路径不同：

Hermes 的 Fallback 逻辑在 AIAgent 内，是 Agent 自己的重试行为
OpenClaw 的 Fallback 在 runWithModelFallback() 层，是 Runtime 层面的 Provider 切换

十五、扩展机制：事后通知 vs 流水线拦截

	Hermes（Callback，事后通知型）	OpenClaw（Hook + Plugin，可拦截型）
工具回调	`tool_progress_callback`	`before_tool_call` 🔴 可终止
思考状态	`thinking_callback`	---
推理内容	`reasoning_callback`	---
用户澄清	`clarify_callback`	---
步骤追踪	`step_callback / stream_delta_callback`	---
模型解析	---	`before_model_resolve`
Prompt 构建	---	`before_prompt_build`（可注入内容）
Agent 回复	---	`before_agent_reply` 🔴 可接管整轮
消息发送	---	`message_sending` 🔴 可终止发送
工具结果	---	`tool_result_persist`（落盘前变换）
压缩	---	`before/after_compaction`
安装	---	`before/after_install`

🔴 = 可终止流水线的节点

OpenClaw AGENTS.md 明确规定了 Hook 的决策规则：

before_tool_call: { block: true } → 终态，停止低优先级 handler
before_tool_call: { block: false } → 空操作，不清除已有阻止
message_sending: { cancel: true } → 终态

十六、上下文压缩

核心差异

	Hermes	OpenClaw
压缩层数	双压缩层（Agent 50% + Gateway 85%）	单层触发型
摘要格式	结构化摘要（Goal / Constraints / Progress / Key Decisions / Relevant Files / Next Steps / Critical Context）	通用摘要
迭代合并	✅ 第二次压缩时更新上次摘要（"items move from In Progress to Done"）	❌
引擎可插拔	✅ `ContextEngine` ABC 允许替换	❌
Hook 可见	❌ 内部行为	✅ `before/after_compaction` 事件
压缩策略	先 flush Memory 到磁盘 → 压缩中间轮次 → 保留末尾 `protect_last_n`（默认 20）条 → 创建子 session lineage	触发 → 执行压缩 → 可能触发重试 → 重置内存 buffer

Hermes 的双压缩层

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context > 50% → 预检压缩

Agent 层
先 flush Memory 到磁盘

（MEMORY.md / USER.md）
context > 85% → Gateway 触发

Gateway 层
ContextCompressor.compress()

摘要中间轮次

保留末尾 protect_last_n 条（默认20）

工具调用对保持完整
生成新 session lineage ID

压缩 = 创建子 session

OpenClaw 的压缩生命周期

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compaction 事件
before_compaction Hook

外部可注入 / 观察
执行压缩算法

（pi-coding-agent 层）
after_compaction Hook

外部可感知
可能触发 Agent 重试

重置内存 buffer 避免重复

十七、自进化能力：Hermes 独有

这是 Hermes 最重要的差异化特性，OpenClaw 完全没有对应物：
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完成任务
自主创建 SKILL.md

可复用的程序化记忆
技能在使用中自我改进

（类 DSPy GEPA 进化）
持久化 Memory

SQLite FTS5 搜索
USER.md 用户画像

Honcho dialectic 建模
下次对话

跨 session / 跨平台 / 跨设备记忆

技能自动创建

agent/background_review.py（~31KB）实现了后台自我回顾：

python 复制代码

# 伪代码还原自源码
class BackgroundReviewer:
    def review_and_create_skill(self, tool_calls_count, task_result):
        if tool_calls_count < 5:
            return  # 简单任务不触发

        review_prompt = (
            "Review this completed task and its tool calls. "
            "If the workflow is reusable, create a concise SKILL.md..."
        )
        skill_md = self._call_auxiliary_llm(review_prompt)
        self._dispatch_tool("skill_manage", action="create", name=..., content=skill_md)

Memory Nudge（记忆自驱）

每个 turn 结束后，后台线程自动运行：

python 复制代码

# 伪代码还原自源码
def _background_memory_nudge(self):
    nudge_prompt = (
        "Review this conversation turn. What durable facts about the user, "
        "their environment, preferences, or recurring patterns should be "
        "remembered for future sessions?"
    )
    memories = self._call_auxiliary_llm(nudge_prompt)
    for m in memories:
        self._dispatch_tool("memory", action="add", content=m)

# 在独立线程中启动，不阻塞主响应
threading.Thread(target=background_memory_nudge).start()

技能自我改进

Agent 可以随时通过 skill_manage 工具修改已有技能：

python 复制代码

# Agent 可直接调用的工具
skill_manage(action="create", name="deploy-k8s", content=full_skill_md)
skill_manage(action="patch", name="deploy-k8s", old_string="...", new_string="...")
skill_manage(action="edit", name="deploy-k8s", file_path="SKILL.md", ...)
skill_manage(action="delete", name="obsolete-skill")

衍生项目

Nous Research 还开了独立仓库 hermes-agent-self-evolution，用 DSPy + GEPA 做技能文件的进化优化：

阶段 1：SKILL.md 内容进化 ✅
阶段 2：工具描述进化（计划中）
阶段 3：系统提示进化（计划中）
阶段 4：工具实现代码进化（计划中）

这是 Hermes 与 OpenClaw 在 AI 能力维度上的本质差异。

十八、OpenClaw 独有的 Post-Turn 设计细节

OpenClaw 虽然没有 Hermes 的「回头看」自进化机制，但 Agent Loop 退出后有完整的收尾流程，体现在 runs.ts 的 run 生命周期管理中：

Run 生命周期管理

复制代码

ACTIVE_EMBEDDED_RUNS     → 当前活跃的 run handle
ACTIVE_EMBEDDED_RUN_SNAPSHOTS → run 状态快照
ABANDONED_EMBEDDED_RUNS  → 被放弃的 run（session 断开后追踪）
EMBEDDED_RUN_WAITERS     → 等待 run 结束的外部观察者
EMBEDDED_RUN_MODEL_SWITCH_REQUESTS → 运行中切换模型的请求队列

消息队列（Run 内 Steering）

typescript 复制代码

// 运行中向 agent 注入消息的能力
queueEmbeddedAgentMessage(sessionId, text, options)
// 返回状态：
// - { queued: true, target: "embedded_run" }      → 成功注入
// - { queued: true, target: "reply_run" }         → 转入 reply 管道
// - { queued: false, reason: "no_active_run" }    → 无活跃 run
// - { queued: false, reason: "not_streaming" }    → run 已结束流
// - { queued: false, reason: "compacting" }       → run 正在压缩
// - { queued: false, reason: "runtime_rejected" }  → run 内部拒绝

Abort 机制

typescript 复制代码

// 按 sessionId 终止单个 run
abortEmbeddedAgentRun(sessionId)

// 批量终止模式
abortEmbeddedAgentRun(undefined, { mode: "all" })        // 全部
abortEmbeddedAgentRun(undefined, { mode: "compacting" })  // 仅压缩中的

这套 Run 生命周期管理是 OpenClaw 作为「生产级 Gateway」的体现------hermes 的 post-turn 更多是「自我改进」，而 OpenClaw 的 post-turn 更多是「运行态管理」。

十九、多渠道支持：OpenClaw 独有

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path,#mermaid-svg-R6u3O3u3YjTshGKL .section-root circle,#mermaid-svg-R6u3O3u3YjTshGKL .section-root polygon{fill:hsl(240, 100%, 46.2745098039%);}#mermaid-svg-R6u3O3u3YjTshGKL .section-root text{fill:#ffffff;}#mermaid-svg-R6u3O3u3YjTshGKL .section-root span{color:#ffffff;}#mermaid-svg-R6u3O3u3YjTshGKL .section-2 span{color:#ffffff;}#mermaid-svg-R6u3O3u3YjTshGKL .icon-container{height:100%;display:flex;justify-content:center;align-items:center;}#mermaid-svg-R6u3O3u3YjTshGKL .edge{fill:none;}#mermaid-svg-R6u3O3u3YjTshGKL .mindmap-node-label{dy:1em;alignment-baseline:middle;text-anchor:middle;dominant-baseline:middle;text-align:center;}#mermaid-svg-R6u3O3u3YjTshGKL :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} OpenClaw Gateway
消息平台
WhatsApp
Telegram
Discord
Slack
Signal
iMessage
Microsoft Teams
WeChat / QQ
Matrix / IRC
LINE / Feishu
还有 10 余个
伴侣应用
macOS 菜单栏
iOS Node
Android Node
Windows Hub
特殊能力
Voice Wake（macOS/iOS）
Talk Mode（Android，连续语音）
Live Canvas（A2UI）
Web Chat

Hermes 支持 Telegram、Discord、Slack、WhatsApp、Signal 等，但渠道广度和多平台伴侣应用是 OpenClaw 的核心护城河。

二十、架构定位重新校准

基于源码的真实结论：
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OpenClaw 优势：

多渠道接入广度

Runtime 生产稳定性

Hook 可拦截扩展

社区规模
Hermes 优势：

跨 session 持久记忆

闭环技能自进化

多 Provider 统一抽象

Agent Tree 子任务
真实的关系（基于源码）
Hermes 填补 OpenClaw 的记忆短板

hermes claw migrate 内置迁移工具
OpenClaw

2025-11 起源

TypeScript / 消息平台优先

Gateway + 多渠道 + Runtime

开源社区驱动
Hermes

2026-02 发布

Python / 终端 + 记忆优先

闭环学习 + Agent 能力

Nous Research 主导

二十一、核心指标真实速查表

维度	Hermes	OpenClaw
起源时间	2026-02	2025-11（as Clawdbot）
关系	在 OpenClaw 基础上发展，内置迁移工具	原版，更早
主语言	Python 83% + TypeScript 13%	TypeScript（Node.js 22+）
核心入口	`run_agent.py: AIAgent`（6,933 行）	`pi-embedded-runner/run.ts: runEmbeddedPiAgent`
Agent Loop	同步 while + `_interruptible_api_call`	异步事件流（pi-agent-core）
Loop 结构	Agent 直连 LLM，while 循环内聚	Gateway 代理 LLM，事件驱动
Post-Turn	✅ 技能自动生成 + Memory Nudge	❌ 直接结束，无「回头看」
工具并发	`_should_parallelize_tool_batch()` + ThreadPool（max 8）	事件化，异步原生
工具护栏	✅ 3 种 loop guardrail（exact_failure / same_tool / no_progress）	❌ 无
迭代预算	✅ 90 次软限制 + refund 机制	❌ 仅硬超时（600s 默认）
上下文文件扫描	✅ 注入检测 + HERMES.md 发现	❌ 无
模型探测	✅ 阶梯式上下文探测 + 30+ Provider 自动推断	pi-ai 层处理
Prompt 组装	砖块式 + 按模型品牌定制指导	分层模板注入
Agent Tree	✅ 原生，`delegate_task()` 内置	主动拒绝（VISION.md）
Fallback	✅ 内置	✅ `runWithModelFallback()`
压缩层数	双压缩层（Agent 50% + Gateway 85%）	单层触发型
压缩格式	8 字段结构化摘要 + 迭代合并	通用摘要
上下文压缩引擎	可插拔（`ContextEngine` ABC）	内置
扩展机制	Callback（通知型）	Hook + Plugin（可拦截型，可终止）
记忆系统	🔥 核心优势，跨 session + 自进化 SKILL + 8 个外部 Provider	需手动配置
跨会话搜索	`session_search`（SQLite FTS5 全文检索，搜索原始消息）	`memory_search`（语义搜索，搜索记忆摘要）
多渠道	Telegram/Discord/Slack 等主流	🔥 20+ 渠道 + 全平台伴侣应用
Run 生命周期	阻塞式返回值	Queue / Abort / Waiter / Model Switch
定位	个人助手 + AI 研究工具（Nous Research）	个人助手 + 消息平台中枢（社区项目）

结语

OpenClaw 解决：怎么接入所有消息平台，怎么稳定运行。

Hermes 解决：怎么让 Agent 记住你，怎么让 Agent 变得更聪明。

两者不是同一赛道的竞争对手，而是从 OpenClaw 这个共同根基上长出的两个方向。Hermes 是在承认「OpenClaw 在渠道和 Runtime 上已经很好了」之后，专门去补它记忆能力不足的缺口。

理解这个关系，比任何架构框架都更接近事实。

附录：论断溯源索引

本文基于对以下源文件的直接读取：

Hermes: run_agent.py（6,933行）、agent/tool_guardrails.py（_{500行）、`agent/background_review.py`（}31KB）、agent/prompt_builder.py（_{19KB）、`agent/context_compressor.py`（}22KB）、agent/model_metadata.py（_{21KB）、`agent/display.py`（}20KB）、agent/iteration_budget.py（_{3.4KB）、`agent/tool_dispatch_helpers.py`（}17KB）、agent/agent_runtime_helpers.py（_{110KB）、`agent/agent_init.py`（}88KB）

OpenClaw: src/agents/pi-embedded-runner/run.ts、src/auto-reply/reply/agent-runner.ts、VISION.md（已验证）、AGENTS.md（已验证）、src/agents/embedded-agent-runner/runs.ts

以下是本文关键论断的验证状态，顺便作做个记录感兴趣的筒子们可以自己对照着看一下：

#	论断	验证状态	源文件
1	`_NEVER_PARALLEL_TOOLS = frozenset({"clarify"})`	✅ 已验证	`agent/tool_dispatch_helpers.py:59`
2	`_PARALLEL_SAFE_TOOLS` 含 `ha_get_state`, `skills_list` 等 11 个	✅ 已验证	`agent/tool_dispatch_helpers.py:61-72`
3	`_PATH_SCOPED_TOOLS = frozenset({"read_file", "write_file", "patch"})`	✅ 已验证	`agent/tool_dispatch_helpers.py:75`
4	`_should_parallelize_tool_batch()` 逻辑（路径重叠检测等）	✅ 已验证	`agent/tool_dispatch_helpers.py:132-177`
5	`_is_destructive_command()` --- 终端命令破坏性检测	✅ 已验证	`agent/tool_dispatch_helpers.py:78-128`
6	`IDEMPOTENT_TOOL_NAMES` / `MUTATING_TOOL_NAMES` 完整名单	✅ 已验证	`agent/tool_guardrails.py:22-59`
7	`ToolCallGuardrailConfig` 默认阈值（2/5, 3/8, 2/5）	✅ 已验证	`agent/tool_guardrails.py:63-97`
8	Guardrail 决策级别：ALLOW / WARN / BLOCK / HALT	✅ 已验证	`agent/tool_guardrails.py:149-162`
9	Warnings 默认开启 + Hard Stops 需显式开启	✅ 已验证	`agent/tool_guardrails.py:80-82`
10	`IterationBudget` 类：`consume()`, `refund()`, 线程安全	✅ 已验证	`agent/iteration_budget.py:26-61`
11	`max_iterations` 默认 90（`agent_init.py`）	✅ 已验证	`agent/agent_init.py:228`
12	子 Agent 默认 50 次（来自 `delegation.max_iterations`）	✅ 已验证	`agent/iteration_budget.py:17-19`
13	`BackgroundReviewer` 后台线程 + fork AIAgent	✅ 已验证	`agent/background_review.py:1-26`
14	`_MEMORY_REVIEW_PROMPT` / `_SKILL_REVIEW_PROMPT` 完整内容	✅ 已验证	`agent/background_review.py:38-178`
15	`_scan_context_content()` --- 上下文文件注入检测	✅ 已验证	`agent/prompt_builder.py`
16	`_URL_TO_PROVIDER` --- 30+ Provider 自动推断	✅ 已验证	`agent/model_metadata.py`
17	`CONTEXT_PROBE_TIERS` 阶梯探测 + `MINIMUM_CONTEXT_LENGTH = 64000`	✅ 已验证	`agent/model_metadata.py`
18	`TOOL_USE_ENFORCEMENT_MODELS` 名单	✅ 已验证	`agent/prompt_builder.py`
19	VISION.md "What We Will Not Merge" --- Agent Tree 拒绝	✅ 已验证	OpenClaw `VISION.md`
20	AGENTS.md Hook 决策规则（`{ block: true }` 终态等）	✅ 已验证	OpenClaw `AGENTS.md`
21	`execution_context` 区分 foreground/background/trajectory	✅ 已验证	`agent/background_review.py:185-201`
22	api_mode 4 级自动检测（chat/codex/anthropic/bedrock）	✅ 已验证	`agent/agent_init.py`
23	IterationBudget 父子共享 vs 独立（代码内矛盾）	✅ 矛盾已确认	`agent_init.py:251`："Shared --- consumed across parent + all subagents"；`iteration_budget.py:15-20`："Each subagent gets an independent budget"
24	`_interruptible_api_call()` 函数	✅ 已验证	`run_agent.py:3968`（thin forwarder）→ 实现在 `agent/chat_completion_helpers.py:125`，后台线程 + 请求级取消标志 + stale-call 检测
25	`_MAX_TOOL_WORKERS = 8`	✅ 已验证	`run_agent.py` 模块级常量（位于 `_openrouter_prewarm_done` 上方）
26	`_SafeWriter` 类	✅ 已验证	`agent/process_bootstrap.py:62-103`，包装 stdout/stderr 静默捕获 broken pipe 的 `OSError`/`ValueError`

读者复核指南：

全部 26 项关键论断均已完成源码交叉验证，标记 ✅ 的可直接引用对应文件行号
本文于 2026-06-10 完成验证；两个项目的仓库持续更新中，关心的童鞋请以最新源码为准

本文在发布前经过了系统性的源码交叉验证。如果你发现错误，请在评论区给我留言或者私信俺。