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默认就是批派发，主 Agent 不阻塞

news 2026/6/10 0:24:24

Hermes 是谁？跟 Claude Code 差在哪
用过 Claude Code 或 Cursor 的话，Hermes 上手几乎没成本。它就是一个跑在终端里的 AI 代理，但多了一层派发能力。

最直观的对比：

Claude Code / Cursor：你打开一个 tab，AI 在里面干活，干完你接着提需求。
Hermes：你打开一个 tab，AI 叫出另外几个 tab 一起干。Hermes 负责拆任务、传上下文、收结果。
Hermes 比单 Agent 工具多出来的核心能力就三件：

多 Provider 路由。一条命令切 OpenAI / Anthropic / Google / xAI 后端，业务代码不用动。
多 Agent 派发。delegate_task() 拉起子 Agent，每个子 Agent 独立上下文，互不污染。
Skills 系统。把写公众号、做调研、出配图 prompt 这些套路写成 SKILL.md，Agent 加载就会用。
剩下的 toolsets（白名单授权）、profiles（多场景隔离）、depends_on（DAG 调度）都是配套的工程能力。

只想跑单 Agent 串行活，Claude Code 够用。但凡你想让多个步骤并发、让上下文拆分不互相污染、让工具按 Agent 最小授权，Hermes 的多 Agent 模型就是为这些场景做的。

二、10 分钟起一个 3 Agent 流水线
不再绕弯子。咱们直接上手。

2.1 装环境
# PyPI 一行装（推荐新手）
pip install hermes-agent

# 启动 REPL
hermes chat

# 切到你想用的模型
hermes config set provider anthropic
hermes config set model claude-sonnet-4
装完跑 hermes tools 看下当前账号可用的工具集。默认会有 browser / file / terminal / web / image_gen / delegation / search 这几类。

2.2 写一份 YAML 配置
Hermes 的精髓在 YAML 流水线配置文件。把 Agent 当成函数声明，依赖关系写在 depends_on 里，Hermes 自己按 DAG 排调度。

下面这份配置就是公众号写作流水线的最小可运行版本。5 个 Agent、5 个文件产物、链式 DAG 调度。

# =========================================================
# Hermes 多 Agent 流水线：公众号写作 5 步曲
# 跑法：hermes run --config hermes_agents.yaml --topic "Hermes 入门"
# =========================================================
agents:
topic_scout: # 第 1 步：选题侦察兵
model: anthropic/claude-sonnet-4
toolsets: [web, search] # 只给联网 + 搜索，文件操作不要开
role: leaf # 关键：声明 leaf，禁止递归 spawn

researcher: # 第 2 步：资料研究员
model: anthropic/claude-sonnet-4
toolsets: [file, web] # file 用来读 01-topics.md、写出 02-research.md
role: leaf

writer: # 第 3 步：执笔写手
model: anthropic/claude-sonnet-4
toolsets: [file, skills] # skills 让它能加载「公众号写作」技能
role: leaf

editor: # 第 4 步：审校编辑
model: anthropic/claude-sonnet-4
toolsets: [file] # 最小集：只让它改文件
role: leaf

illustrator: # 第 5 步：配图提示词
model: anthropic/claude-sonnet-4
toolsets: [skills] # 用 image_gen 技能生成配图 prompt
role: leaf

pipeline:
- call: topic_scout # 入口节点，无需 depends_on
goal: "为「{TOPIC}」出 3 个公众号选题候选，输出到 01-topics.md"

- call: researcher
goal: "基于 01-topics.md 整理 6 section 研究资料包到 02-research.md"
depends_on: topic_scout

- call: writer
goal: "据 02-research.md 起草 2500-3000 字实操教程到 03-draft.md"
depends_on: researcher

- call: editor
goal: "审校 03-draft.md，输出三栏 Markdown 表格存到 04-review.md"
depends_on: writer

- call: illustrator
goal: "为 03-draft.md 配 2-3 张图，给出图名+节点文字+用途"
depends_on: editor
这里有几个细节需要注意一下：

role: leaf 必须显式写。不写默认是 general，子 Agent 自己会 spawn 孙子 Agent，token 几分钟烧光。
toolsets 走白名单，能少一个就少一个。Editor 只给 file，连 web 都不开。
depends_on 替代手写串行，你只声明依赖，Hermes 自己排调度。
2.3 嫌 YAML 不够灵活？上 Python
YAML 适合配置固定的流水线。想动态生成任务、加条件分支、用 Python 拼装任务列表，就走 delegate_task API。

下面这段 Python 是同样的 5 步流水线，能看出它和 YAML 的对应关系。YAML 是声明式，Python 是命令式，干的事一样。

"""
Hermes 多 Agent 流水线 —— Python 入口
跑法：python run_pipeline.py
依赖：pip install hermes-agent
"""
from hermes_tools import delegate_task # Hermes 官方派发 API

# ----------------------------------------------------------------
# 1) 定义 5 个子 Agent 任务（顺序由 depends_on 字段控制）
# ----------------------------------------------------------------
tasks = [
{
"goal": "你是 topic_scout。用户主题：{TOPIC}。请用 web+search 工具调研，"
"输出 3 个公众号选题候选到 01-topics.md。",
"toolsets": ["web", "search"],
"role": "leaf", # 关键：明确 leaf，禁止递归
},
{
"goal": "你是 researcher。据 01-topics.md 整理 6 section 资料包"
"（核心概念/代码/坑点/最佳实践/参考/配图）写到 02-research.md。",
"toolsets": ["file", "web"],
"role": "leaf",
"depends_on": ["topic_scout"],
},
{
"goal": "你是 writer。据 02-research.md 起草 2800 字实操教程，"
"输出到 03-draft.md，必须有 YAML + Python 两段代码。",
"toolsets": ["file", "skills"],
"role": "leaf",
"depends_on": ["researcher"],
},
{
"goal": "你是 editor。审校 03-draft.md，输出三栏 Markdown 表格"
"（原文 / 问题 / 建议）到 04-review.md。",
"toolsets": ["file"],
"role": "leaf",
"depends_on": ["writer"],
},
{
"goal": "你是 illustrator。为 03-draft.md 配 2-3 张图，"
"给出图名+用途+节点文字到 05-illustrations.md。",
"toolsets": ["skills"],
"role": "leaf",
"depends_on": ["editor"],
},
]

# ----------------------------------------------------------------
# 2) 一次性派发：Hermes 默认就是批派发，主 Agent 不阻塞
# Hermes 会按 tasks 里的 depends_on 字段自动排 DAG，
# 没有依赖的同级任务自动并发跑。
# ----------------------------------------------------------------
results = delegate_task(
tasks=tasks,
shared_context={"TOPIC": "Hermes Agent 入门：多 Agent 协作"},
)

# ----------------------------------------------------------------
# 3) 打印每个子 Agent 的 summary（调试用）
# ----------------------------------------------------------------
for r in results:
print(f"[{r['agent']}] -> {r['summary']}")
注意我没像一些老博客那样写 parallel=True。Hermes 的 delegate_task(tasks=[...]) 默认就是批派发，依赖关系由 depends_on 字段决定。它会自动识别哪些任务没依赖、把没依赖的扔到并发队列里跑。parallel=True 是早期接口的遗留，新版 API 不用管。


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https://github.com/krishnbang/one/tree/465d31f8282274d4d59592b577a6b312ce3e5298
https://github.com/krishnbang/one/tree/07a4e39a02776b4a28bb81f49e53a75ffe30f85b
https://github.com/krishnbang/one/tree/8c019844257e7c953b8ba35f672d5c8a86091f9f
https://github.com/krishnbang/one/tree/5d1b2cf1d81169125d63236c23114486a16c2dba
https://github.com/krishnbang/one/tree/97b7295d13e566d5dd83620702c2f8effbd80a55
https://github.com/krishnbang/one/tree/a42504c679e18a7b6a1e6057e240ca6c91ce6c90
https://github.com/krishnbang/one/tree/eb08bc977b6954a64d1735723e42f2e747b245b5
https://github.com/krishnbang/one/tree/36361a71549289797197209b1fa2fb88db7cd1fb
https://github.com/krishnbang/one/tree/982fbbe5d205abdb87639c4d75a28e98ccb5f325
https://github.com/krishnbang/one/tree/5817045274721ad095634a5525dab04b2f03dc56
https://github.com/krishnbang/one/tree/1367eb9d34288dd987acdbd9957eb3f9e1e8e7ea
https://github.com/krishnbang/one/tree/19cfb2134b2229677da3b294809409317edb9a8a
https://github.com/krishnbang/one/tree/37474b6b5d51e19657ff816ab219ad5e22bd1ccc
https://github.com/krishnbang/one/tree/d368811590faa14519e50ef5010f67d3307d0f54
https://github.com/krishnbang/one/tree/85aea40988e85c866356be4e511aa7269972186a
https://github.com/krishnbang/one/tree/3caec7298e113ef5c5c394ab7af18d404888499f
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https://github.com/krishnbang/one/tree/79e3a8ecf762b6468568eee0cf550fd3efdbdc6b
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https://github.com/krishnbang/one/tree/6c88e63d3021c8fdd8898202de45f96e611cab04
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https://github.com/krishnbang/one/tree/592acfd7cbd8efa40a55148edf5ceca636a3f24c
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https://github.com/krishnbang/one/tree/8e9b4fff792f1dd60e86f3af366941246b00e14b
https://github.com/krishnbang/one/tree/4f255eaf96dc7b8851d03dded02441eacc2dc892
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https://github.com/krishnbang/one/tree/b5ff0603642db16d67939c6a4be4dbfa720344db
https://github.com/krishnbang/one/tree/7dfbe2726b15d915fa777d0a4d25753533c53e92
https://github.com/krishnbang/one/tree/4401e64c4f916f313e9b8c4a6d2b66021153256c
https://github.com/krishnbang/one/tree/badaaed9f356d84c0bb17cb2e4213c8b39a10db0
https://github.com/krishnbang/one/tree/79befcd49ed76f0abb4452bbcb8eaa6913a23f93
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https://github.com/krishnbang/one/tree/7cd1a99afbf3d360f9869926e24546a6668246f3
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https://github.com/krishnbang/one/tree/b0865c771e3f0169f9f8ff15f7afa02ff65c6cf9
https://github.com/krishnbang/one/tree/07bbf02f11b4e679d4498199b79dd3af10b10da8
https://github.com/krishnbang/one/tree/02dc9d7059756fa297271a801c4e2e1b973aced4
https://github.com/krishnbang/one/tree/db17e31d8bf58c7dcc700a70cd16e0094669e5ec
https://github.com/krishnbang/one/tree/ee6bad1a46ba05043c4b154293949d3d94ffaa8a
https://github.com/krishnbang/one/tree/a30b357613146c0f28ba9ebfb0b63ea5dfc73760
https://github.com/krishnbang/one/tree/4d85cbb52dc923efba1a7a2a2c2cb7358bed4d78
https://github.com/krishnbang/one/tree/9bcf2950498674409c26b9b1fe81353276e1b86d
https://github.com/krishnbang/one/tree/72c158c8af0046f5e69a446ef718b15e4a45ae5c
https://github.com/krishnbang/one/tree/19e377e0874ae713bd0010912607ea9aa2a61268
https://github.com/krishnbang/one/tree/f2a28e6536fd7b70e63445c64fc758d412a74bd5
https://github.com/krishnbang/one/tree/9bcf7e0fe9011379ab57afa2243efb3968d924d0
https://github.com/krishnbang/one/tree/fcaaa51a9ba5d91b969d46d7abc028258ee0e833
https://github.com/krishnbang/one/tree/47d5a05ca7d53892efd034eeca2567672ed2df4c
https://github.com/krishnbang/one/tree/304ce67124e70cb71ab8a908a6b6782695c95311
https://github.com/krishnbang/one/tree/2a30b3bf3b9d3ac77600902dc061bd9ca82e09b1
https://github.com/krishnbang/one/tree/03bcaa8c77e798d5f5ac3c97ac1f2e0c066d06b2
https://github.com/krishnbang/one/tree/dfe812d2e37387084f6c3ae9ecb98aff517d1f7c
https://github.com/krishnbang/one/tree/60793eefbabc82e8ebe02455ed4f139184d9b585
https://github.com/krishnbang/one/tree/80b74bc37baca7d0995768e88a91fc151f62031d
https://github.com/krishnbang/one/tree/e82adabb04fb6d2ca66507df33485005d419bd39
https://github.com/krishnbang/one/tree/59c0e3807acc79a03e6179779f8240fbd21dc485
https://github.com/krishnbang/one/tree/6ce3bfdb4f7fcabbe652d97ee51201dab1e8ce75
https://github.com/krishnbang/one/tree/1b13d440b4cc22cb0dd83520b515892dfe0014a0
https://github.com/krishnbang/one/tree/87119933917cb15916173715882654c65ae68361
https://github.com/krishnbang/one/tree/7050f2629f47c185098064a85882fb620947695e
https://github.com/krishnbang/one/tree/90ee4ecf29e1bd29e23c4bc4029c34b00ef4e9f3
https://github.com/krishnbang/one/tree/ec061c4ae40a3d791d0e958e617f04ebe50f8842
https://github.com/krishnbang/one/tree/64d4e8c5fe9c0516fe64c46241da069265bfe60e
https://github.com/krishnbang/one/tree/39d7fb1d8ddf9e2690d62501efc59817d19136e4
https://github.com/krishnbang/one/tree/8c36ddb05a6ad2291d8666ce58afba73e0666287

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