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Agent Skill · 研究检索

agentmesh(agentmesh 搜索)

agentmesh 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

agentmesh 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 5,085。

总安装 27,956周安装 1,142GitHub Stars 公开资料未说明下载 9,045核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agentmesh(agentmesh 搜索)
来源仓库:https://github.com/cerbug45/agentmesh
安装命令:
openclaw skills install agentmesh
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install agentmesh

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

在具有加密身份和防篡改传输的 AI 代理之间提供端到端加密、经过身份验证和前向保密的消息传递。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install agentmesh;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

agentmesh 怎么安装?

在终端执行 openclaw skills install agentmesh。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

agentmesh 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

agentmesh 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

agentmesh 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

agentmesh 用的人多吗?

公开数据是累计安装 27,956 次,近一周安装 1,142 次,下载 9,045 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

agentmesh 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
agentmesh

agentmesh 搜索 · 本页

27,956—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

AgentMesh SKILL.md

WhatsApp-style end-to-end encrypted messaging for AI agents. GitHub: https://github.com/cerbug45/AgentMesh | Author: cerbug45

What Is AgentMesh?

AgentMesh gives every AI agent a cryptographic identity and lets agents exchange messages that are:

PropertyMechanism
EncryptedAES-256-GCM authenticated encryption
AuthenticatedEd25519 digital signatures (per message)
Forward-secretX25519 ECDH ephemeral session keys
Tamper-proofAEAD authentication tag
Replay-proofNonce + counter deduplication
PrivateThe Hub (broker) never sees message contents

No TLS certificates. No servers required for local use. One pip install.


Installation

Requirements

  • Python 3.10 or newer
  • pip

Option 1 – Install from GitHub (recommended)

pip install git+https://github.com/cerbug45/AgentMesh.git

Option 2 – Clone and install locally

git clone https://github.com/cerbug45/AgentMesh.git
cd AgentMesh
pip install .

Option 3 – Development install (editable, with tests)

git clone https://github.com/cerbug45/AgentMesh.git
cd AgentMesh
pip install -e ".[dev]"
pytest           # run all tests

Verify installation

python -c "import agentmesh; print(agentmesh.__version__)"
# → 1.0.0

Quick Start (5 minutes)

from agentmesh import Agent, LocalHub

hub   = LocalHub()                  # in-process broker
alice = Agent("alice", hub=hub)     # keys generated automatically
bob   = Agent("bob",   hub=hub)

@bob.on_message
def handle(msg):
    print(f"[{msg.recipient}] ← {msg.sender}: {msg.text}")

alice.send("bob", text="Hello, Bob! This is end-to-end encrypted.")

Output:

[bob] ← alice: Hello, Bob! This is end-to-end encrypted.

Core Concepts

Agent

An Agent is an AI agent with a cryptographic identity (two key pairs):

  • Ed25519 identity key – signs every outgoing message
  • X25519 exchange key – used for ECDH session establishment
from agentmesh import Agent, LocalHub

hub   = LocalHub()
alice = Agent("alice", hub=hub)

# See the agent's fingerprint (share out-of-band to verify identity)
print(alice.fingerprint)
# → a1b2:c3d4:e5f6:g7h8:i9j0:k1l2:m3n4:o5p6

Hub

A Hub is the message router. It stores public key bundles (for discovery) and routes encrypted envelopes. It cannot decrypt messages.

HubUse case
LocalHubSingle Python process (demos, tests, notebooks)
NetworkHubMulti-process / multi-machine (production)

Message

@bob.on_message
def handle(msg):
    msg.sender     # str  – sender agent_id
    msg.recipient  # str  – recipient agent_id
    msg.text       # str  – shortcut for msg.payload["text"]
    msg.type       # str  – shortcut for msg.payload["type"] (default: "message")
    msg.payload    # dict – full decrypted payload
    msg.timestamp  # int  – milliseconds since epoch

Usage Guide

Sending messages with extra data

alice.send(
    "bob",
    text     = "Run this task",
    task_id  = 42,
    priority = "high",
    data     = {"key": "value"},
)

All keyword arguments beyond text are included in msg.payload.

Chaining handlers

# Handler as decorator
@alice.on_message
def handler_one(msg):
    ...

# Handler as lambda
alice.on_message(lambda msg: print(msg.text))

# Multiple handlers – all called in registration order
alice.on_message(log_handler)
alice.on_message(process_handler)

Persistent keys

Save keys to disk so an agent has the same identity across restarts:

alice = Agent("alice", hub=hub, keypair_path=".keys/alice.json")
  • File is created on first run (new keys).
  • File is loaded on subsequent runs (same keys = same fingerprint).
  • Store this file securely – it contains the private key.

Peer discovery

# List all agents registered on the hub
peers = alice.list_peers()   # → ["bob", "carol", "dave"]

# Check agent status
print(alice.status())
# {
#   "agent_id": "alice",
#   "fingerprint": "a1b2:…",
#   "active_sessions": ["bob"],
#   "known_peers": ["bob"],
#   "handlers": 2
# }

Network Mode (multi-machine)

1. Start the hub server

On the broker machine (or in its own terminal):

# Option A – module
python -m agentmesh.hub_server --host 0.0.0.0 --port 7700

# Option B – entry-point (after pip install)
agentmesh-hub --host 0.0.0.0 --port 7700

2. Agents connect from anywhere

# Machine A
from agentmesh import Agent, NetworkHub
hub   = NetworkHub(host="192.168.1.10", port=7700)
alice = Agent("alice", hub=hub)

# Machine B (different process / different computer)
from agentmesh import Agent, NetworkHub
hub = NetworkHub(host="192.168.1.10", port=7700)
bob = Agent("bob", hub=hub)

bob.on_message(lambda m: print(m.text))
alice.send("bob", text="Cross-machine encrypted message!")

Network hub architecture

┌──────────────────────────────────────────────────────┐
│                   NetworkHubServer                   │
│  Stores public bundles.  Routes encrypted envelopes. │
│  Cannot read message contents.                       │
└──────────────────────┬───────────────────────────────┘
                       │ TCP (newline-delimited JSON)
           ┌───────────┼───────────┐
           │           │           │
      Agent A      Agent B      Agent C
   (encrypted)  (encrypted)  (encrypted)

Security Architecture

Cryptographic stack

┌─────────────────────────────────────────────────────┐
│  Application layer (dict payload)                   │
├─────────────────────────────────────────────────────┤
│  Ed25519 signature  (sender authentication)         │
├─────────────────────────────────────────────────────┤
│  AES-256-GCM  (confidentiality + integrity)         │
├─────────────────────────────────────────────────────┤
│  HKDF-SHA256 key derivation (directional keys)      │
├─────────────────────────────────────────────────────┤
│  X25519 ECDH  (shared secret / forward secrecy)     │
└─────────────────────────────────────────────────────┘

Security properties

AttackDefence
EavesdroppingAES-256-GCM encryption
Message tamperingAES-GCM authentication tag (AEAD)
ImpersonationEd25519 signature on every message
Replay attackNonce + monotonic counter deduplication
Key compromiseX25519 ephemeral sessions (forward secrecy)
Hub compromiseHub stores only public keys; cannot decrypt

What the Hub can see

  • ✅ Agent IDs (to route messages)
  • ✅ Public key bundles (required for discovery)
  • ✅ Metadata: sender, recipient, timestamp, message counter
  • ❌ Message contents (always encrypted)
  • ❌ Payload data (always encrypted)

Examples

FileWhat it shows
examples/01_simple_chat.pyTwo agents, basic send/receive
examples/02_multi_agent.pyCoordinator + 4 workers, task distribution
examples/03_persistent_keys.pyKeys saved to disk, identity survives restart
examples/04_llm_agents.pyLLM agents (OpenAI / any API) in a pipeline

Run any example:

python examples/01_simple_chat.py

API Reference

Agent(agent_id, hub=None, keypair_path=None, log_level=WARNING)

MethodDescription
send(recipient_id, text="", **kwargs)Send encrypted message
send_payload(recipient_id, payload: dict)Low-level send
on_message(handler)Register message handler (decorator or call)
connect(peer_id)Pre-establish session (optional, auto-connects)
connect_with_bundle(bundle)P2P: connect using public bundle directly
list_peers()List all peer IDs on the hub
status()Dict with agent state
fingerprintHuman-readable hex identity fingerprint
public_bundleDict with public keys (share with peers)

LocalHub()

MethodDescription
register(agent)Register an agent (called automatically)
deliver(envelope)Route an encrypted envelope
get_bundle(agent_id)Get a peer's public bundle
list_agents()List all registered agent IDs
message_count()Number of messages routed

NetworkHub(host, port=7700)

Same interface as LocalHub, but communicates with a NetworkHubServer over TCP.

NetworkHubServer(host="0.0.0.0", port=7700)

MethodDescription
start(block=True)Start listening (block=False for background thread)

Low-level crypto (advanced)

from agentmesh.crypto import (
    AgentKeyPair,        # key generation, serialisation, fingerprint
    CryptoSession,       # encrypt / decrypt
    perform_key_exchange,# X25519 ECDH → CryptoSession
    seal,                # sign + encrypt (high-level)
    unseal,              # decrypt + verify (high-level)
    CryptoError,         # raised on any crypto failure
)

Troubleshooting

CryptoError: Replay attack detected

You are sending the same encrypted envelope twice. Each call to send() produces a fresh envelope – do not re-use envelopes.

CryptoError: Authentication tag mismatch

The envelope was modified in transit. Check that your transport does not corrupt binary data (use JSON-safe base64).

ValueError: Peer 'xxx' not found on hub

The recipient has not registered with the hub yet. Ensure both agents are created with the same hub instance (LocalHub) or connected to the same hub server (NetworkHub).

RuntimeError: No hub configured

You created Agent("name") without a hub. Pass hub=LocalHub() or hub=NetworkHub(...) to the constructor.


Contributing

git clone https://github.com/cerbug45/AgentMesh.git
cd AgentMesh
pip install -e ".[dev]"
pytest -v

Issues and PRs welcome at https://github.com/cerbug45/AgentMesh/issues


License

MIT © cerbug45 – see LICENSE

平台分布

OpenClaw

94.62%
按下载量换算8,558

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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