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openclaw-agent-meshOpenClaw Agent mesh 搜索

Agent Skill

openclaw-agent-mesh 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。

总安装

8,138

周安装

346

GitHub Stars

公开资料未说明

下载量

2,851
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:openclaw-agent-mesh(OpenClaw Agent mesh 搜索)
来源仓库:https://github.com/clawdpi-ai/openclaw-agent-mesh
安装命令:
openclaw skills install openclaw-agent-mesh
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 openclaw-agent-mesh

简介

openclaw-agent-mesh 实现 OpenClaw 实例间的对等发现与通信。

  • 适用于构建去中心化代理网络场景。openclaw-agent-mesh 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 当用户希望节点间自动发现并建立联系时使用。
  • 安装前需确认权限范围与维护状态,可能涉及网络通信与身份验证。
  • 建议结合来源仓库和原始 README 核验 P2P 协议与安全机制。

SKILL.md

name
openclaw-agent-mesh
description
Peer discovery and agent-to-agent communication for OpenClaw instances. Use when the user wants nearby OpenClaw nodes to discover each other, request contact, require explicit approval, establish trust, and exchange direct messages. Supports V1 workflows for identity initialization, LAN scanning, contact requests, request approval/rejection, point-to-point messaging, and a lightweight HTTP server for discovery and inbox handling.

OpenClaw Agent Mesh

Provide a minimal but real agent-to-agent communication layer for OpenClaw instances. Use the bundled scripts to initialize identity, scan a local network range, exchange contact requests, approve peers, and send signed direct messages. Require explicit acceptance before trusted communication begins.

V1 scope

Implement only these capabilities:

  • local identity generation
  • LAN discovery by probing peer endpoints
  • contact request creation
  • contact approval or rejection
  • trusted peer storage
  • direct signed message creation and delivery
  • inbox verification and acknowledgement
  • lightweight HTTP server for discovery, contact-request intake, and message intake

Do not claim NAT traversal, full mesh routing, or multi-party consensus in V1.

Files and local state

Store mesh state outside the skill folder. Use this default path unless the user specifies another one:

  • ~/.openclaw/agent-mesh/

Expected files:

  • identity.json — local agent identity
  • private_key.pem — local signing key
  • peers/<agent_id>.json — trusted peers
  • requests/incoming/*.json — pending inbound contact requests
  • requests/outgoing/*.json — outbound contact requests
  • messages/incoming/*.json — verified inbound messages
  • messages/outgoing/*.json — sent messages
  • groups/ — reserved for future versions

Workflow

1. Initialize local identity

Run scripts/mesh.py init. This creates a signing keypair and an identity card with:

  • agent_id
  • display_name
  • public_key
  • endpoint
  • created_at
  • fingerprint

Set the endpoint to a reachable HTTP URL if the node should receive requests from peers.

2. Scan for nearby peers

Run scripts/mesh.py scan with a base URL template or a list of candidate URLs. Scanning in V1 is HTTP discovery, not raw port scanning. Probe each candidate at:

  • /agent-mesh/discovery

Treat discovered nodes as untrusted until approved.

3. Send a contact request

Run scripts/mesh.py request-contact. Send a signed request to a discovered node’s inbox endpoint. The receiver stores the request as pending.

4. Approve or reject the request

Run scripts/mesh.py list-requests then approve-request or reject-request. Approval writes the peer into the trust store. Rejection leaves no trusted relationship.

5. Send a direct message

Run scripts/mesh.py send-message only after trust exists. The sender signs the message envelope. The receiver verifies signature, timestamp, and trust status before accepting.

6. Verify delivery

Run scripts/mesh.py list-messages or inspect stored message JSON files. Use acknowledgements to confirm receipt.

Transport model

V1 uses simple HTTP JSON endpoints:

  • GET /agent-mesh/discovery
  • POST /agent-mesh/contact-request
  • POST /agent-mesh/message

Run scripts/server.py to expose these endpoints from a node that should be discoverable or receive peer traffic. Example:

  • python3 scripts/server.py --host 0.0.0.0 --port 8787 --state-dir ~/.openclaw/agent-mesh

If the user does not yet have a server to receive HTTP traffic, use the scripts to generate and inspect signed payloads locally first.

Guardrails

  • Require explicit approval before trusting a peer.
  • Never auto-accept unknown peers.
  • Never send private keys over the network.
  • Prefer signed JSON envelopes with timestamps and message IDs.
  • Reject stale or malformed messages.
  • Keep V1 limited to point-to-point trust and messaging.

References

  • Read references/protocol.md for the JSON message model.
  • Read references/verification.md for trust and signature checks.

Deliverables

When using this skill, produce one or more of:

  • a configured local mesh identity
  • a peer discovery result set
  • a pending or approved contact request
  • a verified direct-message flow
  • a troubleshooting checklist for failed trust or message delivery

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

75.82%
按下载量换算2,162

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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