节点互联
openclaw-agent-mesh
by clawdpi-ai
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.
安装
claude skill add --url github.com/openclaw/skills/tree/main/skills/clawdpi-ai/openclaw-agent-mesh文档
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 identityprivate_key.pem— local signing keypeers/<agent_id>.json— trusted peersrequests/incoming/*.json— pending inbound contact requestsrequests/outgoing/*.json— outbound contact requestsmessages/incoming/*.json— verified inbound messagesmessages/outgoing/*.json— sent messagesgroups/— 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_iddisplay_namepublic_keyendpointcreated_atfingerprint
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/discoveryPOST /agent-mesh/contact-requestPOST /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.mdfor the JSON message model. - Read
references/verification.mdfor 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
相关 Skills
MCP构建
by anthropics
聚焦高质量 MCP Server 开发,覆盖协议研究、工具设计、错误处理与传输选型,适合用 FastMCP 或 MCP SDK 对接外部 API、封装服务能力。
✎ 想让 LLM 稳定调用外部 API,就用 MCP构建:从 Python 到 Node 都有成熟指引,帮你更快做出高质量 MCP 服务器。
Slack动图
by anthropics
面向Slack的动图制作Skill,内置emoji/消息GIF的尺寸、帧率和色彩约束、校验与优化流程,适合把创意或上传图片快速做成可直接发送的Slack动画。
✎ 帮你快速做出适配 Slack 的动图,内置约束规则和校验工具,少踩上传与播放坑,做表情包和演示都更省心。
MCP服务构建器
by alirezarezvani
从 OpenAPI 一键生成 Python/TypeScript MCP server 脚手架,并校验 tool schema、命名规范与版本兼容性,适合把现有 REST API 快速发布成可生产演进的 MCP 服务。
✎ 帮你快速搭建 MCP 服务与后端 API,脚手架完善、扩展顺手,尤其适合想高效验证服务能力的开发者。
相关 MCP 服务
Slack 消息
编辑精选by Anthropic
Slack 是让 AI 助手直接读写你的 Slack 频道和消息的 MCP 服务器。
✎ 这个服务器解决了团队协作中需要 AI 实时获取 Slack 信息的痛点,特别适合开发团队让 Claude 帮忙汇总频道讨论或发送通知。不过,它目前只是参考实现,文档有限,不建议在生产环境直接使用——更适合开发者学习 MCP 如何集成第三方服务。
by netdata
io.github.netdata/mcp-server 是让 AI 助手实时监控服务器指标和日志的 MCP 服务器。
✎ 这个工具解决了运维人员需要手动检查系统状态的痛点,最适合 DevOps 团队让 Claude 自动分析性能数据。不过,它依赖 NetData 的现有部署,如果你没用过这个监控平台,得先花时间配置。
by d4vinci
Scrapling MCP Server 是专为现代网页设计的智能爬虫工具,支持绕过 Cloudflare 等反爬机制。
✎ 这个工具解决了爬取动态网页和反爬网站时的头疼问题,特别适合需要批量采集电商价格或新闻数据的开发者。不过,它依赖外部浏览器引擎,资源消耗较大,不适合轻量级任务。