🧪 Skills

Swarm Orchestrator

AI Agent cluster orchestration platform - manage, schedule, and coordinate multiple AI agents locally with FastAPI backend and React dashboard

v1.0.0
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Description

name: swarm-orchestrator display_name: "Swarm Orchestrator / 群智调度器" description: AI Agent cluster orchestration platform - manage, schedule, and coordinate multiple AI agents locally with FastAPI backend and React dashboard version: 0.1.0 author: OpenClaw Team license: MIT tags: [orchestration, multi-agent, ai-agents, swarm, automation, fastapi, python, react, local-first]

Security & Requirements Declaration

requires:

System tools (must be pre-installed)

tools: - name: python version: ">=3.11" purpose: Backend runtime - name: node version: ">=18" purpose: Frontend build and runtime - name: redis version: ">=6" purpose: Task queue and caching - name: docker version: ">=20" purpose: Optional containerized deployment optional: true

Environment variables (all optional)

env: - name: DATABASE_URL description: Database connection string default: "sqlite+aiosqlite:///./data/swarm.db" required: false sensitive: false - name: REDIS_URL description: Redis connection URL default: "redis://localhost:6379" required: false sensitive: false - name: SECRET_KEY description: Application secret key for sessions default: "generated-on-first-run" required: false sensitive: true - name: OPENAI_API_KEY description: Optional OpenAI API key for LLM agents required: false sensitive: true - name: ANTHROPIC_API_KEY description: Optional Anthropic API key for Claude agents required: false sensitive: true

Package to install

packages: - name: openclaw-swarm-orchestrator source: npm version: "0.1.0" verified_repo: https://github.com/ZhenRobotics/openclaw-swarm-orchestrator verified_commit: acae6e5 install_command: "npm install -g openclaw-swarm-orchestrator"

Network & Security Policy

network: external_servers: - description: "No external servers required for core functionality" - description: "Optional: OpenAI/Anthropic APIs if using LLM agents (user-controlled)" data_collection: none telemetry: none local_only: true

Installation verification

verification: check_commands: - "swarm-orchestrator --version" - "curl http://localhost:8000/health" expected_files: - "~/.swarm-orchestrator/config.yml" - "./data/swarm.db"

🇨🇳 中文版

Swarm Orchestrator 技能

状态： 🟢 本地优先的 AI 智能体编排平台 类型： 自托管，无需外部依赖 隐私： 100% 本地处理（可选的大模型 API 调用除外）

🔒 安全与信任

本技能的功能

  • 本地运行 - 在您的机器上运行（后端 + 前端）
  • 无遥测 - 不收集数据
  • 无需外部服务器 - 核心功能无需外部依赖
  • 可选 API 密钥 - 仅在使用大模型智能体时需要（OpenAI/Claude）
  • 开源 - 所有代码可审计

本技能不会做的事

  • ❌ 不向外部服务器发送数据（可选的大模型 API 除外）
  • ❌ 不收集分析或遥测数据
  • ❌ 不需要账号注册
  • ❌ 不会未经许可访问您的文件
  • ❌ 不会在您不知情的情况下运行后台进程

数据存储

  • 数据库： SQLite 文件存储在 ./data/swarm.db（仅本地）
  • 日志： 文本文件存储在 ./logs/（仅本地）
  • 缓存： 本地 Redis（仅本地）
  • 无云同步 或远程存储

📋 概述

OpenClaw Swarm Orchestrator 是一个本地优先的平台，用于构建和管理多智能体 AI 系统。可以把它想象成协调多个 AI 智能体协同工作的"控制塔"。

核心功能

  • 智能体注册表 - 注册 LLM、工具、人类和自定义智能体
  • 任务队列 - 基于优先级的任务分发与依赖管理
  • 实时仪表板 - Web UI 监控智能体和任务
  • RESTful API - 完整的 REST API 用于程序化控制
  • 本地存储 - 所有数据保留在您的机器上

架构

┌─────────────────┐
│  Web 仪表板     │  http://localhost:3000
│   (React UI)    │
└────────┬────────┘
         │
┌────────▼────────┐
│  FastAPI 服务器 │  http://localhost:8000
│  (后端 API)     │
└────────┬────────┘
         │
┌────────▼────────┐
│   本地存储      │
│ • SQLite 数据库 │  ./data/swarm.db
│ • Redis 缓存    │  localhost:6379
│ • 日志文件      │  ./logs/*.log
└─────────────────┘

🚀 安装

前置条件检查

安装前，请验证您已安装：

# Python 3.11+
python --version

# Node.js 18+
node --version

# Redis
redis-cli ping  # 应该返回 PONG

# (可选) Docker
docker --version

方法一：使用 Docker（推荐 - 最简单）

这是最安全的方法 - 所有内容都在容器中运行。

# 1. 克隆仓库（先检查代码！）
git clone https://github.com/ZhenRobotics/openclaw-swarm-orchestrator.git
cd openclaw-swarm-orchestrator

# 2. 启动前查看 docker-compose.yml
cat docker-compose.yml

# 3. 启动服务
docker-compose up -d

# 4. 验证
curl http://localhost:8000/health
# 应该返回: {"status": "healthy"}

访问：

  • 前端： http://localhost:3000
  • 后端 API： http://localhost:8000
  • API 文档： http://localhost:8000/docs

方法二：本地安装（用于开发）

# 1. 通过 npm 安装（审查包后）
npm view openclaw-swarm-orchestrator  # 安装前查看
npm install -g openclaw-swarm-orchestrator

# 2. 验证安装
swarm-orchestrator --version

# 3. 启动服务（在不同终端中）

# 终端 1: 启动 Redis
redis-server

# 终端 2: 启动后端
cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload

# 终端 3: 启动前端
cd frontend
npm install
npm run dev

方法三：从源码安装（最透明）

# 1. 克隆并检查
git clone https://github.com/ZhenRobotics/openclaw-swarm-orchestrator.git
cd openclaw-swarm-orchestrator

# 2. 验证提交哈希（安全检查）
git log -1 --format="%H"
# 应该是: acae6e5... (或最新发布标签)

# 3. 运行前查看代码
cat backend/requirements.txt  # 检查依赖
cat package.json              # 检查 npm 依赖
cat docker-compose.yml        # 检查容器配置

# 4. 安装依赖
cd backend && pip install -r requirements.txt
cd ../frontend && npm install

# 5. 运行（详细步骤见方法二）

⚙️ 配置

最小配置（仅本地）

在项目根目录创建 .env：

# 最小配置 - 无需外部服务
DATABASE_URL=sqlite+aiosqlite:///./data/swarm.db
REDIS_URL=redis://localhost:6379
SECRET_KEY=your-random-secret-key-here
DEBUG=true

可选：大模型智能体支持

如果您想使用大模型智能体（OpenAI、Anthropic），添加：

# 可选 - 仅在使用大模型智能体时需要
OPENAI_API_KEY=sk-your-key-here
ANTHROPIC_API_KEY=sk-ant-your-key-here

⚠️ 安全提示：

  • 仅在计划使用大模型智能体时添加 API 密钥
  • 安全存储 .env 文件（不要提交到 git）
  • API 密钥永远不会发送到我们的服务器（仅发送到官方大模型提供商）

💻 基本使用

1. 启动系统

# 使用 Docker
docker-compose up -d

# 或手动启动
redis-server &
cd backend && uvicorn app.main:app --reload &
cd frontend && npm run dev &

2. 创建您的第一个智能体

通过 Web UI：

  1. 打开 http://localhost:3000
  2. 转到"智能体"页面
  3. 点击"新建智能体"
  4. 填写：
    • 名称："我的助手"
    • 类型："llm"（或"tool"、"human"、"custom"）
    • 配置：{"model": "gpt-4"}（如果使用大模型）

通过 API：

curl -X POST http://localhost:8000/api/agents \
  -H "Content-Type: application/json" \
  -d '{
    "name": "我的助手",
    "type": "llm",
    "config": {"model": "gpt-4"}
  }'

3. 创建任务

curl -X POST http://localhost:8000/api/tasks \
  -H "Content-Type: application/json" \
  -d '{
    "title": "分析数据",
    "description": "处理销售报告",
    "priority": "high"
  }'

4. 监控状态

Web 仪表板： http://localhost:3000

API：

# 系统状态
curl http://localhost:8000/api/orchestrator/status

# 列出智能体
curl http://localhost:8000/api/agents

# 列出任务
curl http://localhost:8000/api/tasks

🔧 智能体类型

1. LLM 智能体（需要 API 密钥）

使用外部大模型 API（OpenAI、Anthropic）。

{
  "name": "GPT-4 助手",
  "type": "llm",
  "config": {
    "model": "gpt-4",
    "temperature": 0.7
  }
}

需要： OPENAI_API_KEY 或 ANTHROPIC_API_KEY

2. 工具智能体（仅本地）

执行本地函数/脚本。

{
  "name": "数据处理器",
  "type": "tool",
  "config": {
    "script_path": "./tools/process_data.py"
  }
}

无需外部服务。

3. 人类智能体（仅本地）

人在回路工作流。

{
  "name": "管理员审批",
  "type": "human",
  "config": {
    "notification": "email"
  }
}

无需外部服务。

4. 自定义智能体（用户定义）

您定义行为。

from swarm_orchestrator.base import BaseAgent

class MyCustomAgent(BaseAgent):
    async def execute(self, task):
        # 您的自定义逻辑
        return result

无需外部服务。

📊 监控与日志

Web 仪表板

访问 http://localhost:3000：

  • 实时智能体状态
  • 任务队列监控
  • 系统统计
  • 执行日志

日志文件

所有日志本地存储：

# 应用日志
tail -f logs/swarm.log

# Docker 日志（如果使用 Docker）
docker-compose logs -f

🔐 安全最佳实践

1. API 密钥

  • ✅ 存储在 .env 文件中（不在代码中）
  • ✅ 设置文件权限：chmod 600 .env
  • ✅ 将 .env 添加到 .gitignore
  • ✅ 永远不要将 API 密钥提交到 git

2. 网络安全

  • ✅ 防火墙：阻止端口 8000、3000 的外部访问
  • ✅ 开发时仅使用 localhost（不是 0.0.0.0）
  • ✅ 生产环境启用 HTTPS

3. 数据隐私

  • ✅ 所有数据本地存储在 ./data/
  • ✅ 数据库文件权限：chmod 600 data/swarm.db
  • ✅ 定期备份：cp data/swarm.db backups/

🐛 故障排除

后端无法启动

# 检查 Python 版本
python --version  # 必须是 3.11+

# 检查 Redis
redis-cli ping  # 必须返回 PONG

# 检查日志
tail -f logs/swarm.log

前端无法启动

# 检查 Node 版本
node --version  # 必须是 18+

# 清除缓存
cd frontend
rm -rf node_modules
npm install

端口冲突

# 检查端口
lsof -i :8000  # 后端
lsof -i :3000  # 前端
lsof -i :6379  # Redis

# 如需终止进程
kill -9 <PID>

📚 文档

  • 主文档： README.md
  • 快速开始： QUICKSTART.md
  • API 参考： http://localhost:8000/docs（启动后端后）

🤝 支持与社区

✅ 安装前检查清单

使用本技能前：

  • 在 GitHub 上查看源代码
  • 验证提交哈希：acae6e5
  • 检查 requirements.txt 和 package.json
  • 阅读上述安全策略
  • 了解数据存储位置
  • 知道需要哪些 API 密钥（如果有）
  • 首先在隔离环境中运行（可选）

版本： 0.1.0 状态： Alpha - 活跃开发中 本地优先： ✅ 所有核心功能离线工作 隐私： ✅ 数据不离开您的机器（可选的大模型调用除外）

🇬🇧 English Version

Swarm Orchestrator Skill

Status: 🟢 Local-First AI Agent Orchestration Platform Type: Self-hosted, no external dependencies required Privacy: 100% local processing (except optional LLM API calls)

🔒 Security & Trust

What This Skill Does

  • Runs locally on your machine (backend + frontend)
  • No telemetry or data collection
  • No external servers required for core functionality
  • Optional API keys only needed if you use LLM agents (OpenAI/Claude)
  • Open source - all code is auditable

What This Skill Does NOT Do

  • ❌ Does not send data to external servers (except optional LLM APIs)
  • ❌ Does not collect analytics or telemetry
  • ❌ Does not require account registration
  • ❌ Does not access your files without permission
  • ❌ Does not run background processes without your knowledge

Data Storage

  • Database: SQLite file in ./data/swarm.db (local only)
  • Logs: Text files in ./logs/ (local only)
  • Cache: Redis on localhost (local only)
  • No cloud sync or remote storage

📋 Overview

OpenClaw Swarm Orchestrator is a local-first platform for building and managing multi-agent AI systems. Think of it as a "control tower" for coordinating multiple AI agents working together.

Core Features

  • Agent Registry - Register LLM, Tool, Human, and Custom agents
  • Task Queue - Priority-based task distribution with dependencies
  • Real-time Dashboard - Web UI to monitor agents and tasks
  • RESTful API - Complete REST API for programmatic control
  • Local Storage - All data stays on your machine

Architecture

┌─────────────────┐
│  Web Dashboard  │  http://localhost:3000
│   (React UI)    │
└────────┬────────┘
         │
┌────────▼────────┐
│  FastAPI Server │  http://localhost:8000
│  (Backend API)  │
└────────┬────────┘
         │
┌────────▼────────┐
│ Local Storage   │
│ • SQLite DB     │  ./data/swarm.db
│ • Redis Cache   │  localhost:6379
│ • Log Files     │  ./logs/*.log
└─────────────────┘

🚀 Installation

Prerequisites Check

Before installing, verify you have:

# Python 3.11+
python --version

# Node.js 18+
node --version

# Redis
redis-cli ping  # Should return PONG

# (Optional) Docker
docker --version

Method 1: Using Docker (Recommended - Easiest)

This is the safest method - everything runs in containers.

# 1. Clone repository (inspect code first!)
git clone https://github.com/ZhenRobotics/openclaw-swarm-orchestrator.git
cd openclaw-swarm-orchestrator

# 2. Review docker-compose.yml before starting
cat docker-compose.yml

# 3. Start services
docker-compose up -d

# 4. Verify
curl http://localhost:8000/health
# Should return: {"status": "healthy"}

Access:

  • Frontend: http://localhost:3000
  • Backend API: http://localhost:8000
  • API Docs: http://localhost:8000/docs

Method 2: Local Installation (For Development)

# 1. Install via npm (after reviewing package)
npm view openclaw-swarm-orchestrator  # Review before installing
npm install -g openclaw-swarm-orchestrator

# 2. Verify installation
swarm-orchestrator --version

# 3. Start services (in separate terminals)

# Terminal 1: Start Redis
redis-server

# Terminal 2: Start backend
cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload

# Terminal 3: Start frontend
cd frontend
npm install
npm run dev

Method 3: From Source (Most Transparent)

# 1. Clone and inspect
git clone https://github.com/ZhenRobotics/openclaw-swarm-orchestrator.git
cd openclaw-swarm-orchestrator

# 2. Verify commit hash (security check)
git log -1 --format="%H"
# Should be: acae6e5... (or latest release tag)

# 3. Review code before running
cat backend/requirements.txt  # Check dependencies
cat package.json              # Check npm deps
cat docker-compose.yml        # Check container config

# 4. Install dependencies
cd backend && pip install -r requirements.txt
cd ../frontend && npm install

# 5. Run (see Method 2 for detailed steps)

⚙️ Configuration

Minimal Configuration (Local Only)

Create .env in project root:

# Minimal config - no external services needed
DATABASE_URL=sqlite+aiosqlite:///./data/swarm.db
REDIS_URL=redis://localhost:6379
SECRET_KEY=your-random-secret-key-here
DEBUG=true

Optional: LLM Agent Support

If you want to use LLM agents (OpenAI, Anthropic), add:

# Optional - only if using LLM agents
OPENAI_API_KEY=sk-your-key-here
ANTHROPIC_API_KEY=sk-ant-your-key-here

⚠️ Security Note:

  • Only add API keys if you plan to use LLM agents
  • Store .env file securely (not in git)
  • API keys are never sent to our servers (only to official LLM providers)

💻 Basic Usage

1. Start the System

# Using Docker
docker-compose up -d

# Or manually
redis-server &
cd backend && uvicorn app.main:app --reload &
cd frontend && npm run dev &

2. Create Your First Agent

Via Web UI:

  1. Open http://localhost:3000
  2. Go to "Agents" page
  3. Click "New Agent"
  4. Fill in:
    • Name: "My Assistant"
    • Type: "llm" (or "tool", "human", "custom")
    • Config: {"model": "gpt-4"} (if using LLM)

Via API:

curl -X POST http://localhost:8000/api/agents \
  -H "Content-Type: application/json" \
  -d '{
    "name": "My Assistant",
    "type": "llm",
    "config": {"model": "gpt-4"}
  }'

3. Create a Task

curl -X POST http://localhost:8000/api/tasks \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Analyze data",
    "description": "Process the sales report",
    "priority": "high"
  }'

4. Monitor Status

Web Dashboard: http://localhost:3000

API:

# System status
curl http://localhost:8000/api/orchestrator/status

# List agents
curl http://localhost:8000/api/agents

# List tasks
curl http://localhost:8000/api/tasks

🔧 Agent Types

1. LLM Agents (Requires API Key)

Uses external LLM APIs (OpenAI, Anthropic).

{
  "name": "GPT-4 Assistant",
  "type": "llm",
  "config": {
    "model": "gpt-4",
    "temperature": 0.7
  }
}

Required: OPENAI_API_KEY or ANTHROPIC_API_KEY

2. Tool Agents (Local Only)

Executes local functions/scripts.

{
  "name": "Data Processor",
  "type": "tool",
  "config": {
    "script_path": "./tools/process_data.py"
  }
}

No external services needed.

3. Human Agents (Local Only)

Human-in-the-loop workflows.

{
  "name": "Manager Approval",
  "type": "human",
  "config": {
    "notification": "email"
  }
}

No external services needed.

4. Custom Agents (User-Defined)

You define the behavior.

from swarm_orchestrator.base import BaseAgent

class MyCustomAgent(BaseAgent):
    async def execute(self, task):
        # Your custom logic
        return result

No external services needed.

📊 Monitoring & Logs

Web Dashboard

Access at http://localhost:3000:

  • Real-time agent status
  • Task queue monitoring
  • System statistics
  • Execution logs

Log Files

All logs stored locally:

# Application logs
tail -f logs/swarm.log

# Docker logs (if using Docker)
docker-compose logs -f

🔐 Security Best Practices

1. API Keys

  • ✅ Store in .env file (not in code)
  • ✅ Set file permissions: chmod 600 .env
  • ✅ Add .env to .gitignore
  • ✅ Never commit API keys to git

2. Network Security

  • ✅ Firewall: Block ports 8000, 3000 from external access
  • ✅ Use localhost only (not 0.0.0.0) for development
  • ✅ Enable HTTPS in production

3. Data Privacy

  • ✅ All data stored locally in ./data/
  • ✅ Database file permissions: chmod 600 data/swarm.db
  • ✅ Regular backups: cp data/swarm.db backups/

🐛 Troubleshooting

Backend won't start

# Check Python version
python --version  # Must be 3.11+

# Check Redis
redis-cli ping  # Must return PONG

# Check logs
tail -f logs/swarm.log

Frontend won't start

# Check Node version
node --version  # Must be 18+

# Clear cache
cd frontend
rm -rf node_modules
npm install

Port conflicts

# Check ports
lsof -i :8000  # Backend
lsof -i :3000  # Frontend
lsof -i :6379  # Redis

# Kill processes if needed
kill -9 <PID>

📚 Documentation

  • Main Docs: README.md
  • Quick Start: QUICKSTART.md
  • API Reference: http://localhost:8000/docs (after starting backend)

🤝 Support & Community

✅ Pre-Installation Checklist

Before using this skill:

  • Review source code on GitHub
  • Verify commit hash: acae6e5
  • Check requirements.txt and package.json
  • Read security policy above
  • Understand data storage locations
  • Know which API keys you need (if any)
  • Run in isolated environment first (optional)

Version: 0.1.0 Status: Alpha - Active Development Local-First: ✅ All core features work offline Privacy: ✅ No data leaves your machine (except optional LLM calls)

Built with privacy and transparency in mind. Inspect the code before you trust it.

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