Agent Swarm Intelligence Core for Openclaw

A sophisticated multi-agent orchestration framework enabling collective intelligence through coordination, self-organization, and emergent behavior.

yuyonghao-123
v0.1.0
Mar 31, 2026
0
912
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install yuyonghao-agent-swarm

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install yuyonghao-agent-swarm using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is Agent Swarm Intelligence Core?

Agent Swarm is a high-performance core system designed to implement true collective intelligence within the Openclaw Skills ecosystem. It provides a comprehensive suite of tools for managing the lifecycle of multiple agents, allowing them to collaborate on complex tasks that a single agent could not handle alone. By integrating this system, developers can create resilient Openclaw Skills that feature autonomous task decomposition, load balancing, and self-healing capabilities.

The framework focuses on four pillars of swarm intelligence: coordination, sharding, organization, and emergence. This ensures that every agent within the Openclaw Skills environment operates with a clear role while contributing to a unified collective goal, making it ideal for large-scale automation and complex problem-solving scenarios.

Agent Swarm Intelligence Core Use Cases

  • Orchestrating large-scale code generation projects across multiple specialized agents.
  • Implementing distributed research tasks where information is gathered by shards and merged into a final report.
  • Building self-healing agent networks that automatically reorganize roles when an agent fails.
  • Developing consensus-based decision systems for governance or complex logic within Openclaw Skills.

How Agent Swarm Intelligence Core Works

  1. The SwarmCoordinator initializes the environment, setting maximum agent limits and choosing a communication protocol like gossip or broadcast.
  2. Specialized swarms are created to handle specific domains, such as code generation or data analysis.
  3. Agents are registered to the swarm with defined capabilities and processing capacities.
  4. Complex tasks are passed to the TaskSharder, which breaks the work into manageable pieces based on dependencies.
  5. The SelfOrganizer assigns roles and monitors the swarm, ensuring tasks are completed even if individual agents encounter errors.
  6. The EmergentBehavior module identifies patterns and reaches consensus, allowing the Openclaw Skills to learn and adapt over time.

Agent Swarm Intelligence Core Setup

To integrate this core into your project, install the dependencies using the following command:

npm install

To verify the installation and ensure the swarm protocols are functioning correctly, run the test suite:

npm test

Agent Swarm Intelligence Core Data Schema & Taxonomy

The system organizes data through several specialized modules to ensure efficient Openclaw Skills execution:

Module Data Handled Description
SwarmCoordinator Swarm Metadata Manages agent IDs, registration, and consensus thresholds.
TaskSharder Shard Map Tracks task decomposition, execution strategies, and result merging.
SelfOrganizer Role Mapping Maps agents to specific functional roles and handles failover states.
SwarmMonitor Metrics Logs performance indicators, anomaly detection data, and visualization exports.

Agent Swarm Intelligence Core Advanced Features

  • Support for multiple communication protocols including broadcast, gossip, and direct messaging for Openclaw Skills.
  • Dynamic fault-over handling that triggers automatic reorganization when an agent becomes unresponsive.
  • Collective pattern recognition and learning from experience to improve swarm efficiency over time.
  • Real-time visualization of agent interactions and performance metrics via the SwarmMonitor.

SKILL.md


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