Welcome to CleverClaude - an advanced AI agent orchestration system that enables sophisticated multi-agent coordination, swarm intelligence, and task automation.

📚 Documentation Index

Installation Guide - Get CleverClaude up and running
Quick Start - Your first CleverClaude project
Architecture Overview - System design and components
CLI Reference - Complete command-line interface guide
API Documentation - HTTP API endpoints and usage
Agent Management - Creating and managing AI agents
Swarm Coordination - Multi-agent swarm orchestration
MCP Integration - Model Context Protocol tools and usage
Configuration - System configuration options
Testing Guide - Running and writing tests
Deployment - Production deployment strategies
Examples - Code examples and tutorials
Migration Guide - Migrating from claude-flow
Contributing - How to contribute to the project
Troubleshooting - Common issues and solutions

🌟 Key Features

Multi-Agent Orchestration

Agent Types: Researcher, Coder, Analyst, Coordinator, Reviewer, Tester
Lifecycle Management: Create, pause, resume, destroy agents
Task Assignment: Intelligent task routing based on capabilities
Health Monitoring: Real-time agent performance tracking

Swarm Coordination

Multiple Topologies: Mesh, Hierarchical, Star, Ring architectures
Dynamic Scaling: Auto-scale swarms based on workload
Load Balancing: Intelligent task distribution
Fault Tolerance: Automatic failure detection and recovery

MCP Integration

87+ Tools: Comprehensive tool ecosystem for AI operations
Protocol Support: Full MCP (Model Context Protocol) implementation
Tool Discovery: Dynamic tool loading and metadata
Batch Operations: Execute multiple tools concurrently

Modern Architecture

Async-First: Built on Python AsyncIO for high performance
Type Safe: Comprehensive type hints with Pydantic validation
Configurable: YAML-based configuration with environment overrides
Observable: Structured logging with performance metrics

🚀 Quick Example

import asyncio
from cleverclaude import CleverClaudeApp, AgentManager, SwarmCoordinator

async def main():
    # Initialize CleverClaude
    app = CleverClaudeApp()
    await app.initialize()
    
    # Create agents
    researcher = await app.agents.create_agent(
        agent_type="researcher",
        name="Research Agent",
        capabilities={"research", "analysis", "documentation"}
    )
    
    # Create swarm
    swarm_id = await app.swarms.create_swarm(
        name="Research Swarm",
        topology="mesh"
    )
    
    # Add agent to swarm
    await app.swarms.add_agent(swarm_id, researcher)
    
    # Execute task
    task = {
        "type": "research_query",
        "data": {
            "query": "Latest developments in AI agent coordination",
            "scope": "academic_papers",
            "depth": "comprehensive"
        }
    }
    
    result = await app.swarms.submit_task(swarm_id, task)
    print(f"Research completed: {result}")

if __name__ == "__main__":
    asyncio.run(main())

📦 Installation

# Install with uv (recommended)
uv pip install cleverclaude

# Or with pip
pip install cleverclaude

# Development installation
git clone https://github.com/your-org/cleverclaude.git
cd cleverclaude
uv pip install -e .[dev]

🛠️ CLI Usage

# Initialize new project
cleverclaude init my-project

# Start the orchestration system
cleverclaude start

# Check system status
cleverclaude status

# Monitor real-time metrics
cleverclaude monitor --watch

📖 Learn More

Architecture Guide: Deep dive into system design
Agent Development: Creating custom agent types
Swarm Patterns: Advanced coordination strategies
MCP Tools: Leveraging the 87+ tool ecosystem
Production Deployment: Scaling CleverClaude

🤝 Community

GitHub: CleverClaude Repository
Documentation: docs.cleverclaude.ai
Discord: CleverClaude Community
Issues: Bug Reports & Feature Requests

📄 License

CleverClaude is released under the MIT License. See LICENSE for details.