AgentZero - Advanced Multi-Agent System with Hive Intelligence

Overview

AgentZero is a sophisticated multi-agent system that leverages hive intelligence for collaborative problem-solving. It features specialized agents coordinated through a central hive system, enabling complex task execution through distributed intelligence.

Features

🧠 Core Capabilities

• Hive Coordination: Central orchestrator managing multi-agent collaboration
• Specialized Agents: Purpose-built agents for specific domains
• Distributed Intelligence: Agents work together to solve complex problems
• Adaptive Learning: Agents learn from experience and improve over time
• Scalable Architecture: Easily add more agents as needed

🤖 Agent Types

1. Research Agent

   • Web searching and data gathering
   • Content extraction and analysis
   • Fact verification and validation
   • Sentiment analysis
   • Knowledge graph construction

2. Code Agent

   • Code generation in multiple languages
   • Code analysis and optimization
   • Debugging and error fixing
   • Refactoring suggestions
   • Test generation

3. Planning Agent (Coming Soon)

   • Strategic task decomposition
   • Resource allocation
   • Timeline management
   • Dependency resolution

4. Execution Agent (Coming Soon)

   • Task execution monitoring
   • System operations
   • Integration management

Quick Start

Prerequisites

• Python 3.9+
• Docker & Docker Compose (optional)
• Redis (for message queue)
• PostgreSQL (for persistent storage)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/agentzero.git
cd agentzero

2. Install dependencies:

pip install -r requirements.txt

3. Run the system:

python main.py

Docker Deployment

docker-compose up -d

This will start:

• Hive Coordinator
• Multiple Research Agents
• Multiple Code Agents
• Redis for messaging
• PostgreSQL for storage
• Prometheus & Grafana for monitoring

Usage

Interactive Mode

python main.py --mode interactive

Commands:

• research <query> - Research a topic
• code <description> - Generate code
• analyze <file> - Analyze code file
• status - Show system status
• quit - Exit

Batch Mode

python main.py --mode batch --tasks tasks.txt

Python API

from agentzero import AgentZeroSystem
from src.core.agent import Task, Priority

# Initialize system
system = AgentZeroSystem()
await system.initialize()

# Submit a research task
task_id = await system.submit_task(
    "Research latest developments in quantum computing",
    task_type="research"
)

# Submit a code generation task
task_id = await system.submit_task(
    "Generate a REST API for user management",
    task_type="code_generation"
)

# Get system status
status = await system.get_status()
print(f"Active tasks: {status['hive']['active_tasks']}")

Architecture

┌─────────────────────────────────────────┐
│           Hive Coordinator              │
│  • Task Distribution                    │
│  • Agent Management                     │
│  • Resource Optimization                │
└────────────┬────────────────────────────┘
             │
    ┌────────┴────────┬────────┬────────┐
    │                 │        │        │
┌───▼───┐      ┌──────▼──┐ ┌──▼───┐ ┌──▼───┐
│Research│      │  Code   │ │Plan  │ │Exec  │
│ Agent  │      │  Agent  │ │Agent │ │Agent │
└────────┘      └─────────┘ └──────┘ └──────┘

Task Distribution Strategies

1. Capability-Based: Match tasks to agents with required skills
2. Load-Balanced: Distribute evenly across available agents
3. Auction-Based: Agents bid for tasks they can handle
4. Priority-Based: Consider agent performance metrics
5. Round-Robin: Simple sequential distribution

Configuration

Environment Variables

# Hive Configuration
HIVE_ID=main-hive
DISTRIBUTION_STRATEGY=capability_based

# Agent Configuration
RESEARCH_AGENTS=2
CODE_AGENTS=2

# Database
POSTGRES_URL=postgresql://user:pass@localhost/agentzero
REDIS_URL=redis://localhost:6379

# Monitoring
LOG_LEVEL=INFO

Config File

# config/agentzero.yml
hive:
  distribution_strategy: capability_based
  max_tasks_per_agent: 5
  heartbeat_interval: 30

agents:
  research:
    count: 2
    max_depth: 3
    max_pages: 50
  code:
    count: 2
    languages:
      - python
      - javascript
      - typescript

Monitoring

Access Grafana dashboard at http://localhost:3000 (default: admin/admin)

Key metrics:

• Agent utilization
• Task completion rate
• Average task duration
• Hive efficiency score
• Memory usage per agent

Development

Project Structure

agentzero/
├── src/
│   ├── core/           # Core agent framework
│   ├── hive/           # Hive coordination system
│   ├── agents/         # Specialized agent implementations
│   ├── tools/          # Agent tools and utilities
│   ├── memory/         # Memory and learning systems
│   └── communication/  # Inter-agent communication
├── tests/              # Test suite
├── config/             # Configuration files
├── docker/             # Docker configurations
└── docs/               # Documentation

Creating Custom Agents

from src.core.agent import BaseAgent, Task

class CustomAgent(BaseAgent):
    async def execute(self, task: Task):
        # Implement task execution logic
        result = await self.process_custom_task(task)
        return result

Running Tests

pytest tests/
pytest tests/ --cov=src --cov-report=html

Roadmap

• Core agent framework
• Hive coordinator
• Research agent
• Code agent
• Planning agent
• Execution agent
• Web UI dashboard
• REST API
• Agent marketplace
• Distributed deployment
• GPU acceleration for AI tasks

Contributing

Contributions are welcome! Please read our Contributing Guidelines first.

License

MIT License - see LICENSE file for details

Support

• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions

Acknowledgments

Built with cutting-edge technologies and inspired by swarm intelligence principles.