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Welcome to PilottAI Wiki

Welcome to the official documentation for PilottAI, a Python framework designed for building scalable multi-agent systems. This wiki provides comprehensive guidance for developers creating intelligent, autonomous applications.

Table of Contents

1. Introduction
2. Getting Started
3. Core Components
4. Advanced Usage
5. Best Practices
6. API Reference
7. Contributing
8. Troubleshooting
9. Roadmap

Introduction

What is PilottAI?

PilottAI is a modern Python framework for building hierarchical multi-agent systems. It provides developers with tools and infrastructure to create autonomous agents that collaborate on complex tasks.

Key Features

• Agent System

  • Hierarchical agent architecture
  • Task orchestration
  • Flexible communication
  • Memory management
  • Context preservation

• Performance & Reliability

  • Asynchronous processing
  • Dynamic scaling
  • Load balancing
  • Fault tolerance
  • Comprehensive logging

• Integration & Tools

  • LLM provider support
  • Extensible tool system
  • Document processing
  • WebSocket support

Getting Started

Installation

pip install pilott

Basic Setup

from pilott import Serve
from pilott.core import AgentConfig, AgentRole

config = AgentConfig(
    role="processor",
    role_type=AgentRole.WORKER,
    goal="Process tasks efficiently",
    description="Worker agent"
)

pilott = Serve(
    name="MySystem",
    verbose=True
)

async def main():
    await pilott.start()
    
    agent = await pilott.add_agent(
        agent_type="processor",
        config=config
    )
    
    result = await pilott.execute_task({
        "type": "process",
        "data": {"key": "value"}
    })

Core Components

Agent System

Base Agent

The foundation of PilottAI's agent architecture:

from pilott.core import BaseAgent

class CustomAgent(BaseAgent):
    async def execute_task(self, task):
        # Custom task execution logic
        pass

Agent Roles

• Orchestrator: Manages task distribution and coordination
• Worker: Executes specific tasks
• Hybrid: Combines orchestration and worker capabilities

Task Processing

PilottAI includes an intelligent task router that distributes tasks based on:

• Agent capabilities
• Current load
• Task priority
• Resource availability

Load Balancing

load_balancer = LoadBalancer(
    check_interval=30,
    overload_threshold=0.8,
    underload_threshold=0.2
)

Advanced Usage

Custom Tool Development

from pilott.tools import Tool

class CustomTool(Tool):
    def execute(self, **kwargs):
        # Tool implementation
        pass

Agent Communication

await agent.send_message(
    recipient_id="agent2",
    message={"type": "update", "data": data}
)

Scaling Configuration

scaling_config = ScalingConfig(
    scale_up_threshold=0.8,
    scale_down_threshold=0.3,
    min_agents=2,
    max_agents=10
)

Best Practices

Performance Optimization

1. Task Design

   • Create atomic tasks
   • Define clear success criteria
   • Implement error handling

2. Resource Management

   • Monitor agent health
   • Use circuit breakers
   • Set appropriate timeouts

3. State Management

   • Use memory efficiently
   • Implement cleanup
   • Handle edge cases

Security Considerations

1. Authentication

   • Implement proper authentication
   • Use secure connections
   • Validate inputs

2. Data Protection

   • Encrypt sensitive data
   • Implement access controls
   • Conduct regular security audits

API Reference

Serve Class

class Serve:
    async def start(self):
        """Start the system"""
        pass
    
    async def add_agent(self, agent_type: str, config: AgentConfig):
        """Add new agent"""
        pass
    
    async def execute_task(self, task: dict):
        """Execute task"""
        pass

Agent Configuration

class AgentConfig:
    role: str
    role_type: AgentRole
    goal: str
    description: str
    memory_enabled: bool = True
    verbose: bool = False

Contributing

Development Setup

1. Clone repository
2. Install dependencies
3. Set up development environment
4. Run tests

Pull Request Guidelines

1. Fork repository
2. Create feature branch
3. Implement changes
4. Add tests
5. Submit PR

Code Style

• Follow PEP 8
• Use type hints
• Write documentation
• Include tests

Troubleshooting

Common Issues

1. Task Processing

   • Issue: Tasks not being processed
   • Solution: Check agent status and queue size

2. Memory Usage

   • Issue: High memory usage
   • Solution: Implement cleanup and optimize storage

3. Performance

   • Issue: Slow task processing
   • Solution: Adjust load balancing and scaling thresholds

Support Channels

• GitHub Issues
• Discord Community
• Email Support
• Stack Overflow Tag

Roadmap

Current Version (0.1.0)

• Core functionality
• Basic agent system
• Task processing
• Memory management

Future Plans

Short Term:

• Enhanced monitoring
• Additional LLM providers
• Improved documentation

Long Term:

• Advanced orchestration
• ML-based optimization
• Custom DSL for configuration

Last updated: January 2025