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A powerful Python framework for building scalable multi-agent systems with built-in orchestration, LLM integration, and intelligent task processing. Features dynamic scaling, fault tolerance, and advanced load balancing.

Project description

PilottAI

PilottAI Framework Logo

Build Intelligent Multi-Agent Systems with Python

Scale your AI applications with orchestrated autonomous agents

PyPI version Python 3.10+ License: MIT Documentation Status Code style: black Commit Activity

Overview

PilottAI is a Python framework for building autonomous multi-agent systems with advanced orchestration capabilities. It provides enterprise-ready features for building scalable AI applications.

Key Features

  • 🤖 Hierarchical Agent System

    • Manager and worker agent hierarchies
    • Intelligent task routing
    • Context-aware processing
    • Specialized agent implementations
  • 🚀 Production Ready

    • Asynchronous processing
    • Dynamic scaling
    • Load balancing
    • Fault tolerance
    • Comprehensive logging
  • 🧠 Advanced Memory

    • Semantic storage
    • Task history tracking
    • Context preservation
    • Knowledge retrieval
  • 🔌 Integrations

    • Multiple LLM providers (OpenAI, Anthropic, Google)
    • Document processing
    • WebSocket support
    • Custom tool integration

Installation

pip install pilottai

Quick Start

from pilottai import Pilott
from pilottai.core import AgentConfig, AgentRole, LLMConfig

# Configure LLM
llm_config = LLMConfig(
  model_name="gpt-4",
  provider="openai",
  api_key="your-api-key"
)

# Setup agent configuration
config = AgentConfig(
  role="processor",
  role_type=AgentRole.WORKER,
  goal="Process documents efficiently",
  description="Document processing worker",
  max_queue_size=100
)


async def main():
  # Initialize system
  pilott = Pilott(name="DocumentProcessor")

  try:
    # Start system
    await pilott.start()

    # Add agent
    agent = await pilott.add_agent(
      agent_type="processor",
      config=config,
      llm_config=llm_config
    )

    # Process document
    result = await pilott.execute_task({
      "type": "process_document",
      "file_path": "document.pdf"
    })

    print(f"Processing result: {result}")

  finally:
    await pilott.stop()


if __name__ == "__main__":
  import asyncio

  asyncio.run(main())

Specialized Agents

PilottAI includes ready-to-use specialized agents:

Documentation

Visit our documentation for:

  • Detailed guides
  • API reference
  • Examples
  • Best practices

Example Use Cases

  • 📄 Document Processing

    # Process PDF documents
    result = await pilott.execute_task({
        "type": "process_pdf",
        "file_path": "document.pdf"
    })
    
  • 🤖 AI Agents

    # Create specialized agents
    researcher = await pilott.add_agent(
        agent_type="researcher",
        config=researcher_config
    )
    
  • 🔄 Task Orchestration

    # Orchestrate complex workflows
    task_result = await manager_agent.execute_task({
        "type": "complex_workflow",
        "steps": ["extract", "analyze", "summarize"]
    })
    

Advanced Features

Memory Management

# Store and retrieve context
await agent.enhanced_memory.store_semantic(
    text="Important information",
    metadata={"type": "research"}
)

Load Balancing

# Configure load balancing
config = LoadBalancerConfig(
    check_interval=30,
    overload_threshold=0.8
)

Fault Tolerance

# Configure fault tolerance
config = FaultToleranceConfig(
    recovery_attempts=3,
    heartbeat_timeout=60
)

Project Structure

pilott/
├── core/            # Core framework components
├── agents/          # Agent implementations
├── memory/          # Memory management
├── orchestration/   # System orchestration
├── tools/           # Tool integrations
└── utils/           # Utility functions

Contributing

We welcome contributions! See our Contributing Guide for details on:

  • Development setup
  • Coding standards
  • Pull request process

Support

License

PilottAI is MIT licensed. See LICENSE for details.


Built with ❤️ by the PilottAI Team

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