pilott 0.2.5
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.
Requires Python: <4.0,>=3.10

<h1 align="center" style="margin-bottom: -100px;">PilottAI</h1>

<div align="center" style="margin-top: 20px;">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/anuj0456/pilottai/main/docs/assets/logo.svg">
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    <img alt="PilottAI Framework Logo" src="https://raw.githubusercontent.com/anuj0456/pilottai/main/docs/assets/logo.svg" width="500">
  </picture>
  <h3>Build Intelligent Multi-Agent Systems with Python</h3>
  <p><em>Scale your AI applications with orchestrated autonomous agents</em></p>
</div>

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## 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

```bash
pip install pilott
```

## Quick Start

```python
from pilott import Pilott
from pilott.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:

- 🎫 [Customer Service Agent](pilott/examples/customer_service.md): Ticket and support management
- 📄 [Document Processing Agent](pilott/examples/document_processing.md): Document analysis and extraction
- 📧 [Email Agent](pilott/examples/email_agent.md): Email handling and template management
- 🧠 [Learning Agent](pilott/examples/learning_agent.md): Knowledge acquisition and pattern recognition
- 📢 [Marketing Expert Agent](pilott/examples/marketing_expert.md): Campaign management and content creation
- 📊 [Research Analyst Agent](pilott/examples/research_analyst.md): Data analysis and research synthesis
- 💼 [Sales Representative Agent](pilott/examples/sales_rep.md): Lead management and proposals
- 🌐 [Social Media Agent](pilott/examples/social_media_agent.md): Content scheduling and engagement
- 🔍 [Web Search Agent](pilott/examples/web_search.md): Search operations and analysis

## Documentation

Visit our [documentation](https://pilottai.readthedocs.io) for:
- Detailed guides
- API reference
- Examples
- Best practices

## Example Use Cases

- 📄 **Document Processing**
  ```python
  # Process PDF documents
  result = await pilott.execute_task({
      "type": "process_pdf",
      "file_path": "document.pdf"
  })
  ```

- 🤖 **AI Agents**
  ```python
  # Create specialized agents
  researcher = await pilott.add_agent(
      agent_type="researcher",
      config=researcher_config
  )
  ```

- 🔄 **Task Orchestration**
  ```python
  # Orchestrate complex workflows
  task_result = await manager_agent.execute_task({
      "type": "complex_workflow",
      "steps": ["extract", "analyze", "summarize"]
  })
  ```

## Advanced Features

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

### Load Balancing
```python
# Configure load balancing
config = LoadBalancerConfig(
    check_interval=30,
    overload_threshold=0.8
)
```

### Fault Tolerance
```python
# 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](.github/CONTRIBUTING.md) for details on:
- Development setup
- Coding standards
- Pull request process

## Support

- 📚 [Documentation](https://pilottai.readthedocs.io)
- 💬 [Discord](https://discord.gg/pilottai)
- 📝 [GitHub Issues](https://github.com/pilottai/pilott/issues)
- 📧 [Email Support](mailto:support@pilottai.com)

## License

PilottAI is MIT licensed. See [LICENSE](LICENSE) for details.

---

<div align="center">
  <sub>Built with ❤️ by the PilottAI Team</sub>
</div>