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https://github.com/pilottai/pilottai

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.
https://github.com/pilottai/pilottai

agent-framework agents ai generative-ai llm multi-agent-systems opensource pilottai pydantic python

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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.

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PilottAI





PilottAI Framework Logo

Build Intelligent Multi-Agent Systems with Python


Scale your AI applications with orchestrated autonomous agents


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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation Status](https://github.com/pilottai/pilottai/actions/workflows/deploy-docs.yml/badge.svg?branch=main)](https://docs.pilottai.com)
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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 job routing
- Context-aware processing
- Specialized agent implementations

- 🚀 **Production Ready**
- Asynchronous processing
- Dynamic scaling
- Load balancing
- Fault tolerance
- Comprehensive logging

- 🧠 **Advanced Memory**
- Semantic storage
- Job history tracking
- Context preservation
- Knowledge retrieval

- 🔌 **Integrations**
- Multiple LLM providers (OpenAI, Anthropic, Google)
- Document processing
- WebSocket support
- Custom tool integration

## Installation

```bash
pip install pilottai
```

## Quick Start

```python
from pilottai import Pilott
from pilottai.core import AgentConfig, AgentType, LLMConfig

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

# Setup agent configuration
config = AgentConfig(
title="processor",
agent_type=AgentType.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_job({
"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](pilottai/examples/customer_service.py): Ticket and support management
- 📄 [Document Processing Agent](pilottai/examples/document_processing.py): Document analysis and extraction
- 📧 [Email Agent](pilottai/examples/email_service.py): Email handling and template management
- 🧠 [Learning Agent](pilottai/examples/teacher.py): Knowledge acquisition and pattern recognition
- 📢 [Marketing Expert Agent](pilottai/examples/marketing_expert.py): Campaign management and content creation
- 📊 [Research Analyst Agent](pilottai/examples/research_analyst.py): Data analysis and research synthesis
- 💼 [Sales Representative Agent](pilottai/examples/sales_rep.py): Lead management and proposals
- 🌐 [Social Media Agent](pilottai/examples/social_media_manager.py): Content scheduling and engagement
- 🔍 [Web Search Agent](pilottai/examples/web_search.py): Search operations and analysis

## 📚 Documentation

👉 **[Read the full documentation here](https://docs.pilottai.com)**

The documentation includes:
- Detailed guides
- API reference
- Examples
- Best practices

## Example Use Cases

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

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

- 🔄 **Job Orchestration**
```python
# Orchestrate complex workflows
job_result = await manager_agent.execute_job({
"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://docs.pilottai.com)
- 💬 [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.

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Built with ❤️ by the PilottAI Team