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
Build Intelligent Multi-Agent Systems with Python
Scale your AI applications with orchestrated autonomous agents
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:
- 🎫 Customer Service Agent: Ticket and support management
- 📄 Document Processing Agent: Document analysis and extraction
- 📧 Email Agent: Email handling and template management
- 🧠 Learning Agent: Knowledge acquisition and pattern recognition
- 📢 Marketing Expert Agent: Campaign management and content creation
- 📊 Research Analyst Agent: Data analysis and research synthesis
- 💼 Sales Representative Agent: Lead management and proposals
- 🌐 Social Media Agent: Content scheduling and engagement
- 🔍 Web Search Agent: Search operations and analysis
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.
Project details
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