🤖 AIOrchestrator

AI Orchestrator is a powerful, easy-to-use library that helps you integrate and manage AI capabilities in your applications. It provides intelligent orchestration of AI tasks using advanced language models like Claude 3.5 Sonnet.

🌟 Features

• 🔌 Plug-and-Play Integration: Easy integration with existing applications
• 🧠 Intelligent Task Management: Automatic task analysis and orchestration
• 🔄 Flexible Execution Modes: Sequential, parallel, or adaptive execution
• 💾 Built-in State Management: Session-based context and history tracking
• 🛠️ Customizable: Extensible for different AI models and use cases
• 🔐 Error Handling: Robust error recovery mechanisms
• 📈 Scalable: Async support for high-performance applications

🚀 Quick Start

Installation

pip install ai-orchestrator

Basic Usage

from ai_orchestrator import AIOrchestrator

# Initialize orchestrator
orchestrator = AIOrchestrator(
    api_key="your-api-key",
    base_config={
        "default_model": "claude-3-5-sonnet-20241022",
        "default_temperature": 0.7
    }
)

# Use in async context
async def process_task():
    result = await orchestrator.process_input(
        session_id="unique-session-id",
        user_input="Analyze this text for sentiment",
        context_updates={"domain": "sentiment-analysis"}
    )
    print(result)

# Run the task
import asyncio
asyncio.run(process_task())

FastAPI Integration Example

from fastapi import FastAPI
from ai_orchestrator import AIOrchestrator

app = FastAPI()
orchestrator = AIOrchestrator(api_key="your-api-key")

@app.post("/analyze")
async def analyze_text(text: str):
    result = await orchestrator.process_input(
        session_id="unique-session-id",
        user_input=text
    )
    return result

🎯 Use Cases

• 📊 Data Analysis: Intelligent processing of complex datasets
• 📝 Content Generation: Orchestrated content creation and modification
• 🔍 Research Assistance: Coordinated research and analysis tasks
• 🤝 Customer Support: Intelligent routing and handling of support queries
• 🎨 Creative Tasks: Coordinated creative content generation
• 📈 Business Intelligence: Complex analysis and reporting

🛠️ Advanced Configuration

Custom Agent Configuration

from ai_orchestrator import AIOrchestrator, AgentConfig

custom_agents = {
    "analyst": AgentConfig(
        role="data_analyst",
        capabilities=["statistical_analysis", "visualization"],
        model="claude-3-5-sonnet-20241022",
        temperature=0.3,
        context_window=100000,
        max_tokens=4000
    )
}

orchestrator = AIOrchestrator(
    api_key="your-api-key",
    custom_agents=custom_agents
)

Execution Modes

# Sequential Execution
result = await orchestrator.process_input(
    session_id="session-id",
    user_input="Complex task requiring steps",
    context_updates={"mode": "sequential"}
)

# Parallel Execution
result = await orchestrator.process_input(
    session_id="session-id",
    user_input="Multiple independent subtasks",
    context_updates={"mode": "parallel"}
)

# Adaptive Execution (Default)
result = await orchestrator.process_input(
    session_id="session-id",
    user_input="Dynamic task",
    context_updates={"mode": "adaptive"}
)

📚 Documentation

For detailed documentation, visit our documentation site.

Key Concepts

• Sessions: Maintain context and state across multiple interactions
• Execution Modes: Different strategies for task execution
• Agents: Specialized AI models for specific tasks
• Context Management: State and history tracking
• Error Handling: Recovery and fallback mechanisms

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Anthropic for Claude 3.5 Sonnet
• The open-source community

📮 Contact

• Create an issue for bug reports or feature requests
• Connect with me on LinkedIn
• Star the repository if you find it helpful!