duke-agents 1.0.0

DUKE Agents - Advanced AI Agent Framework with IPO Architecture

DUKE Agents

DUKE Agents is an advanced AI agent framework implementing the IPO (Input-Process-Output) architecture with enriched memory and feedback loops. It provides autonomous agents powered by Mistral LLMs for complex task execution.

🚀 Features

• IPO Architecture: Structured Input-Process-Output workflow with memory persistence
• Multiple Agent Types: AtomicAgent for simple tasks, CodeActAgent for code generation and execution
• Mistral Integration: Native support for Mistral and Codestral models
• Memory Management: Rich workflow memory with feedback loops
• Auto-correction: Built-in retry logic with satisfaction scoring
• Flexible Orchestration: Linear and LLM-driven workflow execution
• Type Safety: Full Pydantic models for type validation

📦 Installation

pip install duke-agents

Prerequisites

• Python 3.8 or higher
• Mistral API key (get one at console.mistral.ai)

🔧 Quick Start

Basic Usage

from duke_agents import AtomicAgent, ContextManager, Orchestrator
from duke_agents.models import AtomicInput

# Set your Mistral API key
import os
os.environ["MISTRAL_API_KEY"] = "your-api-key"

# Initialize context manager
context = ContextManager("Process customer data")

# Create orchestrator
orchestrator = Orchestrator(context)

# Create and register an agent
agent = AtomicAgent("data_processor")
orchestrator.register_agent(agent)

# Define workflow
workflow = [{
    'agent': 'data_processor',
    'input_type': 'atomic',
    'input_data': {
        'task_id': 'task_001',
        'parameters': {'data': 'customer info'}
    }
}]

# Execute workflow
results = orchestrator.execute_linear_workflow(workflow)

Code Generation Example

from duke_agents import CodeActAgent, ContextManager, Orchestrator

# Initialize
context = ContextManager("Generate data analysis code")
orchestrator = Orchestrator(context)

# Create code generation agent
code_agent = CodeActAgent("analyst")
orchestrator.register_agent(code_agent)

# Execute
workflow = [{
    'agent': 'analyst',
    'input_type': 'codeact',
    'input_data': {
        'prompt': 'Create a function to analyze sales data and return top 5 products'
    }
}]

results = orchestrator.execute_linear_workflow(workflow)

# Generated code is in results[0].generated_code
if results[0].success:
    print(f"Generated code:\n{results[0].generated_code}")
    print(f"Execution result: {results[0].execution_result}")

📚 Documentation

Full documentation is available at duke-agents.readthedocs.io.

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide first.

📄 License

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

🙏 Acknowledgments

• Built with Mistral AI models
• Implements IPO architecture for robust agent workflows