A powerful Python framework for building and deploying AI agents that automate complex business processes. Combines the latest advances in AI with practical automation needs.

Key Features

  • Modular Agent Architecture: Build custom agents for specific tasks
  • Natural Language Processing: Process and understand unstructured data
  • Task Orchestration: Coordinate multiple agents for complex workflows
  • Monitoring & Analytics: Track agent performance and efficiency
  • Easy Integration: Simple API for integrating with existing systems

Technical Stack

  • Python 3.9+
  • Flask
  • LangChain
  • OpenAI GPT
  • PostgreSQL
  • Redis for caching
  • Docker for containerization

Impact

  • 70% reduction in manual processing time
  • 85% accuracy in automated decision making
  • Significant cost savings in operational processes

Code Example

from ai_agent_framework import Agent, Workflow

# Define a custom agent
class DocumentProcessor(Agent):
    def __init__(self):
        super().__init__()
        self.model = self.load_model("gpt-4")

    async def process(self, document):
        # Extract key information
        extracted_data = await self.model.extract(document)
        # Validate and transform
        return self.validate_and_transform(extracted_data)

# Create a workflow
workflow = Workflow("Document Processing")
workflow.add_agent(DocumentProcessor())
workflow.add_agent(DataValidator())
workflow.add_agent(ReportGenerator())

# Execute the workflow
result = await workflow.execute(document_path)

Project Structure

├── src/
│   ├── agents/         # Agent implementations
│   ├── core/           # Core framework functionality
│   ├── models/         # AI model integrations
│   ├── workflows/      # Workflow definitions
│   └── utils/          # Utility functions
├── tests/              # Test suite
├── examples/           # Example implementations
└── docs/              # Documentation

Getting Started

  1. Install the framework:
pip install ai-agent-framework
  1. Create your first agent:
from ai_agent_framework import Agent

class MyAgent(Agent):
    async def process(self, input_data):
        # Your agent logic here
        return processed_data
  1. Start building your automation workflows!

Documentation

For detailed documentation, visit our GitHub repository.

Contributing

We welcome contributions! Please read our contributing guidelines to get started.

License

MIT License - See LICENSE for details.