grami-ai 0.3.0

Growth and Relationship AI Management Infrastructure

GRAMI-AI: Adaptive AI Agent Orchestration Framework

🚀 Overview

GRAMI-AI is a cutting-edge, async-first AI agent framework designed to solve complex computational challenges through intelligent, collaborative agent interactions. Built with unprecedented flexibility, this library empowers developers to create sophisticated, context-aware AI systems that can adapt, learn, and collaborate across diverse domains.

🌟 Key Innovations

• Modular Agent Architecture: Seamlessly compose and deploy AI agents with dynamic capabilities
• Multi-Provider LLM Integration: Leverage models from OpenAI, Anthropic, Google Gemini, and more
• Async-First Design: High-performance, non-blocking agent interactions
• Extensible Tool Ecosystem: Easily integrate custom tools and expand agent capabilities
• Advanced Memory Management: Intelligent context retention and retrieval

🔍 Use Cases

While initially conceived for marketing and growth solutions, GRAMI-AI's flexible architecture supports a wide range of applications:

• Marketing Intelligence
• Research Automation
• Complex Problem Solving
• Interactive AI Assistants
• Cross-Domain Knowledge Synthesis

📦 Installation

pip install grami-ai

🚀 Quick Start

Basic Agent Creation

from grami_ai.core.agent import AsyncAgent
from grami_ai.llms.gemini_llm import GeminiLLMProvider

# Create an AI agent for marketing
async def main():
    agent = await AsyncAgent.create(
        name="MarketingAssistant",
        llm="gemini",
        tools=["content_generation", "web_search"]
    )

    # Generate marketing content
    response = await agent.process({
        "type": "content_request",
        "platform": "instagram",
        "niche": "tech",
        "content_type": "post"
    })
    print(response)

# Run the agent
asyncio.run(main())

🛠 Core Components

Agent

• Orchestrates LLM, memory, tools, and interfaces
• Async message processing
• Dynamic tool selection

Tools

• Extensible async tool base class
• Metadata-driven tool configuration
• Support for various tool categories

Memory

• Async memory providers
• In-memory and Redis backends
• Conversation and state management

Logging

• Async logging with structured output
• Configurable log levels
• Context-aware logging

🔧 Configuration

GRAMI-AI supports environment-based configuration:

• Development
• Testing
• Production

from grami_ai.core.config import get_settings

# Get environment-specific settings
settings = get_settings()

📡 Interfaces

• WebSocket
• Kafka Consumer
• Custom Interface Support

🔒 Security

• Environment variable management
• Configurable token expiration
• Resource limits

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines.

📄 License

MIT License, Copyright (c) 2024 WAFIR-Cloud

📞 Contact

For support, collaboration, or inquiries:

• Email: [e-mail]
• GitHub: WAFIR-Cloud/grami-ai

Repository Information

Repository: WAFIR-Cloud/grami-ai Issues: GitHub Issues Documentation: README

Python Compatibility

• Supported Python Versions: 3.10 - 3.12
• Recommended Python Version: 3.11

🌐 Roadmap

• Enhanced LLM Provider Support
• Advanced Tool Ecosystem
• Comprehensive Documentation
• Performance Benchmarking
• Community Extensions

🏆 Acknowledgements

Built with ❤️ by YAFATek Solutions, pushing the boundaries of AI-powered solutions.