grami-ai 0.3.110

A dynamic and flexible AI agent framework for building intelligent, multi-modal AI agents

GRAMI-AI: Dynamic AI Agent Framework

Vision and Purpose

GRAMI-AI is a revolutionary, async-first AI agent framework designed to solve complex computational challenges through intelligent, collaborative agent interactions. Our mission is to create a highly flexible, extensible platform that empowers developers to build sophisticated, context-aware AI systems capable of adapting and collaborating across diverse domains.

Framework Philosophy

The core philosophy of GRAMI-AI is to provide an abstraction layer that:

• Enables dynamic creation of AI agents with specific roles and capabilities
• Supports multiple Language Models (LLMs) with their unique interfaces
• Allows seamless integration of communication protocols
• Provides flexible memory management
• Supports extensible tool ecosystems

Key Architectural Goals

1. Dynamic Agent Creation

• Define agents with precise roles (e.g., Growth Manager, Content Creator)
• Customize system instructions and behavior
• Support multi-modal capabilities (text, image, video generation)

2. LLM Abstraction

• Unified interface for different LLMs (Gemini, OpenAI, Anthropic, Ollama)
• Handle provider-specific nuances (prompt building, chat interfaces)
• Flexible function/tool integration

3. Communication Interfaces

• Support multiple protocols (WebSocket, REST, Kafka)
• Enable inter-agent communication
• Implement global state management

4. Memory Management

• Pluggable memory providers (In-Memory, Redis, DynamoDB)
• Persistent conversation and task history
• Scalable state storage

5. Tool Ecosystem

• Default utility tools (Kafka publisher, web search)
• Easy custom tool development
• Provider-specific function handling

Roadmap and TODO List

Short-term Goals

• Complete core agent abstraction
• Implement multi-LLM support
• Develop comprehensive documentation
• Create robust testing framework
• Implement basic memory providers

Mid-term Goals

• Advanced inter-agent communication
• Kafka integration for agent crews
• WebSocket chat interfaces
• S3 and cloud storage integrations
• Payment solution abstractions

Long-term Vision

• Machine learning-driven agent optimization
• Advanced context and memory management
• Multi-language support
• Enterprise-grade security features
• Community-driven tool marketplace

Development Checklist

Core Framework Development

• Implement base Agent abstract class
• Create LLM provider abstraction layer
• Develop async communication interfaces
• Implement memory management system
• Design tool integration mechanism

LLM Provider Support

• Google Gemini integration
• OpenAI GPT integration
• Anthropic Claude integration
• Ollama local LLM support
• Add provider-specific function calling

Communication Protocols

• WebSocket implementation
• REST API endpoint design
• Kafka message broker integration
• gRPC support
• Inter-agent communication protocol

Memory Management

• In-memory state storage
• Redis persistent storage
• DynamoDB scalable storage
• Conversation history tracking
• State serialization/deserialization

Tool Ecosystem

• Web search utility tool
• Data analysis tool
• Image generation tool
• Code generation tool
• Custom tool development framework

Testing and Quality

• Unit tests for core components
• Integration tests for agent interactions
• Performance benchmarking
• Security vulnerability scanning
• Continuous integration setup

Documentation

• Comprehensive API documentation
• Detailed usage examples
• Developer guides
• Architecture overview
• Contribution guidelines

Community and Extensibility

• Open-source contribution model
• Plugin architecture
• Community tool repository
• Regular maintenance and updates
• Feedback and feature request system

Conceptual Agent Example

agent = AsyncAgent(
    llm=GeminiProvider(),            # LLM Provider
    memory=RedisMemoryProvider(),    # Memory Management
    tools=[                          # Extensible Tools
        KafkaPublisher(),
        WebSearchTool(),
        ContentAnalysisTool()
    ],
    system_instructions="You are a Growth Manager...",  # Role Definition
    communication_interface=WebSocketInterface(),
    storage=S3StorageProvider(),     # Optional Storage
    crew_config={                    # Optional Crew Configuration
        'team_members': ['ContentCreator', 'Researcher'],
        'communication_protocol': 'kafka'
    }
)

Community Contribution

We envision GRAMI-AI as a collaborative ecosystem where developers can:

• Create and share custom tools
• Develop new LLM integrations
• Build memory providers
• Implement communication interfaces
• Contribute to core framework development

Current Capabilities

• Async-first architecture
• Basic agent creation
• Gemini LLM integration
• Simple tool support
• Advanced inter-agent communication
• Multiple memory providers
• Comprehensive LLM support

Getting Started

pip install grami-ai

License

MIT License - Empowering open-source innovation

About YAFATEK Solutions

Pioneering AI innovation through flexible, powerful frameworks.

Contact & Support

• Email: [e-mail]
• GitHub: GRAMI-AI Issues

---

Star ⭐ the project if you believe in collaborative AI innovation!