grami-ai 0.3.114

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

GRAMI-AI: Dynamic AI Agent Framework

Overview

GRAMI-AI is a revolutionary, async-first AI agent framework designed to create intelligent, collaborative, and highly customizable AI agents across multiple domains.

Key Features

• Dynamic AI Agent Creation
• Multi-LLM Support (Gemini, OpenAI, Anthropic, Ollama)
• Extensible Tool Ecosystem
• Multiple Communication Interfaces
• Flexible Memory Management
• Secure and Scalable Architecture

Installation

Using pip

pip install grami-ai

From Source

git clone https://github.com/YAFATEK/grami-ai.git
cd grami-ai
pip install -e .

Quick Start

Basic Agent Creation

from grami.agent import Agent
from grami.providers import GeminiProvider

# Initialize a Gemini-powered Agent
agent = Agent(
    name="AssistantAI",
    role="Helpful Digital Assistant",
    llm_provider=GeminiProvider(api_key="YOUR_API_KEY"),
    tools=[WebSearchTool(), CalculatorTool()]
)

# Send a message
response = await agent.send_message("Help me plan a trip to Paris")
print(response)

Examples

We provide a variety of example implementations to help you get started:

Available Examples

1. Basic Agents

   • examples/gemini_example.py: Multi-tool Gemini Agent
   • examples/openai_example.py: OpenAI-powered Agent
   • examples/anthropic_example.py: Claude Agent Implementation

2. Advanced Scenarios

   • examples/agent_crew_example.py: Multi-Agent Collaboration
   • examples/web_research_agent.py: Web Research Specialist
   • examples/content_creation_agent.py: Content Generation Agent

3. Tool Integration

   • examples/custom_tool_example.py: Creating Custom Tools
   • examples/kafka_communication_example.py: Inter-Agent Communication

Documentation

For detailed documentation, visit our Documentation Website

Contributing

We welcome contributions! Please see our Contribution Guidelines

License

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

Community

• Star the project on GitHub
• Join our Discord Community
• Follow us on Twitter/X

Development Checklist

Core Framework Design

• Implement AsyncAgent base class with dynamic configuration
• Create flexible system instruction definition mechanism
• Design abstract LLM provider interface
• Develop dynamic role and persona assignment system
• Implement multi-modal agent capabilities (text, image, video)

LLM Provider Abstraction

• Unified interface for diverse LLM providers
  • Google Gemini integration (start_chat(), send_message())
  • OpenAI ChatGPT integration
  • Anthropic Claude integration
  • Ollama local LLM support
• Standardize function/tool calling across providers
• Dynamic prompt engineering support
• Provider-specific configuration handling

Communication Interfaces

• WebSocket real-time communication
• REST API endpoint design
• Kafka inter-agent communication
• gRPC support
• Event-driven agent notification system
• Secure communication protocols

Memory and State Management

• Pluggable memory providers
  • In-memory state storage
  • Redis distributed memory
  • DynamoDB scalable storage
  • S3 content storage
• Conversation and task history tracking
• Global state management for agent crews
• Persistent task and interaction logs

Tool and Function Ecosystem

• Extensible tool integration framework
• Default utility tools
  • Kafka message publisher
  • Web search utility
  • Content analysis tool
• Provider-specific function calling support
• Community tool marketplace
• Easy custom tool development

Agent Crew Collaboration

• Inter-agent communication protocol
• Workflow and task delegation mechanisms
• Approval and review workflows
• Notification and escalation systems
• Dynamic team composition
• Shared context and memory management

Use Case Implementations

• Digital Agency workflow template
  • Growth Manager agent
  • Content Creator agent
  • Trend Researcher agent
  • Media Creation agent
• Customer interaction management
• Approval and revision cycles

Security and Compliance

• Secure credential management
• Role-based access control
• Audit logging
• Compliance with data protection regulations

Performance and Scalability

• Async-first design
• Horizontal scaling support
• Performance benchmarking
• Resource optimization

Testing and Quality

• Comprehensive unit testing
• Integration testing for agent interactions
• Mocking frameworks for LLM providers
• Continuous integration setup

Documentation and Community

• Detailed API documentation
• Comprehensive developer guides
• Example use case implementations
• Contribution guidelines
• Community tool submission process
• Regular maintenance and updates

Future Roadmap

• Payment integration solutions
• Advanced context understanding
• Multi-language support
• Enterprise-grade security features
• AI agent marketplace