hermes-ai 0.2.3

Lightweight framework for modular, multi-provider AI agents.

Hermes AI Agent Framework

🚀 Smart LlamaIndex Abstraction for AI Agents

Hermes is a lightweight, powerful abstraction layer over LlamaIndex that simplifies building production-ready AI agents with essential utilities and multi-agent capabilities.

✨ Features

• 🛠️ Automatic Tool Integration - Convert Python functions to tools effortlessly
• 🤖 Multi-Agent Orchestration - Coordinate multiple specialized agents
• 🧠 Enhanced Prompt Management - Built-in Chain of Thought and context awareness
• 💬 Smart Memory Management - Automatic chat history handling with configurable limits
• 🔌 Multi-Provider Support - OpenAI, Azure, Anthropic, and more
• 📊 Text Processing Utilities - Keyword extraction, formatting, and enhancement

🚀 Quick Start

pip install hermes-ai

import asyncio
from hermes.core import Agent

async def main():
    # Create a simple tool
    def get_weather(location: str) -> str:
        """Get weather information for a location."""
        return f"Weather in {location}: Sunny, 25°C"

    # Initialize your agent
    agent = Agent(
        provider="openai",
        model="gpt-4o-mini",
        name="WeatherAssistant",
        description="Helps with weather information",
        prompt="Provide accurate and helpful weather updates.",
        tools=[get_weather]
    )

    # Execute the agent
    response = await agent.execute("What's the weather in Tokyo?")
    print(response)

asyncio.run(main())

🏗️ Multi-Agent System

# Create specialized agents
market_agent = Agent(
    name="FinancialAnalyst",
    description="Stock market expert",
    tools=[get_market_data]
)

investment_agent = Agent(
    name="InvestmentAdvisor", 
    description="Investment strategy expert",
    tools=[calculate_returns]
)

# Coordinate with master agent
coordinator = Agent(
    name="FinanceCoordinator",
    description="Coordinates financial experts",
    tools=[market_agent.execute, investment_agent.execute]
)

🔧 Core Components

Agent Configuration

Agent(
    provider="openai",           # LLM provider
    model="gpt-4",              # Model name
    name="Assistant",           # Agent identity
    description="Helpful AI",   # Agent purpose
    prompt="Behavior guidelines", # System prompt
    tools=[function1, function2], # Available tools
    temperature=0.7,            # Creativity control
    max_chat_history_length=20  # Memory management
)

Built-in Utilities

• Automatic tool conversion from Python functions
• Enhanced prompt formatting with current context
• Chat history management with configurable limits
• Input enhancement with keyword extraction
• Multi-provider LLM support

📁 Project Structure

hermes/
├── core.py          # Main Agent class
├── utils.py         # Text processing utilities
├── tools.py         # Tool creation helpers
└── examples/        # Usage examples

🎯 Use Cases

• Customer Support Agents
• Multi-Domain Expert Systems
• Research Assistants
• Data Analysis Tools
• Content Generation Systems

🔮 Roadmap

• Vector database integration
• Advanced memory backends
• Streaming responses
• Plugin system
• Web interface

💡 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

MIT License - see LICENSE file for details.

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Build intelligent agents faster with Hermes - The messenger of AI capabilities