agentarium 0.2.0

A framework for managing and orchestrating AI agents

🌿 Agentarium

A powerful Python framework for managing and orchestrating AI agents with ease. Agentarium provides a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments.

Installation • Quick Start • Features • Examples • Documentation • Contributing

🚀 Installation

pip install agentarium

🎯 Quick Start

from agentarium import Agent

# Create agents
agent1 = Agent(name="agent1")
agent2 = Agent(name="agent2")

agent1.talk_to(agent2, "Hello, how are you?")
agent2.talk_to(agent1, "I'm fine, thank you!")

agent1.act() # Same as agent.talk_to but it's the agent who decides what to do

✨ Features

• 🤖 Advanced Agent Management: Create and orchestrate multiple AI agents with different roles and capabilities
• 🔄 Robust Interaction Management: Coordinate complex interactions between agents
• 💾 Checkpoint System: Save and restore agent states and interactions
• 📊 Data Generation: Generate synthetic data through agent interactions
• ⚡ Performance Optimized: Built for efficiency and scalability
• 🌍 Flexible Environment Configuration: Define custom environments with YAML configuration files
• 🛠️ Extensible Architecture: Easy to extend and customize for your specific needs

📚 Examples

Basic Chat Example

Create a simple chat interaction between agents:

# examples/basic_chat/demo.py
from agentarium import Agent

alice = Agent.create_agent()
bob = Agent.create_agent()

alice.talk_to(bob, "Hello Bob! I heard you're working on some interesting data science projects.")
bob.act()

Synthetic Data Generation

Generate synthetic data through agent interactions:

# examples/synthetic_data/demo.py
from agentarium import Agent
from agentarium.CheckpointManager import CheckpointManager

checkpoint = CheckpointManager("demo")

alice = Agent.create_agent()
bob = Agent.create_agent()

alice.talk_to(bob, "What a beautiful day!")
checkpoint.update(step="interaction_1")

checkpoint.save()

More examples can be found in the examples/ directory.

📖 Documentation

Environment Configuration

Configure your environment using YAML files:

llm:
  provider: "openai" # any provider supported by aisuite
  model: "gpt-4-mini" # any model supported by the provider

aisuite: # optional, credentials for aisuite
  openai:
    api_key: "sk-..."

Key Components

• Agent: Base class for creating AI agents
• CheckpointManager: Handles saving and loading of agent states

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Commit your changes (git commit -m 'feat: add amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

🙏 Acknowledgments

• Thanks to all contributors who have helped shape Agentarium
• Special thanks to the open-source community

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