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https://github.com/Thytu/Agentarium

open-source framework for creating and managing simulations populated with AI-powered agents. It provides an intuitive platform for designing complex, interactive environments where agents can act, learn, and evolve.
https://github.com/Thytu/Agentarium

Last synced: 14 days ago
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Host: GitHub
URL: https://github.com/Thytu/Agentarium
Owner: Thytu
License: apache-2.0
Created: 2024-12-20T19:04:40.000Z (30 days ago)
Default Branch: main
Last Pushed: 2024-12-31T13:56:12.000Z (19 days ago)
Last Synced: 2024-12-31T14:02:58.620Z (19 days ago)
Language: Python
Size: 57.6 KB
Stars: 44
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome - Thytu/Agentarium - open-source framework for creating and managing simulations populated with AI-powered agents. It provides an intuitive platform for designing complex, interactive environments where agents can act, learn, and evolve. (Python)
awesome-LLM-resourses - Agentarium - source framework for creating and managing simulations populated with AI-powered agents. (智能体 Agents)
awesome-generative-ai-data-scientist - Agentarium - source framework for creating and managing simulations populated with AI-powered agents. It provides an intuitive platform for designing complex, interactive environments where agents can act, learn, and evolve. [GitHub](https://github.com/Thytu/Agentarium) (Agents and Tools (Build Your Own))

README

        # 🌿 Agentarium



[![License: Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-yellow.svg)](https://opensource.org/licenses/Apache-2.0)

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)

[![PyPI version](https://badge.fury.io/py/agentarium.svg)](https://badge.fury.io/py/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](#installation) •

[Quick Start](#quick-start) •

[Features](#features) •

[Examples](#examples) •

[Documentation](#documentation) •

[Contributing](#contributing)



## 🚀 Installation

```bash

pip install agentarium

```

## 🎯 Quick Start

```python

from agentarium import Agent

# Create agents

agent1 = Agent(name="agent1")

agent2 = Agent(name="agent2")

# Direct communication between agents

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

# Agent autonomously decides its next action based on context

bob.act()

```

## ✨ Features

- **🤖 Advanced Agent Management**: Create and orchestrate multiple AI agents with different roles and capabilities

- **🔄 Autonomous Decision Making**: Agents can make decisions and take actions based on their context

- **💾 Checkpoint System**: Save and restore agent states and interactions for reproducibility

- **🎭 Customizable Actions**: Define custom actions beyond the default talk/think capabilities

- **🧠 Memory & Context**: Agents maintain memory of past interactions for contextual responses

- **⚡ AI Integration**: Seamless integration with various AI providers through aisuite

- **⚡ Performance Optimized**: Built for efficiency and scalability

- **🛠️ Extensible Architecture**: Easy to extend and customize for your specific needs

## 📚 Examples

### Basic Chat Example

Create a simple chat interaction between agents:

```python

from agentarium import Agent

# Create agents with specific characteristics

alice = Agent.create_agent(name="Alice", occupation="Software Engineer")

bob = Agent.create_agent(name="Bob", occupation="Data Scientist")

# Direct communication

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

# Let Bob autonomously decide how to respond

bob.act()

```

### Adding Custom Actions

Add new capabilities to your agents:

```python

from agentarium import Agent, Action

# Define a simple greeting action

def greet(name: str, **kwargs) -> str:

    return f"Hello, {name}!"

# Create an agent and add the greeting action

agent = Agent.create_agent(name="Alice")

agent.add_action(

    Action(

        name="GREET",

        description="Greet someone by name",

        parameters=["name"],

        function=greet

    )

)

# Use the custom action

agent.execute_action("GREET", "Bob")

```

### Using Checkpoints

Save and restore agent states:

```python

from agentarium import Agent

from agentarium.CheckpointManager import CheckpointManager

# Initialize checkpoint manager

checkpoint = CheckpointManager("demo")

# Create and interact with agents

alice = Agent.create_agent(name="Alice")

bob = Agent.create_agent(name="Bob")

alice.talk_to(bob, "What a beautiful day!")

checkpoint.update(step="interaction_1")

# Save the current state

checkpoint.save()

```

More examples can be found in the [examples/](examples/) directory.

## 📖 Documentation

### Agent Creation

Create agents with custom characteristics:

```python

agent = Agent.create_agent(

    name="Alice",

    age=28,

    occupation="Software Engineer",

    location="San Francisco",

    bio="A passionate developer who loves AI"

)

```

### LLM Configuration

Configure your LLM provider and credentials using a YAML file:

```yaml

llm:

  provider: "openai"  # The LLM provider to use (any provider supported by aisuite)

  model: "gpt-4"      # The specific model to use from the provider

aisuite:              # (optional) Credentials for aisuite

  openai:            # Provider-specific configuration

    api_key: "sk-..."  # Your API key

```

### Key Components

- **Agent**: Core class for creating AI agents with personalities and autonomous behavior

- **CheckpointManager**: Handles saving and loading of agent states and interactions

- **Action**: Base class for defining custom agent actions

- **AgentInteractionManager**: Manages and tracks all agent interactions

## 🤝 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](LICENSE) file for details.

## 🙏 Acknowledgments

Thanks to all contributors who have helped shape Agentarium 🫶