Multi-Agent Alpha is a robust, open-source framework for building, managing, and deploying intelligent multi-agent systems. It provides a flexible architecture for creating custom agents, with a focus on code quality, testing, and continuous integration.
Project description
Multi-Agent Alpha
Multi-Agent Alpha is a robust, open-source framework designed to simplify the process of building, managing, and deploying intelligent multi-agent systems. This project serves as a stepping stone for individuals and organizations to automate complex multi-agent workflows with ease, including support for custom hooks and capabilities.
Key Features
- Modular Architecture: Easily build and manage multi-agent systems with a flexible and scalable architecture.
- Custom Hooks: Extend the functionality of your agents with custom hooks, allowing for seamless integration with other tools and systems.
- Non-Commercial Use: Built with open-source principles, supporting non-commercial use while encouraging community contributions.
- PEP 8 Compliance: Ensures code quality and consistency through adherence to Python’s PEP 8 standards, with built-in linters and formatters.
- Integration with LangChain: Leverage advanced agent workflows with integration support for LangChain.
- Continuous Integration: Automatically test and validate your code with GitHub Actions, ensuring that every change is stable and reliable.
Getting Started
To get started with Multi-Agent Alpha, follow these steps:
Clone the Repository
git clone https://github.com/nishgaba-ai/multi-agent-alpha.git
cd multi-agent-alpha
Install Dependencies
Use Poetry to install the project dependencies:
poetry install
Run Tests
Ensure everything is working by running the tests:
poetry run pytest
Installation
To install Multi-Agent Alpha in your project, use Poetry:
poetry add multi-agent-alpha
Usage
Here's a simple example of how to create and run a custom agent:
from multi_agent_alpha.agents import CustomAgent
# Initialize a custom agent
agent = CustomAgent(name="MyAgent")
# Use the agent to perform a task
result = agent.example_usage(query="Post this message to LinkedIn")
print(result)
Contributing
We welcome contributions from the community! Please follow these steps to contribute:
- Fork the repository and clone it locally.
- Create a new branch for your feature or bug fix.
- Commit your changes with clear commit messages.
- Push to your branch and submit a pull request.
Please ensure that your code adheres to the existing code style and passes all tests before submitting.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. See the LICENSE file for more details.
Acknowledgements
Special thanks to all the contributors and the open-source community for their support and collaboration.
Contact
For any questions, feel free to reach out to the project maintainer, Nishchal Gaba, at nishgaba.ai@gmail.com.
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