This Streamlit application showcases the Mixture of Agents (MOA) architecture proposed by Together AI, powered by any Ollama compatible API (including Ollama). It allows users to interact with a configurable multi-agent system for enhanced AI-driven conversations.

Agent responses (layer agents):

Final (mixture) response (main model):

Source: Adaptation of Together AI Blog - Mixture of Agents

• Interactive chat interface powered by MOA
• Configurable main model and layer agents
• Real-time streaming of responses
• Visualisation of intermediate layer outputs
• Customisable agent parameters through the UI

1. Clone the repository:

   git clone https://github.com/sammcj/moa.git
   cd moa
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Set up your environment variables: Create a .env file in the root directory and add your Ollama compatible base URL and API key:

   OLLAMA_HOST=https://localhost:11434
   OLLAMA_API_KEY=ollama
   

1. Run the Streamlit app:

   streamlit run app.py
   

2. Open your web browser and navigate to the URL provided by Streamlit (usually https://localhost:8501).

3. Use the sidebar to configure the MOA settings:

   • Select the main model
   • Set the number of cycles
   • Customize the layer agent configuration

4. Start chatting with the MOA system using the input box at the bottom of the page.

• app.py: Main Streamlit application file
• moa/: Package containing the MOA implementation
  • __init__.py: Package initializer
  • moa.py: Core MOA agent implementation
  • prompts.py: System prompts for the agents
• main.py: CLI version of the MOA chat interface
• requirements.txt: List of Python dependencies
• static/: Directory for static assets (images, etc.)

The MOA system can be configured through the Streamlit UI or by modifying the default configuration in app.py. The main configurable parameters are:

• Main model: The primary language model used for generating final responses
• Number of cycles: How many times the layer agents are invoked before the main agent
• Layer agent configuration: A JSON object defining the system prompts, model names, and other parameters for each layer agent

Contributions to this project are welcome! Please follow these steps to contribute:

1. Fork the repository
2. Create a new branch for your feature or bug fix
3. Make your changes and commit them with descriptive commit messages
4. Push your changes to your fork
5. Submit a pull request to the main repository

Please ensure that your code adheres to the project's coding standards and includes appropriate tests and documentation.

• The project was forked from skapadia3214/moa
  • groq-moa was stated to be licensed under the MIT License - but it's LICENSE.md file contained the license for Apache 2.0.

• skapadia3214 for their groq based MOA implementation
• Ollama for providing the Ollama compatible API
• Together AI for proposing the Mixture of Agents architecture and providing the conceptual image
• Streamlit for the web application framework

This project implements the Mixture-of-Agents architecture proposed in the following paper:

@article{wang2024mixture,
  title={Mixture-of-Agents Enhances Large Language Model Capabilities},
  author={Wang, Junlin and Wang, Jue and Athiwaratkun, Ben and Zhang, Ce and Zou, James},
  journal={arXiv preprint arXiv:2406.04692},
  year={2024}
}

For more information about the Mixture-of-Agents concept, please refer to the original research paper and the Together AI blog post.