A Mixture-of-Agents Demo Using Groq and Streamlit

I am trying a Mixture-of-Agents application using Groq. The idea is to combine responses from multiple language models in layers, then have a main model synthesize an answer. The [original research](https://arxiv.org/abs/2406.04692) reports improvements on particular evaluations, including AlpacaEval 2.0, but that does not mean every multi-agent setup reaches GPT-4o quality or improves every task.

The [groq-moa project](https://github.com/skapadia3214/groq-moa) provides a Streamlit interface for this workflow using Groq’s inference API. In the diagram, a prompt goes to several agents, later layers receive previous outputs, and a final model aggregates the results. I expect layered inference to add latency, and I am interested in whether fast model serving can make this practical. This is an application using Groq, not evidence that the service automatically applies MoA to every request.

In the 2024 setup, I clone the repository in VS Code, enter the project directory, and open the folder. I create and activate a Conda environment with Python 3.11, then install dependencies with `pip install -r requirements.txt`. The repository also includes a Dockerfile. Software dependencies and available models may have changed since the recording, so check the project’s current instructions.

I create a `.env` file containing `GROQ_API_KEY` and obtain a key from the Groq console. I say I will revoke the key displayed in the video before publication. Keep your own key private, exclude the environment file from version control, and revoke or rotate an exposed key. I then run `streamlit run app.py`, opening the interface locally; model requests still go to the remote API.

The sidebar lets me choose the main model, layer count, main-model temperature, and agent settings. In this example I select Llama 3 70B as the aggregator, leave the default at three layers, and use Llama 3 8B, Gemma 7B, and Llama 3 8B as agents. The paper and this demo examine particular configurations; three layers should be treated as a choice to test, not a universal optimum.

I ask for ten sentences that end with the word “apple.” The interface shows the response from each agent at each layer, so I can inspect where individual agents succeed or miss the instruction. In the example, one first-layer agent appears to satisfy it, another performs poorly, and another comes close. The final aggregated list meets the requested ending in the displayed example.

The result arrives quickly in my demonstration, but I do not measure latency or compare it against a single-model baseline. Multiple calls can add cost and latency, and an aggregator can still make mistakes. To know whether the approach helps a particular application, compare quality, speed, and cost on representative tasks.

The Streamlit interface also offers deployment, rerun, appearance settings, printing, screencast recording, and cache clearing. I hope inference interfaces make techniques such as agent mixtures, model routing, and reasoning workflows easier to use. This project is a starting point for experimenting with those ideas rather than a guarantee of better answers.