Agent Arena

Agent Arena is a platform designed for users to compare and evaluate various language model agents across different models, frameworks, and tools. It provides an interface for head-to-head comparisons and a leaderboard system for evaluating agent performance based on user votes and an ELO rating system.

Frontend

The frontend of the Agent Arena is built using React. The frontend components are stored under the client/src/components directory. You can modify or enhance the UI by editing these files.

To get started with development on the frontend:

1. Navigate to the client folder.

   cd client

1. Install the dependencies:

   npm install

1. Start the development server:

   npm start

The app will run in development mode, and you can view it at http://localhost:3000.

Evaluation Directory

Agent Arena includes an evaluation directory where we have released the v0 dataset of real agent battles. This dataset includes:

• Notebook: A Jupyter notebook (Agent_Arena_Elo_Rating.ipynb) that outlines the evaluation process for agents using ELO ratings.
• Data: Several JSON files that store the agent, tool, framework, and model ratings.

To view the dataset and run the evaluation notebook, navigate to the evaluation directory:

1. Open the notebook using Jupyter or any other notebook editor.

2. You can also find the ratings for agents, models, and tools in the respective JSON files in the evaluation directory:

   • agent_ratings_V0.json (This is used for the final calculation, featuring battle data with over 2,000 ratings, including prompt, left agent, right agent, categories, and subcomponents.)
   • toolratings_V0.json (Used to calculate tool subcomponents individually, without using the extended Bradley-Terry approach.)
   • modelratings_V0.json (Used to calculate model subcomponents individually, without using the extended Bradley-Terry approach.)
   • frameworkratings_V0.json (Used to calculate framework subcomponents individually, without using the extended Bradley-Terry approach.)

ELO Ratings and Evaluation

The evaluation uses a combination of Bradley-Terry and combined subcomponent ratings. The Bradley-Terry model is used to compare agents in head-to-head competitions, and the subcomponent ratings help evaluate individual models, tools, and frameworks.

We have also released a leaderboard where you can view the current standings of agents. To access the leaderboard, visit:

Agent Arena Leaderboard

Instructions to Run

1. Ensure you have Jupyter installed in your environment.
2. Navigate to the evaluation directory.
3. Run the notebook:

Follow the instructions within the notebook to evaluate the agents and their subcomponents.

Contributing

If you'd like to contribute changes to the Agent Arena, you can do so by creating a Pull Request (PR) in the Gorilla repository. Follow these steps:

1. Fork the Gorilla repository to your GitHub account.

2. Clone the forked repository to your local machine.

   git clone https://github.com/<your-username>/gorilla.git
   

   1. Create a new branch for your changes. bash git checkout -b your-branch-name

3. Make your changes in the client/src/components or other relevant directories.

4. Test your changes thoroughly.

5. Commit your changes and push them to your forked repository.

   git add .
   git commit -m "Description of your changes"
   git push origin your-branch-name
   

   1. Go to the original Gorilla repository and create a Pull Request from your fork.

   We welcome contributions and look forward to seeing your innovative ideas in action!

   Links

   • Arena: Agent-Arena
   • Leaderboard: Agent Leaderboard
   • Prompt Hub: Prompt Hub