• 🏆 Rank outputs from different LLMs, RAG setups, and prompts to find the best configuration of your system
• ⚔️ Perform automated head-to-head evaluation using judges from OpenAI, Anthropic, Cohere, and more
• 🤖 Define and run your own custom judges, connecting to internal services or implementing bespoke logic
• 💻 Run application locally, getting full control over your environment and data

🤔 Why Head-to-Head Evaluation?

• LLMs are better at judging responses head-to-head than they are in isolation (arXiv:2408.08688) — leaderboard rankings computed using Elo scores from many automated side-by-side comparisons should be more trustworthy than leaderboards using metrics computed on each model's responses independently!
• The LMSYS Chatbot Arena has replaced benchmarks for many people as the trusted true leaderboard for foundation model performance (arXiv:2403.04132). Why not apply this approach to your own foundation model selection, RAG system setup, or prompt engineering efforts?
• Using a "jury" of multiple smaller models from different model families like gpt-4o-mini, command-r, and claude-3-haiku generally yields better accuracy than a single frontier judge like gpt-4o — while being faster and much cheaper to run. AutoArena is built around this technique, called PoLL: Panel of LLM evaluators (arXiv:2404.18796).
• Automated side-by-side comparison of model outputs is one of the most prevalent evaluation practices (arXiv:2402.10524) — AutoArena makes this process easier than ever to get up and running.

🔥 Getting Started

Install from PyPI:

pip install autoarena

Run as a module and visit localhost:8899 in your browser:

python -m autoarena

With the application running, getting started is simple:

1. Create a project via the UI.
2. Add responses from a model by selecting a CSV file with prompt and response columns.
3. Configure an automated judge via the UI. Note that most judges require credentials, e.g. X_API_KEY in the environment where you're running AutoArena.
4. Add responses from a second model to kick off an automated judging task using the judges you configured in the previous step to decide which of the models you've uploaded provided a better response to a given prompt.

That's it! After these steps you're fully set up for automated evaluation on AutoArena.

📄 Formatting Your Data

AutoArena requires two pieces of information to test a model: the input prompt and corresponding model response.

• prompt: the inputs to your model. When uploading responses, any other models that have been run on the same prompts are matched and evaluated using the automated judges you have configured.
• response: the output from your model. Judges decide which of two models produced a better response, given the same prompt.

📂 Data Storage

Data is stored in ./data/<project>.duckdb files in the directory where you invoked AutoArena. See data/README.md for more details on data storage in AutoArena.

🦾 Development

AutoArena uses uv to manage dependencies. To set up this repository for development, run:

uv venv && source .venv/bin/activate
uv pip install --all-extras -r pyproject.toml
uv tool run pre-commit install
uv run python3 -m autoarena serve --dev

To run AutoArena for development, you will need to run both the backend and frontend service:

• Backend: uv run python3 -m autoarena serve --dev (the --dev/-d flag enables automatic service reloading when source files change)
• Frontend: see ui/README.md

To build a release tarball in the ./dist directory:

./scripts/build.sh