{"data":{"slug":"kolenaio-autoarena","name":"autoarena","tagline":"Rank LLMs, RAG systems, and prompts using automated head-to-head evaluation","github_url":"https://github.com/kolenaIO/autoarena","owner":"kolenaIO","repo":"autoarena","owner_avatar_url":"https://avatars.githubusercontent.com/u/77010818?v=4","primary_language":"TypeScript","stars":108,"forks":9,"topics":["ai","evaluation","hacktoberfest","llm","llm-evaluation","rag","testing"],"archived":false,"github_pushed_at":"2024-12-16T12:25:44+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/kolenaio-autoarena","markdown_url":"https://www.graphcanon.com/tools/kolenaio-autoarena.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/kolenaio-autoarena","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=kolenaio-autoarena","description":"Rank LLMs, RAG systems, and prompts using automated head-to-head evaluation","homepage_url":"https://www.kolena.com/autoarena/","license":"Apache-2.0","open_issues":4,"watchers":6,"ai_summary":null,"readme_excerpt":"## 🔥 Getting Started\n\nInstall from [PyPI](https://pypi.org/project/autoarena/):\n\n```shell\npip install autoarena\n```\n\nRun as a module and visit [localhost:8899](http://localhost:8899/) in your browser:\n\n```shell\npython -m autoarena\n```\n\nWith the application running, getting started is simple:\n\n1. Create a project via the UI.\n1. Add responses from a model by selecting a CSV file with `prompt` and `response` columns.\n1. Configure an automated judge via the UI. Note that most judges require credentials, e.g. `X_API_KEY` in the\n   environment where you're running AutoArena.\n1. Add responses from a second model to kick off an automated judging task using the judges you configured in the\n   previous step to decide which of the models you've uploaded provided a better `response` to a given `prompt`.\n\nThat's it! After these steps you're fully set up for automated evaluation on AutoArena.","github_created_at":"2024-08-28T19:55:28+00:00","created_at":"2026-07-11T12:03:09.066972+00:00","updated_at":"2026-07-11T12:03:26.337844+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"evaluation","name":"evaluation"},{"slug":"llm","name":"llm"},{"slug":"ai","name":"ai"},{"slug":"testing","name":"testing"},{"slug":"hacktoberfest","name":"hacktoberfest"},{"slug":"rag","name":"rag"},{"slug":"typescript","name":"typescript"},{"slug":"llm-evaluation","name":"llm evaluation"}],"trust":{"provenance":{"is_fork":false,"github_id":849004809,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T12:03:09.676Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":571,"last_release_at":"2024-10-07T13:52:21Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T12:03:16.691Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T12:03:16.225Z"},"languages":{"value":["typescript","python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-11T12:03:16.225Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-11T12:03:16.225Z"}}}}