Agents IA
SWE-bench_Pro-os
A benchmark testing whether agents can solve long-horizon coding tasks.
★ 539 étoilesMIT
Voir le guide →gpqa est un projet de agents IA open source, créé par idavidrein (idavidrein/gpqa sur GitHub). Il compte 540 étoiles sur GitHub et 58 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 30 septembre 2024.
A graduate-level benchmark even Google can't crack.
Pick one concrete task so you can judge whether the project fits.
Clone the repo and check the prerequisites listed in the README.
Install what it needs and configure credentials or model access.
Run a minimal example end to end before wiring it into your stack.
Questions written by domain experts.
Resistant to simple search-based solving.
Tests deep understanding, not trivia.
Widely cited in frontier-model evals.
Explore via Jupyter.
Permissive licensing.
Download the source and move into the project folder.
git clone https://github.com/idavidrein/gpqa.git
cd gpqaSet up a Python environment with the notebook dependencies.
pip install -r requirements.txtOpen the notebooks and inspect the questions.
jupyter notebookIt is maintained by idavidrein at idavidrein/gpqa, with 58 forks. The last push was September 2024.
Yes. It is released under the MIT license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
The dataset is free; you need model access to evaluate against it.
GPQA is deliberately harder and more reasoning-focused; MMLU is broader.
Read the paper, clone the repo, and explore the notebooks.
A benchmark testing whether agents can solve long-horizon coding tasks.
The tougher, more robust successor to the MMLU benchmark.
The benchmark that grades AI on real GitHub issues.
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See what your AI agents do — at the system level.
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A benchmark testing whether agents can solve long-horizon coding tasks.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.