Agentes de IA
SWE-bench_Pro-os
A benchmark testing whether agents can solve long-horizon coding tasks.
★ 539 estrellasMIT
Ver la guía →MMLU-Pro es un proyecto de agentes de IA de código abierto de TIGER-AI-Lab (TIGER-AI-Lab/MMLU-Pro en GitHub). Tiene 429 estrellas en GitHub y 58 bifurcaciones, se publica bajo la licencia Apache-2.0 y se actualizó por última vez en GitHub el 18 de marzo de 2026.
The tougher, more robust successor to the MMLU benchmark.
Write down the outcome you want in one sentence.
Clone it locally and check the README.
Get dependencies and tooling in place per the docs.
Follow the minimal example, then adapt it to your needs.
Beyond what MMLU could separate.
Ten options per question raise the bar.
Breadth across knowledge domains.
Code and data, open to all.
Peer-reviewed benchmark design.
Increasingly the test labs must pass.
Download the source and move into the project folder.
git clone https://github.com/TIGER-AI-Lab/MMLU-Pro.git
cd MMLU-ProIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m mmlu_pro # check README for the exact commandIt is an open-source project at TIGER-AI-Lab/MMLU-Pro with 58 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — Apache 2.0 allows commercial use and modification. Keep the license and notice files, and note it includes a patent grant.
More answer choices per question and harder items overall — designed to separate frontier models.
Clone the repo and follow the eval instructions; it's code and data, not a service.
Clone the repo and follow the README quickstart — the README is the best starting point.
A benchmark testing whether agents can solve long-horizon coding tasks.
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A graduate-level benchmark even Google can't crack.
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Proyectos de Agentes de IA cercanos en el catálogo, los más próximos en estrellas a MMLU-Pro: sigue explorando a ambos lados.
A harness-oriented framework for production-grade agent applications.
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Snowflake's Agent World Model: synthesise 1,000 executable MCP tool-use environments for agentic RL.
Agentic memory for LLM agents, from a published research project.
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Benchmark computer-use agents on long, real-world desktop tasks.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.