KI-Agenten
SWE-bench_Pro-os
A benchmark testing whether agents can solve long-horizon coding tasks.
★ 539 SterneMIT
Anleitung ansehen →MMLU-Pro ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von TIGER-AI-Lab (TIGER-AI-Lab/MMLU-Pro auf GitHub). Es hat 429 Sterne auf GitHub und 58 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 18. März 2026 auf GitHub aktualisiert.
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.
The benchmark that grades AI on real GitHub issues.
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A benchmark for tool-agent-user interaction in real-world domains.
An open benchmark for browser AI agents on everyday tasks.
A graduate-level benchmark even Google can't crack.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls MMLU-Pro nicht das Richtige ist.
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DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die MMLU-Pro bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
A harness-oriented framework for production-grade agent applications.
Safety testing for agents, right in pytest.
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.
Guardrails for building secure, robust agents.
Benchmark computer-use agents on long, real-world desktop tasks.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.