SWE-bench_Pro-os
A benchmark testing whether agents can solve long-horizon coding tasks.
gpqaは、idavidreinが公開しているオープンソースのAIエージェントプロジェクトです(GitHub: idavidrein/gpqa)。GitHubスターは540、フォークは58で、MITライセンスの下で公開されています。GitHubでの最終更新は2024年9月30日です。
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.
SEO, GEO, and marketing skills your agents can use.
Control Gmail, Calendar, Docs, Sheets, Drive and more from AI — a full Google Workspace MCP server.
See what your AI agents do — at the system level.
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — gpqaが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でgpqaとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
Take multi-agent AutoGen workflows to production, fast.
A benchmark testing whether agents can solve long-horizon coding tasks.
A very large curated list of AI tools, updated for 2026.
Detect prompt injections and jailbreaks before they hit your LLM.
See what your AI agents do — at the system level.
Guardrails for building secure, robust agents.