datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
webchain
WebChain v2
A large-scale, human-annotated dataset of real-world web interaction trajectories for training and evaluating web agents.
[Paper] [Code] [Dataset]
WebChain captures how people complete real tasks on live websites. It is designed for agents that must both identify the correct interface element and reason through a sequence of actions. Each trajectory aligns screenshots, web structure, grounded actions, and reasoning signals instead of treating web navigation as… See the full description on the dataset page: https://huggingface.co/datasets/webagentlab/webchain.agent-web-index
Agent Web Index — how much of the web can AI assistants actually read?
48,154 domains measured live. 25% of them cannot be read by at least one of
ChatGPT, Claude, Perplexity or Gemini. Updated daily. Live index: https://shop.lumnika.com/ai-readiness/
Every row here is the result of real HTTP requests, not an estimate and not a re-publication of
someone else's crawl: each domain's homepage is requested once as a browser and once as each of the
published AI crawler user-agents… See the full description on the dataset page: https://huggingface.co/datasets/DeusHorizon/agent-web-index.web-agent-trajectory-testkoen-web-agent-sft-mixes
Korean-English Web Agent SFT Mixes
브라우저 GUI 에이전트 SFT 용 한국어·영어 혼합 데이터. 언어 비율만 다르고 나머지는
동일하게 통제된 4개 구성이라, 비율이 성능에 미치는 영향을 직접 비교할 수 있다.
구성
config
ko : en
스텝
궤적
ko_only_20k
10 : 0
20,001
3,042
mix_ko_en_5050
5 : 5
20,008
2,663
mix_ko_en_2080
2 : 8
20,003
2,436
en_only_20k
0 : 10
20,009
2,274
비율은 궤적 수가 아니라 스텝 수 기준이다. 스텝 하나가 학습 샘플 하나인데
한국어 궤적은 평균 6.6스텝, 영어는 8.7스텝이라, 궤적 수로 5:5 를 맞추면 실제
gradient 기여가 5:5 가 되지 않는다. 궤적은 절대 쪼개지 않는다.
네 구성의 표본은 서로 중첩된다.… See the full description on the dataset page: https://huggingface.co/datasets/GUIAgentt/koen-web-agent-sft-mixes.agentic_ii_agent_Qwen3_coder_prompt_web_bench_verified