datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
webagentswebchain
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.webchain-legacy
WebChain
WebChain is a large-scale, human-annotated dataset of real-world web interaction trajectories for training and evaluating GUI agents and web agents. WebChain contains 31,725 trajectories, 317,993 steps, and 428 unique domains. Its core contribution is a Triple Alignment of visual context, structural context, and action grounding, enabling supervision for both spatial grounding and long-horizon planning.
Paper: https://arxiv.org/abs/2603.05295
Open access… See the full description on the dataset page: https://huggingface.co/datasets/webagentlab/webchain-legacy.AFM-WebAgent-RL-Dataset
Data Introduction
This dataset serves as the core training data for Agent Foundation Models (AFMs), specifically designed to elicit end-to-end multi-agent reasoning capabilities in large language models. Built on the novel "Chain-of-Agents (CoA)" paradigm, the dataset leverages a multi-agent distillation framework to transform collaboration processes from state-of-the-art multi-agent systems into trajectory data suitable for supervised fine-tuning (SFT), simulating dynamic… See the full description on the dataset page: https://huggingface.co/datasets/PersonalAILab/AFM-WebAgent-RL-Dataset.AFM-WebAgent-SFT-Dataset
Data Introduction
This dataset serves as the core training data for Agent Foundation Models (AFMs), specifically designed to elicit end-to-end multi-agent reasoning capabilities in large language models. Built on the novel "Chain-of-Agents (CoA)" paradigm, the dataset leverages a multi-agent distillation framework to transform collaboration processes from state-of-the-art multi-agent systems into trajectory data suitable for supervised fine-tuning (SFT), simulating dynamic… See the full description on the dataset page: https://huggingface.co/datasets/PersonalAILab/AFM-WebAgent-SFT-Dataset.execution-time-warnings-web-agents
Trustworthy Completion for Web Agents
This release contains audited run-level results from a controlled study of execution-time safeguards for web agents under deceptive consumer interfaces.
The benchmark independently scores nominal completion (C) and trajectory safety (S): trustworthy completion (C=1,S=1), unsafe completion (C=1,S=0), safe non-completion (C=0,S=1), and unsafe failure (C=0,S=0).
Study design
One frozen vision-capable web-agent configuration
12… See the full description on the dataset page: https://huggingface.co/datasets/deceptive-web-benchmark/execution-time-warnings-web-agents.st-webagentbench
A Benchmark for Evaluating Safety & Trustworthiness in Web Agents
Accepted at ICLR 2026
Overview
ST-WebAgentBench is a policy-enriched evaluation suite for web agents, built on BrowserGym. It measures not only whether agents complete tasks, but whether they do so while respecting safety and trustworthiness (ST) policies — the constraints that govern real enterprise deployments.
The… See the full description on the dataset page: https://huggingface.co/datasets/ST-WebAgentBench/st-webagentbench.web-agent-graph-dataset
Web Agent Grouped Graph Dataset
This dataset contains web navigation tasks in grouped graph format with full history and candidate actions for training reward models.
Data Format
Each line in graph_dataset.jsonl represents a single step with all candidate actions grouped together:
{
"task_id": "...",
"goal": "Find product X and add to cart",
"domain": "shopping",
"step_index": 3,
"history": [
{"state_id": "S0", "screenshot": "...", "url": "...", "obs":… See the full description on the dataset page: https://huggingface.co/datasets/Anish13/web-agent-graph-dataset.Execution-Time-Warnings-for-Web-Agents
Trustworthy Completion for Web Agents
This release contains audited run-level results from a controlled study of execution-time safeguards for web agents under deceptive consumer interfaces.
The benchmark independently scores nominal completion (C) and trajectory safety (S): trustworthy completion (C=1,S=1), unsafe completion (C=1,S=0), safe non-completion (C=0,S=1), and unsafe failure (C=0,S=0).
Study design
One frozen vision-capable web-agent configuration
12… See the full description on the dataset page: https://huggingface.co/datasets/EvaNing123/Execution-Time-Warnings-for-Web-Agents.execution-time-warnings-web-agents
Deception Warning Study — benchmark runs (staging)
This repository will host run-level rows for the controlled benchmark described in the companion paper (NeurIPS-style release).
Contents (when populated)
Artifact
Description
run_level.jsonl / run_level.csv
One row per merged run: task, condition, repeat, outcome, flags
run_level.parquet
Optional if pyarrow is installed (Hub-friendly)
manifest.yaml (optional)
benchmark_version, repeats_per_task_condition… See the full description on the dataset page: https://huggingface.co/datasets/deceptive-web/execution-time-warnings-web-agents.agentic-web-cheatsheets
Bowmark: Agentic Web Cheatsheets — Free Sample
Bowmark indexes how websites actually work, for AI agents.
Each row is a cheatsheet for one task on one site: the behavioral gotchas you
only learn by driving the site, a deep-link shortcut where one exists, and a
verification stamp saying how many times it worked and as of when. Every row
was run end-to-end and proven to work — that's the gate to be included.
This repository is a free, curated sample — the strongest… See the full description on the dataset page: https://huggingface.co/datasets/bowmark-ai/agentic-web-cheatsheets.webagent-dom-with-imagesweb-search-agent-sft-traces
Dataset Card for "web-search-agent-sft-traces"
More Information needed
lumos_web_agent_plan_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_web_agent_plan_iterative.lumos_web_agent_ground_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_web_agent_ground_iterative.web-search-agent-sft-traces2
Dataset Card for "web-search-agent-sft-traces2"
More Information needed
agent-web-index
Agent Web Index — how much of the web can AI assistants actually read?
47,979 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.ai-research-berkeley-webagent
Berkeley WebAgent Experiment Artifacts
Native GEPA, CLUE and ACE experiment logs and available actor trajectory evidence.
Files require manual access approval. Request access with your Hugging Face account.
Results — September 21, 2026
Non-WebArena results: training, validation and five-repeat held-out tables/plots.
Every iteration: training and validation by iteration; separate ACE training windows.
WebArena speed: component benchmarks, direct commit comparison… See the full description on the dataset page: https://huggingface.co/datasets/MinjaeLee-FuriosaAI-Ext/ai-research-berkeley-webagent.WebAgentOpenDataembodied_web_agent_outdoor_trajectorywebagent_policy_rationale_formattedmini_rm_benchmark_for_web_agent
Dataset Card for "mini_rm_benchmark_for_web_agent"
More Information needed
webagent-r1-distillqwen-web-agentweb-agent-trajectory-testweb-agent-trajectory-multimodal-testembodied-web-agent-indoorembodied_web_agent_outdoor_annotationembodied-web-agent-geoguessrwebagent-dom