README.md

---
pretty_name: WebChain
license: other
license_name: webchain-academic-research-license-v1.0
license_link: LICENSE
gated: true
size_categories:
  - 10K<n<100K
tags:
  - web-agent
  - gui-agent
  - multimodal
  - trajectory
  - grounding
  - planning
  - accessibility-tree
  - screenshot
  - reinforcement-learning
extra_gated_heading: Request access to WebChain
extra_gated_description: Academic and educational use is free upon approval. Commercial use requires a separate license agreement from the authors.
extra_gated_button_content: Submit access request
extra_gated_prompt: By submitting this request, you confirm that the information you provide is accurate, that you will use the dataset only under the LICENSE terms, and that any commercial use requires separate written permission from the authors.
extra_gated_fields:

  Full Name:
    type: text

  Organization Type:
    type: select
    options:
      - "Academic / University Research Lab"
      - "Non-profit Research Institute"
      - "Independent Researcher / Open Source Contributor"
      - "Company employee — personal learning only (no work use)"
      - "Company / Commercial use ⚠️ Please email sicheng_fan@foxmail.com instead"

  Organization Name + Homepage URL:
    type: text
    description: "e.g., 'Stanford NLP Group, https://nlp.stanford.edu'. We may verify."

  Supervisor / Manager Name + Verifiable Contact:
    type: text
    description: "e.g., 'Prof. Chris Manning, manning@stanford.edu'. Required for verification."

  Intended Use (2-3 sentences, please be specific):
    type: text
    description: "e.g., 'Pretraining a 3B agent model for ICLR 2027 submission on long-horizon planning; weights to be released under MIT.'"

  I confirm my use is strictly non-commercial:
    type: checkbox

  I agree to cite WebChain in any publications and not redistribute raw data:
    type: checkbox
---

# 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

## Access policy

This repository is **publicly visible but access-gated**.

- **Academic and educational use**: free upon approval.
- **Commercial use**: not automatically permitted. Please contact the authors to obtain a separate commercial license.
- **All users** must submit an access request through the Hugging Face gating form.

Access is granted on a **case-by-case basis**. Approval is generally intended for researchers, students, and educators using the data for non-commercial research or teaching.

## What is included

This repository contains the WebChain dataset in both raw and processed forms. Some raw modalities in the public release are partially available, especially full-page screenshots.

```text
WebChain/
├── README.md
├── LICENSE
├── .gitattributes
├── raw/
│   ├── json/
│   ├── img/
│   └── full_screenshots/
└── processed/
    ├── high_parquet/
    └── low_parquet/
```


### Directory overview

- `raw/json/`: raw trajectory and step-level structured records.
- `raw/img/`: viewport-level images or cropped visual observations used for step grounding.
- `raw/full_screenshots/`: partial full-page screenshots aligned with step context where available. This folder is not exhaustive and does not provide full coverage for all trajectories or steps.
- `processed/high_parquet/`: processed Parquet files for high-level task formulation. Samples typically contain the user instruction, prior action history, and the current page screenshot, targeting planning and long-horizon decision-making.
- `processed/low_parquet/`: processed Parquet files for low-level step formulation. Samples typically contain the current-step instruction together with the current observation, targeting grounding and action localization.

## Dataset summary

- **31,725** human-verified trajectories
- **317,993** total interaction steps
- **428** unique domains
- Average trajectory length: **10.02** steps
- Average task duration: **1.07 minutes**

Each step may include the following modalities, depending on the release split and format:

- **Visual context**: viewport screenshots and, where available, full-page screenshots
- **Structural context**: HTML snapshots and Accessibility (AX) tree snapshots
- **Precise grounding**: element bounding boxes, CSS selectors, and pixel coordinates
- **Behavioral annotations**: action type, input text when applicable, timestamps
- **Reasoning traces**: Chain-of-Thought style rationales where included in the release

## Data construction

The paper describes a three-stage construction pipeline:

1. **Constraint-Based Task Synthesis**
   - Structured functionality extraction from target websites
   - Schema-constrained task generation to avoid unsupported or hallucinated tasks
2. **Human-in-the-Loop Trajectory Collection**
   - Human annotators execute tasks on real websites
   - Each step records state, action, and rich alignment signals
3. **Post-processing Contextual Enrichment**
   - Visual grounding densification
   - Synthetic rationale generation for planning supervision

## Intended use

WebChain is intended for research and education in:

- Web agents
- GUI grounding
- Vision-Language-Action (VLA) models
- Long-horizon planning
- Agentic reasoning on websites
- Dataset and benchmark research for multimodal agents

Potential use cases include:

- Supervised fine-tuning (SFT)
- Reward modeling or post-training
- Long-horizon planning experiments
- Benchmarking on held-out web interaction tasks

## Format note

This repository is not intended to be parsed as a single standardized Hugging Face dataset repository with one unified schema across all files. The recommended programmatic entry point is the processed Parquet release under `processed/high_parquet/` and `processed/low_parquet/`.

The raw folders are released as downloadable research artifacts and may require custom parsing scripts depending on the specific subdirectory and file type.

## Quick start

### Option A: load processed Parquet files directly

```python
from datasets import load_dataset

# Example: read all parquet shards in the higher-fidelity release
hf_repo = "computer-use-agent-Lab/WebChain"

dataset = load_dataset(
    "parquet",
    data_files={
        "train": f"hf://datasets/{hf_repo}/processed/high_parquet/*.parquet"
    }
)

print(dataset)
print(dataset["train"][0])
```

### Option B: iterate over downloaded local shards

```python
import glob
import pandas as pd

files = sorted(glob.glob("processed/high_parquet/*.parquet"))
print(f"Found {len(files)} parquet shards")

sample = pd.read_parquet(files[0])
print(sample.head())
print(sample.columns.tolist())
```

### Option C: access raw files after approval

After approval, you can clone or download the repository and work directly with:

- `raw/json/` for raw structured records
- `raw/img/` for viewport images
- `raw/full_screenshots/` for partial page-level images

## Example available fields

A step in WebChain may align the following information:

- task / instruction
- trajectory id
- step id
- action type
- action arguments
- click coordinates
- target bounding box
- input text
- CSS selector
- XPath or DOM reference
- AX tree snapshot
- screenshot path
- full screenshot path
- timestamp
- optional rationale / CoT

## Responsible use

You agree not to:

- attempt to re-identify individuals from the dataset;
- use the dataset in ways that violate privacy, platform rules, or applicable law;
- redistribute the dataset outside the scope permitted by the LICENSE;
- use the dataset commercially without separate written permission.

Please report any privacy, redaction, or policy issue to the maintainers.

## Citation

If you use WebChain in your work, please cite:

```bibtex
@article{fan2026webchain,
  title   = {WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces},
  author  = {Sicheng Fan and Rui Wan and Yifei Leng and Gaoning Liang and Li Ling and Yanyi Shang and Dehan Kong},
  journal = {arXiv preprint arXiv:2603.05295},
  year    = {2026}
}
```

## Contact

For academic access requests, please use the Hugging Face request form.

For **commercial licensing**, please contact:

- **Fan Sicheng**
- **sicheng_fan@foxmail.com**

## Acknowledgements

We thank the annotators, collaborators, and institutions who supported the creation and release of WebChain.