datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
browser-agent-tasks
Browser Agent Tasks
Moderate, multi-step browser tasks for collecting agent trajectories and evaluating screenshot, DOM, and DOM-diff evidence.
Files
tasks.jsonl: one task definition per line.
Each task contains:
task_id: stable identifier
category: task family
instruction: complete instruction given to the browser agent
start_url: suggested public starting page
stopping_condition: when the agent must stop
constraints: safety and scope restrictions
These tasks… See the full description on the dataset page: https://huggingface.co/datasets/ishagarg1103/browser-agent-tasks.browser-agent-failure-corpus
Browser Agent Failure Corpus — case-row view
This view contains 30 controlled local regression cases from one owner-produced deterministic run on 2026-08-31 using Qwen/Qwen3-8B-GGUF:Q4_K_M, llama.cpp b10103-c588c4f47, the Codex in-app browser and the historical Mutant Web guard v2, at temperature 0, reasoning budget 0 and at most eight steps per case. It preserves model proposals, guard decisions, executed actions and recorded outcomes for inspectability and reuse. Allowed… See the full description on the dataset page: https://huggingface.co/datasets/mutantweb/browser-agent-failure-corpus.browser-agent-tasks
Browser Agent Tasks
Moderate, multi-step browser tasks for collecting agent trajectories and evaluating screenshot, DOM, and DOM-diff evidence.
Files
tasks.jsonl: one task definition per line.
Each task contains:
task_id: stable identifier
category: task family
instruction: complete instruction given to the browser agent
start_url: suggested public starting page
stopping_condition: when the agent must stop
constraints: safety and scope restrictions
These tasks… See the full description on the dataset page: https://huggingface.co/datasets/WootzappLab/browser-agent-tasks.browser-agent-phase1-sft-action-only
Browser Agent Phase 1 SFT Action-Only
What this is
Action-only step-level chat SFT data for browser-agent training.
Each example teaches the model to predict the next BrowserGym action from:
the original generation-time system prompt used for data collection
task goal and URL
short recent history
current observation text and diagnostics
Assistant targets contain only the next action.
Why this format
This is the primary training format for small-model SFT… See the full description on the dataset page: https://huggingface.co/datasets/saital/browser-agent-phase1-sft-action-only.tool-calling-browser-agent-tasks
Dataset Card
Created by: DataCreator AI
Overview
Tool Calling for Agentic Tasks with Multi-Step Workflows contains 1,062 synthetic multi-turn conversations between a user and an AI assistant. The examples primarily focus on practical agentic tasks such as train ticket booking, dynamic form filling, and payment processing. It provides diverse scenarios including successful execution, context retrieval, tool integration, and failure recovery.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DataCreatorAI/tool-calling-browser-agent-tasks.browser-agent-phase1-sft-reasoning-action
Browser Agent Phase 1 SFT Reasoning+Action
What this is
Reasoning-plus-action step-level chat SFT data for browser-agent training.
Each example uses the original generation-time system prompt, then appends a short instruction to reason first and output the final action.
Assistant targets contain:
one <think>...</think> block
then one BrowserGym action
Why this format
This is an experimental variant for comparing whether explicit reasoning supervision helps or… See the full description on the dataset page: https://huggingface.co/datasets/saital/browser-agent-phase1-sft-reasoning-action.qwen3-browser-agent-processedAgent-browser-taskarticle-02-ledger-browser-agent-audit
Your browser engine doesn't need the cloud. Cryptographic Audit Ledgers for Autonomous Browser Agents proves it.
Cryptographic Audit Ledgers for Autonomous Browser Agents: Verifiable Action Logging with SHA3-256
The Problem
Autonomous browser agents?AI-driven systems that navigate web pages, fill forms, and execute user-delegated tasks?present a fundamental accountability problem: how can users verify that an agent acted correctly and did not exceed its… See the full description on the dataset page: https://huggingface.co/datasets/kleinnner/article-02-ledger-browser-agent-audit.article-02-ledger-browser-agent-audit
Your browser engine doesn't need the cloud. Cryptographic Audit Ledgers for Autonomous Browser Agents proves it.
Cryptographic Audit Ledgers for Autonomous Browser Agents: Verifiable Action Logging with SHA3-256
The Problem
Autonomous browser agents?AI-driven systems that navigate web pages, fill forms, and execute user-delegated tasks?present a fundamental accountability problem: how can users verify that an agent acted correctly and did not exceed its… See the full description on the dataset page: https://huggingface.co/datasets/Anticloud/article-02-ledger-browser-agent-audit.