datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
course-imagesapex-agents
APEX–Agents
APEX–Agents is a benchmark from Mercor for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by investment banking analysts, management consultants, and corporate lawyers, and require agents to navigate realistic work environments with files and tools (e.g., docs, spreadsheets, PDFs, email, chat, calendar).
Tasks: 480 total (160 per job category)
Worlds: 33 total (10 banking, 11 consulting, 12… See the full description on the dataset page: https://huggingface.co/datasets/mercor/apex-agents.agent-reward-bench
AgentRewardBench
💾Code
📄Paper
🌐Website
🤗Dataset
💻Demo
🏆Leaderboard
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent TrajectoriesXing Han Lù, Amirhossein Kazemnejad*, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stańczak, Peter Shaw, Christopher J. Pal, Siva Reddy*Core Contributor
Loading dataset
You can use the huggingface_hub library to load the dataset. The dataset is available on Huggingface Hub at… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/agent-reward-bench.gaia2_filesystem
GAIA2 Filesystem
This is a dataset containing files for the GAIA2 benchmark. You should not use this dataset on its own, but instead use the Meta Agents Research Environments framework to execute scenarios from that GAIA2 dataset.
Dataset Link
https://huggingface.co/datasets/meta-agents-research-environments/gaia2
Contact Details
Publishing POC: Meta AI Research Team
Affiliation: Meta Platforms, Inc.
Website:… See the full description on the dataset page: https://huggingface.co/datasets/meta-agents-research-environments/gaia2_filesystem.UI-Genie-Agent-16kThis repository contains the Trajectory dataset from the paper UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based
Mobile GUI Agents.
Github: https://github.com/Euphoria16/UI-Genie
Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K.AgenticOCR-SFT
AgenticOCR SFT Training Data
Supervised fine-tuning data for the AgenticOCR project.
The dataset contains 7,631 training records in sft_combined_0422.json. Image paths in each record are relative to the repository root and point into sft_images/.
gelato-osworld-agent-trajectoriesGui-agent
Gui-Agent — GUI trajectories in LIBERO/VLA format
Human GUI demonstrations from four sources, unified into a single VLA-style
intermediate representation and written as LIBERO-layout HDF5, so LIBERO/VLA
dataloaders run against GUI data unchanged.
raw source ──[adapter]──> GuiEpisode ──[writer]──> LIBERO-style HDF5
per-source the IR format- what you train on
only specific
25,872 episodes / 453,264 steps / 235 GB… See the full description on the dataset page: https://huggingface.co/datasets/Yushi123/Gui-agent.Omnimodal-Agent-SFT-2K
OmniGAIA: Omni-Modal General AI Assistant Benchmark
📄 Paper
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💻 Code & Demo
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🤗 Dataset & Model
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📈 Leaderboard
This dataset contains omni-modal agent supervised fine-tuning (SFT) trajectories in the LlamaFactory SFT data format. You can directly follow LlamaFactory's instructions to fine-tune your omni-modal LLMs.OmniGAIA is a benchmark for Omni-Modal General AI Assistants that jointly reason over vision, audio, and language with external tools. It is… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/Omnimodal-Agent-SFT-2K.AgentIF-OneDay
Dataset Card for AgentIF-OneDay
Dataset Details
AgentIF-OneDay is a comprehensive benchmark designed to evaluate AI agents on diverse, daily tasks across work, life, and learning scenarios. Unlike evaluations focused solely on task difficulty, this dataset emphasizes the breadth of general user needs, requiring agents to handle complex attachments, infer implicit instructions, and deliver tangible file-based outputs. It comprises 104 tasks structured around Open… See the full description on the dataset page: https://huggingface.co/datasets/xbench/AgentIF-OneDay.agent-as-policy
Agent as Policy — Real Dual-Arm LLM-Agent Manipulation Trials
Paper · Project page · Code
Summary
162 real-robot trials in which an LLM agent acts directly as the policy on a
bimanual YAM arm setup: it reads camera observations through a tool interface,
issues Cartesian and joint commands, and judges its own completion. Ten
manipulation tasks (block stacking, die flipping, towel folding, part insertion
and assembly, throwing), six models, three reasoning-effort… See the full description on the dataset page: https://huggingface.co/datasets/Agent-as-Policy/agent-as-policy.gui-agent-outputwave-uiLICENSE
Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M.unified-agent-trajectories
Unified Benchmark Agent Trajectories
Dataset release: v2.1.1 (2026-09-18)Record format: unified-agent-sft-v1
A growing collection of benchmark agent execution trajectories converted into one
transparent, multimodal, tool-aware representation. These are complete recorded benchmark
runs—not ordinary chat transcripts—including benchmark tasks, model reasoning and answers,
tool calls, tool observations, runtime status, and benchmark scores when available. The
directory layout is… See the full description on the dataset page: https://huggingface.co/datasets/ChrisDing1105/unified-agent-trajectories.Agentic-MME
Agentic-MME Dataset
This is the official dataset for the Agentic-MME benchmark, featured in Hugging Face Daily Papers.
Agentic-MME is a comprehensive benchmark designed to evaluate the abilities of multimodal agents in tool-use, web searching, and multi-step reasoning through visual clues.
Usage
You can load the dataset using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("Crystal1047/Agentic-MME")
# To see the first record… See the full description on the dataset page: https://huggingface.co/datasets/Agentic-MME/Agentic-MME.Agentic-SLS-ASTM
Agentic-SLS-ASTM
ASTM mechanical-test specimens (D638 tensile, D790 flex) printed on the Inova Mk1 SLS printer and pulled on an MTS / TestWorks Instron. Each row is a single specimen with full geometry, scalar results, stress–strain + raw DAQ curves, and — for SLS rows — FK references and an embedded snapshot of the upstream print profile from ppak10/Agentic-SLS-Database.
Rows are self-contained for ML use: the full PrintProfile JSON is inlined, so features (material/energy… See the full description on the dataset page: https://huggingface.co/datasets/ppak10/Agentic-SLS-ASTM.AgentFlow-DocDancer-benchmarkEMU-Agentic-PostTrain-Dataeasyr1-agent-grounding-dataCBIS-DDSM-SEGfailed_agent_trajectory
Mobile Trajectory Verification Data
This dataset is prepared for verifying how LoT agent can help us identify incorrect actions taken in mobile execution trajectory. As mentioned in our paper (see citation 1), We select 52 mobile agent trajectories with specific task goals from instruction-
guided executions on MagicWand platform(see citation 2). Each folder has a execution trajectory that includes screenshots along this trajectories, json files describing the details of each… See the full description on the dataset page: https://huggingface.co/datasets/orlando23/failed_agent_trajectory.AgentHorizon
AgentHorizon
AgentHorizon is a benchmark for evaluating LLM judges of computer-use agents. Each item is a recorded trajectory of a GUI agent attempting a long-horizon desktop task (often 100 to 300+ steps), paired with a ground-truth label that says whether the agent actually completed the task. A judge reads the trajectory (instruction, action sequence, and screenshots) and predicts success or failure.
Repository contents
Path
What it is… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow/AgentHorizon.agent-tts-libraryapex-agents
APEX–Agents
APEX–Agents is a benchmark from Mercor for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by investment banking analysts, management consultants, and corporate lawyers, and require agents to navigate realistic work environments with files and tools (e.g., docs, spreadsheets, PDFs, email, chat, calendar).
Tasks: 480 total (160 per job category)
Worlds: 33 total (10 banking, 11 consulting, 12 law)… See the full description on the dataset page: https://huggingface.co/datasets/lobinni/apex-agents.agent-reward-bench
AgentRewardBench
💾Code
📄Paper
🌐Website
🤗Dataset
💻Demo
🏆Leaderboard
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent TrajectoriesXing Han Lù, Amirhossein Kazemnejad*, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stańczak, Peter Shaw, Christopher J. Pal, Siva Reddy*Core Contributor
Loading dataset
You can use the huggingface_hub library to load the dataset. The dataset is available on Huggingface Hub at… See the full description on the dataset page: https://huggingface.co/datasets/yuanyyaa/agent-reward-bench.apex-agents
APEX–Agents
APEX–Agents is a benchmark from Mercor for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by investment banking analysts, management consultants, and corporate lawyers, and require agents to navigate realistic work environments with files and tools (e.g., docs, spreadsheets, PDFs, email, chat, calendar).
Tasks: 480 total (160 per job category)
Worlds: 33 total (10 banking, 11 consulting, 12… See the full description on the dataset page: https://huggingface.co/datasets/idleengine/apex-agents.wave-ui-25k
WaveUI-25k
This dataset contains 25k examples of labeled UI elements. It is a subset of a collection of ~80k preprocessed examples assembled from the following sources:
WebUI
RoboFlow
GroundUI-18K
These datasets were preprocessed to have matching schemas and to filter out unwanted examples, such as duplicated, overlapping and low-quality datapoints. We also filtered out many text elements which were not in the main scope of this work.
The WaveUI-25k dataset includes the original… See the full description on the dataset page: https://huggingface.co/datasets/agentsea/wave-ui-25k.agent_paper_review