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.resultsgaia2
Gaia2
Paper | Code | Project Page
Dataset Summary
Gaia2 is a benchmark dataset for evaluating AI agent capabilities in simulated environments. The dataset contains 800 scenarios that test agent performance in environments where time flows continuously and events occur dynamically.
The dataset evaluates seven core capabilities: Execution (multi-step planning and state changes), Search (information gathering and synthesis), Adaptability (dynamic response to environmental… See the full description on the dataset page: https://huggingface.co/datasets/meta-agents-research-environments/gaia2.agents-last-exam-data
Agents Last Exam — Task Input Data
Input files (the materials each task hands to the agent at run start) for the
Agents Last Exam (ALE) benchmark. Browsable per-task directory layout.
The Agents Last Exam dataset family
ALE is published as three companion HuggingFace datasets:
Dataset
Contents
Access
Task Card Metadata
One row per task: titles, prompts, taxonomy, input-file descriptors
Open
Task Input Data
The input/ files each task hands the agent at… See the full description on the dataset page: https://huggingface.co/datasets/agents-last-exam/agents-last-exam-data.ale-images-qcow2
ALE QEMU runner image
agentslastexam/ale-qemu is the container-side runtime used by the ALE qemu
provider. It packages QEMU, KVM integration, NAT networking, noVNC, and process
supervision. The Ubuntu or Windows guest is supplied separately as
/storage/data.qcow2.
Docker is the container runtime. Dockur is the upstream QEMU-in-Docker project
whose startup and networking stack this image inherits. ALE adds a stable
runner contract around that upstream image.
The image is based on… See the full description on the dataset page: https://huggingface.co/datasets/agents-last-exam/ale-images-qcow2.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.certificatesunit4-students-scoresda-code-evaluation-resultsrequestsagents-last-exam-reference
Agents Last Exam — Reference (Ground-Truth) Data
⚠️ Gated dataset. This repo contains the ground-truth / reference outputs
used to score the Agents Last Exam (ALE) benchmark. Access requires login,
agreement to the terms on the access-request form, and manual approval.
Note (06/16/26): This repository was accidentally deleted and has been recreated. The
previous list of approved requesters could not be restored, so even if you
were granted access before, you will need to… See the full description on the dataset page: https://huggingface.co/datasets/agents-last-exam/agents-last-exam-reference.D2E-480p
D2E-480p
Project Page · Paper (arXiv) · GitHub · OWA Toolkit Documentation
This is the dataset for D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI. 268.7 hours of synchronized video, audio, and input events from 29 PC games across diverse genres (FPS, open-world, sandbox, and more), for training vision-action models and game agents.
What's included:
Video + Audio: H.264 encoded at 480p 60fps with game audio. Fixed 0.5s keyframe intervals and… See the full description on the dataset page: https://huggingface.co/datasets/open-world-agents/D2E-480p.unit_1_quiz_student_responsesD2E-Original
D2E-Original
Project Page · Paper (arXiv) · GitHub · OWA Toolkit Documentation
This is the dataset for D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI. 273.4 hours of synchronized video, audio, and input events from 29 PC games across diverse genres (FPS, open-world, sandbox, and more), for training vision-action models and game agents.
What's included:
Video + Audio: H.264 encoded at FHD/QHD 60fps with game audio.
Input events: Keyboard… See the full description on the dataset page: https://huggingface.co/datasets/open-world-agents/D2E-Original.Nexus-Agents-ToolCalling
Nexus Agents — Tool-Calling Conversations
Synthetic, schema-verified tool-calling conversations for training the Nexus Projects
agents. This is the exact data behind
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF),
including the verification transcripts that scored it (27/27 on the behavioral
interview eval, vs 13/27 for the base model).
Links: the fine-tuned model →
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF) ·
the generator + seed data + eval harness →
Nexus Training Studio ·… See the full description on the dataset page: https://huggingface.co/datasets/NexusProjectsAI/Nexus-Agents-ToolCalling.final-certificatesagents-last-exam-data-archive
Agents Last Exam — Task Data Archive (input + reference)
⚠️ Gated dataset. This repo packages each task's input, software, and
reference (ground-truth) data into a single archive (ale-tasks-data.tar.gz)
for convenient one-shot download — in particular for running ALE locally with
the local Docker provider,
which fetches it and mounts each task's data at run time. Because it includes
the reference outputs used to score runs, access requires login, agreement to
the terms on the… See the full description on the dataset page: https://huggingface.co/datasets/agents-last-exam/agents-last-exam-data-archive.vpt-owamcapThis dataset is an OWAMcap conversion from the Video PreTraining (VPT) minecraft dataset.
It is compressed with the WebDataset Format, which is essentially a series of compressed tar files.
Each sample contains:
.mp4 (containing video, from the original VPT dataset)
.jsonl (containing actions, from the original VPT dataset)
.mcap (OWAMcap format containing actions, which is converted from jsonl)
There are 26322 numbers of samples, a total volume of 5.2TB.
For reading OWAMcap data, please… See the full description on the dataset page: https://huggingface.co/datasets/open-world-agents/vpt-owamcap.trustworthy-biology-agents-traces
Trustworthy Biology Agents — Run Traces
Raw execution traces from 1,329 agent runs across three coding agents on three
biology benchmarks — BiomniBench-DA, BixBench, and CompBioBench. This is the scrubbed
trace bundle for the study in
manu-tej/ai-scientists; the write-up
lives in that repo's RESULTS.md.
The motivating question is not only whether an agent reaches the right answer, but
whether it behaves like a trustworthy analyst when the task is ambiguous,
under-specified, or… See the full description on the dataset page: https://huggingface.co/datasets/amanutej/trustworthy-biology-agents-traces.agents-last-exam
Agents Last Exam — Task Card Metadata (v1.1)
A metadata-only release (v1.1) of 152 tasks from the Agents Last Exam (ALE)
benchmark for evaluating computer-use agents on long-horizon professional work.
The Agents Last Exam dataset family
ALE is published as three companion HuggingFace datasets:
Dataset
Contents
Access
Task Card Metadata
One row per task: titles, prompts, taxonomy, input-file descriptors
Open
Task Input Data
The input/ files each task… See the full description on the dataset page: https://huggingface.co/datasets/agents-last-exam/agents-last-exam.agentshot
Wan2.2-Lightning
We are excited to release the distilled version of Wan2.2 video generation model family, which offers the following advantages:
Fast: Video generation now requires only 4 steps without the need of CFG trick, leading to x20 speed-up
High-quality: The distilled model delivers visuals on par with the base model in most scenarios, sometimes even better.
Complex Motion Generation: Despite the reduction to just 4 steps, the model retains excellent motion dynamics in… See the full description on the dataset page: https://huggingface.co/datasets/xingzhaohu/agentshot.TIR-Bench
TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning
Introduction:
TIR-Bench is a comprehensive benchmark designed to evaluate the "thinking-with-images" capabilities of Multimodal Large Language Models (MLLMs), addressing a gap left by existing benchmarks like Visual Search which only test basic operations. As models like OpenAI o3 begin to intelligently create and operate tools to transform images for problem-solving, TIR-Bench provides 13… See the full description on the dataset page: https://huggingface.co/datasets/Agents-X/TIR-Bench.Social-Media-Agents-Benchmark
🤖 SoMe: A Realistic Benchmark for LLM-based Social Media Agents
📋 Overview
SoMe is a comprehensive benchmark designed to evaluate the capabilities of Large Language Model (LLM)-based agents in realistic social media scenarios. This benchmark provides a standardized framework for testing and comparing social media agents across multiple dimensions of performance.
SoMe comprises a diverse collection of:
8 social media agent tasks
9,164,284 posts from various… See the full description on the dataset page: https://huggingface.co/datasets/LivXue/Social-Media-Agents-Benchmark.agent-sft-stitch-zh-tts
agent-sft-stitch-zh-tts
Voiced version of voidful/agent-sft-stitch-zh: the STITCH-S spoken chunks synthesized with BlueMagpie-TTS (hung_yi_lee voice), per-utterance loudness-aligned to -23 LUFS, best-of-N + Whisper-CER accepted.
Configs
records (default): one row per agent dialogue — id/source/user/msg (full STITCH-S trajectory) + available_tools + STITCH quality scores + spoken (ordered list of the utterances, each with audio, text, seg_index, cer, accepted… See the full description on the dataset page: https://huggingface.co/datasets/voidful/agent-sft-stitch-zh-tts.AgentSearch-V1
Getting Started
The AgentSearch-V1 dataset boasts a comprehensive collection of over one billion embeddings, produced using jina-v2-base. The dataset encompasses more than 50 million high-quality documents and over 1 billion passages, covering a vast range of content from sources such as Arxiv, Wikipedia, Project Gutenberg, and includes carefully filtered Creative Commons (CC) data. Our team is dedicated to continuously expanding and enhancing this corpus to improve the search… See the full description on the dataset page: https://huggingface.co/datasets/SciPhi/AgentSearch-V1.course-certificates-of-excellenceexample_datasetDataset preview available at: https://huggingface.co/spaces/open-world-agents/visualize_dataset
apex-agents-v1.1
APEX-Agents 1.1
APEX-Agents 1.1 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. They require agents to work across realistic project files and applications such as documents, spreadsheets, PDFs, email, chat, and calendar.
Tasks: 240 total (80 per job category)
Worlds: 31 total (8 investment… See the full description on the dataset page: https://huggingface.co/datasets/mercor/apex-agents-v1.1.wave-uiLICENSE