datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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/mercor/apex-agents.gaia2
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.unit4-students-scoresgaia2_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.agents-last-exam
Agents Last Exam — Task Card Metadata (v1.1)
A metadata-only release (v1.1) of 151 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.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.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.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.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.course-certificates-of-excellencewave-uiLICENSE
agent-sft-10B
Dataset: agent-sft-10B
This dataset was uploaded from /mnt/yulan_pretrain/mount/data_final_train/agent-sft-10B/no-curriculum/tmp.
unit3-inviteesapex-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.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.evovling_agents
Evolving Agents Benchmark
This repository contains the data presented in EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?.
Project page: https://mas-orchestra.salesforceresearch.ai/evoharness/
A versioned, per-split, multi-domain library of given Codex subagents,
produced by evovle_agents. It is the agent-track
analogue of evovling_tools: where
evovling_skills evaluates a model that generates
skills, evolving-agents evaluates a model that orchestrates given… See the full description on the dataset page: https://huggingface.co/datasets/ZixuanKe/evovling_agents.finance-agents-benchmark-traces
FAB — Agent Traces and Grading
600 completed agent runs: four models × 50 tasks × three trials.
Agents investigate a synthetic company's data room and answer financial
due-diligence questions. Each row pairs a full execution trace with the task,
final answer, grading criteria, pass/fail verdicts and judge explanations.
Tasks 041 and 049 are included.
The benchmark dataset
contains the shared data room and tasks. The
GitHub repository
contains the execution and grading harness.… See the full description on the dataset page: https://huggingface.co/datasets/secondstate/finance-agents-benchmark-traces.lectura-agents-data
LectūraAgents Dataset
Overview
This dataset is in support of findings in our paper LectūraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching. LectūaAgents is a hierarchical multi-agent framework that enables end-to-end personalized learning experiences through adaptive embodied teaching. It mirrors a professor–students’ relationship, wherein a ProfessorAgent guides a collaborative team of specialized subordinate… See the full description on the dataset page: https://huggingface.co/datasets/Jaward/lectura-agents-data.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.SWITCH
SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios
[arXiv]
[leaderboard]
[dataset]
[PDF]
Dataset Summary
SWITCH (Semantic World Interface Tasks for Control & Handling) is a multimodal embodied-interaction benchmark for understanding, modeling, and evaluating actions over Tangible Control Interfaces (TCIs) in egocentric real-world scenarios.
TCIs include everyday interfaces such as appliance panels, lighting… See the full description on the dataset page: https://huggingface.co/datasets/BAAI-Agents/SWITCH.agent-simulations
Agent Simulations
Made with the whileai SDK · Collections: Simulation, Start here: foundational post-training datasets
53,971 synthetic agent trajectories generated by simulations
across 34 agent types. The rows include successful and failed
trajectories for supervised fine-tuning, preference work, reinforcement learning, and
evaluation.
NOTE: This is generated test and training data, not curated ground truth. Review and
filter it for your application before training or… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/agent-simulations.finance-agents-benchmark
FAB — Finance Agents Benchmark
FAB is an open-source project for benchmarking LLM agents' ability to perform
financial due diligence in a synthetic company data room.
FAB consists of a dataset of tasks containing agent instructions, documents and
rubrics, and an execution harness for running and evaluating agents. This
repository contains the dataset; the harness is available on
GitHub.
Dataset
50 tasks · 160 documents · 231 grading criteria · One shared data room… See the full description on the dataset page: https://huggingface.co/datasets/secondstate/finance-agents-benchmark.agents-index
AgentCrush Agent Index
Evidence-ranked index of the AI agent economy. Updated daily from agentcrush.xyz.
Overview
1,450 agents indexed across categories: developer tools, tokenized agents, service agents, model families
221 evidence-ranked with verified multi-signal scores
Updated: 2026-10-10
Configs
Config
Description
Rows
agents
All indexed agents with metadata
~1,450
evidence_ranked
Evidence-ranked tier only
~221
snapshots_latest… See the full description on the dataset page: https://huggingface.co/datasets/AgentCrush/agents-index.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 law)… See the full description on the dataset page: https://huggingface.co/datasets/neyralabs/apex-agents.gaia2-cli
GAIA2 CLI
Benchmark dataset for gaia2-cli, the CLI-based agent evaluation harness.
Schema
Each row has two columns:
Column
Type
Description
scenario_id
string
Unique scenario identifier (e.g. scenario_universe_21_1qgjj6)
scenario
string
Complete scenario as a JSON string
Usage
from datasets import load_dataset
import json
# Load a specific config (160 scenarios)
ds = load_dataset("meta-agents-research-environments/gaia2-cli", "adaptability"… See the full description on the dataset page: https://huggingface.co/datasets/meta-agents-research-environments/gaia2-cli.SI2CA-Training-TrajectoriesDataset Card for SI2CA-Training-Trajectories
[🌐 Website] •
[🤗 Dataset] •
[📜 Paper] •
[🐱 GitHub]
💡 Introduction
This dataset consists of 32,340 coding-agent trajectories generated by Qwen3.5-122B-A10B on the same 10,780 executable Python SWE tasks under the three trajectory-curation settings of Section 4.4 of the paper: standard sampling, full self-judgement, and an efficient discovered strategy found by the recursive self-improvement framework. Each task is… See the full description on the dataset page: https://huggingface.co/datasets/Self-Improving-Coding-Agents/SI2CA-Training-Trajectories.agent-sft-stitch-zh-tts-taste-codec-chat-sample
Gemma 4 E2B Taste-S multi-turn codec SFT
This dataset contains 37,362 complete Traditional Chinese agent
dialogues selected from voidful/agent-sft-stitch-zh-tts. It covers
229,434 synthesized speech segments, approximately
520.5 hours of audio before codec extraction.
Every assistant speech segment is represented without Gemma native audio tags:
<SAY> text_token <a_code> <b_code> ... <p_code> ... </SAY>
The first assistant output starts immediately with <SAY>.
[SOPR]...[EOPR]… See the full description on the dataset page: https://huggingface.co/datasets/voidful/agent-sft-stitch-zh-tts-taste-codec-chat-sample.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 law)… See the full description on the dataset page: https://huggingface.co/datasets/abridges/apex-agents.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.