datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
UltraData-SFT-Agent-2609
UltraData-SFT-Agent-2609
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-SFT-Agent-2609 is the L3 refined data for Agent instruction-tuning within UltraData's L0-L4 tiered data management framework. Built for the post-training of MiniCPM5-2B, it complements UltraData-SFT-2605 (core-domain SFT) with executable Agent trajectories. The release contains approximately 500,000 samples spanning tool use… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-SFT-Agent-2609.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.Omnimodal-Agent-SFT-2K
OmniGAIA: Omni-Modal General AI Assistant Benchmark
📄 Paper
•
💻 Code & Demo
•
🤗 Dataset & Model
•
📈 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.Nemotron-AIQ-Agentic-Safety-Dataset-1.0
Nemotron-AIQ Agentic Safety Dataset
Dataset Summary
Nemotron-AIQ-Agentic-Safety-Dataset is a comprehensive dataset that captures a broad range of novel safety and security contextual risks that can emerge within agentic systems. It highlights the robustness of NVIDIA's open model, llama-3.3-nemotron-super-49b-v1, when deployed as a research assistant inside AIQ, demonstrating its ability to handle a diverse spectrum of agentic safety and security challenges. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-AIQ-Agentic-Safety-Dataset-1.0.orca-agentinstruct-1M-v1
Dataset Card
This dataset is a fully synthetic set of instruction pairs where both the prompts and the responses have been synthetically generated, using the AgentInstruct framework.
AgentInstruct is an extensible agentic framework for synthetic data generation.
This dataset contains ~1 million instruction pairs generated by the AgentInstruct, using only raw text content publicly avialble on the Web as seeds. The data covers different capabilities, such as text editing, creative… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1.agent-llm-traces-v2
Exgentic Agent LLM Traces v2 — Agent Chat Only
OpenTelemetry-shaped execution traces for 10,057 agent runs across 6 benchmarks (AppWorld, SWE-bench, BrowseCompPlus, τ²-bench Airline/Retail/Telecom), filtered to the agent under test's chat-only LLM calls. This is the dataset for replay testing, behavioral analysis, or any task where you care about what the benchmarked model actually did — not the eval scaffolding around it.
This v2 release expands upon Exgentic/agent-llm-traces… See the full description on the dataset page: https://huggingface.co/datasets/Exgentic/agent-llm-traces-v2.jupyter-agent-dataset
Jupyter Agent Dataset
Dataset Details
Dataset Description
The dataset uses real Kaggle notebooks processed through a multi-stage pipeline to de-duplicate, fetch referenced datasets, score educational quality, filter to data-analysis–relevant content, generate dataset-grounded question–answer (QA) pairs, and produce executable reasoning traces by running notebooks. The resulting examples include natural questions about a dataset/notebook, verified answers, and… See the full description on the dataset page: https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset.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.White-Hat-Security-Agent-Prompts-600K
White Hat Security Agent Prompts 600K
Overview
The White-Hat-Security-Agent-Prompts-600K dataset is a practitioner-perspective security prompts corpus of 596,295 richly contextualized queries, designed to represent how real-world defensive security professionals communicate, interrogate, and reason through active threat scenarios.
Where most security datasets catalogue CVEs, malware signatures, or CTF write-ups, this collection teaches models to operate from inside the… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/White-Hat-Security-Agent-Prompts-600K.AgentTrap
AgentTrap
AgentTrap is a runtime benchmark for evaluating whether LLM agents can use third-party skills while resisting malicious workflow behavior.
Contents
data/tasks.*: the task registry with resolved release paths for each skill package.
data/raw/cases.json: the canonical case registry.
skills/: all multi-file skill packages used by the tasks, normalized into skills/malicious/ and skills/benign/.
fixtures/workspace/: shared workspace fixtures used by the… See the full description on the dataset page: https://huggingface.co/datasets/zhmzm/AgentTrap.deepseek-v4-pro-0813-agentic
DeepSeek-V4-Pro 0813 Agentic (DS4)
A standalone, verifiable-first agentic training corpus: 19,072 training traces
plus 2,135 held-out evaluation rows (validation 1,070 / test 1,065), generated by
DeepSeek-V4-Pro 0813 (deepseek-v4-pro-0813, official API, thinking mode) across 13 verifiable task families,
each row admitted only after passing a deterministic programmatic verifier. The corpus is
designed to be directly usable for SFT, GRPO/RLVR, and NeMo Gym / NeMo RL
(verified… See the full description on the dataset page: https://huggingface.co/datasets/r0b0tlab/deepseek-v4-pro-0813-agentic.Paper-Review-Dataset
Dataset Card for Paper Review Dataset (ICLR 2023-2025)
Dataset Description
This dataset contains paper submissions and review data from the International Conference on Learning Representations (ICLR) for the years 2023, 2024, and 2025. The data is sourced from OpenReview, an open peer review platform that hosts the review process for top ML conferences.
Focus on Review Data
This dataset emphasizes the peer review ecosystem surrounding academic papers. Each… See the full description on the dataset page: https://huggingface.co/datasets/AgentAlphaAGI/Paper-Review-Dataset.Audio-Video-Engineering-Agentic-Tasks-1M
Audio/Video Engineering Agentic Tasks (1M)
Abstract
A highly specialized dataset comprising 1,029,459 in-context troubleshooting prompts and execution commands built for the deepest levels of media production. Unlike standard datasets that simulate clean, theoretical instructions, this matrix captures the chaotic, highly-detailed, and conversational reality of professional audio engineers, composers, and video editors mid-session. It is engineered to train multimodal AI… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M.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.agent-think-tool_use
Agent Think Tool Use
Датасет многошаговых агентных сессий для дообучения моделей работе с кодом, инструментами и инженерными задачами. Записи содержат пользовательские требования, комментарии агента во время работы, decision summaries, вызовы инструментов, результаты запусков, обработку ошибок и финальную проверку.
Каждый shard представляет отдельную связанную сессию, а не отдельный вопрос и ответ. Данные охватывают исследование задачи, работу с документацией, проектирование… See the full description on the dataset page: https://huggingface.co/datasets/ru-dataset/agent-think-tool_use.DEBATE
DEBATE: Diverse Multi-Agent Debates
This dataset is presented in the paper "MALLM: Multi-Agent Large Language Models Framework".
Citation
comming soon.
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/BlueIsGreen/Edge-Agent-Reasoning-WebSearch-260K.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/DEMIRUNC/Edge-Agent-Reasoning-WebSearch-260K.Agentic-Long-Context-Understanding-QA 📖 Agentic Long Context Understanding 📖
Self-Taught Agentic Long Context Understanding (Arxiv).
AgenticLU refines complex, long-context queries through self-clarifications and contextual grounding, enabling robust long-document understanding in a single pass.
Installation Requirements
This codebase is largely based on OpenRLHF and Helmet, kudos to them.
The requirements are the same
pip install openrlhf
pip install -r ./HELMET/requirements.txt… See the full description on the dataset page: https://huggingface.co/datasets/yzhuang/Agentic-Long-Context-Understanding-QA.piaf
Dataset Card for Piaf
Dataset Summary
Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
plain_text
Size of downloaded dataset files: 1.31 MB
Size of the generated dataset: 3.18 MB
Total amount of disk used: 4.49 MB
An… See the full description on the dataset page: https://huggingface.co/datasets/AgentPublic/piaf.agents-index
AgentCrush Agent Index
Evidence-ranked index of the AI agent economy. Updated daily from agentcrush.xyz.
Overview
1,443 agents indexed across categories: developer tools, tokenized agents, service agents, model families
207 evidence-ranked with verified multi-signal scores
Updated: 2026-09-23
Configs
Config
Description
Rows
agents
All indexed agents with metadata
~1,443
evidence_ranked
Evidence-ranked tier only
~207
snapshots_latest… See the full description on the dataset page: https://huggingface.co/datasets/AgentCrush/agents-index.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/rAVEUK/Creative-Professionals-Agentic-Tasks-1M.AgentJudgeBench
AgentJudgeBench: Evaluating LLM Judge Reliability on Agentic Tool-Calling
A benchmark for systematically evaluating how reliably LLM judges assess
agentic tool-calling workflows across structured, dependency-driven tasks.
Why this benchmark?
AgentJudgeBench measures how reliably LLM judges assess agentic tool-calling outputs. It provides 3,808 benchmark records spanning six DAG topologies and three difficulty… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow-AI/AgentJudgeBench.agent-finance-reasoning
Agent Finance Reasoning
This dataset includes traces and associated metadata from agentic interactions on a Financial Reasoning task. We built the agentic system in langgraph and ReAct agents. In each sample, the agent is given a question that must be answered with information from one or more company 10-K's. The 10-K's are accessible to the agents through tools.
Our question-answer pairs have been carefully co-created with Snorkel's Expert Data-as-a-Service network of financial… See the full description on the dataset page: https://huggingface.co/datasets/snorkelai/agent-finance-reasoning.orca
Orca Dataset Collection
The Orca Dataset Collection is a unified compilation of multiple datasets from the Microsoft Orca and OpenOrca projects.
All duplicate entries have been removed, and any personally identifiable information (PII) has been carefully redacted.
All rows have been sorted by hash and split into JSONL files containing 100,000 entries each for easier handling and consistency.
Example Entry
id: MD5 hash of the system prompt, question, and answer JSON… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/orca.data-agent
📈 Data Agent
Data-analysis tasks as a plain, load-and-go dataset — no runtime, no framework required. Each
row is one self-contained task: a real tabular dataset, a question about it, and a
deterministically-checkable gold answer. Load it, prompt any model however you like, and grade the
result with the bundled grader.
Where it comes from
Built from the jupyter-agent dataset
— real data-science notebooks over Kaggle datasets. Every question–answer pair was… See the full description on the dataset page: https://huggingface.co/datasets/FineEnvs/data-agent.lightblue-tagengo-gpt4
lightblue/tagengo-gpt4
An unofficial, reformatted version of lightblue/tagengo-gpt4.
Tagengo is described by its author as the world's largest high-quality multilingual chat dataset - containing over 75,000 single-turn conversations between humans and GPT‑4 (gpt-4-0125-preview) across 74 languages. It fills a major gap in multilingual chat data, which has so far been limited compared to English.
Additional Processing
Split by language
Kept only entries with both… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/lightblue-tagengo-gpt4.data-agent-benchmarks
LongHorizon Full Data-Agent Benchmarks
Companion data artifacts for five complete evaluation tracks:
DataSciBench full55 / 167 metric entries
DABStep full450
DABStep-Research full100
DSBench Modeling full74
LongDS full68 / 2,225 turns
The companion GitHub repository contains processed manifests, evaluation code,
historical API ReAct baseline code, download/preparation tools, and the frozen
source lock. artifact_manifest.json records every uploaded object's size,
SHA-256… See the full description on the dataset page: https://huggingface.co/datasets/noel7Y/data-agent-benchmarks.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/Torenn/Edge-Agent-Reasoning-WebSearch-260K.text-sft-questions-answers-only
text-sft: Questions and Answers
This dataset consists of question-and-answer pairs generated from short excerpts drawn from Wikipedia, Cosmopedia, and FineWeb-Edu. It is an adapted version of agentlans/text-sft.
Overview
The dataset provides compact examples of English question-and-answer relationships that can help models learn linguistic patterns, syntactic structures, and semantic associations between questions and their corresponding answers.
Intended Use… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/text-sft-questions-answers-only.