datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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-scoresAgentTrove
AgentTrove
AgentTrove is the largest open-source collection of agentic interaction traces to date, released by the OpenThoughts-Agent team. It contains 1,696,847 rows drawn from 219 source datasets spanning code repair, shell scripting, mathematical problem-solving, competitive programming, and general computer-use tasks.
At 1.7 million rows, AgentTrove is 4× the size of the Nemotron Terminal Corpus (430 K rows), the previous largest open-source agentic trace dataset.… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/AgentTrove.SWE-agent-trajectories
Dataset Summary
This dataset contains 80,036 trajectories generated by a software engineering agent based on the SWE-agent framework, using various models as action generators. In these trajectories, the agent attempts to solve GitHub issues from the nebius/SWE-bench-extra and the dev split of princeton-nlp/SWE-bench.
Dataset Description
This dataset was created as part of a research project focused on developing a software engineering agent using open-weight models… See the full description on the dataset page: https://huggingface.co/datasets/nebius/SWE-agent-trajectories.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.Toucan-1.5M
🦤 Toucan-1.5M:
Toucan-1.5M is the largest fully synthetic tool-agent dataset to date, designed to advance tool use in agentic LLMs. It comprises over 1.5 million trajectories synthesized from 495 real-world Model Context Protocols (MCPs) spanning 2,000+ tools. By leveraging authentic MCP environments, Toucan-1.5M generates diverse, realistic, and challenging tasks requires using multiple tools, with trajectories involving real tool executions across multi-round, multi-turn… See the full description on the dataset page: https://huggingface.co/datasets/Agent-Ark/Toucan-1.5M.DeepCoder-Preview-Dataset
Data
Our training dataset consists of 24K problems paired with their test cases:
7.5K TACO Verified problems.
16K verified coding problems from PrimeIntellect’s SYNTHETIC-1.
600 LiveCodeBench (v5) problems submitted between May 1, 2023 and July 31, 2024.
Our test dataset consists of:
LiveCodeBench (v5) problems between August 1, 2024 and February 1, 2025.
Codeforces problems from Qwen/CodeElo.
Format
Each row in the dataset contains:
problem: The coding problem… See the full description on the dataset page: https://huggingface.co/datasets/agentica-org/DeepCoder-Preview-Dataset.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.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.SWE-smith-mini_swe_agent_plus-trajectories-66k
Dataset: SWE-smith-mini_swe_agent_plus-trajectories-66k
A corpus of ~66k issue-solving trajectories collected with mini-swe-agent-plus on issues derived from SWE-smith. Each trajectory records the agent’s end-to-end process.
We training the Qwen3-8B model on different sizes of the training data. The results are shown in the figure, it could be observed that the solve rate on SWE-bench Verified improves approximately linearly with the logarithm of the data scale (1k → 66k… See the full description on the dataset page: https://huggingface.co/datasets/Kwai-Klear/SWE-smith-mini_swe_agent_plus-trajectories-66k.legi
📢 Sondage 2026 : Utilisation des datasets publiques de MediaTech
Vous utilisez ce dataset ou d’autres datasets de notre collection MediaTech ? Votre avis compte !
Aidez-nous à améliorer nos datasets publiques en répondant à ce sondage rapide (5 min) : 👉 https://grist.numerique.gouv.fr/o/albert/forms/gF4hLaq9VvUog6c5aVDuMw/11
Merci pour votre contribution ! 🙌
🇫🇷 French Consolidated Legislation Dataset (LEGI)
This dataset contains a semantic-ready and… See the full description on the dataset page: https://huggingface.co/datasets/AgentPublic/legi.open_government
Open Government Dataset
Open Government is the largest agregation of governement text and data made available as part of open data programs.
In total, the dataset contains approximately 380B tokens. While Open Government aims to become a global resource, in its current state it mostly features open datasets from the US, France, European and international organizations.
The dataset comprises 16 collections curated through two different initiaties: Finance commons and Legal commons.… See the full description on the dataset page: https://huggingface.co/datasets/AgentPublic/open_government.OpenThoughts-Agent-v1-SFT
Project |
SFT dataset |
RL dataset |
SFT model |
RL model
OpenThinker-Agent-v1-SFT
OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our first release includes datasets, models and our research codebase.
OpenThinker-Agent-v1 is a model trained for agentic tasks such as Terminal-Bench 2.0 and SWE-Bench.
The OpenThinker-Agent-v1 model is post-trained from Qwen/Qwen3-8B.
It is SFT-ed on the OpenThoughts-Agent-v1-SFT dataset, then… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT.Gui-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.agent-trajectory-sentinel
AgentTrajectorySentinel — 3581 agent episodes across 33 corpora
Committed agent trajectories with step-level telemetry, used to fit and
evaluate one-class monitors for real-time failure detection.
Paper: https://arxiv.org/abs/2608.02464
Code and the full evaluation harness:
https://github.com/sunnydubey1111/agent-trajectory-sentinel
A recorded walkthrough of the method, ending with the live demo
detecting and repairing a real failure:
https://youtu.be/a05n_000klE?t=0… See the full description on the dataset page: https://huggingface.co/datasets/sunnydubey1111/agent-trajectory-sentinel.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.AgentInstruct
AgentInstruct Dataset
🤗 [Models] • 💻 [Github Repo] • 📌 [Project Page] • 📃 [Paper]
AgentInstruct is a meticulously curated dataset featuring 1,866 high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like Task Derivation and Self-Instruct.
🔍 CoT - Harness the power of ReAct, offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/AgentInstruct.sol-max-opusnode-data
sol-max-opusnode-data
Training data built by the AgentPTB arm for cell sol-max-opusnode — Codex / gpt-5.6-sol @ effort max.
This is the corpus the arm itself assembled during its 100-hour run: what it downloaded,
filtered, rewrote and mixed. It is the input side of the checkpoints published as
agentic-ptb/sol-max-opusnode.h*, and the companion to the run record in agentic-ptb/sol-max-opusnode-record.
field
value
plot cell
sol-max-opusnode
driver
Codex / gpt-5.6-sol… See the full description on the dataset page: https://huggingface.co/datasets/agentic-ptb/sol-max-opusnode-data.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.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.agent-traces
Trace Commons — Agent Traces
Trace Commons is one open, public dataset of coding-agent sessions — the
back-and-forth between a developer and an AI coding agent, including prompts,
model responses, tool calls, and command output — contributed voluntarily as an
open resource for studying, evaluating, and building on how these agents
actually work.
Every trace here was donated only from a public, open-source repository, was
anonymized on the contributor's own machine before upload… See the full description on the dataset page: https://huggingface.co/datasets/trace-commons/agent-traces.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.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.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.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.course-certificates-of-excellenceagentlogs
AgentLogs
AgentLogs is a dataset of activity related to the GitHub agents functionality: repository metadata, agent tasks, sessions, session logs (messages, tool calls, usage details, etc.), and user records.
This dataset is described in:
Jonan Richards, Kosei Horikawa, Youmei Fan, Yutaro Kashiwa, and Mairieli Wessel (2026), AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agent. arXiv: 2608.29204 (preprint).
# Records
Size
Table
Content… See the full description on the dataset page: https://huggingface.co/datasets/risenlab/agentlogs.Agentic-Multi-SWE-RLagentic-redteam-benchmark
agentic-redteam-benchmark
v0.8 preview · 2,288 multi-step agent trajectories · 513 hand-authored gold + 1,775 provenance-flagged augmented.
A per-step benchmark that scores whether a verifier catches drift inside an agent's trajectory — not whether a prompt is harmful.
📦 Code, eval harness & issues: github.com/Alkur123/agentic-redteam-benchmark · 📄 Paper: A Per-Step Trajectory Benchmark for AI-Agent Governance Verifiers and a Corrected Catch-at-Drift Metric (Aegis AI, 2026)… See the full description on the dataset page: https://huggingface.co/datasets/jash-ai/agentic-redteam-benchmark.