datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.data-gouv-datasets-catalog
📢 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 ! 🙌
🇫🇷 Data.gouv.fr Datasets Catalog
This dataset contains a processed and embedded version of the… See the full description on the dataset page: https://huggingface.co/datasets/AgentPublic/data-gouv-datasets-catalog.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.DATA_SOURCEdpsk-v4-flash-data
dpsk-v4-flash-data
Training data built by the AgentPTB arm for cell dpsk-v4-flash — pi / DeepSeek v4-flash @ effort thinking.
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/dpsk-v4-flash.h*, and the companion to the run record in agentic-ptb/dpsk-v4-flash-record.
field
value
plot cell
dpsk-v4-flash
driver
pi / DeepSeek v4-flash… See the full description on the dataset page: https://huggingface.co/datasets/agentic-ptb/dpsk-v4-flash-data.terminal-agent-sft-data-v2Agent-R1-data
Agent-R1 Data
Preprocessed datasets and runtime artifacts for reproducing the agentic reinforcement learning experiments in Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning.
These files cover the main benchmarks used in the Agent-R1 paper and codebase: ALFWorld, WebShop (full), HotpotQA, Paper Search (PaSa), and cross-corpus retrieval corpora (HotpotQA, 2WikiMultiHopQA, MuSiQue).
Total size: ~19 GB (compressed where noted).
Repository… See the full description on the dataset page: https://huggingface.co/datasets/Melmaphother/Agent-R1-data.insta-150k-v3
InSTA: Towards Internet-Scale Training For Agents
Brandon Trabucco (1) Gunnar Sigurdsson (2) Robinson Piramuthu (2) Ruslan Salakhutdinov (1)
(1) Carnegie Mellon University, Machine Learning Department (2) Amazon
This is a dataset from the authors of the paper Towards Internet-Scale Training For Agents, and contains 150k web navigation tasks to facilitate internet-scale training of LLM agents without relying heavily on human annotations. The dataset is split into… See the full description on the dataset page: https://huggingface.co/datasets/data-for-agents/insta-150k-v3.tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified
Text to Terminal, v2 — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned, combined, and thinking-augmented version of muellerzr/text_to_terminal_v2. It pairs natural language instructions with their corresponding terminal/bash commands, now augmented with explicit <think> reasoning traces that model the step-by-step thought process before producing the final command.The restructuring approach is directly… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified.terminal-agent-sft-data-raw-sampledata-agent-sft
🛠️ Data Agent — SFT
4,677 worked examples of an agent doing data science the right way. Each row is a complete,
verified-correct trajectory: read the question, poke at the data with a shell tool, reason,
compute, and write the answer. Every one of these solved its task and passed a deterministic grader
— so you're fine-tuning on demonstrations that are known to be correct, not just plausible.
Drop-in ready for TRL: conversational messages + tools.
Where it comes… See the full description on the dataset page: https://huggingface.co/datasets/FineEnvs/data-agent-sft.tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified
ToolACE - Tool-Use Agent Data Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of the Team-ACE/ToolACE dataset. ToolACE is a high-quality conversational tool-use dataset containing 11,300+ examples of natural language interactions requiring function calling across diverse domains. This version converts the original OpenAI function-call format into a standardized multi-turn tool-use… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified.opus-max-data
opus-max-data
Training data built by the AgentPTB arm for cell opus-max — Claude Code / claude-opus-5 @ 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/opus-max.h*, and the companion to the run record in agentic-ptb/opus-max-record.
field
value
plot cell
opus-max
driver
Claude Code / claude-opus-5
reasoning effort
max… See the full description on the dataset page: https://huggingface.co/datasets/agentic-ptb/opus-max-data.tool-reasoning-sft-MEMORY-mem_agent-sft-data-cleaned-rectified-408k
mem_agent-sft-data-cleaned-rectified
Multi-turn long-context memory-agent SFT dataset with explicit reasoning traces, structured tool calls, and sequential chunk-processing sub-chains.
Schema
Column
Type
Description
messages
string (JSON)
JSON-serialized list of {role, content} dicts. Roles: system, user, reasoning, tool_call, tool_output, answer
core_chain_OR_subcall
string
"core_chain" (full orchestration trace) or "subcall" (single chunk-processing step)… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-MEMORY-mem_agent-sft-data-cleaned-rectified-408k.kto_redteaming_data_for_secret_loyaltygrok-data
grok-data
Training data built by the AgentPTB arm for cell grok — pi / grok-4.6 @ effort xhigh.
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/grok.h*, and the companion to the run record in agentic-ptb/grok-record.
field
value
plot cell
grok
driver
pi / grok-4.6
reasoning effort
xhigh
total size
2.54 GB
path in run… See the full description on the dataset page: https://huggingface.co/datasets/agentic-ptb/grok-data.insta-150k-v1
InSTA: Towards Internet-Scale Training For Agents
Brandon Trabucco (1) Gunnar Sigurdsson (2) Robinson Piramuthu (2) Ruslan Salakhutdinov (1)
(1) Carnegie Mellon University, Machine Learning Department (2) Amazon
This dataset, presented in the paper Towards Internet-Scale Training For Agents, contains 150k web navigation tasks generated to facilitate Internet-scale training of agents without relying heavily on human annotations. The dataset is split into training and… See the full description on the dataset page: https://huggingface.co/datasets/data-for-agents/insta-150k-v1.gui-agent-desktop-sft-datatool-reasoning-sft-RESEARCH-dr-tulu-sft-deep-research-agent-data-cleaned-rectified
Deep Research - Tulu SFT Data Cleaned Rectified
👥 Follow the Author
Supriti Vijay
Overview
This dataset is a cleaned and restructured version of the DR-TULU SFT dataset released by AllenAI's RL Research team. The original DR-TULU dataset represents significant work in creating high-quality training data for reasoning-enhanced language models with tool use capabilities. This version addresses structural issues in the original release while preserving… See the full description on the dataset page: https://huggingface.co/datasets/SupritiVijay/tool-reasoning-sft-RESEARCH-dr-tulu-sft-deep-research-agent-data-cleaned-rectified.AgenticQwen-Data
Dataset Overview
This dataset contains synthetic training examples for agentic RL.
Rather than simple prompt-response pairs, each sample is a self-contained agent task with a user goal, hidden scenario context, tool interfaces, operational constraints, adversarial pressure, and verifiable success criteria.
The data is generated or expanded by LLMs to create diverse workflows, tool ecosystems, and failure modes. As a result, the dataset is designed not just to train models to respond… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/AgenticQwen-Data.data_agent
data_agent
Plain, Harbor-free version of the data-analysis agent tasks — usable directly via load_dataset.
Splits: train 5000, test 250, eval 144. Deterministic grading, no LLM judge.
Columns
task_id, source_row_id — ids
question — the question to answer
answer — gold answer; reward_mode (numeric/exact_short/exact_bool/list/list_csv/flexible), atol/rtol — how to grade
difficulty_level (1-5), difficulty_tier (easy/medium/hard)
kaggle_dataset — source Kaggle… See the full description on the dataset page: https://huggingface.co/datasets/AdithyaSK/data_agent.data_agent_harbor_train_sft
data_agent_harbor_train_sft
4677 verified agent trajectories (SFT) for the data_agent_harbor_train environments. TRL-ready tool-calling format: messages + tools columns.
Each row is a reward=1 rollout — instruction -> bash tool calls (shell commands) -> final answer — graded deterministically (no LLM judge). Single bash tool throughout.
Columns
messages: OpenAI/TRL chat format (system, user, assistant+tool_calls, tool, ...). tool_calls[].function.arguments are… See the full description on the dataset page: https://huggingface.co/datasets/AdithyaSK/data_agent_harbor_train_sft.sft_data_vsi_wo_video_hint
agent-traces-data-pipeline-debugging
Agent Traces: data-pipeline-debugging
Synthetic multi-agent workflow traces with LLM-enriched content for the data-pipeline-debugging domain.
Part of the juliensimon/open-agent-traces collection — 10 datasets covering diverse domains and workflow patterns.
What is this dataset?
This dataset contains 2,033 events across 50 workflow runs, each representing a complete multi-agent execution trace. Every trace includes:
Agent reasoning — chain-of-thought for each… See the full description on the dataset page: https://huggingface.co/datasets/tesraghavan/agent-traces-data-pipeline-debugging.sft_data_longvila_wo_video_hint
agent-traces-data-pipeline-debugging
Agent Traces: data-pipeline-debugging
Synthetic multi-agent workflow traces with LLM-enriched content for the data-pipeline-debugging domain.
Part of the juliensimon/open-agent-traces collection — 10 datasets covering diverse domains and workflow patterns.
What is this dataset?
This dataset contains 2,033 events across 50 workflow runs, each representing a complete multi-agent execution trace. Every trace includes:
Agent reasoning — chain-of-thought for each agent step… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/agent-traces-data-pipeline-debugging.kimi-data
kimi-data
Training data built by the AgentPTB arm for cell kimi — kimi-code / kimi-k3 @ effort high.
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/kimi.h*, and the companion to the run record in agentic-ptb/kimi-record.
field
value
plot cell
kimi
driver
kimi-code / kimi-k3
reasoning effort
high
total size
0.75 GB
path in run… See the full description on the dataset page: https://huggingface.co/datasets/agentic-ptb/kimi-data.misc-merged-claude-code-traces-v1
MISC Unification of Public Claude Code Traces
A unified dataset of 32,133 deduplicated Claude API conversation traces focused on software engineering and code generation tasks. This dataset merges and normalizes traces from 10 different source datasets into a single, consistent format.
Dataset Description
This dataset contains real Claude API interaction traces capturing software engineering workflows including:
Code generation and modification
Bug fixing and… See the full description on the dataset page: https://huggingface.co/datasets/agent-data/misc-merged-claude-code-traces-v1.insta-150k-v2
InSTA: Towards Internet-Scale Training For Agents
Brandon Trabucco (1) Gunnar Sigurdsson (2) Robinson Piramuthu (2) Ruslan Salakhutdinov (1)
(1) Carnegie Mellon University, Machine Learning Department (2) Amazon
This is a revised dataset, from the authors of the paper Towards Internet-Scale Training For Agents, contains 150k web navigation tasks generated to facilitate Internet-scale training of agents without relying heavily on human annotations. The dataset is split… See the full description on the dataset page: https://huggingface.co/datasets/data-for-agents/insta-150k-v2.