datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
AgentDoG1.0-Training-Data
AgentDoG1.0 Training Data
[💻 GitHub] | [📊 ATBench Dataset] | [📄 ATBench Paper] | [📄 AgentDoG Paper] | [🤗 Collection]
AgentDoG1.0 Training Data releases supervised instruction-tuning data for trajectory-level AI-agent safety modeling. It is paired with the AgentDoG and ATBench line of work: ATBench is the benchmark release, while this repository contains training-oriented data for binary safety classification and fine-grained taxonomy diagnosis.
Introduction… See the full description on the dataset page: https://huggingface.co/datasets/AI45Research/AgentDoG1.0-Training-Data.Agent-G2-ALFWorld-Webshop-sft-data
Agent-G2 SFT Data
Agent-G2 SFT Data contains reasoning and action trajectories for supervised
fine-tuning (SFT) in the Agent-G2 project.
Associated paper: Agent-G2: Gaussian Guidance for Agentic Reinforcement
Learning — accepted to the EMNLP 2026 Main Conference.
The dataset covers two interactive agent environments:
WebShop: agents search for products, select options, and complete
purchases according to user requirements.
ALFWorld: agents interact with household environments… See the full description on the dataset page: https://huggingface.co/datasets/xiamoent/Agent-G2-ALFWorld-Webshop-sft-data.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.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.data-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.sft-datatool-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.tool-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.proper-agents-data
ProPer Agents — data
Data for ProPer Agents: Proactivity Driven Personalized Agents for Advancing
Knowledge Gap Navigation (ACL 2026).
Paper ·
Adapters
Three domains: code, medical, pwab (product recommendation).
Layout
{domain}/
raw/train.jsonl source examples
raw/test.jsonl
raw/{domain}_rga_{train,test}.jsonl RGA SFT data (Alpaca format)
raw/{domain}_dga_{train,test}.jsonl DGA SFT data (Alpaca format)… See the full description on the dataset page: https://huggingface.co/datasets/itsgupta/proper-agents-data.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.TCNNet-SFT-NetCom-zhTW-1.1M
[TCNNet] A Traditional Chinese Networking and Communication Instruction Fine-Tuning Dataset (zh-TW)
A large-scale supervised fine-tuning (SFT) dataset created specifically for TCNNet-9B, a Chinese language model specialized in networking and communications domains. The dataset contains question-answer pairs generated from various networking, cybersecurity, and tech review articles written in Traditional Chinese.
Dataset Description
Dataset Summary
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/DataAgent/TCNNet-SFT-NetCom-zhTW-1.1M.dataagent-dabench-altimate-noctx-sft-410
DataAgent DAB Altimate NoCtx SFT 410
This dataset contains 410 validated SFT trajectories generated by an Altimate-centered DataAgent pipeline. The paired RL release is forseasons/dataagent-dabench-altimate-noctx-rl-500.
Contents
train_sft.jsonl: final SFT rows, one selected trajectory per task.
train_sft.parquet: parquet version of the same final rows.
summary.json: merge/filter summary.
dataset_package_manifest.json: row counts, statistics, checksums.
audit/:… See the full description on the dataset page: https://huggingface.co/datasets/forseasons/dataagent-dabench-altimate-noctx-sft-410.agent-data-collection
Dataset Card for OpenHands Agent Logs
This dataset consists of multi-turn dialogues between a simulated human and an LLM-based agent interacting in a virtual operating system environment. Each conversation involves the agent reasoning about and solving command-line tasks through execute_bash and related actions.
Dataset Format
Each file in the dataset is a .json file, structured as a list of instances. Each instance contains:
id: A unique identifier for the interaction… See the full description on the dataset page: https://huggingface.co/datasets/fantos/agent-data-collection.Pretrain-Taiwan-DentistKnowledge-zhTW-290KLaplaceAI 繁中領域知識資料集計畫
利用我在爬蟲自動化與資料後處理上的專業，針對不同大小的領域知識資料集進行建立與維護。
在 LaplaceAI 的 huggingface 頁面，你可以找到許多不同領域的資料集。
這項 datasets 是由 LaplaceAI 整理維護的牙科相關知識。
ptdbench-data-format-task-agent-loop-022-llama-dapo-math-dataset
PTDBench dataset snapshot: task_agent_loop_022-llama-dapo-math
This repository stores the immutable runtime dataset snapshot for one
materialized PTDBench task. It intentionally excludes model weights and
training checkpoints.
PTDBench family: data_format
Source evaluation metric: val-core/math_dapo/reward/mean@1
Provenance: Processed from BytedTsinghua-SIA/DAPO-Math-17k; task-specific bytes are pinned.
License: Apache-2.0
The artifact manifest records every hydrated runtime… See the full description on the dataset page: https://huggingface.co/datasets/LIF1014/ptdbench-data-format-task-agent-loop-022-llama-dapo-math-dataset.agentdog-lite-qwen35-08b-base-training-data-suite
AgentDoG-Lite Qwen3.5-0.8B 基座训练数据套件
本数据集用于 AgentDoG-Lite Summer Camp 轨迹级 Agent 安全诊断任务，目标是训练模型判断完整 agent trajectory 是否安全。
核心判断标准不是风险词匹配，而是：
Agent 是否实际执行了 unsafe action。
即：
风险出现 != unsafe
风险被执行 == unsafe
相关模型
Full-SFT 完整权重：https://huggingface.co/hhhggfdd/doc-was-wrong-because-training-started-from-qwen3.5-0.8b-base-not-agentdog-full-sft
LoRA adapter：https://huggingface.co/hhhggfdd/doc-was-wrong-because-training-started-from-qwen3.5-0.8b-base-not-agentdog-lora… See the full description on the dataset page: https://huggingface.co/datasets/hhhggfdd/agentdog-lite-qwen35-08b-base-training-data-suite.agent-data-collection
Dataset Card for OpenHands Agent Logs
This dataset consists of multi-turn dialogues between a simulated human and an LLM-based agent interacting in a virtual operating system environment. Each conversation involves the agent reasoning about and solving command-line tasks through execute_bash and related actions.
Dataset Format
Each file in the dataset is a .json file, structured as a list of instances. Each instance contains:
id: A unique identifier for the… See the full description on the dataset page: https://huggingface.co/datasets/Artificial-Production-Units/agent-data-collection.TCNNet-Pretrain-NetCom-zhTW-3.7M
[TCNNet] A Large-scale Traditional Chinese Networking and Communication Continuous Pretraining Dataset (zh-TW)
A specialized domain knowledge dataset created for continuous pretraining of TCNNet-9B, a Chinese language model based on Yi-9B and specialized in networking and communications domains. The dataset contains articles from various networking, cybersecurity, and tech review sources written in Traditional Chinese.
Dataset Description
Dataset Summary
This… See the full description on the dataset page: https://huggingface.co/datasets/DataAgent/TCNNet-Pretrain-NetCom-zhTW-3.7M.AgentSkiller-11Ktoucan-agentic-thinking
Toucan Agentic with Thinking Dataset
This dataset contains agentic reasoning responses generated by MiniMax-M2.1 based on questions from Agent-Ark/Toucan-1.5M_SFT.
Dataset Description
For each user question, the model generates:
Thinking process: The model's reasoning wrapped in <think> tags
Response: A complete, helpful answer in natural language
The original tool definitions are preserved in the tools field for reference.
Statistics
Split
Examples… See the full description on the dataset page: https://huggingface.co/datasets/agent-data/toucan-agentic-thinking.