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.agent-data-collection
Agent Data Collection
A comprehensive collection of agent interaction datasets for training and evaluating AI agents across diverse domains and tasks.
This dataset aggregates high-quality agent trajectories from various environments including web browsing, code generation, household tasks, knowledge base querying, and software engineering.
The dataset is collected through methods described in Agent Data Protocol.
Dataset Splits
Each dataset configuration provides up… See the full description on the dataset page: https://huggingface.co/datasets/neulab/agent-data-collection.AgentTrove
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.Nemotron-SFT-Agentic-v2
Dataset Description
The Nemotron-SFT-Agentic-v2 dataset is a collection of synthetic single-turn and multi-turn tool-use trajectories designed to strengthen models’ capabilities as interactive, tool-using agents. It targets tasks where the model must decompose user goals, decide when to call tools, and reason over tool outputs to complete tasks reliably and safely.
This dataset is ready for commercial use.
The dataset consolidates three internally curated components (described… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2.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.real-pi-coding-agent-traces-sessions
Real Pi Coding Agent Traces Sessions
An aggregated dataset of real human–AI coding agent sessions, collected from 21 independently published Hugging Face datasets and hand-filtered to exclude synthetic or AI-generated content.
Every session is an unedited (but redacted) trace of a real person using pi — an open-source AI coding agent harness — to build, debug, and ship real open-source software. Real prompts, real tool calls, real errors, real backtracking.
Why this… See the full description on the dataset page: https://huggingface.co/datasets/MaxDevv/real-pi-coding-agent-traces-sessions.DeepSeek-v4-Pro-AgentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
DeepSeek v4 Pro Agent Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by deepseek/deepseek-v4-pro.
JSONL files: 4006
Training-ready tools
A complete configured tools schema snapshot is embedded in the collapsed section at the bottom of… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/DeepSeek-v4-Pro-Agent.Agriculture-Agent-RL-Training-Data
Agriculture Agent RL Training Data
A growing dataset of RL rollout trajectories for LLM agents on
natural/regenerative farming — the first RL/trajectory-shaped dataset in the
Copyleft Cultivars collection
(every prior dataset here is SFT/conversational Q&A). Agents call real tools
(primarily cultivars-mcp,
a plant-genomics MCP server) across 9 knowledge categories (plus a 10th,
organic_chemistry_soil_science, added 2026-08-11, and an 11th,
organic_chemistry_synthesis, added… See the full description on the dataset page: https://huggingface.co/datasets/CopyleftCultivars/Agriculture-Agent-RL-Training-Data.common-crawl-sample
Common Crawl sample
A small unofficial random subset of the famous Common Crawl dataset.
60 random segment WET files were downloaded from Common Crawl on 2024-05-12.
Lines between 500 and 5000 characters long (inclusive) were kept.
Only unique texts were kept.
No other filtering.
Languages
Each text was assigned to one of the language codes using the GCLD3 Python package.
The Chinese texts were classified as either simplified, traditional, or Cantonese using the… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/common-crawl-sample.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.novel-agent-sft-dataset
All Novel Can Be Galgame — 完整数据集
中文小说叙事理解项目的完整数据集。包含 669 本中文小说的原始文本、标注和训练数据，用于训练叙事 Agent 系统。
项目地址：https://github.com/lin1753/novel2galgame
训练代码仓库：https://github.com/lin1753/novel-agent
数据规模
目录
文件数
大小
说明
training/
52
689 MB
训练用 SFT 数据 (JSONL)
raw-books/
671
327 MB
669 本原始小说
processed/
39,842
1.2 GB
按章节预处理文本
annotations/
1,626
1 MB
原始标注文件
合计
42,191
2.2 GB
目录结构
datasets/
├── training/
│ ├── base-sft/… See the full description on the dataset page: https://huggingface.co/datasets/mikuhhn1239/novel-agent-sft-dataset.formal-math-autoformalization
Formal Math Autoformalization Dataset
A growing, CC0 public-domain corpus of ⟨natural-language statement ↔ Lean 4 statement + proof⟩ pairs, contributed through the Agentic Commons network.
Why this is scarce data. Mathlib already contains millions of proven Lean theorems — but as bare Lean, with no paired natural language:
theorem add_comm (a b : ℕ) : a + b = b + a := ... -- no "addition on naturals is commutative" attached
The scarce, valuable artifact is the pairing of the… See the full description on the dataset page: https://huggingface.co/datasets/AgenticCommons/formal-math-autoformalization.Unified_Agent_Framework
A Unified Framework for the Evaluation of LLM Agentic Capabilities
This repository contains the dataset (Benchmark, Toolkit, and Environment assets) for the paper A Unified Framework for the Evaluation of LLM Agentic Capabilities.
The official code and agent execution sandbox can be found on GitHub: whfeLingYu/A-Unified-Framework-for-the-Evaluation-of-LLM-Agentic-Capabilities.
Dataset Description
The dataset integrates diverse agent benchmarks into a standardized… See the full description on the dataset page: https://huggingface.co/datasets/whfeLingYu/Unified_Agent_Framework.DTap-Bench-Agent-Trajectories
DecodingTrust-Agent Platform
A Controllable and Interactive Red-Teaming Platform for AI Agents.
This is the full collection of the agent trajectories produced from evaluating the DTap-Bench from DecodingTrust-Agent Platform (DTAP),
spanning 14 real-world domains and 50+ simulation environments that replicate widely-used
systems such as Google Workspace, PayPal, Slack, Salesforce, Snowflake, and Databricks. Each task
ships the configuration the evaluator needs to spin up the… See the full description on the dataset page: https://huggingface.co/datasets/AI-Secure/DTap-Bench-Agent-Trajectories.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.qwen-cpp-agent-0-protocolExperiment in agentic autonomy protocols.
~ everything in this repo was created by Qwen 3.8 27B (Q4) running autonomously inside Deepseek Harness, on a single RTX 3090 GPU, for 3 weeks.
The only human artifacts are:
agents/*
human/*
AGENTS.md
DecodingTrust-Agent-Platform
DecodingTrust-Agent Platform
A Controllable and Interactive Red-Teaming Platform for AI Agents.
This is the per-task dataset for the DecodingTrust-Agent Platform (DTAP),
spanning 14 real-world domains and 50+ simulation environments that replicate widely-used
systems such as Google Workspace, PayPal, Slack, Salesforce, Snowflake, and Databricks. Each task
ships the configuration the evaluator needs to spin up the sandbox, run an agent, and verify the
outcome — config.yaml (task… See the full description on the dataset page: https://huggingface.co/datasets/AI-Secure/DecodingTrust-Agent-Platform.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.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.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.Nemotron-RL-Agentic-Terminal-Pivot-v1
Dataset Description
The Nemotron-RL-Agentic-Terminal-Pivot-v1 dataset provides training samples for reinforcement learning of command-line ("terminal use") LLM agents with the terminus_judge environment in NeMo Gym.
Each record is a single agent decision point extracted from a successful agent trajectory on a terminal task:
responses_create_params.input — the prompt: the task instruction plus the terminal interaction history (prior agent actions and terminal outputs) up to the… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Terminal-Pivot-v1.GLM-5.2-AgentThis dataset was generated using teich by TeichAI
GLM-5.2 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 319
Model metadata: glm-5.2
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/GLM-5.2-Agent.AgentEHR-Bench
AGENTEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization
Paper | Code
AGENTEHR is a novel benchmark designed to bridge the gap between idealized experimental settings and realistic clinical environments. Unlike previous tasks that focus on factual retrieval, AGENTEHR challenges agents to perform complex clinical decision-making—such as diagnosis and treatment planning—directly within raw, high-noise EHR databases.
💡 Key Features… See the full description on the dataset page: https://huggingface.co/datasets/BlueZeros/AgentEHR-Bench.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.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.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.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.Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1
Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1
Dataset Description:
Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1 is an RL dataset for training and evaluating a tool-using agent's ability to resist Indirect Prompt Injection (IPI) attacks hidden inside tool-returned environment data. In each record, the agent receives a benign user request that requires calling a read tool whose output contains an adversarial instruction disguised as legitimate domain content… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1.DeepSeek-v4-Pro-AgentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
DeepSeek v4 Pro Agent Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by deepseek/deepseek-v4-pro.
JSONL files: 4006
Training-ready tools
A complete configured tools schema snapshot is embedded in the collapsed section at the bottom of… See the full description on the dataset page: https://huggingface.co/datasets/ronaldcmz/DeepSeek-v4-Pro-Agent.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.