oncody/AI_Agent_Task_Dataset

---
license: mit
task_categories:
- text-generation
- text-classification
- token-classification
- question-answering
- table-question-answering
- summarization
- fill-mask
- text-to-speech
- automatic-speech-recognition
- audio-classification
- translation
- zero-shot-classification
- text-to-audio
- depth-estimation
- object-detection
- feature-extraction
language:
- en
tags:
- ai-agents
- synthetic
- tool-use
- procedural-generation
- RahulChaube1
- EverestQ
size_categories:
- 10G<n<100G
---

# 🤖 Massive AI Agent Task Dataset (10.5GB)

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---

## 📌 Overview

Welcome to the **AI Agent Task Dataset**, a massive **10.5GB procedural dataset** designed for training, fine-tuning, and evaluating **autonomous AI agents and LLMs**.

This dataset focuses on:

- Multi-step reasoning
- Tool usage (APIs, frameworks, systems)
- Real-world execution workflows

Perfect for building **agentic AI systems, copilots, and automation models**.

---

## 📑 Table of Contents

- Dataset Details
- Dataset Structure
- Tech Stack & Tool Coverage
- How to Use (Quickstart)
- Use Cases & Applications
- Dataset Creation & Curation
- License & Copyright

---

## 📊 Dataset Details

- **Curator:** Rahul Chaube (oncody)
- **Format:** `.jsonl` (JSON Lines)
- **Size:** ~10.5 GB
- **Language:** English
- **Scale:** Tens of millions of structured task records

---

## 🏗️ Dataset Structure

Each row represents a **complete reasoning workflow**.

### Fields

- **goal** → High-level objective
- **steps** → Step-by-step execution plan
- **tools** → Required technologies/APIs
- **output** → Expected result
- **reasoning** → Why this approach works

---

### 📌 Example

```json
{
"goal": "Automate daily price monitoring for top competitors",
"steps": [
"Fetch list of competitor URLs from Google Sheets API.",
"Scrape HTML content of each URL.",
"Parse price elements using CSS selectors.",
"Compare scraped prices with the internal product database.",
"Send a notification if a competitor's price drops below a defined threshold."
],
"tools": [
"Python",
"BeautifulSoup",
"Google Sheets API",
"Slack API"
],
"output": "A daily Slack message listing price changes and an updated Google Sheet with current competitor prices.",
"reasoning": "Google Sheets acts as an easily updatable database while automation ensures real-time monitoring and alerts."
}