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dataset_info:
features:
- name: agent_id
dtype: string
- name: platform_name
dtype: string
- name: agent_name
dtype: string
- name: agent_description
dtype: string
- name: agent_category
dtype: string
- name: agent_usage
dtype: string
- name: usage_example
dtype: float64
- name: agent_url
dtype: string
- name: agent_accessibility
dtype: string
- name: agent_pricing
dtype: string
- name: base_model
dtype: string
- name: update_time
dtype: string
- name: misc
dtype: string
- name: platform_id
dtype: string
splits:
- name: agents
num_bytes: 14529673
num_examples: 9759
download_size: 6044862
dataset_size: 14529673
configs:
- config_name: default
data_files:
- split: agents
path: data/train-*
---
# AgentSearchBench Agents
**AgentSearchBench** is a large-scale benchmark for AI agent search, built from nearly 10,000 real-world agents sourced from the [GPT Store](https://chatgpt.com/gpts), [Google Cloud Marketplace](https://cloud.google.com/marketplace), and [AgentAI Platform](https://agent.ai/).
🌐 [Project Page](https://bingo-w.github.io/AgentSearchBench) • 💻 [Codebase](https://github.com/Bingo-W/AgentSearchBench)
---
## Overview
This repository contains **AgentBase**, the agent corpus underlying AgentSearchBench. It comprises 9,759 real-world AI agents crawled from public platforms, capturing practical challenges such as capability overlap and inconsistent documentation that reflect the complexity of real agent ecosystems.
Each agent entry includes metadata such as name, description, capabilities, and platform of origin, forming the candidate pool against which benchmark tasks are evaluated.
---
## Dataset Statistics
| Platform | Description |
|----------|-------------|
| GPT Store | Custom GPT agents from OpenAI's public marketplace |
| Google Cloud Marketplace | Cloud-native agents and tools |
| AgentAI Platform | General-purpose agents from agent.ai |
| Total Agents |
|---|
| 9,759 |
---
## Data Fields
- `agent_id`: Unique identifier for the agent.
- `platform_name`: Name of the platform where the agent is hosted.
- `agent_name`: Display name of the agent.
- `agent_description`: Text description of the agent's purpose and capabilities.
- `agent_category`: Category or domain the agent belongs to.
- `agent_usage`: Instructions or notes on how to use the agent.
- `usage_example`: Example input or interaction demonstrating the agent's functionality.
- `agent_url`: URL linking to the agent's page on its host platform.
- `agent_accessibility`: Accessibility status of the agent, e.g. public or restricted.
- `agent_pricing`: Pricing model or cost information associated with using the agent.
- `base_model`: Underlying language model powering the agent.
- `update_time`: Timestamp of the agent's last update.
- `misc`: Miscellaneous metadata not captured by other fields.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("AgentSearch/AgentSearchBench-Agents")
```
---
## Related Datasets
| Dataset | Description |
|---------|-------------|
| [AgentSearchBench-Tasks](https://huggingface.co/datasets/AgentSearch/AgentSearchBench-Tasks) | Benchmark tasks: single-agent queries, multi-agent queries, and task descriptions |
| [AgentSearchBench-Responses](https://huggingface.co/datasets/AgentSearch/AgentSearchBench-Responses) | 60K+ raw agent execution responses from the validation set |
---
## Citation
```bibtex
@article{}
```
|