README.md

metadata

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-*
task_categories:
  - text-retrieval

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, Google Cloud Marketplace, and AgentAI Platform.

📄 Paper • 🌐 Project Page • 💻 Codebase

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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.

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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

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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

from datasets import load_dataset

ds = load_dataset("AgentSearch/AgentSearchBench-Agents")

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Related Datasets

Dataset	Description
AgentSearchBench-Tasks	Benchmark tasks: single-agent queries, multi-agent queries, and task descriptions
AgentSearchBench-Responses	60K+ raw agent execution responses from the validation set

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Citation

@misc{wu2026agentsearchbench,
      title={AgentSearchBench: A Benchmark for AI Agent Search in the Wild}, 
      author={Bin Wu and Arastun Mammadli and Xiaoyu Zhang and Emine Yilmaz},
      year={2026},
      eprint={2604.22436},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
}