---
language:
- en
license: apache-2.0
task_categories:
- question-answering
tags:
- agent
- benchmark
- tool-use
- korean
---

<p align="center">
  <img src="banner.png" />
</p>

# **🇰🇷 Ko-AgentBench v1**

**"Korean Agent Benchmark Project"**

**English | [한국어](README.md)**

As AI agents become more sophisticated, it has become crucial to precisely measure their performance under conditions similar to real-world environments. However, most benchmarks are designed based on English-speaking environments, which limits their ability to reflect Korea's unique usage contexts.

To address this issue, we have developed a high-quality agent benchmark specialized for the Korean real-world usage environment.

# Ko-AgentBench Key Features ✨
**1. Step-by-step Task Design**

We have comprehensively analyzed agent capabilities across 7 levels, from simple tool calls to long-term contextual abilities and robustness handling capabilities.

**2. 18 Korean-specific APIs and High-quality Scenarios Tailored to Real-life Environments**

Based on APIs from Korean real-world usage environments such as Naver, Maps, Kakao, and websites, we have implemented realistic problem-solving scenarios closely related to domestic users' daily lives, such as 'appointment booking' and 'blog review search'.

**3. Cache-based Iterative Evaluation and Robustness Testing**

We solve chronic problems of existing benchmarks, such as 'information attribute inconsistency changes'.
By improving failed API responses, we ensure benchmark consistency and reliability.

By evaluating error recognition/response capabilities (strategies) in intentional error situations, we select models that operate stably even in real-world environments.

**4. Step-specific Precision Metrics**

We evaluate the necessity/requirements of problem-solving step by step, including tool selection, parameter configuration, and data flow. Through this, we quantitatively identify the strengths and weaknesses of models.

## **Data Loading**

```bash
from datasets import load_dataset

# Load specific level
dataset = load_dataset("Hugging-Face-KREW/Ko-AgentBench", data_files="L1.json")

# Or load all levels
dataset = load_dataset("Hugging-Face-KREW/Ko-AgentBench", data_files="*.json")
```

# Dataset Overview

- Define task classification system for agent benchmark design
- Design to evaluate agent's tool calling capabilities in a step-by-step manner

## Dataset Scope

- Evaluation Target: Open-weight sLLM (supports tool calling), Commercial APIs
- Evaluation Scope: Agent tool calling performance in single-turn and multi-turn conversation situations
- Applied APIs: 18 Korean-specific open APIs


# Task Levels

## Single-Turn

**L1. Single Tool Call**
- Goal: Verify the most basic API calling capability
- Description: Check if the given tool can be executed with correct parameters
- Feature: Evaluate "accuracy only" by performing requests with specified API names or natural language requests as-is
- Example: "Search for 'Rapid Current' using Naver Book API and tell me the price."
- Example: "Tell me the price of the 'Rapid Current' book"

**L2. Tool Selection**
- Goal: Verify the ability to select the optimal API among multiple candidate tools
- Description: Users make requests in natural language, and the model must select the most suitable tool from the given tool list
- Feature: Evaluate accurate tool mapping with input natural language
- Example: "Check the price of the 'All Back English Middle 2-1 Cheonjae (Kim)' book."
- Candidate tools: `hotel_booking_api`, `aladin_books_api`
    - Candidate tools must have no mutual correlation.

**L3. Sequential Tool Reasoning**
- Goal: Verify planning and execution capabilities through multi-step reasoning
- Description: Check if a correct pipeline can be constructed by connecting the results of one tool as input to another tool
- Feature: Evaluate "planned chain-of-tools" rather than simple calls
- Example: "Tell me when the Instax11 I bought from 11st Amazon will be delivered"
- Candidate tools: `11st_order_api`, `customs_api`, `cj_delivery_api`
    - Tools must be callable sequentially (11st delivery number inquiry → customs clearance → courier company)

**L4. Parallel Tool Reasoning**
- Goal: Collect information in parallel and derive conclusions by synthesizing it
- Description: Simultaneously call multiple independent tools, compare and analyze results, then produce final answers
- Feature: Evaluate multi-source aggregation (information synthesis and comparison ability)
- Example: "Check the stock of the 'Hanroro Grapefruit Apricot Club' book."
- Candidate tools: `kyobo_books_api`, `aladin_books_api`
- Expected answer: There are 12 books at Kyobo Book Centre and 18 books at Aladin, totaling 30 books.
    - At this time, candidate tools must handle the same function in parallel.

**L5. Error Handling and Robustness**
- Goal: Verify coping ability in error situations
- Description: Evaluate how various failure modes are handled, not just "failed"
- **Sub-items:**
- A. Request for additional questions
    - Guide users to make clearer requests when information is insufficient
- B. Hallucination prevention
    - Prohibit calling non-existent APIs
    - Prohibit "pretending to succeed" answers when failed
- C. Fallback maneuvers
    - Whether alternative APIs with the same function can be utilized when specific API errors occur
- Example: "When Naver Movie API call fails → Report 'API call failed' or call Kakao Movie API as alternative"

## Multi-Turn

**L6. Efficient Tool Utilization**
- Goal: Verify the ability to efficiently reuse previous tool results
- Description: While recalling APIs in all situations is accurate, it's inefficient in terms of cost and delay. Conversely, unconditionally reusing old information also causes accuracy problems.
- Feature: Evaluate whether reasonable choices can be made between "recall vs reuse"
- Example:
    - User: "Compare Coupang and Naver prices." → Result: Coupang 80, Naver 85
    - User: "What was the Naver price?"
    - Correct answer: 85 (utilize past information, avoid unnecessary recalls)
    - Wrong answer: Call API again or "I don't know"

**L7. Long-Context Reasoning**
- Goal: Verify the ability to maintain long-term context in multi-turn conversations
- Description: Remember information from several turns ago and correctly perform tool calling by connecting it with new questions
- Example:
    - User's first question: "I'm going to travel to Jeju Island."
    - Later: "How's the weather?" → Call weather API using Jeju Island context
    - (Additional turn) "If it rains, find places where I can buy an umbrella." → Utilize all previous Jeju Island + weather context

## Links
You can check more detailed information about Ko-AgentBench.
- 🏆 [Live Leaderboard](https://huggingface.co/spaces/huggingface-KREW/Ko-AgentBench)
- 📊 [Dataset](https://huggingface.co/datasets/huggingface-KREW/Ko-AgentBench)
- 📝 [Github](https://github.com/Hugging-Face-KREW/Ko-AgentBench)

## Contact
If you have any questions about the dataset and benchmark, please contact us!

Hugging Face KREW is a Korean non-profit research organization that strives to deeply understand artificial intelligence through Hugging Face and contribute to open source.
- ✍🏻 Blog: [KREW-blog](https://hugging-face-krew.github.io/)
- 🐦 HuggingFace Community: [@huggingface-KREW](https://huggingface.co/huggingface-KREW)
- 💼 LinkedIn: [Hugging Face KREW](https://www.linkedin.com/company/hugging-face-krew/)