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license: mit
tags:
- benchmark
linked_spaces:
- FlyaiLab/ecommerce_last_exam_leaderboard
dataset_info:
features:
- name: instance_id
dtype: string
- name: domain
dtype: string
- name: config
dtype: string
- name: user_question
dtype: string
- name: docker_image
dtype: string
splits:
- name: test
num_examples: 120
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: travel
data_files:
- split: test
path: data/travel/test-*
- config_name: e_commerce
data_files:
- split: test
path: data/e_commerce/test-*
---
# E-Commerce Last Exam
<iframe
src="https://FlyaiLab-ecommerce_last_exam_leaderboard.hf.space"
frameborder="0"
width="100%"
height="600"
></iframe>
A benchmark for evaluating LLM agents on **120 real-world travel planning and e-commerce tool-use tasks**. Each task runs in an isolated Docker container with domain-specific CLI tools and SQLite databases. Agents must search, analyze, and produce structured recommendations.
- **Repository**: [alibaba-flyai/ecommerce_last_exam](https://github.com/alibaba-flyai/ecommerce_last_exam)
- **Evaluation CLI**: [flyai-bench](https://pypi.org/project/flyai-bench/) (`pip install flyai-bench`)
- **Leaderboard**: [FlyaiLab/ecommerce_last_exam_leaderboard](https://huggingface.co/spaces/FlyaiLab/ecommerce_last_exam_leaderboard)
## Dataset Summary
E-Commerce Last Exam consists of 120 tasks across two configs:
| Config | Tasks | Domains |
|--------|-------|---------|
| `travel` | 77 | Hotel booking, transport routing, attraction planning |
| `e_commerce` | 43 | Travel gear, food, electronics, lifestyle shopping |
Each task provides a natural-language user question and a pre-built Docker image containing the environment (CLI tools, databases, test harness). Agents interact with the environment via tool calls and produce a structured answer scored 0.00 - 1.00.
## Data Fields
| Field | Type | Description |
|-------|------|-------------|
| `instance_id` | string | Unique task identifier (e.g., `attraction_auckland_extreme_sports_415`) |
| `domain` | string | Task domain (e.g., `attraction`, `hotel`, `transport`, `consume`) |
| `config` | string | Benchmark config: `travel` or `e_commerce` |
| `user_question` | string | Natural-language task description |
| `docker_image` | string | Docker image for the task environment |
## Usage
```python
from datasets import load_dataset
# Load all 120 tasks
ds = load_dataset("FlyaiLab/ecommerce_last_exam", split="test")
# Load only travel tasks (77)
ds_travel = load_dataset("FlyaiLab/ecommerce_last_exam", "travel", split="test")
# Load only e-commerce tasks (43)
ds_ecom = load_dataset("FlyaiLab/ecommerce_last_exam", "e_commerce", split="test")
```
## Evaluation
```bash
pip install flyai-bench
# Run evaluation on travel config
flyai-bench run --dataset-config travel --limit 5 --dry-run
flyai-bench run --dataset-config travel
# Generate report and submit
flyai-bench report
flyai-bench submit --model your-model --provider your-provider
```
See the [GitHub repository](https://github.com/alibaba-flyai/ecommerce_last_exam) for full documentation.
## Citation
If you use this benchmark, please cite:
```bibtex
@misc{ecommerce_last_exam,
title={E-Commerce Last Exam: A Benchmark for LLM Agent Evaluation on Real-World Tool-Use Tasks},
author={FlyaiLab},
year={2026},
url={https://huggingface.co/datasets/FlyaiLab/ecommerce_last_exam}
}
```
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