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[ICLR 2026] DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle
| Date | Stars |
|---|---|
| 2026-07-31 | 433 |
| 2026-08-06 | 433 |
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<div align="center">
<h1 align="center">
<img src="assets/DAComp-logo.png" width="230" alt="DAComp Logo" />
<br>
Benchmarking Data Agents across the Full Data Intelligence Lifecycle
</h1>
<p>
🌐 <a href="https://da-comp.github.io/">Website</a> |
📑 <a href="https://arxiv.org/abs/2512.04324">Paper</a> |
🤗 <a href="https://huggingface.co/DAComp">Dataset</a> |
🐥 <a href="https://x.com/fangyu_lei/status/1998458208160198714?s=20">Twitter</a>
</p>
</div>
## 📰 News
- **2025-12-08**: 🔥 We release the [DAComp dataset](https://huggingface.co/DAComp) and the [paper](https://arxiv.org/abs/2512.04324).
## 👋 Overview
DAComp offers a research-grade benchmark spanning full data intelligence workflows: repository-level data engineering (DAComp-DE), open-ended data analysis (DAComp-DA), a Chinese-localized split (DAComp-zh), and accompanying baseline agents with evaluation suites curated in this repository.
<div align="center">
<img src="assets/dacomp.png" width="800" alt="DAComp_Main_Figure">
</div>
## 🔍 Installation
Set up the environment using the following commands:
```
conda create -n dacomp python=3.12
conda activate dacomp
pip install -r requirements.txt
pip install openhands-ai
conda install -c conda-forge nodejs
conda install -c conda-forge poetry
```
## 🚀 Quick access DAComp Dataset
DAComp consists of two subsets: DA (Analysis) and DE (Engineering). You can download the dataset from [DAComp](https://huggingface.co/DAComp).
Please use the provided scripts in [dacomp-da/download.py](./dacomp-da/README.md) and [dacomp-de/download.py](./dacomp-de/README.md) to download the data automatically.
```
# --- Download DAComp-DA Dataset ---
cd dacomp-da
# Download DAComp-DA dataset，English tasks into `dacomp-da/tasks` and Chinese tasks into `dacomp-da/tasks_zh`. Change repo_id and download_dir in download.py.
python download.py
# --- Download DAComp-DE Dataset ---
cd dacomp-de
# Download DAComp-DE dataset，English tasks into `dacomp-de/tasks` and Chinese tasks into `dacomp-de/tasks_zh`. Change repo_id and download_dir in download.py.
python download.py
```
## 🚀 Quickstart
### DAComp-DA
- Agents: pick `methods/da-agent` (three-stage baseline), `methods/spider-agent` (single, image-first baseline), or OpenHands; fill in your model config, install requirements, and run `run.py` as shown in each agent [README](./methods/README.md).
### DAComp-DE
- Agents: pick `methods/de-agent` (OpenHands integration); fill in your model config, install requirements, as shown in [README](./methods/de-agent/README.md).
## ⚖️ Evaluation
### DAComp-DA
- Standard DAComp-DA Tasks: follow [dacomp-da/evaluation_suite/README.md](./dacomp-da/evaluation_suite/README.md) to evaluate **DA** tasks.
- Results: export a run to `dacomp-da/evaluation_suite/agent_results` with `get_results.py` from the agent folder.
### DAComp-DE
- Standard DAComp-DE Tasks: follow [dacomp-de/evaluation_suite/README.md](./dacomp-de/evaluation_suite/README.md) to evaluate **DE-Impl** and **DE-Evol** tasks.
- DE-Arch Unified Evaluator: follow [dacomp-de/evaluation_suite_arch/README.md](./dacomp-de/evaluation_suite_arch/README.md) to evaluate **DE-Arch** tasks.
# 📋 Leaderboard Submission
To submit your agent results to the leaderboard, please follow the instructions in [DAComp Submission Guidelines](https://docs.google.com/document/d/1t93acmrwBmJQ_I6bzlnqHq1y8G5N2kwJU_ZfeVNG3yE/edit?usp=sharing).
# 🙇♂️ Acknowledgement
We thank the [OpenHands](https://github.com/OpenHands/OpenHands) team for their valuable contributions to the open-source community.
# ✍️ Citation
If you find our work helpful, please cite as
```
@misc{lei2025dacomp,
title={DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle},
author={Fangyu Lei and Jinxiang Meng and Yiming Huang and Junjie Zhao and Yitong Zhang and Jianwen Luo and XinExcerpt of 5,618 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:46dcec0a27184e38, llm:Repository title and description: 'DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle' (ICLR 2026).
matched fp:46dcec0a27184e38, llm:Repository title and description: 'DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle' (ICLR 2026).
matched fp:46dcec0a27184e38, llm:Repository title and description: 'DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle' (ICLR 2026).