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kddcup2026-data-agents-starter-kit

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# Installation Guide

1. Get the code

git clone https://github.com/HKUSTDial/kddcup2026-data-agents-starter-kit

Downloads the entire project code from GitHub to your computer.

cd kddcup2026-data-agents-starter-kit

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install .

Installs the package published on PyPI directly — no need to clone the source.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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<div align="center">

# KDD Cup 2026 DataAgent-Bench Starter Kit

English | [中文](README.zh.md)

[![Official Website](https://img.shields.io/badge/Official%20Website-Visit%20dataagent.top-0ea5e9?style=for-the-badge&logo=googlechrome&logoColor=white&labelColor=0f172a)](https://dataagent.top)
[![Discord](https://img.shields.io/badge/Discord-Join%20Community-5865F2?style=for-the-badge&logo=discord&logoColor=white&labelColor=0f172a)](https://discord.com/invite/7eFwJQN3Fx)

</div>

This repository provides baseline starter kits for KDD Cup 2026 DataAgent-Bench. It is organized by competition phase so participants can start from the package that matches the phase they are working on.

## Repository Layout

| Directory | Purpose |
| --- | --- |
| `PHASE_1/` | Starter kit for Phase 1 tasks. |
| `PHASE_2/` | Starter kit for Phase 2 tasks and demo data format. |

Each phase directory is self-contained and includes its own README, configuration files, source code, dependency lock file, and baseline command-line entry points.

## Which Directory Should I Use?

Use `PHASE_1/` if you are working with the Phase 1 task format.

Use `PHASE_2/` if you are working with the Phase 2 demo release or Phase 2 task format. Phase 2 keeps the same general agent workflow while allowing richer task context files.

## Quick Start

Choose the phase directory first, then follow that directory's README.

```bash
cd PHASE_1
# or
cd PHASE_2
```

Then install dependencies and run the baseline from inside the selected directory:

```bash
uv sync
uv run dabench status --config configs/react_baseline.example.yaml
uv run dabench run-benchmark --config configs/react_baseline.example.yaml
```

The exact dataset layout, configuration options, tools, and output paths are documented in each phase-specific README.

## Baseline Overview

The starter kit contains a minimal ReAct-style data agent. The baseline is intentionally simple: it reads task metadata, inspects files under each task's `context/` directory through a small set of tools, calls an OpenAI-compatible model endpoint, and writes `prediction.csv` outputs.

Participants are expected to adapt or replace the baseline logic for their own methods. The code reads model endpoint settings from configuration or environment-compatible values rather than hardcoding service credentials.

## Common Project Structure

Inside each phase directory, the main files are organized as follows:

```text
configs/                         # Example baseline configuration
src/data_agent_baseline/          # Baseline source code
artifacts/                        # Local run outputs, ignored except .gitkeep
README.md                         # Phase-specific usage guide
README.zh.md                      # Chinese usage guide
pyproject.toml                    # Python project metadata
uv.lock                           # Locked dependency versions
```

## Notes

- Run commands from inside `PHASE_1/` or `PHASE_2/`, not from the repository root.
- Keep local run outputs under `artifacts/`; they are not intended to be committed.
- Review the phase-specific README before packaging or submitting a solution.

## Contact

- Open issues: https://github.com/HKUSTDial/kddcup2026-data-agents-starter-kit/issues
- Official website: https://dataagent.top
- Discord: https://discord.com/invite/7eFwJQN3Fx
- WeChat official account: `数据智能与分析实验室 DIAL`

<div align="center">
  <table>
    <tr>
      <td align="center">
        <a href="https://dataagent.top">
          <img
            src="https://api.qrserver.com/v1/create-qr-code/?size=144x144&data=https://dataagent.top&bgcolor=ffffff&color=111827&margin=8"
            alt="Official website QR code"
            width="144"
          />
        </a>
        <br />
        Official Website
      </td>
      <td align="center">
        <a href="https://discord.com/invite/7eFwJQN3Fx">
          <img
            src="https://api.qrserver.com/v1/create-qr-code/?size=144x144&data=https://discord.com/invite/7eFwJQN3Fx&bgcolor=ffffff&color=111827&margin=8"
            alt="Discord QR code"
            width="144"
          />
        </a>
        <br />
        Discord
      </td>
      <td align="center">
        <img
          src="https://dataagent.top/HKUSTGZ_DIAL.jpg"
          alt="WeChat official account QR code"
          width="144"
        />
        <br />
        WeChat Official Account
      </td>
    </tr>
  </table>
</div>

## Main Modules

The same baseline layout is used inside each phase directory. For example, after entering `PHASE_1/` or `PHASE_2/`, the core modules are:

| Module | Responsibility |
| --- | --- |
| `src/data_agent_baseline/benchmark/dataset.py` | Dataset loader |
| `src/data_agent_baseline/tools/filesystem.py` | `list_context`, `read_csv`, `read_json`, `read_doc` |
| `src/data_agent_baseline/tools/python_exec.py` | `execute_python` |
| `src/data_agent_baseline/tools/sqlite.py` | `inspect_sqlite_schema`, `execute_context_sql` |
| `src/data_agent_baseline/tools/registry.py` | Tool registration and terminal `answer` |
| `src/data_agent_baseline/agents/prompt.py` | System prompt, task prompt, observation prompt |
| `src/data_agent_baseline/agents/react.py` | ReAct runtime with JSON action protocol |
| `src/data_agent_baseline/run/runner.py` | Single-task and benchmark execution |