xhs-research 0.1.0

Search Xiaohongshu and generate AI-powered research reports

xhs-research

Search Xiaohongshu (小红书) and generate AI-powered research reports from the command line.

Instead of scrolling through dozens of posts one by one, type one command and get a structured summary with recommendations, price comparisons, and user sentiment — all powered by AI.

Features

• One command, full report — search a keyword → scrape posts → AI generates a structured Markdown report
• Multi-model support — OpenAI, Claude, DeepSeek, or run locally with llama.cpp / Ollama (zero cost)
• Smart summarization — chunks large result sets and merges summaries to fit any model's context window
• Structured output — recommendations table, buying advice, red flags, sentiment breakdown
• Login state persistence — scan QR code once, cookies saved for reuse

Quick Start

Option A: Install from GitHub (recommended)

pip install git+https://github.com/yongsinfok/xhs-research.git
playwright install firefox

Option B: Clone and install

git clone https://github.com/yongsinfok/xhs-research.git
cd xhs-research
pip install -e .
playwright install firefox

2. Configure AI model

mkdir -p ~/.xhs-research
cp config.example.yaml ~/.xhs-research/config.yaml

Edit ~/.xhs-research/config.yaml:

ai:
  api_key: sk-your-key        # not needed for local models
  base_url: null              # local models: http://localhost:11434/v1
  model: gpt-4o               # or deepseek-chat, llama3, etc.

3. Run

xhs-research search "马来西亚高性价比扫地机器人"

A browser window opens. Scan the QR code with the Xiaohongshu app to log in (only needed the first time). The tool then scrapes posts and generates a report.

Usage

# Basic search (default 20 posts)
xhs-research search "吉隆坡美食推荐"

# More posts for better coverage
xhs-research search "新加坡PR申请攻略" --limit 30

# Use a specific model
xhs-research search "MacBook Pro M4 值得买吗" --model deepseek-chat

# Save to a specific path
xhs-research search "装修避坑指南" --output ./my-report.md

# Also export raw data as JSON
xhs-research search "搬家攻略" --json

# View config file location
xhs-research config-path

Report Example

# 马来西亚扫地机器人推荐 调研报告

> 基于 20 篇小红书帖子 · 2026-05-25

## 核心发现

- 小米 X20+ 关注度最高（447赞），有实测背书
- Dreame D20/Ultra 为热门候选，性价比讨论多
- Mova E40 作为竞品出现

## 推荐清单

| 品牌/型号   | 提及次数 | 最高赞 | 定位       |
|-------------|---------|--------|------------|
| 小米 X20+   | 1+      | 447    | 高关注实测 |
| Dreame D20 Ultra | 2  | 131   | 热门候选   |
| Dreame D20  | 1       | 28     | 性价比讨论 |
| Mova E40    | 1       | 28     | 竞品对比   |

## 购买建议 / 踩坑提醒 / 观点分布
...

Supported AI Models

Provider	base_url	model example	Cost
OpenAI	(default)	gpt-4o	Paid
Anthropic	(default)	claude-sonnet-4-6	Paid
DeepSeek	https://api.deepseek.com/v1	deepseek-chat	Low cost
Ollama	http://localhost:11434/v1	llama3, qwen2	Free
llama.cpp	http://localhost:8080/v1	(local model)	Free
Any OpenAI-compatible API	(your endpoint)	(your model)	Varies

Any endpoint that exposes an OpenAI-compatible /v1/chat/completions API works out of the box.

Project Structure

xhs-research/
├── xhs_research/
│   ├── cli.py              # CLI entry point (typer)
│   ├── config.py           # YAML config loader
│   ├── models/post.py      # Post / Comment data models
│   ├── ai/
│   │   ├── client.py       # Unified AI client (OpenAI SDK)
│   │   └── summarizer.py   # Chunk + merge summarization
│   └── scraper/
│       ├── browser.py      # Playwright browser manager
│       ├── login.py        # QR code login handler
│       └── parser.py       # Search result scraper
├── config.example.yaml
├── requirements.txt
└── README.md

Limitations

• Xiaohongshu web restrictions — post detail pages are often blocked on web, so reports are primarily based on search result titles and card summaries. Increasing --limit improves coverage.
• Anti-scraping — uses standard Playwright Firefox. For better evasion, consider camoufox.
• Login expiration — cookies may expire; re-scan QR code when prompted.
• Personal use only — respect Xiaohongshu's terms of service. Do not use for commercial scraping.

Contributing

Issues and pull requests are welcome. Areas to contribute:

• Mobile API support for full post content
• Better anti-detection (camoufox integration)
• Web UI or API server mode
• Support for other platforms (Douyin, Bilibili, etc.)

License

MIT