GitHub Trending Reporter

Automatically fetch GitHub Trending data, perform intelligent analysis using LLM, and generate a visualized report website.

中文文档 | English

Online Access

Website: https://wayyoungboy.github.io/github-trending-reporter/

Features

• Automatic Scraping - Automatically fetch GitHub Trending hot projects daily
• AI Analysis - Use LLM for deep analysis and trend insights on projects
• Visual Display - Generate beautiful report websites using Docusaurus
• Automated Scheduling - Fully automated through GitHub Actions

Project Structure

github-trending-reporter/
├── .github/workflows/
│   └── daily_report.yml     # Automated workflow
├── src/                     # Docusaurus frontend source
├── reports/                 # Markdown reports
├── data/                    # JSON raw data
├── scripts/                 # Utility scripts
├── main.py                  # Main program
├── trending_scraper.py      # Scraper module
├── llm_analyzer.py          # LLM analysis module
├── data_pusher.py           # Data push module
├── config.py                # Configuration file
├── docusaurus.config.js     # Docusaurus configuration
├── package.json             # Node.js dependencies
└── requirements.txt         # Python dependencies

Quick Start

Local Run (Report Generation Only)

# Install Python dependencies
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env
# Edit .env and fill in API Key

# Generate report
python main.py --local --no-push

Local Preview Website

# Install Node.js dependencies
npm install

# Start development server
npm start

Configuration

Environment Variables

Variable	Description
LLM_API_KEY	LLM API key
LLM_BASE_URL	LLM API URL
LLM_MODEL	Model name
GITHUB_API_TOKEN	GitHub API Token (for fetching detailed info)
GITHUB_TOKEN	GitHub Token (for pushing data)

GitHub Actions Secrets

Configure in repository Settings → Secrets:

• LLM_API_KEY
• LLM_BASE_URL
• LLM_MODEL
• GITHUB_API_TOKEN

Automation Workflow

1. Scheduled Trigger - Runs automatically every day
2. Data Collection - Scrape GitHub Trending page
3. API Enhancement - Fetch detailed info via GitHub API
4. LLM Analysis - AI deep analysis of projects
5. Report Generation - Output Markdown format reports
6. Website Deployment - Automatically build and deploy to GitHub Pages

Command Line Options

python main.py [OPTIONS]

Options:
  -l, --language TEXT    Filter by programming language (e.g., python, javascript)
  -s, --since TEXT       Time range: daily, weekly, monthly (default: daily)
  --no-push              Don't push to GitHub repository
  --local                Save to local files
  --date TEXT            Specify date (format: YYYY-MM-DD)
  --no-detailed          Skip detailed project analysis

Inspiration

This project is inspired by ai-git-trending, an automated bot for analyzing GitHub Trending. During development, we referenced the project's core architecture and implementation ideas, including:

• GitHub Trending data scraping mechanism
• LLM intelligent analysis workflow
• Automated report generation solution
• Overall project architecture design

We appreciate the excellent work of the original project author for providing valuable reference and inspiration for this project.

API Integration

You can fetch GitHub Trending reports via HTTP and integrate them into your own agent or application.

Endpoints

Type	URL Pattern
Report Page (HTML)	https://wayyoungboy.github.io/github-trending-reporter/reports/YYYY/MM/YYYY-MM-DD
Raw Data (JSON)	https://wayyoungboy.github.io/github-trending-reporter/data/YYYY/MM/YYYY-MM-DD.json

Fetch JSON Data

curl https://wayyoungboy.github.io/github-trending-reporter/data/2026/05/2026-05-27.json

JSON Response Format

{
  "date": "2026-05-27",
  "generated_at": "2026-05-27T00:05:00.000000",
  "total_repos": 25,
  "repositories": [
    {
      "owner": "xxx",
      "name": "xxx",
      "full_name": "owner/repo",
      "url": "https://github.com/owner/repo",
      "description": "Original description",
      "llm_summary": "AI-generated concise summary",
      "language": "Python",
      "stars": 12345,
      "stars_today": 567,
      "forks": 890,
      "topics": ["ai", "llm"],
      "license": "MIT"
    }
  ]
}

Key Fields

Field	Description
full_name	Repository full name (owner/name)
url	GitHub repository URL
description	Original description from GitHub
llm_summary	AI-generated concise summary (15-25 chars)
language	Primary programming language
stars	Total star count
stars_today	Stars gained today
forks	Fork count
topics	Repository topics/tags

Agent Integration Example (Python)

import requests
from datetime import datetime

def get_trending_report(date_str=None):
    if date_str is None:
        date_str = datetime.now().strftime("%Y-%m-%d")
    year, month = date_str[:4], date_str[5:7]
    url = f"https://wayyoungboy.github.io/github-trending-reporter/data/{year}/{month}/{date_str}.json"
    resp = requests.get(url)
    resp.raise_for_status()
    return resp.json()

# Usage
data = get_trending_report("2026-05-27")
for repo in data["repositories"][:5]:
    print(f"{repo['full_name']}: {repo.get('llm_summary') or repo['description']}")

Related Links

• GitHub Trending
• Docusaurus
• ai-git-trending - Inspiration Source

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Built with Python + Docusaurus