AI Engineering Trending

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A CLI tool for discovering trending AI Engineering projects from GitHub.

What is AI Engineering?

AI Engineering focuses on practical engineering applications of AI/LLM technologies, including:

• LLM SDK & API Clients - OpenAI, Anthropic, Gemini, Mistral SDKs
• Agent Frameworks - LangChain, LlamaIndex, AutoGPT, CrewAI
• RAG Tools - Retrieval-augmented generation, document processing
• Vector Databases - Chroma, Pinecone, Weaviate, Milvus
• AI Gateways - LiteLLM, OpenRouter, unified API proxies
• AI Code Assistants - Cursor, Copilot, Claude Code
• AI Observability - Langfuse, LangSmith, monitoring tools
• LLM Inference - vLLM, Ollama, inference servers

This tool filters out research papers, datasets, tutorials, and model weights to focus on production-ready projects.

Installation

# Install from PyPI
pip install github-ai-scraper

# Or install from source
pip install -e ".[dev]"

Windows Quick Start

Double-click install.bat after cloning.

If ai-scraper is not recognized, run:

py -m ai_scraper.cli --help

Quick Start

# Show this week's trending AI Engineering projects
ai-scraper trending

# Today's trending
ai-scraper trending --period daily

# This month's trending
ai-scraper trending --period monthly

# Save as Markdown
ai-scraper trending --save output.md

# Show verbose (filtered projects)
ai-scraper trending -v

Commands

Configuration

Create ai-scraper.yaml to customize:

github:
  token: ${GITHUB_TOKEN}  # Optional, for higher rate limits

database:
  path: ./data/ai_scraper.db

trending:
  languages:
    - python
    - typescript
    - javascript
    - go
    - rust
  timeout: 30

summary:
  enabled: false
  provider: anthropic
  api_key: ${ANTHROPIC_API_KEY}
  model: claude-3-5-haiku-20241022

AI Chinese Summaries

Enable AI-powered Chinese summaries for repository descriptions:

pip install "github-ai-scraper[ai]"
set ANTHROPIC_API_KEY=your_api_key
ai-scraper trending --ai-summary

Project Structure

github-ai-scraper/
├── src/ai_scraper/
│   ├── cli.py              # CLI entry point
│   ├── config.py           # Configuration management
│   ├── classifier.py       # AI Engineering classification
│   ├── scraper/
│   │   └── trending.py     # GitHub Trending scraper
│   ├── output/
│   │   ├── exporter.py     # Markdown exporter
│   │   └── summarizer.py   # AI summary generator
│   ├── models/
│   │   └── repository.py   # Data models
│   └── storage/
│       └── database.py     # SQLite storage
├── tests/                  # Test suite
└── pyproject.toml          # Package metadata

How It Works

1. Scrape GitHub Trending - Fetches trending repos from GitHub's trending page
2. Filter Engineering Projects - Removes tutorials, datasets, model weights, etc.
3. Classify AI Projects - Uses keyword/topic matching to identify AI Engineering projects
4. Sort by Growth - Orders by star growth rate
5. Export Results - Outputs to console or Markdown file

Development

pip install -e ".[dev]"
pytest tests/ -v

License

MIT