A production-ready AI agent that evaluates the quality of a GitHub repository's README and assigns a rating out of 5.0. It is built with Python, CrewAI, LangChain, Groq API (Llama 3.3 70B), and Rich for a beautiful CLI experience.
- Automated Fetching: Automatically fetches the README content using the GitHub API without needing to clone the repo.
- AI Evaluation: Employs an expert AI Agent specializing in Open Source Documentation, Developer Experience, and Markdown Best Practices.
- Structured Scoring: Evaluates the README against 15 distinct criteria and computes a weighted overall rating.
- Actionable Feedback: Identifies strengths, weaknesses, missing sections, and top improvements.
- Beautiful CLI: Uses Rich to render an elegant terminal interface with tables, panels, and progress spinners.
readme-rating-agent/
│── agents.py # Defines the Evaluator Agent
│── tasks.py # Defines the Evaluation Task and Pydantic Schema
│── tools.py # Custom CrewAI tool to fetch README via GitHub API
│── run.py # Main CLI entrypoint, Rich UI rendering, and orchestration
│── requirements.txt # Project dependencies
│── .env.example # Example environment variables
│── README.md # Project documentation
- Python 3.11+
- A valid Groq API Key for Llama 3 models.
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Clone the repository (if you haven't already):
git clone https://github.com/sarvan-2187/readme-rating-agent.git cd readme-rating-agent -
Create a virtual environment (recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install dependencies:
pip install -r requirements.txt
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Copy the example environment file:
cp .env.example .env
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Edit
.envand add your Groq API key:GROQ_API_KEY=your_actual_api_key_here MODEL_NAME=llama3-70b-8192
Execute the CLI by running:
python run.pyYou will be prompted to enter a GitHub repository URL:
Enter GitHub Repository URL: https://github.com/owner/repository
The agent will then begin processing the documentation. A spinner will indicate that the evaluation is in progress.
Once finished, the CLI will display:
- Overall Rating: The final score out of 5.0 and star rating.
- Category Breakdown: A table with individual scores for Readability, Accessibility, Developer Experience, etc.
- Strengths & Weaknesses: A side-by-side comparison of the documentation's pros and cons.
- Actionable Feedback: Suggested missing sections and top improvements.
- Final Verdict: A comprehensive summary written by the AI Agent.
This project is licensed under the MIT License. Built by Sarvan Kumar