Transform your GitHub repositories into professional, ATS-optimized resumes using AI.
Try GitResume now: gitresume.live
GitResume is a production-ready FastAPI web app that uses AI to turn your GitHub repositories into polished, ATS-friendly resume content. It analyzes your codebase, extracts technical achievements, and generates impactful resume sections. Designed for engineers at all levels, GitResume supports multiple AI providers and is fully containerized for easy deployment.
Transform this:
A repo with FastAPI backend, Redis caching, and Docker deployment.
Into:
- Developed scalable backend using FastAPI and Redis for caching.
- Implemented CI/CD with Docker and GitHub Actions.
- Designed microservices structure for modular development.
- 🧑🎓 Students: Turn class projects into resume-ready experience.
- 👩💻 Developers: Showcase contributions with tailored resume bullets.
- 🧑💼 Job Seekers: Get ATS-optimized content in minutes.
- 👥 Recruiters: Generate summaries from candidate GitHub links.
- AI-Powered Resume Creation: Converts GitHub repositories into ATS-friendly resumes.
- Smart Code Parsing: Uses tree-sitter to extract tech stack, structure, and key contributions.
- Customizable Output: Tailor resumes to specific job descriptions and formats.
- Secure & Scalable: GitHub OAuth, rate limiting, Redis support, and production-ready Docker setup.
- Python 3.11 (for local development)
- Docker (recommended for production)
- Git (for cloning repositories)
- Redis (optional, for rate limiting and session management)
- API Keys:
- GitHub OAuth (
GITHUB_CLIENT_ID,GITHUB_CLIENT_SECRET,GITHUB_TOKEN) - AI Providers (Gemini, OpenAI, Groq, Claude)
- GitHub OAuth (
- Clone the Repository:
git clone https://github.com/whoisjayd/gitresume.git cd gitresume - Set Up Virtual Environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
- Install Dependencies:
pip install --upgrade pip pip install -r requirements.txt
- Configure Environment Variables:
- Copy
.env.exampleto.envand fill in your API keys and secrets. - Or use
env.yamlfor configuration.
- Copy
- Run the Application:
Access at http://localhost:8080.
uvicorn app:app --host 0.0.0.0 --port 8080
- Build the Docker Image:
docker build -t gitresume . - Run the Container:
Or mount
docker run --env-file .env -p 8080:8080 gitresume
env.yaml:docker run -v $(pwd)/env.yaml:/app/env.yaml -p 8080:8080 gitresume
| Component | Technology |
|---|---|
| Backend | Python 3.11, FastAPI, Starlette, Pydantic, SlowAPI |
| Frontend | Jinja2 Templates, Tailwind CSS (CDN) |
| AI Providers | Gemini, OpenAI, Groq, Claude |
| Caching | Redis (optional) |
| Containerization | Docker, Uvicorn |
| Code Parsing | Tree-sitter |
- Environment Variables:
- Copy
.env.exampleto.envor createenv.yaml. - Key variables include:
GITHUB_CLIENT_ID,GITHUB_CLIENT_SECRET,GITHUB_TOKENREDIS_HOST,REDIS_PORT,REDIS_USERNAME,REDIS_PASSWORDAI_PROVIDER(gemini, openai, groq, claude)- API keys for AI providers
SESSION_SECRET_KEY,CALLBACK_URL,ENVIRONMENT
- Both
.envandenv.yamlare supported for local and Docker setups.
- Copy
- Example
.env:GITHUB_CLIENT_ID=your_client_id GITHUB_CLIENT_SECRET=your_client_secret AI_PROVIDER=gemini GEMINI_API_KEY=your_gemini_key ENVIRONMENT=production
GitResume generates structured JSON output optimized for ATS systems and easy integration into resume templates. The schema includes:
project_title: Project name (string)tech_stack: List of technologies used (array)bullet_points: 4-6 concise achievement statements (array)additional_notes: Unique technical setup, deployment strategies, or noteworthy engineering decisions (string)future_plans: Logical next features or enhancements (string)potential_advancements: Advanced architectural improvements or optimizations (string)interview_questions: Array of objects with question, answer, and category
Example Output:
{
"project_title": "E-Commerce Platform",
"tech_stack": ["Python", "Redis"],
"bullet_points": [
"Developed scalable backend using FastAPI and Redis for caching.",
"Integrated secure payment gateway and OAuth authentication.",
"Implemented CI/CD pipeline with Docker and GitHub Actions.",
"Optimized database queries, reducing latency by 30%."
],
"additional_notes": "Designed modular microservices architecture for rapid feature deployment.",
"future_plans": "Add automated testing and expand API documentation.",
"potential_advancements": "Utilize event-driven design with message queues for order processing.",
"interview_questions": [
{
"question": "How did you ensure scalability in the backend?",
"answer": "By leveraging FastAPI's async capabilities and Redis for caching.",
"category": "Backend Architecture"
}
]
}- Is my GitHub data stored?
No, repositories are cloned temporarily and deleted after analysis. - Which AI models are supported?
Gemini, OpenAI, Groq, and Claude—configurable via.env. - Can I analyze private repositories?
Yes, after authenticating via GitHub OAuth. - How do I deploy in production?
Use the provided Docker setup with environment variables configured. - How do I report bugs or suggest features?
Open an issue on GitHub Issues. - How do I contribute?
See the Contributing section below.
We welcome contributions! To get started:
- Fork and clone the repository.
- Create a feature branch:
git checkout -b feature/your-feature. - Implement and test your changes locally.
- Push to your fork and submit a pull request.
Guidelines:
- Follow PEP 8 for Python code.
- Include tests for new features (use pytest).
- Update documentation as needed.
- Be respectful and inclusive (see Code of Conduct).
This project is licensed under the MIT License.
- Core Technologies: Python, FastAPI, Tree-sitter, Jinja2, Tailwind CSS, Redis, Uvicorn, Docker
- AI Providers: Google Gemini, OpenAI, Groq, Anthropic Claude
GitResume uses a modular, scalable architecture:
- Frontend: Jinja2 templates styled with Tailwind CSS, served by FastAPI.
- Backend: FastAPI handles routing, API endpoints, and WebSocket connections.
- Authentication: GitHub OAuth for secure access to public and private repos.
- Code Analysis: Tree-sitter parses codebases for structure and technology insights.
- AI Integration: Modular support for Gemini, OpenAI, Groq, and Claude.
- Caching & Rate Limiting: Redis (optional) for session management and performance.
- Deployment: Docker with multi-stage builds for minimal, secure images.
| Endpoint | Method | Description |
|---|---|---|
| / | GET | Home page |
| / | POST | Generate resume from repo URL |
| /login | GET | Initiate GitHub OAuth |
| /callback | GET | Handle OAuth callback |
| /logout | GET | Log out |
| /health | GET | Health check endpoint |
| /{user}/{repo} | GET/POST | Dynamic repo analysis |
| /ws/ | WS | WebSocket for real-time updates |
| Module | Purpose |
|---|---|
| app.py | Core FastAPI app with routing and middleware |
| tools/create_resume.py | Orchestrates AI resume generation |
| tools/git_operations.py | Manages repo cloning and validation |
| tools/gitingest.py | Parses and summarizes codebases |
| tools/grammar_check.py | Ensures high-quality AI text output |
| tools/api_utils.py | Integrates with AI provider APIs |
| tools/utils.py | General utility functions |
- No More Blank Pages: Start your resume with content generated from real code.
- Designed for Engineers: Resume bullets highlight your actual skills and impact.
- Recruiter-Ready: Outputs are optimized for ATS and easy formatting.
- Plug-and-Play: Works with public and private repos, supports multiple AI providers, and Docker.
- Open Source: Built for the community—contributions welcome!
Created with ❤ by Jaydeep Solanki.
Questions or feedback? Reach out via GitHub Issues.