Offline AI-Powered Code Analysis Platform
Lintara is an offline full-stack application that uses local LLMs to analyze your code for bugs, security vulnerabilities, and best practices.
- User Authentication — Secure JWT-based login/register
- AI Code Analysis — Local LLM-powered code review
- Multiple Languages — Python, JavaScript, TypeScript, Java, C++, Go, Rust etc.
- Real-time Updates — Auto-refresh during analysis
- Analysis History — View, re-analyze, or delete past submissions
- Clean UI — Modern React + Tailwind interface
Backend:
- FastAPI (Python)
- PostgreSQL
- SQLAlchemy ORM
- Alembic (migrations)
- JWT Authentication
- Ollama (Local LLM)
Frontend:
- React 18
- TypeScript
- Tailwind CSS
- Vite
- React Router
- Axios
Before running Lintara, ensure you have:
Lintara works on all major operating systems. The core technologies (Python, Node.js, PostgreSQL, and Ollama) are all cross-platform. For Linux and macOS users, the setup instructions above will work as is.
For Windows users, there are a few differences to note. Instead of using source venv/bin/activate to activate the Python virtual environment, use venv\Scripts\activate. PostgreSQL can be installed using the Windows installer from the official website, and you'll manage the service through the Windows Services panel or pgAdmin instead of systemctl.
Ollama has a native Windows installer available at ollama.com that works just like the Linux version. All the Python and Node.js commands remain exactly the same across all platforms. If you're on Windows and prefer a Linux-like environment, you can also use WSL2 (Windows Subsystem for Linux) to follow the Linux instructions exactly.
git clone https://github.com/n-dlms/lintara.git
cd lintarasudo systemctl start postgresql
sudo -u postgres psql -c "CREATE DATABASE lintara;"
sudo -u postgres psql -c "ALTER USER postgres PASSWORD 'postgres';"cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cat > .env << 'ENVEOF'
DATABASE_URL=postgresql://postgres:postgres@localhost:5432/lintara
SECRET_KEY=your-secret-key-change-this
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30
ENVEOF
# Run the database migrations using alembic
alembic upgrade headcd ../frontend
npm installcurl -fsSL https://ollama.com/install.sh | sh
ollama pull codellamaYou'll need 3 terminal windows:
ollama servecd lintara/backend
source venv/bin/activate
uvicorn app.main:app --reload --port 8000cd lintara/frontend
npm run devOpen your browser at http://localhost:5173
Once the backend is running, view the interactive API docs at:
- Swagger UI: http://localhost:8000/docs
- ReDoc: http://localhost:8000/redoc
lintara/
├── backend/
│ ├── app/
│ │ ├── routers/ # API endpoints
│ │ ├── services/ # Business logic (LLM integration)
│ │ ├── auth.py # JWT authentication
│ │ ├── database.py # Database connection
│ │ ├── models.py # SQLAlchemy models
│ │ ├── schemas.py # Pydantic schemas
│ │ └── main.py # FastAPI app
│ ├── alembic/ # Database migrations
│ ├── requirements.txt
│ └── .env
├── frontend/
│ ├── src/
│ │ ├── components/ # Reusable UI components
│ │ ├── pages/ # Page components
│ │ ├── services/ # API service layer
│ │ ├── utils/ # Helper functions
│ │ └── App.tsx # Main app component
│ ├── package.json
│ └── vite.config.ts
└── README.md
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License — see the LICENSE file for details.
Ntokozo Dlamini @n-dlms
Once you have the application running, here's how to get started.
First, navigate to http://localhost:5173 in your browser and you'll see the login page. Since you're a new user, click on the "Register" link to create an account. Fill in your email, choose a username, and create a password (minimum 6 characters).
After registering, you'll be redirected to the login page where you can sign in with your new credentials. Once logged in, you'll land on the dashboard where you can immediately start submitting code for analysis. Simply give your submission a title, select the programming language, paste your code, and click "Analyze Code". Your analysis history will appear on the right side, and you can click any previous analysis to view its results.
This is one of the key advantages of using Ollama for AI-powered analysis. Once you've installed all the dependencies and pulled the CodeLlama model, you don't need an internet connection to use the application. Your code never leaves your machine all analysis happens locally using the LLM running on your own hardware.
This makes Lintara perfect for analyzing sensitive or proprietary code that you wouldn't want to send to cloud-based AI services. The only time you need internet is during the initial setup to download the dependencies and the Ollama model. After that, feel free to disconnect and analyze code anywhere anytime without worrying about internet connectivity.
This application uses Ollama to run large language models locally on your machine. The analysis speed depends entirely on your hardware specifications. If you have a dedicated GPU (NVIDIA with CUDA support), the code analysis will complete in seconds. However, if you're running on CPU only (like an Intel i3 or similar), expect the analysis to take anywhere from 2-5 (even more than 8) minutes per submission, this is completely normal and not a bug. The application handles this gracefully by showing a "Processing your code, please be patient" message and automatically refreshing when the analysis completes.
However, for the best experience, I recommend having at least 8GB of RAM and a modern processor. If you're just testing the application and don't want to wait for the full analysis, you can use the smaller tinyllama model instead of codellama by running ollama pull tinyllama and updating the model name in backend/app/services/llm.py. This significantly reduces wait times but may provide less detailed code analysis.
Enjoy : )