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Contributing to Tulbase

Thanks for your interest in contributing! Tulbase is an open-core project — the core proxy, rule-based optimizer, and injection detection are open source.

Ways to contribute

Injection patterns

The most impactful contribution: add injection detection patterns for new languages or new attack vectors. See tulbase/injection.py — each pattern is a tuple of (regex, name, score).

Optimization rules

Add verbose phrase replacements or filler word lists for new languages. See tulbase/optimizer.py — patterns are organized by language.

Integration examples

Show how Tulbase works with your favorite framework or tool. Add to examples/.

Bug reports

Open an issue with steps to reproduce. Include your Python version and Tulbase version.

Skills & extensions

Build integrations for new platforms (VS Code, Cursor, n8n, etc.). See examples/ for patterns.

Development setup

git clone https://github.com/tulbasetkn-tech/tulbase.git
cd tulbase
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Running tests

pytest

Code style

We use ruff for linting:

ruff check .
ruff format .

Pull requests

  1. Fork the repo
  2. Create a branch (git checkout -b feature/my-feature)
  3. Make your changes
  4. Run tests (pytest)
  5. Run linter (ruff check .)
  6. Open a PR

Keep PRs focused on a single change. Include tests for new patterns.

What stays closed

These components are proprietary (Tulbase Cloud):

  • Tulbase Distill compression algorithm
  • ML model fine-tuning and training data
  • Dashboard and analytics backend
  • Billing and authentication systems

License

By contributing, you agree that your contributions will be licensed under the Apache 2.0 license.