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Contributing to ctxeng

Thank you for helping make ctxeng better! This guide covers everything you need to get started.

Development setup

git clone https://github.com/your-username/python-context-engineer
cd python-context-engineer
pip install -e ".[dev]"

Running tests

pytest                          # run all tests
pytest tests/unit/              # unit tests only
pytest -k "test_scoring"        # filter by name
pytest --cov=ctxeng             # with coverage

Code style

We use ruff for linting:

ruff check ctxeng/
ruff format ctxeng/

Project layout

ctxeng/
├── __init__.py         Public API exports
├── core.py             ContextEngine main class
├── builder.py          ContextBuilder fluent API
├── models.py           Data classes (Context, ContextFile, TokenBudget)
├── scorer.py           File relevance scoring (keyword, AST, git, path)
├── optimizer.py        Token counting, budget fitting, smart truncation
├── cli.py              CLI entry point
├── sources/            File collectors (filesystem, git, explicit)
└── integrations/       LLM client helpers (Claude, OpenAI, LangChain)

How to add a new scoring signal

  1. Add a function _my_signal_score(content, query, ...) -> float in scorer.py
  2. Call it from score_file() and add it to the weighted average
  3. Add a unit test in tests/unit/test_core.py
  4. Document it in the README scoring table

How to add a new LLM integration

  1. Add an ask_mymodel(ctx, ...) -> str function in ctxeng/integrations/__init__.py
  2. Follow the pattern of ask_claude / ask_openai
  3. Add it to pyproject.toml optional-dependencies
  4. Document it in the README

Submitting a PR

  1. Fork the repo and create a branch: git checkout -b feat/my-feature
  2. Write code + tests
  3. Run pytest and ruff check — both must pass
  4. Open a PR with a clear description of what it does and why

Reporting bugs

Open an issue with:

  • Python version
  • ctxeng version
  • Minimal reproduction case
  • Expected vs actual behavior