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Contributing to Tau

Thanks for helping improve Tau. Tau is both a usable terminal coding agent and a teaching codebase for understanding how coding agents are built. Contributions should preserve that dual purpose: make the tool better while keeping the architecture small, readable, and easy to learn from.

Project philosophy

Tau is organized around three layers:

tau_ai      provider/model streaming layer
tau_agent   portable agent harness, loop, tools, events, sessions
tau_coding  CLI app, resources, skills, extensions, commands, TUI integration

The key boundary is:

AgentHarness = reusable agent brain
AgentSession = coding-agent environment
TUI = one possible frontend

Please keep these principles in mind:

  • Small layers beat magic. Each package should have one clear job.
  • Events are the contract. The harness emits typed events; UI and renderers consume them.
  • The core stays portable. tau_agent should not depend on the CLI, Textual, Rich, local config paths, or Tau-specific resource loading.
  • Tools are ordinary typed functions. Prefer explicit schemas and structured results.
  • Sessions are durable and inspectable. Avoid changes that make history hard to read, resume, or export.
  • Documentation follows implementation. User-facing behavior and architectural decisions should be documented.

Local development

Use uv for Python commands so they run in the project environment.

uv sync --dev
uv run tau --version

Run Tau from the checkout:

uv run tau
uv run tau -p "explain this repo"

To expose the checkout as a global tau command, use:

uv tool install --editable --force .

Repeat that command after git pull. The editable link makes source changes live, but uv must reinstall the tool to refresh package metadata (including the version), dependencies, and entry points.

Checks before submitting

Run the relevant focused tests while developing, then run the full checks before opening a pull request when practical:

uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run mypy

For the documentation site (a Hugo project):

cd website
hugo server -D
hugo --minify

Where changes belong

Use the layer boundaries to decide where code should live:

  • Provider integrations, model adapters, and provider-neutral streaming belong in tau_ai.
  • Agent loop behavior, tool abstractions, events, messages, harnesses, and portable session primitives belong in tau_agent.
  • CLI behavior, slash commands, TUI integration, local config, resources, skills, prompt templates, and coding-specific tools belong in tau_coding.
  • Textual-specific code should stay behind the TUI layer.
  • Rich rendering should not leak into the reusable agent harness.

If a change crosses layers, prefer adding a small typed boundary instead of importing app-specific details into core code.

Adding a provider or model

The built-in provider catalog is data, not code: edit src/tau_coding/data/catalog.toml and open a PR — no Python changes needed. Each [[providers]] table declares the provider's name, kind (openai-compatible, anthropic, or openai-codex), base URL, models, default model, context windows, and thinking configuration. Validation happens at load time, so a typo fails tests with a pointed error message.

For personal or unreleased providers, create ~/.tau/catalog.toml with the same schema — it is overlaid on the built-in catalog (your values win, models are unioned) and needs no PR at all.

Testing expectations

  • Add or update tests for behavior changes.
  • Use fake providers and fake tools for deterministic agent-loop tests.
  • Keep core tests free of provider-specific assumptions.
  • Add regression tests for bugs.
  • Prefer focused tests that describe the behavior being protected.

Documentation expectations

For substantial architectural or phase-oriented work, add beginner-friendly notes under dev-notes/ explaining:

  • what changed
  • why it exists
  • how it maps to Tau's architecture
  • how to test or use it

For user-facing behavior, update the published docs under:

website/content/

Release process

Tau is published to PyPI as tau-ai. Publishing is a production release action, not a side effect of every commit merged to main.

To prepare a release, intentionally bump [project].version in pyproject.toml and merge that change through a pull request. The PyPI workflow publishes only when it detects that version change, or when a maintainer uses an explicit release trigger such as a published GitHub Release or manual workflow dispatch. See dev-notes/release-process.md for the full process.

Pull request guidelines

Good Tau pull requests are small, focused, and easy to review. Please include:

  • the motivation for the change
  • a summary of behavior changes
  • tests or checks you ran
  • screenshots or terminal output for TUI/CLI changes when useful
  • notes about compatibility, migrations, config changes, or provider-specific behavior

Avoid unrelated refactors in feature or bug-fix PRs. If a larger design change is needed, open an issue or discussion first.

Roadmap alignment

Tau is developed incrementally. For larger changes, check the roadmap issue before starting:

#1

When in doubt, favor the smallest step that preserves the architecture and teaches the design clearly.