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How to contribute

We'd love to accept your patches and contributions to this project.

Before you begin

Sign our Contributor License Agreement

Contributions to this project must be accompanied by a Contributor License Agreement (CLA). You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project.

If you or your current employer have already signed the Google CLA (even if it was for a different project), you probably don't need to do it again.

Visit https://cla.developers.google.com/ to see your current agreements or to sign a new one.

Review our community guidelines

This project follows Google's Open Source Community Guidelines.

Code reviews

All submissions, including submissions by project members, require review. We use GitHub pull requests for this purpose. Consult GitHub Help for more information on using pull requests.

Contribution workflow

Finding Issues to Work On

  • Browse issues labeled good first issue (newcomer-friendly) or help wanted (general contributions).
  • For other issues, please kindly ask before contributing to avoid duplication.

Requirement for PRs

  • All PRs, other than small documentation or typo fixes, should have an Issue associated. If a relevant issue doesn't exist, please create one first or you may instead describe the bug or feature directly within the PR description, following the structure of our issue templates.
  • Small, focused PRs. Keep changes minimal—one concern per PR.
  • For bug fixes or features, please provide logs or screenshot after the fix is applied to help reviewers better understand the fix.
  • Please include a testing plan section in your PR to describe how you will test. This will save time for PR review. See Testing Requirements section for more details.

Large or Complex Changes

For substantial features or architectural revisions:

  • Open an Issue First: Outline your proposal, including design considerations and impact.
  • Gather Feedback: Discuss with maintainers and the community to ensure alignment and avoid duplicate work

Testing Requirements

To maintain code quality and prevent regressions, all code changes must include comprehensive tests and verifiable end-to-end (E2E) evidence.

Unit Tests

Please add or update unit tests for your change. Please include a summary of passed pytest results.

Requirements for unit tests:

  • Coverage: Cover new features, edge cases, error conditions, and typical use cases.
  • Location: Add or update tests under tests/unittests/, following existing naming conventions (e.g., test_<module>_<feature>.py).
  • Framework: Use pytest. Tests should be:
    • Fast and isolated.
    • Written clearly with descriptive names.
    • Free of external dependencies (use mocks or fixtures as needed).
  • Quality: Aim for high readability and maintainability; include docstrings or comments for complex scenarios.

Manual End-to-End (E2E) Tests

Manual E2E tests ensure integrated flows work as intended. Your tests should cover all scenarios. Sometimes, it's also good to ensure relevant functionality is not impacted.

Depending on your change:

  • ADK Web:

    • Use the adk web to verify functionality.
    • Capture and attach relevant screenshots demonstrating the UI/UX changes or outputs.
    • Label screenshots clearly in your PR description.

  • Runner:

    • Provide the testing setup. For example, the agent definition, and the runner setup.
    • Execute the runner tool to reproduce workflows.
    • Include the command used and console output showing test results.
    • Highlight sections of the log that directly relate to your change.

Documentation

For any changes that impact user-facing documentation (guides, API reference, tutorials), please open a PR in the adk-docs repository to update the relevant part before or alongside your code PR.

Development Setup

  1. Clone the repository:

    gh repo clone google/adk-python
cd adk-python

  2. Install uv:

    Check out uv installation guide.

  3. Create and activate a virtual environment:

    NOTE: ADK supports Python 3.10+. Python 3.11 and above is strongly recommended.

    Create a workspace venv using uv.

    uv venv --python "python3.11" ".venv"

    Activate the workspace venv.

    source .venv/bin/activate

    Windows

    source .\.venv\Scripts\activate

  4. Install dependencies:

    uv sync --all-extras

    NOTE: for convenience, installing all extra deps as a starting point.

  5. Run unit tests:

    pytest ./tests/unittests

    NOTE: for accurate repro of test failure, only include test as extra dependencies.

    uv sync --extra test
pytest ./tests/unittests

    Alternatively, use the included unittests.sh script which handles environment setup and restoration automatically:

    ./scripts/unittests.sh

    This script will:

    • Set up the test environment with minimal dependencies (test, eval, a2a)
    • Run the unit tests
    • Restore the full development environment (--all-extras)

  6. Auto-format the code:

    NOTE: We use isort and pyink for styles. Use the included autoformat.sh to auto-format.

    ./autoformat.sh

  7. Build the wheel file:

    uv build

  8. Test the locally built wheel file: Have a simple testing folder setup as mentioned in the quickstart.

    Then following below steps to test your changes:

    Create a clean venv and activate it:

    VENV_PATH=~/venvs/adk-quickstart
    command -v deactivate >/dev/null 2>&1 && deactivate
    rm -rf $VENV_PATH \
  && python3 -m venv $VENV_PATH \
  && source $VENV_PATH/bin/activate

    Install the locally built wheel file:

    pip install dist/google_adk-<version>-py3-none-any.whl

Contributing Resources

Contributing folder has resources that are helpful for contributors.

Vibe Coding

If you want to contribute by leveraging vibe coding, the AGENTS.md (https://github.com/google/adk-python/tree/main/AGENTS.md) could be used as context to your LLM.