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feat: Add databricks-agents framework for building discoverable agents
Add databricks-agents, a lightweight Python framework for building discoverable AI agents on Databricks Apps. Key features: - AgentApp: FastAPI wrapper with auto-generated A2A protocol endpoints - AgentDiscovery: Workspace scanning and agent discovery - A2AClient: Agent-to-agent communication - UCAgentRegistry: Unity Catalog integration - MCPServer: Model Context Protocol support - UCFunctionAdapter: Expose UC Functions as tools The framework enables building agents with just 5 lines of code: app = AgentApp(name="my_agent", description="...", capabilities=[...]) @app.tool(description="...") async def my_tool(param: str) -> dict: ... This auto-generates: - /.well-known/agent.json (A2A protocol agent card) - /.well-known/openid-configuration (OIDC delegation) - /health (health check) - /api/mcp (MCP server) - /api/tools/* (tool endpoints) Includes: - Complete test suite - CI/CD workflows (test, publish, docs) - MkDocs documentation - 4 working examples - Apache 2.0 license Project structure: - src/databricks_agents/ - Core framework (~2000 LOC) - examples/ - Complete working examples - tests/ - Test suite - docs/ - MkDocs documentation - .github/workflows/ - CI/CD pipelines Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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‎databricks-agents/.github/workflows/docs.yml‎

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name: Documentation
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on:
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push:
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branches: [ main ]
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workflow_dispatch:
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permissions:
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contents: write
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jobs:
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deploy-docs:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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- name: Install dependencies
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run: |
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pip install mkdocs-material mkdocstrings[python] pymdown-extensions
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- name: Build and deploy
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run: |
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mkdocs gh-deploy --force

‎databricks-agents/.github/workflows/publish.yml‎

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name: Publish to PyPI
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on:
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release:
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types: [published]
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permissions:
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contents: read
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jobs:
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build-and-publish:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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- name: Install build dependencies
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run: |
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python -m pip install --upgrade pip
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pip install build twine
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- name: Build package
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run: |
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python -m build
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- name: Check package
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run: |
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twine check dist/*
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- name: Publish to PyPI
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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twine upload dist/*

‎databricks-agents/.github/workflows/test.yml‎

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name: Tests
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on:
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push:
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branches: [ main, develop ]
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pull_request:
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branches: [ main, develop ]
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -e ".[dev]"
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- name: Lint with ruff
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run: |
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ruff check src/
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- name: Check formatting with black
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run: |
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black --check src/
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- name: Type check with mypy (informational)
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continue-on-error: true
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run: |
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pip install mypy types-httpx
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mypy src/ --ignore-missing-imports
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- name: Run tests with pytest
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run: |
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pytest tests/ -v --cov=databricks_agents --cov-report=xml --cov-report=term
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- name: Upload coverage to Codecov
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uses: codecov/codecov-action@v4
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if: matrix.python-version == '3.11'
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with:
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file: ./coverage.xml
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flags: unittests
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name: codecov-umbrella
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fail_ci_if_error: false

‎databricks-agents/.gitignore‎

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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual environments
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venv/
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env/
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ENV/
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.venv
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# IDEs
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# Testing
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.pytest_cache/
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.coverage
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htmlcov/
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.tox/
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.hypothesis/
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# Documentation
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docs/_build/
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site/
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# OS
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.DS_Store
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Thumbs.db
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# Project specific
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*.log
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.env
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.env.local

‎databricks-agents/CONTRIBUTING.md‎

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# Contributing to databricks-agents
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Thank you for your interest in contributing to databricks-agents! This project is a Databricks Labs initiative to make it easier to build discoverable AI agents on Databricks Apps.
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## Development Setup
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1. Clone the repository
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2. Install dependencies:
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```bash
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pip install -e ".[dev]"
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```
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3. Run tests:
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```bash
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pytest
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```
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## Code Style
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- Use Black for code formatting: `black src/`
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- Use Ruff for linting: `ruff check src/`
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- Line length: 100 characters (configured in `pyproject.toml`)
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- Type hints are encouraged for public APIs
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## Testing
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- Write tests for new features using pytest
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- Place tests in the `tests/` directory
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- Run tests with: `pytest tests/`
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- Aim for >80% code coverage
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## Pull Requests
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1. Fork the repository
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2. Create a feature branch: `git checkout -b feature/my-feature`
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3. Make your changes
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4. Add tests for new functionality
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5. Run tests and linting: `pytest && black src/ && ruff check src/`
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6. Commit with a clear message describing the change
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7. Push and create a pull request
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## PR Guidelines
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- **Clear description**: Explain what the PR does and why
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- **Tests included**: All new features should have tests
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- **Documentation updated**: Update README.md and docstrings as needed
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- **Small, focused changes**: One feature or fix per PR
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- **Passes CI**: All tests and linting must pass
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## Areas for Contribution
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We welcome contributions in these areas:
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- **Unity Catalog integration**: Register agents as UC catalog objects
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- **MCP server support**: Add Model Context Protocol server capabilities
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- **Orchestration patterns**: Multi-agent coordination utilities
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- **RAG utilities**: Built-in vector search and retrieval patterns
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- **Observability**: Logging, metrics, and tracing integrations
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- **Documentation**: Examples, guides, and API documentation
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- **Testing**: Improve test coverage and test utilities
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## Questions?
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- Open an issue for bugs or feature requests
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- Start a discussion for design questions
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- Check existing issues and PRs before starting work
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## Code of Conduct
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Be respectful, inclusive, and constructive in all interactions. This is a professional community focused on building great tools together.
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## License
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By contributing, you agree that your contributions will be licensed under the Apache 2.0 License.

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