agent-framework/python/.github/skills/python-package-management/SKILL.md

name, description

name	description
python-package-management	Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.

Python Package Management

Monorepo Structure

python/
├── pyproject.toml              # Root package (agent-framework)
├── packages/
│   ├── core/                   # agent-framework-core (main package)
│   ├── azure-ai/               # agent-framework-azure-ai
│   ├── anthropic/              # agent-framework-anthropic
│   └── ...                     # Other connector packages

• agent-framework-core contains core abstractions and OpenAI/Azure OpenAI built-in
• Provider packages extend core with specific integrations
• Root agent-framework depends on agent-framework-core[all]

Dependency Management

Uses uv for dependency management and poethepoet for task automation.

# Full setup (venv + install + prek hooks)
uv run poe setup

# Install dependencies from lockfile (frozen resolution with prerelease policy)
uv run poe install

# Create venv with specific Python version
uv run poe venv --python 3.12

# Intentionally upgrade a specific dependency to reduce lockfile conflicts
uv lock --upgrade-package <dependency-name> && uv run poe install

# Refresh all dev dependency pins, lockfile, and validation in one run
uv run poe upgrade-dev-dependencies

# First, run workspace-wide lower/upper compatibility gates
uv run poe validate-dependency-bounds-test
# Defaults to --project "*"; pass a package to scope test mode
uv run poe validate-dependency-bounds-test --project <workspace-package-name>

# Then expand bounds for one dependency in the target package
uv run poe validate-dependency-bounds-project --mode both --project <workspace-package-name> --dependency "<dependency-name>"

# Repo-wide automation can reuse the same task
uv run poe validate-dependency-bounds-project --mode upper --project "*"

# Add a dependency to one project and run both validators for that project/dependency
uv run poe add-dependency-and-validate-bounds --project <workspace-package-name> --dependency "<dependency-spec>"

Dependency Bound Notes

• Stable dependencies (>=1.0) should typically be bounded as >=<known-good>,<next-major>.
• Prerelease (dev/a/b/rc) and <1.0 dependencies should use hard bounds with an explicit upper cap (avoid open-ended ranges).
• For <1.0 dependencies, prefer the broadest validated range the package can really support. That may be a patch line, a minor line, or multiple minor lines when checks/tests show the broader lane is compatible.
• Prefer supporting multiple majors when practical; if APIs diverge across supported majors, use version-conditional imports/paths.
• For dependency changes, run workspace-wide bound gates first, then validate-dependency-bounds-project --mode both for the target package/dependency to keep minimum and maximum constraints current. The same task can also drive repo-wide upper-bound automation by using --project "*" and omitting --dependency.
• Prefer targeted lock updates with uv lock --upgrade-package <dependency-name> to reduce uv.lock merge conflicts.
• Use add-dependency-and-validate-bounds for package-scoped dependency additions plus bound validation in one command.
• Use upgrade-dev-dependencies for repo-wide dev tooling refreshes; it repins dev dependencies, refreshes uv.lock, and reruns check, typing, and test.

Lazy Loading Pattern

Provider folders in core use __getattr__ to lazy load from connector packages:

# In agent_framework/azure/__init__.py
_IMPORTS: dict[str, tuple[str, str]] = {
    "AzureAIAgentClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
}

def __getattr__(name: str) -> Any:
    if name in _IMPORTS:
        import_path, package_name = _IMPORTS[name]
        try:
            return getattr(importlib.import_module(import_path), name)
        except ModuleNotFoundError as exc:
            raise ModuleNotFoundError(
                f"The package {package_name} is required to use `{name}`. "
                f"Install it with: pip install {package_name}"
            ) from exc

Adding a New Connector Package

Important: Do not create a new package unless approved by the core team.

Initial Release (Preview)

1. Create directory under packages/ (e.g., packages/my-connector/)
2. Add the package to tool.uv.sources in root pyproject.toml
3. Include samples inside the package (e.g., packages/my-connector/samples/)
4. Do NOT add to [all] extra in packages/core/pyproject.toml
5. Do NOT create lazy loading in core yet

Recommended dependency workflow during connector implementation:

1. Add the dependency to the target package: uv run poe add-dependency-to-project --project <workspace-package-name> --dependency "<dependency-spec>"
2. Implement connector code and tests.
3. Validate dependency bounds for that package/dependency: uv run poe validate-dependency-bounds-project --mode both --project <workspace-package-name> --dependency "<dependency-name>"
4. If the package has meaningful tests/checks that validate dependency compatibility, you can use the add + validation flow in one command: uv run poe add-dependency-and-validate-bounds --project <workspace-package-name> --dependency "<dependency-spec>" If compatibility checks are not in place yet, add the dependency first, then implement tests before running bound validation.

Promotion to Stable

1. Move samples to root samples/ folder
2. Add to [all] extra in packages/core/pyproject.toml
3. Create provider folder in agent_framework/ with lazy loading __init__.py

Versioning

• All non-core packages declare a lower bound on agent-framework-core
• When core version bumps with breaking changes, update the lower bound in all packages
• Non-core packages version independently; only raise core bound when using new core APIs

Installation Options

pip install agent-framework-core          # Core only
pip install agent-framework-core[all]     # Core + all connectors
pip install agent-framework               # Same as core[all]
pip install agent-framework-azure-ai      # Specific connector (pulls in core)

Maintaining Documentation

When changing a package, check if its AGENTS.md needs updates:

• Adding/removing/renaming public classes or functions
• Changing the package's purpose or architecture
• Modifying import paths or usage patterns