Publish to PyPI workflow (FalkorDB/GraphRAG-SDK)

The Publish to PyPI workflow from FalkorDB/GraphRAG-SDK, explained and optimized by Latchkey.

What it does

This is the Publish to PyPI workflow from the FalkorDB/GraphRAG-SDK repository, a real project running GitHub Actions. It is shown here with attribution under its Apache-2.0 license.

Below, Latchkey shows a faster, safer version produced by its optimization engine.

The workflow

name: Publish to PyPI

on:
  release:
    types: [published]

permissions:
  contents: read
  id-token: write

jobs:
  build-and-publish:
    runs-on: ubuntu-latest
    environment: release
    defaults:
      run:
        working-directory: graphrag_sdk
    steps:
      - uses: actions/checkout@v6

      - uses: actions/setup-python@v6
        with:
          python-version: "3.12"

      - name: Verify tag matches pyproject.toml version
        run: |
          PKG_VERSION=$(python -c "import tomllib; print(tomllib.load(open('pyproject.toml','rb'))['project']['version'])")
          TAG_VERSION="${GITHUB_REF_NAME#v}"
          if [ "$PKG_VERSION" != "$TAG_VERSION" ]; then
            echo "::error::Tag $GITHUB_REF_NAME ($TAG_VERSION) does not match pyproject.toml version ($PKG_VERSION)"
            exit 1
          fi

      - run: pip install build
      - run: pip install twine

      - run: python -m build
      - run: twine check dist/*

      - uses: actions/upload-artifact@v7
        with:
          name: python-package-distributions
          path: graphrag_sdk/dist/

      - uses: pypa/gh-action-pypi-publish@release/v1
        with:
          packages-dir: graphrag_sdk/dist/

The same workflow, on Latchkey

Estimated ~20% faster on cache hits, plus fewer wasted runs and a safer supply chain. Added and changed lines are highlighted.

name: Publish to PyPI
 
on:
  release:
    types: [published]
 
permissions:
  contents: read
  id-token: write
 
jobs:
  build-and-publish:
    timeout-minutes: 30
    runs-on: latchkey-small
    environment: release
    defaults:
      run:
        working-directory: graphrag_sdk
    steps:
      - uses: actions/checkout@v6
 
      - uses: actions/setup-python@v6
        with:
          cache: 'pip'
          python-version: "3.12"
 
      - name: Verify tag matches pyproject.toml version
        run: |
          PKG_VERSION=$(python -c "import tomllib; print(tomllib.load(open('pyproject.toml','rb'))['project']['version'])")
          TAG_VERSION="${GITHUB_REF_NAME#v}"
          if [ "$PKG_VERSION" != "$TAG_VERSION" ]; then
            echo "::error::Tag $GITHUB_REF_NAME ($TAG_VERSION) does not match pyproject.toml version ($PKG_VERSION)"
            exit 1
          fi
 
      - run: pip install build
      - run: pip install twine
 
      - run: python -m build
      - run: twine check dist/*
 
      - uses: actions/upload-artifact@v7
        with:
          name: python-package-distributions
          path: graphrag_sdk/dist/
 
      - uses: pypa/gh-action-pypi-publish@release/v1
        with:
          packages-dir: graphrag_sdk/dist/
 

What changed

• Run on Latchkey managed runners with one line (runs-on), which apply the fixes below automatically and self-heal transient failures. This example uses latchkey-small; pick the runner size that fits the job.
• Cache dependency installs on the setup step so they are served from cache.
• Add a job timeout so a hung step cannot burn hours of runner time.

1 third-party action is referenced by a movable tag. Pin it to the commit SHA (Latchkey resolves and applies this automatically) so a repointed tag cannot change what runs.

What Latchkey heals here

This workflow has steps that commonly fail on transient issues (network, registries, flaky browsers). On Latchkey managed runners they are detected, retried, and self-healed instead of failing your build:

• Dependency installs

This workflow runs 1 job per trigger. On Latchkey the same minutes cost up to 58% less than GitHub-hosted, with zero queue time.

Actions used in this workflow