--- title: "Restore compatible Python environments" description: "Restore the resolved virtual environment and keep the install step after it." url: https://starsling.dev/ci-playbooks/techniques/cache-python-environments canonicalUrl: https://starsling.dev/ci-playbooks/techniques/cache-python-environments --- # Restore compatible Python environments Restore the resolved virtual environment and keep the install step after it. ## How to choose an approach This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it. - **[OpenAI](https://starsling.dev/ci-playbooks/openai#cache-python-environments)**: Use it when Dependency installation repeats on fresh CI runners despite unchanged interpreter and lockfiles. Check before adopting: Virtual environments can hold interpreter-specific paths and binaries. Key on everything that changes them, and keep the install step so a stale cache is repaired. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Restore compatible Python environments. Restore the resolved virtual environment and keep the install step after it. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/cache-python-environments.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How OpenAI used this technique Read what OpenAI changed and follow the original sources. ### OpenAI: Restore compatible Python environments The Python SDK added a virtual-environment cache to three jobs, keyed by operating system, architecture, resolved Python version and both lockfiles, with dependency sync still running after the restore. The repository now uses uv's built-in cache (openai/openai-python#3653). Evidence: Source PR/commit evidence Environment cache keyed by OS, architecture, Python version and lockfiles ([Source](https://github.com/openai/openai-python/pull/3573)) Repository moved to uv, with uv's built-in cache in CI ([Source](https://github.com/openai/openai-python/pull/3653)) Use it when: Dependency installation repeats on fresh CI runners despite unchanged interpreter and lockfiles. Before applying it: Virtual environments can hold interpreter-specific paths and binaries. Key on everything that changes them, and keep the install step so a stale cache is repaired. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#cache-python-environments) [Primary source](https://github.com/openai/openai-python/pull/3573) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Related techniques ### [Bake stable dependencies into runner images](https://starsling.dev/ci-playbooks/techniques/prebuild-runner-images) Prepare versioned system dependencies and build downloads once in the runner image instead of installing them on every job. 2 implementations · 2 companies (Anthropic, Linear) ### [Cache package downloads and dependency stores](https://starsling.dev/ci-playbooks/techniques/reuse-package-downloads) Restore package-manager downloads or dependency stores while keeping a locked install that validates the requested dependency graph. 7 implementations · 6 companies (DoorDash, Linear, Shopify, Spotify, Stripe, Uber) ### [Cache the directories the build actually uses](https://starsling.dev/ci-playbooks/techniques/cache-real-output-paths) Align restored and saved cache paths with the tool’s real output directories so the next run can use the cached work. 2 implementations · 2 companies (OpenAI, Uber) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed ## Related guides Implementation guides for this technique. ### [Cache workflow dependencies](https://starsling.dev/best-practices/github-actions/cache-dependencies) Choose dependency cache paths and keys for your package manager. ### [Cut repeated test setup](https://starsling.dev/best-practices/github-actions/cut-repeated-test-setup) Share expensive setup at the scope your tests need. ### [Share setup steps across jobs](https://starsling.dev/github-actions/optimizations/avoid-duplicated-setup) Centralize repeated setup definitions and remove duplicate or unused steps. ### [Optimize npm installs](https://starsling.dev/github-actions/optimizations/optimize-npm-install) Cache npm dependencies and large downloads and remove unused tool installs. ### [Optimize pnpm installs](https://starsling.dev/github-actions/optimizations/optimize-pnpm-install) Set up the pnpm store cache in the right order and scope recursive commands.