StarSling vs GitHub Actions

Keep your GitHub Actions workflows and use StarSling to improve both job execution and the workflow itself.

• Give each job faster compute: Adaptive Compute combines Sandbox Router's placement on the fastest available sandbox with Afterburner's extra CPU and RAM during a running step, at no extra cost.
• Improve the work your pipeline performs: StarSling agents deliver reviewable optimization PRs for workflow improvements. Inspect the changes in real repositories and merge the ones that fit your team.
• Turn CI findings into fixes: ci-speedup, ci-score, and ci-secure give your coding agent workflow-specific findings and fixes for speed, configuration, and security, applying CI optimization best practices.
• Own your code review: Review Runners run your chosen model, repo-native skills, scripts, and team-defined reviewers. Version review logic alongside your code.
• Give GPU tests their own hardware: GPU Runners provide dedicated NVIDIA GPUs for GPU tests, CUDA builds, and model evaluations, keeping CI off your production GPUs.
• Diagnose failures and wasted time: the sling CLI explains failed jobs with evidence and next steps and breaks wall-clock time into runner-lifecycle phases.

Explore fast GitHub Actions, GitHub Actions alternatives, GitHub Actions CI/CD pipeline.

In RunsOn's public GitHub Actions runner benchmark of Rust and TypeScript builds on x64 runners at 8 to 12 vCPU (p90 wait + job time, September 30 to October 4, 2026), StarSling ranked first on both builds with Rust 1m 54s and TypeScript 1m 07s. GitHub-hosted runners recorded Rust 2m 36s and TypeScript 1m 34s.

Each figure is a provider's best runner in that size range, on a cold Rust build and a cold TypeScript build. RunsOn runs the benchmark and sells one of the runners it measures. Its harness and raw results carry all nine providers, and our write-up walks through them.

ubuntu-latest is a moving alias. It tracks GitHub's current stable image, which might not be the most recent version of the operating system available from the operating system vendor (hosted-runner reference, checked 2026-07-14). What actually changes inside that image, and when it rolls out, is published in actions/runner-images, so a workflow pinned to ubuntu-latest can start running on a different OS build without anything in your repository changing. GitHub's move of ubuntu-latest to Ubuntu 26.04 is the current example, and Ubuntu 26.04 and ubuntu-latest: which of your jobs break lists what changed in the image.

That is fine until it is not: an image rollout is a plausible explanation for a build that was green on Friday and red on Monday with no diff. StarSling's label names the OS version explicitly (starsling-ubuntu-26.04), so the image you get is not delivered through a moving -latest alias, and an OS bump is a label change you make on purpose rather than one that arrives on its own.

These customer measurements come from real GitHub Actions workflows before and after migration. The linked case studies carry the measurement windows, the source method, and the optimization PRs where the repository is public.

• Better Auth: StarSling agents fixed Turborepo caching, Docker Compose readiness, and Playwright browser installs. E2E went from 2m 22s to 1m 04s, and CI went from 1m 40s to 1m 02s.
• Mastra: agents opened nineteen PRs across sleeps, polling, Docker healthchecks, sharding, race fixes, and runner migration. Combined store Tests measured 29m 56s to 5m 06s in the post-migration window, and E2E measured 14m 16s to 10m 50s.
• Partcl: a synthesis-bound CI suite did not become a headline wall-clock speed story, but the agents shipped eight CI optimization PRs, including 13x lower compute cost per run and p95 queue time from 9.5 minutes to 35 seconds.