Install and run in 2 easy steps
Requires Go 1.26+ - install Go.
Install
$ curl -fsSL https://sparkwing.dev/install.sh | sh
Works on macOS, Linux, & Windows (bash or WSL) - arm64 & amd64. Verifies SHA-256 before installing.
Run and view the dashboard
$ sparkwing pipeline new --name ci --template build-test-deploy
$ sparkwing run ci
$ sparkwing dashboard start
Watch an agent build a pipeline
Pipelines run locally with fast iteration loops and structured, clear log and error output - the exact debug surface AI agents need. Embedded offline docs, clear examples, and a composable module ecosystem help them build highly efficient pipelines with zero guesswork.
One CLI, everything you or an agent needs
Run pipelines, start the dashboard, scaffold new templates, read embedded docs, manage secrets, view old runs, surface logs or errors, install pre-commit or pre-push sparkwing hooks, and more. No web access required.
The Dashboard you’ve dreamed of
Information-dense and easily parsed. Sparkwing visualizes your runs the way you’ve always wanted, featuring a triage view, built-in metrics for failure rates, common errors, and insane grep-ability. Seamlessly explore the DAG, jump straight to errors, and filter by log adjacency. Packed with useful features like quick keyboard navigation, full pipeline log search, and step and job summary views. If there’s a feature you’re missing, just let us know. We’re building this for you!
Stop debugging YAML indentation
Sparkwing replaces fragile, sprawling YAML with a strongly-typed Go DSL. It brings robust library support, native autocomplete, and advanced build caching directly into your workflow definitions. Compile and catch errors locally before you ever push to the cloud, easily import legacy bash scripts, and build declarative dependency graphs that do exactly what you expect.
Four ways to host
Lightest to heaviest. Pipeline code stays the same as you scale - and all three shared modes give the team a centralized dashboard.
Local
Lightest
SQLite + filesystem. Zero infrastructure.
For: Trying sparkwing out, solo work, fast dev loops.
Shared object storage
Light
N runners write to the same bucket (S3, GCS, Azure Blob). One shared dashboard, shared caches, no database to operate. Buffers writes offline and syncs when reachable.
For: Teams keeping GitHub Actions runners but moving pipeline logic to typed, composable Go. Or any team that wants a unified dashboard and shared cache without operating Postgres.
Postgres + object storage
Heavier
Add Postgres for state and real-time coordination. Exactly-one execution for cached steps; triggers, approvals, and debug pauses work across runners.
For: Teams that have outgrown shared-bucket - expensive cached work where coalescing matters, or workflows that need cross-runner triggers and approvals.
Cloud or self-hosted controller
Heaviest
A central controller owns the DB, dispatches jobs to worker pools, and serves the dashboard. Auth (tokens, sessions), trusted-team workflows, optional laptop-as-worker registration to save costs or increase efficiency.
For: Organizations with dedicated infrastructure and team-scale workflows.
