Workflow automation for developers
Workflows that
don't fall over.
A parallel-by-default automation engine built for real data. Branches run concurrently, million-row runs finish in steady memory, and you can drop into real Python whenever the canvas isn't enough — with a one-paste importer for your existing n8n workflows.
Free demo workspace — the full product, while we head to launch.
engine
Independent branches run concurrently on a frontier scheduler. Node 7 doesn't wait for node 3 unless it actually depends on it.
data
Items stream from node to node; binary payloads spill to disk instead of living in RAM. A million-row run finishes in steady memory.
migration
Paste an n8n export. Nodes map to natives, and your JavaScript code nodes get translated to Python — with the original kept as comments.
What you get
An engine worth switching for
Parallel frontier engine
The scheduler advances a frontier of ready nodes, not a queue. Fan out three API calls and they run at once — with per-node concurrency limits when you need them.
Built for real datasets
Streaming transforms, bounded previews, disk-spilled binaries. The engine is designed around the biggest run you'll ever throw at it.
n8n importer
Bring the workflows you already have. Imports map nodes to natives, flag what needs attention, and open arranged on the canvas.
Real Python, real imports
The code node runs actual Python with package imports — pick from the modules already installed, enable more in one click. Imported n8n JavaScript? AI translates it for you.
Live execution
Every node lights up the instant it runs, streamed over SSE. Inspect any node's real input and output items, mid-run or after.
Sub-workflows & error alerts
Compose workflows from workflows, and designate an error workflow that fires whenever a run fails — your pager, not your polling.
Why we built it
The engine came first
Most visual automation tools run one node at a time and hold every row in memory — a design that works beautifully right up until the data gets real. We started from that ceiling and built downward: scheduler first, canvas second.
Compared to the tools you already know, Parallix runs independent branches in parallel on a frontier scheduler, and streams data through the run — so a fan-out actually fans out, and a million-row workflow finishes in steady memory instead of growing with the dataset.
The rest is built for developers: real Python where you need code, expressions everywhere else, credentials in an encrypted vault, versioned publishing, and role-based access for your team.
The same 1,000,000-item run
processing…
streaming…
Representative animation — the reproducible benchmark ships with launch.
Live demo · launch TBA
You're looking at the real thing
This site runs the live demo instance. Create an account and build real workflows in your own workspace — free, and your feedback shapes the launch.