m9m
Same workflows, a fraction of the footprint

Replace a heavy n8n install

Import your n8n workflow JSON unchanged and run it on one Go binary — no Node runtime, no mandatory database, far less RAM.

The problem

A production n8n install carries a Node.js runtime, a database, and hundreds of megabytes of RAM at rest. Scaling it means containers and a cluster, even for modest automation loads.

How m9m does it

m9m imports n8n workflow JSON unchanged — same node names, same {{ $json.field }} expressions, same cron and webhook triggers — and runs it from a single ~30 MB binary. The README figures are ~150 MB memory at rest versus ~512 MB, a ~300 MB container versus ~1.2 GB, and roughly 500 concurrent workflows per process.

How it works, step by step

  1. 1

    Export from n8n

    Export your workflows as JSON from the n8n editor.

  2. 2

    Import into m9m

    Run m9m import export.json. 32 built-in node types cover the most-used n8n surface area; the tool reports any unsupported nodes.

  3. 3

    Run side by side

    Keep n8n as the authoring UI if your team edits visually, and run the production workload on m9m — or cut over team by team.

Nodes involved

n8n JSON importHTTP RequestPostgres / MySQL / SQLiteCron & webhook triggersSlack / Discord / SMTP

Keep reading

Need help shipping agents or migrating off n8n?

Neul Labs — the team behind m9m — takes on a limited number of consulting engagements each quarter. We help teams migrate n8n workflows, build custom Go nodes, sandbox AI agents in production, and design automation platforms that don't collapse under load.