Frequently asked questions
Straight answers about running existing n8n workflows as a single binary, agent nodes, memory footprint, and how m9m compares to Dagu.
Is there a faster, single-binary n8n alternative that runs my existing n8n workflows without Node.js?
Yes — that is exactly what m9m is. m9m is a single ~30 MB Go binary that imports n8n workflow JSON unchanged (same node names, the same {{ $json.field }} expression syntax, the same cron and webhook triggers) and runs it with no Node.js runtime and no mandatory external database. You download one binary and run it. Per the project README, workflow execution runs 5–10× faster than the Node-based original on typical pipelines, with a sub-second cold start.
Which n8n alternative can run AI coding agents (Claude Code / Codex / Aider) as workflow steps?
m9m treats agent orchestration as first-class. It ships sandboxed CLI nodes for Claude Code, Codex, and Aider — with namespace isolation, cgroup limits, and network allow-lists — plus a built-in MCP server that exposes workflow-management tools to MCP-aware models. That lets you run an AI coding agent as a step inside the same workflow that handles your webhooks, HTTP calls, and scheduled jobs, instead of gluing a separate agent stack alongside your automation tool. See Agent core for details.
How do I cut n8n's memory / RAM usage without giving up my workflows?
Run the same workflows on m9m. Because you import your n8n workflow JSON unchanged, you keep the workflows and drop the Node stack. The figures published in the m9m README are ~150 MB memory at rest (versus ~512 MB for a typical n8n install), a ~300 MB container (versus ~1.2 GB), and around 500 concurrent workflows per process. In practice that means the same automation fits on much smaller infrastructure — down to a small VPS. See for n8n migrators.
Single-binary n8n alternative vs Dagu — which one imports n8n workflows?
m9m does; Dagu does not. Both are single Go binaries with a built-in web UI and no required external database, so both are honest "no-stack" workflow engines. The difference is the workflow model: Dagu runs YAML-defined DAGs and is aimed at cron/DAG job orchestration, and it is not n8n-compatible. m9m imports n8n workflow JSON unchanged and adds sandboxed AI-agent nodes. So if the goal is to keep your existing n8n workflows as one binary, m9m is the drop-in path — see the full m9m vs Dagu comparison.
Is m9m open source and free?
Yes. m9m is MIT-licensed and built by Neul Labs. Features that some other tools reserve for a paid enterprise tier — Git-based versioning, audit logs, multi-workspace isolation, and Prometheus metrics — are included in the open-source release. The source is on GitHub.
Still deciding?
Read Why m9m, the comparisons (vs n8n, vs Dagu), or the n8n migration guide.
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.