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Gumloop vs n8n

Both draw automations on a canvas, and both are built for different people. Gumloop is aimed at business teams building AI agents. n8n is aimed at technical users who want control and the option to self-host.

The short answer

These two rarely lose deals to each other. They tend to be chosen by different people in the building.

Gumloop

  • The people building the automations are not engineers.
  • AI steps are the point, rather than an addition to an integration flow.
  • You want browser automation over sites with no API.
  • Results should land in Slack where the team asks follow-up questions.

n8n

  • You want to self-host, for data residency, air-gapped operation or cost.
  • The automation is mostly integration plumbing with some AI in it.
  • You need a community node for something niche.
  • You want to watch a run move through the graph step by step.

Gumloop vs n8n vs Aisle

The first two columns answer the question in the title. The third is the same work written rather than drawn.

FeatureGumloopn8nAisle
What you buildA node graph on a canvasA node graph with expressionsA Python program
Built forBusiness teamsTechnical usersTeams with a developer
HostingHosted onlySelf-host or n8n CloudManaged only
Multi-modelYesYesYes, per step
PromptsIn the nodeIn the nodeVersioned, called by slug
Model outputNot constrainedNot constrainedConstrained by an output schema
Change historyCanvas historyWorkflow JSON you exportA revision on every save, with a diff
RollbackUndo, or rebuildRe-import an older exportOne click
TestsNot includedNot includedA tests.py in the same revision
Retrying a partial runThe flow runs againThe workflow runs againCheckpoints skip finished items
Follow-up questions on a resultSlack threadsChat TriggerOutput as a chat
Non-developer run surfaceInterfacesFormsTyped inputs render a form
Multiple accounts per providerWorkspace credentialsChosen per nodeNamed, selectable per call
Browser automationYesVia community nodesNot included
What you build
GumloopA node graph on a canvas
n8nA node graph with expressions
AisleA Python program
Built for
GumloopBusiness teams
n8nTechnical users
AisleTeams with a developer
Hosting
GumloopHosted only
n8nSelf-host or n8n Cloud
AisleManaged only
Multi-model
GumloopYes
n8nYes
AisleYes, per step
Prompts
GumloopIn the node
n8nIn the node
AisleVersioned, called by slug
Model output
GumloopNot constrained
n8nNot constrained
AisleConstrained by an output schema
Change history
GumloopCanvas history
n8nWorkflow JSON you export
AisleA revision on every save, with a diff
Rollback
GumloopUndo, or rebuild
n8nRe-import an older export
AisleOne click
Tests
GumloopNot included
n8nNot included
AisleA tests.py in the same revision
Retrying a partial run
GumloopThe flow runs again
n8nThe workflow runs again
AisleCheckpoints skip finished items
Follow-up questions on a result
GumloopSlack threads
n8nChat Trigger
AisleOutput as a chat
Non-developer run surface
GumloopInterfaces
n8nForms
AisleTyped inputs render a form
Multiple accounts per provider
GumloopWorkspace credentials
n8nChosen per node
AisleNamed, selectable per call
Browser automation
GumloopYes
n8nVia community nodes
AisleNot included

Both leave the same two things to you.

Whichever canvas you pick, prompts live inside nodes with no version history, and neither constrains the shape of what a model returns. Those are the two that hurt once an AI automation runs on every record.

In a task the prompt is a platform object with version history, and it carries an output schema that constrains the model to JSON matching it. Edit a prompt and every task calling it runs the new version, with no code change and no redeploy.

  • Prompts are versioned objects, shared by chat, projects and tasks.
  • A bad prompt edit reverts in one click, with a history of who changed what.
  • A malformed answer fails at the schema boundary instead of three steps downstream.
  • A revision on every save, including the tests.py, with a line-level diff.
  • A retry resumes at the failure rather than re-firing what already succeeded.
score_leads.py
score = aisle.ai.run_prompt(
    slug="score-lead",
    variables={"lead": lead},
)

if score["tier"] == "hot":
    aisle.integrations.slack.create_message("#sales", text=score["summary"])

Questions

Is Gumloop or n8n better for AI agents?

Gumloop, if AI is the point and the builders are not engineers. Its interface, credential handling and Slack surface are designed around that. n8n fits better when the AI step sits inside a larger integration flow and you want self-hosting.

Is Gumloop or n8n cheaper?

Self-hosted n8n is usually cheapest on paper, because you pay for a server rather than per unit of work, but you also pay in maintenance time. Compare on your own volume: credit and execution pricing behave very differently once an automation runs on every record.

Can Gumloop be self-hosted?

No, Gumloop is a hosted product. If self-hosting is a requirement, n8n, Activepieces and Windmill are the realistic options.

Do either of them version prompts?

Neither surfaces prompt versioning. In both, the prompt text lives inside the node it belongs to, so there is no history, no rollback, and no way to reuse the same prompt across automations.

What if I want AI automations but not a canvas?

Write them as code on a managed runtime. Aisle is one option, where a task is Python and the model is called at the steps that need judgment. Temporal, Inngest and Trigger.dev are alternatives if you would rather have durable execution as a library inside an application you already run.

Competitor details reviewed . Vendors change plans and features without notice, so check theirs before deciding.

Versioned prompts and structured output, in code.

The two things both canvases leave to you, built into the platform.