The Gumloop alternative, written in Python.
Gumloop builds AI agents on a canvas. A Task is a program that calls a model at the steps that need judgment. Hosted and run on secure cloud infrastructure.
for lead in aisle.integrations.hubspot.list_contacts(limit=100): score = aisle.ai.run_prompt( slug="score-lead", variables={"lead": lead}, ) if score["tier"] == "hot": aisle.integrations.slack.create_message("#sales", text=score["summary"])
Model and provider agnostic.
Anthropic
OpenAI
Gemini
xAI
OpenRouter
Amazon
Perplexity
MoonshotAI
Meta
Qwen
Aisle vs Gumloop
Gumloop is well built and its non-developer experience is genuinely good. These rows matter once an automation runs on every record, and once more than one person has to maintain it.
| Feature | Aisle | Gumloop |
|---|---|---|
| What you build | A Python program | A node graph on a canvas |
| Change history | A revision on every save, with a line-level diff | Canvas history |
| Rollback | Restore any revision in one click | Undo, or rebuild |
| Tests | A tests.py rides with the task, in the same revision | Not included |
| Model output | An output schema constrains the model to your JSON | Not constrained |
| Prompts | Versioned in a prompt platform, called by slug | Prompt text sits in the node |
| Swapping models | The model is a field on the prompt | Chosen per node |
| Retrying a partial run | Checkpoints skip the items that finished | The flow runs again |
| Control flow | Loops, branches and functions in code | Wired on the canvas |
| Reviewing a change | Read the diff | Inspect the canvas |
| Multiple accounts per provider | Named credentials, selectable per call | Shared per workspace |
| Multi-model | Yes, per step | Yes |
| Run surface for non-developers | Typed inputs render a form | Interfaces |
| Follow-up questions on a result | Output as a chat | Slack threads |
| Building without code | The builder drafts the task as code you edit | Yes, the canvas is the product |
| Browser automation | Not included | Yes |
| Integrations | 45+ integrations, 390+ operations, plus raw HTTP | Broad catalogue plus browser automation |
A task is an application.
The AI flows you build today resemble complex applications: they have logic, control flow, state, dependencies, and a maintenance cost.
Versioned prompts, called by slug
A model-agnostic prompt platform. Prompts are objects with version history, shared by chat, projects and tasks. Edit one and every task calling it runs the new version, with no code change and no redeploy, and a bad edit reverts in one click.
score = aisle.ai.run_prompt( slug="score-lead", variables={"lead": lead}, )
Revisions and rollback
Every save writes a revision with a line-level diff and a changelog note. The tests.py is versioned in the same revision, so a diff shows whether the tests moved with the code, and any revision restores in one click.
Predictable token spend
A prompt is only used when you call it, so you decide when to spend tokens, not an agent. Cache results between runs, checkpoint long ones, and read the call sites off the file to work out the bill before it arrives.
summary = aisle.cache.get_or_set( f"account-brief:{lead['domain']}", lambda: research(lead), ttl="7d", )
Moving a flow across.
There is no importer. Start with a flow whose failure would be cheap rather than the one carrying the most volume.
- Describe the flow to the builder and it drafts the task as code your developer edits.
- Connect the accounts once at the org level.
- Run both side by side and compare the output.
- Move the trigger over once they agree.
Where Gumloop fits better.
Gumloop does something Aisle deliberately does not try to do.
- The people building the automations do not write code, and a canvas keeps them independent.
- You want non-developers building new automations, not running ones a developer wrote.
- Browser automation over sites with no API is central to the work.
- You want to watch a run move through a graph while you debug.
Questions
What is the difference between Aisle and Gumloop?
Gumloop automations are built on a canvas. Aisle automations are Python programs on a managed runtime. Most of the other differences follow from that: code can be diffed, reviewed, unit tested and rolled back line by line, and it expresses loops and branches directly rather than as wired nodes.
Do I need to write Python to use Aisle?
To build or change a task, yes, though the builder drafts the first version from a description. To run one, no. Typed inputs render a form, so anyone on the team runs it with their own parameters and never sees the code.
Does Aisle support multiple models like Gumloop?
Yes, and per step rather than per automation. OpenAI, Anthropic, Gemini, Grok and OpenRouter sit behind one call, and the model is a field on the prompt, so switching a step is one edit and no caller changes.
Can a task post into Slack?
Yes. A task can write to Slack, return an API response, store to memories, or publish its result as a chat the team asks follow-up questions in. Slack also works as a trigger, so a task can start from a message.
How does the cost compare to Gumloop credits?
The units differ, so compare on your own volume. The structural difference is that a prompt is only used when you call it, so the model spend sits at points you chose in the code rather than being metered across a canvas.
Competitor details reviewed . Vendors change plans and features without notice, so check theirs before deciding.
More comparisons
Ship your first task.
Open the editor, write a script against your connected accounts, and put it on a trigger. Or describe it, and the builder drafts the task as code you edit.