Latenode AI Workflow Builder
Create AI automations & agents using prompts
34 followers
Create AI automations & agents using prompts
34 followers
Stop building automations manually. Just describe what you want to automate, and AI Agent Builder creates a production-ready AI agent connected to your tools, business data, and AI models. With 5,500+ integrations and built-in orchestration, teams can launch complex AI workflows in minutes.
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Hey Product Hunt! 👋
Excited to share something big we've been building.
At Latenode, our mission has always been to make serious automation accessible to everyone, not just developers. Today we're launching a big step toward that: the AI Workflow Builder.
Just describe what you want in plain text, and AI Workflow Builder turns it into a working automation, complete with nodes, logic, and the connections between them. It even maps the data fields between nodes for you, so you're not stuck manually wiring inputs and outputs before you can test your idea.
Go from a rough idea to a workflow you can actually run and refine in seconds.
We'd love to hear what you think, drop your feedback in the comments below. Thanks for being part of the Latenode journey!
the auto data-field mapping between nodes is the part i'd want to stress test. when the prompt is ambiguous about which field maps to which, say two similarly named fields coming from different tools, does it pick one silently and let you find out when it breaks, or does it flag the ambiguity and ask before wiring it up? that feels like the real difference between automation in minutes and automation that quietly does the wrong thing in minutes.
@galdayan The builder actually tests the scenario as it wires things up, and if it’s not sure about something — like two plausible field matches from different tools — it’ll ask you rather than guess. It only auto-maps when the match is clear.
@dmitrii_glushakov that answers the "two similarly named fields" case well. the harder version i'm picturing is when the match looks unambiguous at wiring time - same field name, same type - but the actual data diverges at runtime, like a field called "id" that's a UUID in one tool and an integer in another. that wouldn't trigger the ambiguity flag since there's only one plausible match, it would just fail (or worse, silently coerce) once real data hits it. does the builder do any runtime type-checking on the actual values flowing through, or is the "ask when unsure" logic purely a build-time schema thing?
@galdayan Good edge to raise, but in practice those two fields won’t collide — the scenario builds and tests step by step, so each connection gets checked against real data as it’s wired, not all at once at the end. That id mismatch would surface right at the step it happens, not silently downstream.
@dmitrii_glushakov that's the answer i was hoping for honestly - checking per-step against real data as it's wired beats validating the whole graph at the end. good to know it's not just a schema-level sniff test. thanks for going a few rounds on this, appreciate the detail
How does the pricing work once you start using AI models and running lots of runs through the orchestrator, does it get expensive fast compared to just wiring up something in n8n yourself?
@ardaxi50 Fair concern. The difference is we bill on execution time — actual CPU time, pay as you go — not per-step or per-operation. So a 20-step flow doesn’t cost 20× a one-step one, and step count basically stops being the thing you optimize your bill around.
A versioning system for AI agents would be huge. Right now it feels tricky to track what changed between iterations or roll back when a tweak hurts performance. Something like git-style commits with diffs and one-click rollback would make iterating way less scary for production workflows.
@recep743659 Good news on part of this - the AI builder already walks you through every change it makes in the chat, so you’ve got a running record of what it touched and why as you iterate. Git-style commits with diffs and one-click rollback are a nice step beyond that though, and it’s an idea we’ll seriously think about.
Would love to see a version history with diffs for the AI-generated workflows so I can review what changed when I tweak a prompt, instead of wondering why a step suddenly behaves differently after a small edit.
@mcahit388915 Right now the AI builder tells you every change it makes directly in the chat as you go, so there’s a running record instead of guessing after the fact. A proper version history with side-by-side diffs is the natural next step though, and it’s on our radar. Would a plain diff cover it, or would you also want it to flag which change likely caused the behavior shift?
Tried it for a quick lead enrichment flow and it spun up a working agent in like three minutes. The 5,500+ integrations claim actually held up, I hooked it to Notion and Slack without wrestling with auth.
@mustafabfag Thanks for testing it out. Happy to share a few enrichment patterns if you want to push it further.
Tried it with a quick "send new Stripe customers to Slack and summarize" prompt and got a working agent in under a minute, integrations wired up and all. Surprised me how clean the orchestration view is, not the mess of nodes I was expecting.
@atakanlcts Thanks for putting it through a real test. A clean canvas is easy to demo and miserable to maintain, so that’s exactly the reaction we were hoping for. Shout if you hit anything rough.