Overview
Gumloop is a no-code AI automation platform for building agentic workflows without writing code. Launched in 2023 out of Y Combinator’s S23 batch, its thesis is that most ‘AI automation’ tools are 2010s integration platforms with a GPT button attached, whereas real AI work needs a runtime designed around LLM chaining from day one. The result is a visual drag-and-drop canvas where the building blocks are verbs like summarize, extract, research, classify, and generate — not just trigger and filter.
It is aimed at operations leads, RevOps and marketing teams, agency owners, and non-technical founders who lose hours to repetitive work involving data, email, CRMs, and spreadsheets. You assemble a workflow by connecting nodes: pull data from a website, PDF, or Google Sheet; run it through one or more AI models; branch on the result; and write the output to the app of your choice. Because Gumloop treats AI as a reasoning node, workflows can loop, self-correct, and pause for human-in-the-loop approval before continuing — the shift from ‘if this then that’ to ‘if this, then think, then do.’
A key strength is multi-model flexibility. Within a single flow you can route deterministic parsing to a cheap, fast model, creative writing to a high-quality one, and deep analysis to another, optimizing cost against quality per step. It ships with 130+ native integrations plus custom API and webhook nodes, a public template gallery of sales-research, lead-enrichment, and content agents that work out of the box, and MCP server support for governance and per-tool permissions. When a built-in node does not fit, a JavaScript or Python code node keeps the platform no-code by default without becoming a cage.
The honest weaknesses are cost visibility and integration breadth. Pricing is credit-based — every node consumes credits, and heavy nodes like web scraping, OCR, and large LLM responses cost more — so a 20-step research agent can be hard to price before running it. The integration count trails Zapier’s by orders of magnitude, and higher tiers climb quickly at scale. But for automations that genuinely need AI decision-making, Gumloop hits a sweet spot rivals miss.
Key Features
- Visual drag-and-drop builder with AI reasoning nodes
- 130+ native integrations plus custom API and webhook nodes
- Multi-model chaining to balance cost and quality per step
- Template gallery of ready-made research and enrichment agents
- Human-in-the-loop approvals, MCP servers, and a code escape hatch
Pricing
| Plan | Price | For |
|---|---|---|
| Free | $0 | ~2,000-5,000 credits/mo, unlimited draft workflows |
| Pro / Solo | from $37/mo | Automated triggers, more credits, priority support |
| Team | ~$97/mo | Shared library, collaboration seats, version history |
| Enterprise | Custom | SSO/SAML, audit logs, VPC, governance, BYOK |
Comparison
Compared to Lindy, Gumloop’s canvas is more visual and flexible with wider integrations, while Lindy leans into structured, single-purpose AI assistants like schedulers and email responders. Compared to Coze, Gumloop is built around chaining data-and-AI workflows across your business apps rather than publishing chatbots to messaging channels. Against Dify, which targets developers building LLM apps and RAG pipelines, Gumloop is the no-code option non-technical operators reach for first.
Hands-on Verdict
Gumloop is the no-code tool I use to build AI automations as visual flows — scrape, summarize, and pipe data between apps without code. It’s more AI-native than Zapier on LLM steps and a friendlier build than Make. For simple triggers I use Zapier.
Who it’s for: ops, growth, founders. Tip: start with one input node and one output, then add AI steps — Gumloop’s canvas scales, but a small working flow beats an ambitious broken one.