Gumloop Review: A Deep Dive Into an AI-First Automation Tool

Having reviewed the major players like Zapier, Make, and n8n, I thought I'd seen everything in the workflow automation space. But then the AI revolution came, along with platforms like Gumloop that promised a new age of automation driven by AI-native tools.

If you've been on X or LinkedIn lately, you've seen the engagement farming around AI agents. Lead magnets about "bots running entire companies" are everywhere, with one founder even claiming that all his company’s employees disappeared for a week while AI agents handled everything (sales, support, operations) with zero impact on revenue or churn.

Compelling, but does it deliver? Gumloop positions itself at the forefront of this AI-first wave, but after testing countless SaaS tools, I've learned to approach bold promises with skepticism.

The real question isn't just whether Gumloop works, but whether there's a meaningful difference between traditional workflow solutions and these platforms designed for AI. Today, we're going to find out. I thoroughly tested Gumloop and took a deep dive into what it does, what it doesn’t do, and where it delivers. Let’s take a look!

What Is Gumloop?

Gumloop is an AI-native automation platform purpose-built for go-to-market (GTM) teams, revenue operations, and business development. It positions itself as a no-code solution for building AI-powered automations.

In a sense, it works like traditional workflow tools by allowing you to connect steps (called nodes in Gumloop) on a visual canvas to automate tasks. However, unlike established tools that added AI later, Gumloop was built from the ground up with AI as the core focus.

The platform offers advanced AI capabilities with nodes for tasks such as categorization, scoring, data extraction, and image analysis, as well as the ability to switch between different AI models (Claude 3.5 Sonnet, GPT -4, etc.) depending on the task.

It also excels at web scraping through a Chrome extension that embeds AI tools directly into browsers.

Most notably, Gumloop has embraced something called model context protocol (MCP), an open-source standard that acts like a USB-C port for AI applications. This provides a standardized way to connect AI systems to external data sources, tools, and workflows. Gumloop's MCP implementation enables users to describe what they want integrations to do in plain language, with AI communicating with the app and generating all the code behind the scenes. You can use specific queries like, "Retrieve last five emails from my Gmail inbox with the subject 'New lead' from sender sales@domain.com,” and Gumloop will make it happen!

Gumloop’s Key Features

Now that you understand what Gumloop is, let’s take a look at what it does. Here are Gumloop’s key features and what they can help you achieve.

But first, let's clarify some key Gumloop concepts and how they shape the platform's user experience:

• Flows: Gumloop’s automations, a series of steps (nodes) that are visually connected.
• Nodes: Automation steps or actions. These include core nodes (such as filters, if-else conditions, manual inputs, etc.), AI nodes for analyzing and processing content, integration-based nodes, and more advanced nodes, like web scraping, among others.
• Trigger Nodes: This is a specific type of node that allows you to automate flows.
• MCP Nodes: Custom nodes that allow you to talk to a specific app using semantic language. For example, “Get the YouTube video details and transcript.”
• Custom Nodes: Build your own custom node using AI. Describe what you want and have AI build it for you.
• Subflows: Flows within flows that enable you to go into creating some really deep automations.
• Interfaces: Similar to Zapier’s interfaces, these are usually forms that you can connect to a flow. When someone completes the form, information will be sent to the other nodes.

Workflow Builder UI

My initial impression of the visual flow builder in Gumloop was overwhelmingly positive. This is the most beautiful and intuitive builder as far as UI goes. It employs a smooth drag-and-drop interface, allowing you to connect and branch any nodes across the canvas (similar to Make).

Animations, focus, and highlight states are all clear and distinctive across the whole app. This also applies to helpers and testing visualizations.

Little things, such as active triggers, indicate whether this is an automated or manual flow.

Nodes can be auto-aligned or collapsed for better alignment, and you can even select the panning behavior of the canvas (scroll-to-pan vs. drag-to-pan), as well as the edge type and snap-to-grid options.

These are little things, for sure, but they can vastly help with adoption if you're coming from an automation tool that uses scrolling vs. dragging pan behavior, for example. Power users will understand how annoying this can be.

Data Mapping

Node inputs and outputs are clearly indicated in Gumloop.

In the example below, we can see that the Interface node has two outputs. In other words, two data items flow out of the node (name and email). These items will be available as data inputs in the Gmail node underneath. We simply drag them as dynamic data items into the fields of the node (in this case, the email Body).

Lastly, we can view the output of the Gmail node, which displays the Email Status (e.g., Sent, Bounced, etc.).

This method of data mapping is a radical improvement for me, compared to the complex, JSON-driven data structures Make uses, but I can see developers and more advanced automators complaining about this. I couldn’t find a way to use formulas and JSON formatting in data fields, but you can create custom nodes with AI to format text output (more on that later).

You can find out more about what I thought of Make in my Make Review.

Building Automations

My initial positive impressions faded when I attempted to build actual workflows in Gumloop. I found myself genuinely confused about how the platform works.

That's because Gumloop operates on a completely (or at least partially) different paradigm.

Most automation platforms follow the simple Zapier model: something happens in one app, and that triggers something else in another app. For example, a new lead comes in, and that triggers a Slack message. It’s linear and straightforward.

Gumloop can do this (although not as extensively as Zapier), but it's really built for more complex data operations. Think scraping 50 competitor websites, having AI analyze each one for pricing info, and then generating individual reports for every company.

For example, this flow analyzes a LinkedIn profile, then comes up with a personalized outreach message as an email draft. To run it, you use Gumloop’s Chrome extension on a LinkedIn profile.

Instead of reacting to events, teams run these flows when they need them. That might be when launching an outreach campaign, doing market research, or analyzing competitors. You're essentially building mini-apps that others can use by inputting data and receiving results in return.

It's brilliant for teams who work with data in batches rather than individual triggers for lead generation, scraping, and LLM processes, for instance. The value is obvious once it clicks. It just took me a while to wrap my head around the concept, as I was searching for triggers and action steps.

The easiest way to know if you have an automated or on-demand flow is to look for the trigger nodes in your flow.

Loop Mode

Loop Mode in Gumloop is a feature that lets a node (or group of nodes) automatically process lists of inputs. Normally, a node takes one input, does something, and outputs one result. In Loop Mode, when you feed a list (e.g., an array) of items into that node, it processes each item individually and gives you an output list of corresponding results.

To understand this better, let’s look at the difference between triggers and on-demand nodes further by examining the two flows below.

On-demand Flow with Loop (Trigger Not Activated)

In this flow, we have a Google Sheets Reader node connected to a Gmail Sender node. Notice that the trigger option is not activated. This means we’ll get the flow to read and retrieve all of the rows (in this case, contacts) in our Google sheet.

Loop mode will then run the Gmail Sender node for each record in the table one by one. For instance, if we have 10 rows, we’ll receive 10 emails for each record in our Gmail.

This is quite useful if, for example, you want to draft a large number of personalized emails using AI. To run this flow, you must click the “Run Flow” button in Gumloop.

Automated Flow with a Trigger Activated

In the second example, the trigger feature is enabled instead. We also now get the option to select the “Trigger Mode” – in this case, we chose “Create Row.” In other words, the flow will be triggered when a new row is created. When that happens, the Gmail Sender will send an individual email with the details of the person, such as a new contact alert.

Notice that the output items are now simply called “Email” and “Date”, rather than “Email list” and “Date list." This is because we're retrieving the data for a single row, not the whole list.

Triggers

In Gumloop, a trigger is a special kind of node (or event) that automatically starts a flow (automation) when some external condition or event happens. This saves you from having to run automations manually each time. But the trigger catalog reveals where Gumloop falls short compared to traditional automation platforms. With only 11 triggers, it’s clear that automated flows aren't the main focus of this tool.

Diving further, I just couldn’t figure out how to make some triggers work. The HubSpot List Reader, for example, doesn’t allow me to use an individual output and only offers lists. In other words, I can retrieve a list of all of the objects in my CRM, but I can’t trigger them individually. I’m not sure if this is a bug, underdevelopment, or a design choice, but it felt strange, considering that the Google Sheet node had this option.

Subflows

Subflows in Gumloop are essentially complete flows that can be used as nodes within other flows. They allow you to break up complicated logic into separate functions, similar to how programming uses functions to organize code. They're also reusable, so you can drop them into multiple larger flows.

Apart from keeping your flows more manageable and organized (which is critical if you are building large flows), subflows help process nodes faster. Once your subflow works perfectly for a single item, you can connect it to a data source like a Google Sheet and enable Loop Mode for parallel processing. This lets you process multiple items simultaneously with faster execution.

Templates

It’s when I dove into Gumloop’s templates that it became evident to me that Gumloop serves a completely different purpose from Zapier and Make.

Most use cases are geared towards batch operations: scraping, analysis, research, lead generation, etc. Think content repurposing at scale, LinkedIn profile analysis across hundreds of prospects, comprehensive lead research, or personalized outreach campaigns. Make and Zapier, on the other hand, are more about workflow automation and orchestration.

Check out my review of Make vs Zapier to learn more about what they can do and where they shine.

Interfaces

Think of interfaces as dynamic forms that can trigger flows in Gumloop.

For instance, the YouTube summarizer interface asks you to enter a YouTube video URL. Once provided, the flow will create a transcript of the video, and an AI node will summarize it. Pretty neat!

And the flow:

But the most powerful part about this is that it can be used as a launchpad for broader flows for content production, marketing automation, or sales enablement. For example, you could auto-extract some of the best-performing snippets of a marketing video and then feed that to ChatGPT to auto-generate LinkedIn or Twitter Captions before sending it on to a social media management tool for publishing.

Chrome Extension

Gumloop’s Google Chrome extension works in conjunction with the Browser Extension Input node.

It allows you to scrape or screenshot the page that you're currently visiting. You can also scrape the source of the page, something I didn’t see with the Scrape Website node.

For instance, you can scrape LinkedIn profiles. Simply go to the page, click on the extension, and you’ll see a list of the flows that have the Browser Extension Input. You can run any of these flows from the extension.

Custom Nodes

Gumloop custom nodes let you extend the platform with your own code and functionality. They’re like plugins for automations, or reusable building blocks you can drop into any workflow.

You can:

• Write your own code (JavaScript or Python).
• Integrate proprietary APIs.
• Handle specialized tasks not covered by Gumloop’s built-in nodes.

Even better, Gumloop’s AI assistant, Gummie, can help you scaffold these nodes without writing code yourself. You describe what you need, and Gummie drafts the node’s logic.

Once created, a custom node is:

• Reusable across multiple workflows.
• Configurable with inputs and outputs.
• Seamlessly integrated with existing Gumloop nodes.

Sounds beautiful in theory, but does it deliver in practice? Let’s try it out.

I gave my custom node the following request: “Trigger when a new Calendly meeting is booked.” Gummie got to work figuring out if my idea was feasible.

Frankly, I didn’t expect it to say my idea was feasible, because Gumloop doesn’t integrate with Calendly at all. One minute and 170 lines of code later, it came up with this node, which looked surprisingly promising:

…Buut, unfortunately, it errored out when I tested it with a real API key:

After some prompting with Gummie, I got the node to a seemingly functional place. However, once I connected it to another node, I was unable to retrieve actual data from Calendly. It always came out empty.

At this point, this feature feels like a hit or miss to me, and I’d only recommend it to developers who are willing to sift through the AI-generated code to test it out and make changes (yes, you can edit the actual node code).

That said, I had more success with the Contact List Formatter, which you can use to format and process data by giving it natural language instructions. For instance, I asked it to list all data items on one line, instead of pushing them as numbered lists. It worked just fine.

MCPs

Gumloop's model context protocol (MCP) nodes let you connect to external tools and data sources through the open MCP standard. Instead of hand-coding integrations, you describe what you want in plain language, and Gumloop’s AI assistant, Gummie, walks you through the setup.

Behind the scenes, Gumloop uses MCP to:

• Dynamically generate a Python script.
• Handle the communication with the external integration.
• Return structured results back into your flow.

The result: you can spin up a fully working integration node without writing a single line of code and use it just like any other Gumloop node.

This time, I was a bit more skeptical when testing, so I opted for something more conventional and selected one of the suggested MCP strings. This one was designed to retrieve all unread emails in Gmail from the last eight hours. This is what Gummie came up with after a few minutes of thinking and coding:

Then I ran the test. Voila! This time it was successful! The MCP step successfully retrieved all unread messages from the last 24 hours. Well done, Gummie!

Gummie, Gumloop’s AI-First Assistant

Gummie is arguably the best AI automation assistant I’ve tested, or maybe up to par with Zapier’s chatbot. Instead of dragging nodes around, you just describe what you want to automate in plain language. Gummie builds the entire workflow for you: nodes, connections, configurations, everything.

The experience feels like collaborating with an expert automation engineer who works at the speed of thought. While other platforms have bolted on AI assistants that feel like afterthoughts, Gummie feels native to Gumloop.

I created this flow with Gummie. It monitors Reddit for brand mentions, analyzes the sentiment, and sends an email report with all of the information. I was quite impressed with the results out of the box.

Last but not least, Gummie was brilliant in helping me troubleshoot automations, often rebuilding the whole flow from scratch to fix logical issues and errors. While not perfect, I felt like this is the best the current market can offer in terms of AI assistance in the automation space.

Getting Started with Gumloop

So what’s it like getting started with Gumloop? I wanted to find out, so I put myself into the shoes of a new potential user. For this guide, I’ll pretend that I’m selling content marketing services for SaaS products. I’ll use Gumloop to generate leads and a personalized message from a list of email addresses. Data points that I want to populate include:

• Name and email.
• Company, website, company size, industry.
• Website URL and website description.
• Whether the company is SaaS or not.
• A personalized outreach message based on the company and the experience of the person.

This is the Google Spreadsheet before the Gumloop automation:

And after it:

View the spreadsheet

And this is my final flow.

Let’s break down the steps to creating this flow.

1. Starting with a Google Spreadsheet

For this flow, I’ll start with the Google Sheets Reader node, but I won't use a trigger step because I want to enrich a batch of existing leads.

2. Separating the Subflows

The first time I built this flow, I created it in a single flow. But I quickly realized it would get really messy, so I asked Gummie to help me separate them into subflows. Unfortunately, Gummie doesn’t yet have the capabilities to create and arrange subflows, but it was quite helpful in providing info on how to achieve it.

I created two subflows:

• One for contact enrichment.
• One for company analysis.

3. Contact Enrichment Subflow

Subflows work with Input and Output nodes. Think of them as forms that indicate what information is passed into the subflow and what information is passed out of the subflow.

In this case, I started with an email address, so I created an input for it.

Next, I enriched the contact through the email address using the native Enrich Contact Information node. Gumloop supports out-of-the-box access to enrichment providers like Apollo, Hunter.io, and Zoominfo, which is great. That said, credits are quite hefty when not using your own API keys (more on that later).

Something I couldn’t figure out at this stage of the automation was how to obtain the company data of the person. I'm not sure if this is a limitation of the enrichment APIs or Gumloop (it feels more like the latter), but due to this, I had to do a lot of jiu-jitsu to extract the company name and website.

As an optional extra step, I used the Ask AI node to format and summarize the work experience for easier viewing in the table.

4. Company Analysis Subflow

Now that the company has been extracted from the contact enrichment subflow, I can use it to gather and analyze company information.

First, I used the LinkedIn Company Profile Scraper to scrape the company’s website URL (as well as a few other data points about the company) by giving it a “Company name.”

Next, I used the Website Scraper node to scrape the actual content of the company’s website.

Then, I used the Extract Data node with the website’s content to gather the website title, which I’ll use as the company description.

Lastly, I used Ask AI to analyze if the company is a SaaS or not.

5. Putting It All Together

Now that the Contact Enrichment and Company Analysis subflows are complete, I can feed the extracted and enriched data to the final Google Sheet Updater node.

Make sure that the Search Column “Email” is selected and the Search Value is set to “Email List,” so that the spreadsheet updates are matched to the appropriate email.

6. Generating the Personalized Outreach Message

Finally, I used a router that checks if the company is SaaS. If it is, I use the Ask AI node to generate a custom personalized message that's populated back to the spreadsheet.

This is the prompt I used:

“Write a personalized outreach message. I'm selling a content marketing service for SaaS tools. Use the company information, industry, and stress on the fact that they're SaaS. Personalize a short outreach email for the person based on their work experience.

Company info:

[List all relevant company and contact information for personalization]”

Here’s an example result:

“Subject: Helping Encharge-scale SaaS marketing with content that drives growth

Hi [First Name],

I’ve been closely following Encharge.io’s impressive journey—from pioneering seamless SaaS marketing automation to your successful acquisition earlier this year. Your experience integrating CRM, emails, and in-app events into targeted lifecycle campaigns clearly shows your deep understanding of what drives SaaS growth.

Given your background co-founding and scaling multiple SaaS tools, including Encharge and HeadReach, I wanted to introduce our content marketing service designed specifically for SaaS agencies like yours. We specialize in creating data-driven, conversion-focused content that amplifies customer acquisition and retention—key to accelerating SaaS MRR.

I’d love to explore how we can support your growth marketing efforts with tailored content strategies that resonate with SaaS buyers and reduce your reliance on paid channels.

Are you open to a quick call next week? I’m confident we can help you build upon your impressive growth record with content that delivers measurable results.

Looking forward to connecting,
[Your Name]
[Your Company]
[Contact Info] “

It certainly could be better, but it’s a good start. I’d use Clause instead of ChatGPT to get more refined copy and tweak the prompt slightly to make it even more personal.

Gumloop Pricing

Gumloop uses a credit-based pricing model. Here's the breakdown:

• The Free plan offers 2,000 credits per month and 1 active trigger, so think of it as a trial version.
• The solo plan starts at $37/month for 10,000 credits.
• The Team plan starts at $244/month for 60,000 credits and includes 10 seats.

The problem is that it’s hard to predict how many credits you’ll use. Most nodes cost 0 credits, but certain nodes that use expensive APIs cost credits proportional to the API cost. For example, a GPT-4.1 query may cost 20 credits, while enrichment nodes can be even more costly.

Plus, minor modifications of a workflow can mean vastly different costs. One user's LinkedIn workflow went from using 1-2 credits per run to suddenly consuming 70 credits after a minor modification. This kind of unpredictability makes budgeting nearly impossible.

To top it all off, there’s no clear cost calculator to estimate credit usage before building a flow, forcing you to learn by trial and error.

Fortunately, you can authenticate your own OpenAI or other APIs to reduce these costs slightly. That said, you must tread lightly when executing complex flows with many records.

Final Verdict

Don't expect Gumloop to replace your traditional automation stack. Instead, it’s laser-focused on batch data processing and scraping operations for GTM and rev ops teams. It's an apples-to-oranges comparison. In a sense, Gumloop is a more direct competitor to GTM orchestration and enrichment tools, such as Clay.

Also, despite the marketing buzz that you may have heard, this isn't an "agent" platform. It's a workflow automation tool with strong AI capabilities. It has a ton of agentic functions built in, especially in the error handling and troubleshooting process, but at its core, you create workflows, not AI agents.

That said, I loved Gumloop for its intuitive design and well-developed use cases. I only wish it had more integrations and triggers. But, hey, as I said, this is not Zapier and it isn’t trying to be.

The bottom line? If you regularly find yourself thinking "I wish I could analyze 100 LinkedIn profiles at once," or "I need to scrape competitor data and generate reports," or “I have to transcribe and summarize 50 YouTube videos,” Gumloop will save you countless hours of manual work. Just don't expect it to handle all your basic app-to-app automation needs.

For revenue teams dealing with large-scale data operations, it's a solid investment. For everyone else looking for general app-to-app automation, stick with the established players.

Using Gumloop: Pros and Cons