Low-Code and No-Code for AI Agents: a complete comparison of n8n, Make, Zapier, and OpenAI AgentKit

Low-Code and AI Agents are no longer topics reserved for people who breathe code all day long. Over the past few months, platforms like n8n, Zapier, and Make have gone all in on democratizing the creation of artificial intelligence agents, and the market just keeps growing. What used to be exclusive territory for engineers and data scientists is now within reach of anyone with a solid idea and a desire to automate repetitive processes. This changes the game significantly, especially for small teams and companies that need agility without relying on a massive technical team.

But which of these tools actually delivers on its promises when it comes to AI-powered automation? That question sounds simple, but the answer involves a lot of nuance. Each platform has a different value proposition, its own learning curve, and limitations that only surface when you actually roll up your sleeves and get to work.

To answer that, the team at AIMultiple spent three days testing each platform on their free plans, setting up AI Agent workflows from scratch with LLM actions, document parsers, search tools, triggers, conditional steps, tool calls, and webhooks. The focus was on simulating situations you would actually encounter in a real work environment, not just polished demo scenarios designed to look pretty in a tutorial. This kind of testing is what separates a tool that works in the lab from one that can actually hold up under everyday pressure.

The goal was straightforward: see how each system handles multi-step automations, the kind you run into in real-world work scenarios. The comparison included:

• n8n (self-hosted)
• Make
• Zapier
• OpenAI AgentKit (evaluated based on official documentation, announced in October 2025)
• Creatio Studio
• Google Workspace Studio

If you are thinking about building your first AI agents or looking to migrate from one solution to another, this comparison was made for you.

Overview of AI Agent Builder platforms

Before diving into the technical details, it is worth understanding how each tool is positioned and what it brings to the table. The low-code and no-code AI agent builder ecosystem has developed a pretty interesting variety of options, each with a very distinct profile.

Creatio Studio

Creatio Studio is a cloud-based platform focused on enterprise process automation. It uses a visual designer and natural language prompts so that users without technical knowledge can create workflows and automated tasks. The differentiator here is the focus on corporate scenarios, with market integrations, no-code visual components, and a complete view of data at every step of the process. The platform already includes ready-made agents for tasks like sales prospecting, customer service automation, and marketing workflows. On top of that, process blocks and applications can be reused across teams, which helps a lot with consistency when operations scale.

n8n

n8n is open-source, developer-oriented, and highly flexible. Its entire source code is available on GitHub, which allows for deep customizations and an active community contributing new nodes and features. The platform offers more than 1,200 native integrations, support for JavaScript and Python code within workflows, a dedicated AI Agent node for multi-step agent logic, memory and context support, plus multiple triggers, branching, loops, and error handling. It is the option that gives you the most control over infrastructure, especially when self-hosted.

OpenAI AgentKit

Announced in October 2025, OpenAI AgentKit is a toolkit for building and deploying AI agents, designed specifically for teams that already use OpenAI models and tools. It is not a low-code platform in the traditional sense, but rather a framework that focuses on how agents think, reason, and use tools. It includes a visual canvas for building agent flows, native support for memory, tool usage, and delegation between agents, logic blocks like If, While, and Set State, direct integration with OpenAI models and MCP tools, plus evaluation features like automated grading, prompt optimization, and agent performance tracing. ChatKit widgets also allow you to embed agents into websites and applications.

Make

Make is a cloud-based SaaS platform where you connect apps using visual modules. It can run multi-step AI workflows that simulate agent behavior, but it does not offer an agent framework in the strict sense. Workflows are called scenarios, and the platform supports routers and filters for branching, loops, sub-scenarios, and integrations via HTTP modules. The standout feature is the Chrome DevTools extension that enables detailed debugging and clear step-by-step logs. It supports more than 400 native app modules, webhooks, and custom apps.

Zapier

Zapier is the simplest and most beginner-friendly automation tool. It uses a natural language interface to build AI agents, but relies on linear workflows. The ecosystem boasts more than 8,000 app integrations, webhooks, and custom actions. AI Agents are still in beta and are built through natural language instructions. For more advanced conditional logic like branching or feedback loops, you need paid features like Paths or Code by Zapier. Step-level transparency is still limited compared to platforms like n8n and Make.

Google Workspace Studio

Google Workspace Studio is Google’s no-code agent builder, introduced as part of Google Workspace. It uses Gemini AI to work seamlessly with Gmail, Drive, Calendar, Chat, Forms, and other services in the Google ecosystem. Agents can operate directly within these tools, extracting context from files, emails, and events to make smarter decisions. Workflows can be triggered by events like receiving emails, calendar events, new form responses, scheduled times, or Chat mentions. Once created, agents can be shared across teams just like Google Docs.

How the testing actually went

The main testing criterion was each platform’s ability to sustain a complete AI Agent workflow without needing external code to fill in the gaps. This included setting up an agent that receives input via webhook, processes a document with a parser, performs a web search, decides the next step based on the result, and returns a structured response. It sounds complex, but this is exactly the kind of flow that shows up in customer service automations, email triage, and automated report generation.

With n8n, the setup process was the most technical of the three, but also the most flexible. The platform has a specific node called AI Agent that centralizes the agent logic and lets you connect tools like pieces of a puzzle. Integration with models from OpenAI, Anthropic, and other providers is straightforward, and tool calling support works quite reliably. The most challenging part was the initial learning curve around the architecture, especially for anyone unfamiliar with concepts like agent memory and call chaining. But once you understand how the nodes communicate, the speed of creating new flows ramps up considerably. The debug system is thorough, showing data from each step in real time, which makes troubleshooting much faster than on the alternatives.

With Make, the visual experience is the big highlight. Scenarios are built by dragging modules and connecting them intuitively, and the router system lets you create branches without much effort. During testing, the platform performed well in medium-complexity scenarios, with API integrations via HTTP modules running stably. The Chrome DevTools extension for debugging is a feature that makes a real difference when you need to understand what is happening at each step of execution. The most noticeable limitation was the lack of a native agent framework, which forces you to simulate agent behavior using combinations of modules, routers, and webhooks.

Zapier was a pleasant surprise when it came to ease of agent setup. The experience is guided and significantly reduces friction for beginners. On the other hand, testing revealed clear limitations when the flow involved more complex logic or required chained tool calls. In some scenarios, it was necessary to split what would be a single agent into multiple separate Zaps, which fragments the logic and makes maintenance harder. The ready-made templates for common agent tasks help a lot at the start, but as complexity grows, the platform’s linear architecture starts to weigh things down.

Transparency, debugging, and self-hosting

One point that does not always get the attention it deserves in this kind of comparison is each platform’s ability to show what is happening at every step of execution. When an AI agent fails or produces unexpected results, how quickly you can identify and fix the problem depends directly on the quality of the debugging tools.

In this regard, n8n and Creatio Studio stand out by offering a complete view of data at each step, which testing confirmed is essential for more complex workflows. Make and Zapier also offer step-by-step logs, but without the same level of detail. AgentKit provides basic API logs, and Google Workspace Studio has only basic activity logs.

When it comes to self-hosting, n8n is the only platform among those evaluated that offers this option. This means you can run the entire infrastructure on your own servers, which is a dealbreaker for companies with strict privacy and compliance requirements. The other platforms are all cloud-based, with AgentKit being tied to the OpenAI ecosystem.

n8n vs Zapier vs Make: where each one shines

When it comes to AI Agent automation in complex scenarios, n8n comes out ahead in terms of flexibility and cost. The self-hosted architecture eliminates variable per-execution costs, which is a game-changer for teams that need to run agents frequently. On top of that, the ability to customize each node with JavaScript or Python code when needed provides a level of flexibility that the other platforms do not natively offer. For teams that already have some technical maturity or are willing to invest time in the learning curve, n8n is tough to beat. Git-based version control support on higher plans is another important differentiator for teams working collaboratively.

Make stands out when the priority is flow visualization and the ability to understand what is happening in automations with many branches. The interface is visually richer than Zapier and less intimidating than n8n for people without a technical background. The AI features are functional and sufficient for most mid-range use cases, and the free plan lets you run fairly representative tests before committing to a paid subscription. For operations and marketing teams that want to create AI-powered flows without depending on the development team, Make is a well-grounded choice.

Zapier remains the best pick for anyone who needs quick app-to-app integration and does not want to worry about infrastructure setup. The connector library is the largest on the market, and ease of use is still unmatched for simpler automations. The challenge is that as workflows grow in complexity, the per-task pricing model and conditional logic limitations start to add up. The platform is evolving rapidly in this direction, but it still trails n8n and Make when the focus is building robust multi-step AI agents.

Where does OpenAI AgentKit fit into this picture?

OpenAI AgentKit, announced in October 2025, represents a completely different approach from the previous platforms. It is not a Low-Code tool in the traditional sense, but rather a framework aimed at developers who want to build agents directly on top of OpenAI’s infrastructure, with granular control over behavior, memory, tools, and orchestration.

What AgentKit offers is an abstraction layer above OpenAI’s APIs, making it easier to create agents with tool calling, persistent memory, and parallel task execution. The visual canvas for building flows is an interesting differentiator, and the built-in evaluation features like automated grading, prompt optimization, and agent trace tracking are functionalities that none of the other platforms offer natively. For teams already using OpenAI models that want to go beyond simple chatbots, AgentKit can be a significant accelerator.

The learning curve is steeper than any of the Low-Code platforms evaluated, and it requires familiarity with the fundamental concepts of agent architecture. But the level of control it provides is unmatched for advanced use cases. The MCP connector ecosystem and the ability to create custom tool servers significantly expand what is possible with the platform.

In the context of this comparison, AgentKit works more as a reference point than a direct competitor to Low-Code platforms. It shows what is possible when you trade visual simplicity for total control. For most professionals just getting started with AI Agents right now, Low-Code platforms still make more sense as a starting point, but understanding what AgentKit represents helps calibrate expectations about what each solution can deliver in the long run. 🤖

Pricing comparison: how each platform charges

The difference in pricing models across these platforms is one of the heaviest factors in the decision-making process, especially when automation volumes start to grow. Understanding how each one charges is key to avoiding surprises on the bill.

n8n

n8n charges per workflow execution. That means one execution counts as a single charge, regardless of how many nodes the flow has. If your workflow has 10 nodes, it counts as one execution. The platform offers self-hosted and cloud-hosted options, both of which can run on your own infrastructure via Docker. The Community edition is free, but it does not include some enterprise features like SSO, access controls, and global variables. Since August 2025, n8n has removed active workflow limits on all cloud plans, allowing unlimited workflows, steps, and users. The free plan includes 2,500 executions per month.

AgentKit

AgentKit’s cost is tied to API and model usage. You pay for tokens consumed and tools used according to OpenAI’s rates. There is no separate charge for AgentKit itself, which simplifies the math, but it also means costs can vary quite a bit depending on the complexity and frequency of your agents.

Make

Make uses an operation-based pricing model. Each module in a scenario counts as one operation. This means a workflow with many modules burns through your quota quickly. To illustrate: if you have an agent that runs 3 times a day with 5 modules per execution, that is 15 operations per day. Over 30 days, that comes out to roughly 450 operations. The free plan includes 1,000 operations per month and allows up to 2 active scenarios. Paid plans start at 9 dollars per month for 10,000 operations.

Zapier

Zapier charges per task. Each action step after the trigger counts as a task. The free plan offers 100 tasks per month and up to 5 Zaps. Paid plans start at 19.99 dollars per month for 750 tasks. An important detail: when you exceed the task limit, Zapier switches to per-task billing at a higher rate to keep your Zaps running. Zapier also offers AI Agents as part of its AI orchestration package, with the free plan including 400 activities per month.

The difference in practice

This difference in billing models has a direct impact on total cost. If a workflow has 10 nodes and runs once:

• Make and Zapier would count that as 10 operations or tasks.
• n8n would count it as a single execution, regardless of the number of nodes.

For scenarios with high execution frequency and complex workflows, this difference becomes quite significant over the course of a month.

What to consider before choosing

The choice between n8n, Make, Zapier, and other solutions depends heavily on your team’s profile and the type of automation you need to build. If the focus is speed of implementation and simplicity, Zapier is still hard to beat for basic use cases. If you want a balance between ease of use and the ability to handle more elaborate flows, Make is a well-grounded choice. And if you need total control, maximum flexibility, and do not want to worry about per-execution costs, self-hosted n8n is the most solid path.

One point that often goes unnoticed in this kind of evaluation is the total cost of operation over time. Platforms that charge per task may seem affordable at first, but when automation volumes grow, the bill climbs significantly. n8n eliminates this problem by being self-hosted, but shifts the cost to infrastructure and maintenance. There is no universal right answer here — what exists is the right combination for your specific context.

For teams embedded in the Google ecosystem, Google Workspace Studio offers an interesting alternative that works natively within the tools those teams already use daily. The integration with Gemini AI and the ability to share agents across teams like documents are differentiators that make sense for organizations that prioritize simplicity and rapid adoption.

Another important factor is the maturity of AI features on each platform. All three major players are evolving rapidly, and capabilities that were limited six months ago have already been updated. Keeping up with changelogs and release notes for each tool is a practice that makes a real difference for anyone who wants to get the most out of these platforms. The Low-Code market with AI Agents is still taking shape, and the platforms that manage to balance accessibility with technical capability will define how most teams work with automation in the years ahead. 🚀

For anyone looking to explore alternatives outside the low-code universe, it is worth checking out frameworks like LangGraph, CrewAI, and LangChain, which offer even more control over agent orchestration but require programming knowledge. The choice between visual tools and code-based frameworks is not mutually exclusive — many teams use a combination of both approaches to cover different needs within the same organization.