Low/No-Code AI Agent Builders: n8n, AgentKit, make, Zapier
Low- and no-code AI agent builders let users create automated, AI-driven workflows without writing complex code, making agent development faster and accessible to non-technical teams.
We spent three days configuring AI-agent workflows, manually setting up LLM actions, document parsers, search tools, and building pipelines with triggers, conditional steps, tool calls, and webhooks to compare how each system handles multi-step agent automation.
We used the free tiers of popular low/no-code AI agent builders, including n8n (self-hosted), make, and Zapier, and evaluated OpenAI’s AgentKit based on its official documentation.1
AI agent builders
Platform | Agent tools ecosystem | Transparency & debugging | Self-hosting |
|---|---|---|---|
n8n | 1,200+ native integrations + custom nodes | full step data view | ✅ |
AgentKit | MCP connector ecosystem + custom tool servers | basic API logs | ❌ (tied to OpenAI tooling) |
make | 400+ built-in app modules + webhooks + custom apps | step data logs | ❌ |
Zapier | 8,000+ app integrations + webhooks + custom actions | step data logs | ❌ |
Here is a quick review of each platform:
- n8n: Open-source, developer-oriented, code-driven. Also, swings heavily towards SaaS. Support deep agent orchestration features, such as memory or tool reasoning. Provides a dedicated agent node.
- OpenAI AgentKit: Open-source agent builder for teams and users deeply embedded in OpenAI’s ecosystem; not a strong tool for building highly custom agents. It includes agent evaluation tools as built-in features, such as automated grading, prompt optimization, and performance tracking
- make: Cloud-based SaaS tool. Supports multi-step agent workflows, conditional branching, and API integrations. Provides less agentic flexibility and logic than n8n, but it still supports custom configurations setups via HTTP requests, JSON/router modules and webhooks.
- Zapier: Cloud-based SaaS tool. The most beginner-friendly option with a code prompt-based AI agent builder. However, its architecture is linear, and deeper logic (like branching or feedback loops) requires paid features such as Paths or Code by Zapier.
→ For those considering frameworks over no-code tools, read about our hands-on experience building AI agents with LangGraph, CrewAI, Swarm and LangChain.
Read more
If you are looking into the infrastructure that powers web-capable agentic AI, here are our latest benchmarks:
- Remote browsers: How browser infrastructure enables agents to interact with the web securely.
- Browser MCP benchmark: Top MCP servers for tool use and web access.
n8n
n8n lets users build complex AI agent workflows. It is developer-friendly and flexible. You can write real code inside workflows and extend it however you want. What sets n8n apart is that its entire source code is available on GitHub.2
Key features:
- Code support with JavaScript and Python
- Rich node library with hundreds of integrations
- A dedicated AI Agent node for multi-step agent logic
- Create agent nodes via system prompts
- Context and memory support
- Multiple triggers, branching, loops, and error-handling
- External npm packages when self-hosting3
- Git-based version control on higher tiers
OpenAI’s AgentKit
In October 2025, OpenAI announced AgentKit, a toolkit for building and deploying AI agents.4 It is designed for teams already using OpenAI models and tools. It focuses on how agents think, reason, and use tools, not on general automation.
Key features:
- Visual canvas for building agent flows
- Native support for memory, tool use, and agent delegation
- Built-in logic blocks (If, While, Set State)
- Tight integration with OpenAI models and MCP tools
- Built-in evaluation with
- Automated grading
-
- Prompt optimizer
-
- Agent trace grading
- ChatKit widgets to embed AI agents in websites and apps
make
make is a cloud-based automation platform where you connect apps using visual modules. It can run multi-step AI workflows that mimic agent behavior, but it does not provide a true agent framework.
Key features:
- Multi-step workflows called “scenarios”
- Routers and filters for branching
- Loops and sub-scenarios
- API support via HTTP modules
- No code nodes; relies on its own expression language
- Chrome DevTools extension for detailed debugging
- Clear step-by-step logs
Zapier
Zapier is the simplest and beginner-friendly automation tool. It utilizes a natural-language interface to construct AI agents, but relies on linear workflows.
Key features:
- 8,000+ app integrations
- Very simple setup
- AI Agents (beta) built through natural-language instructions
- Code by Zapier for small JS/Python snippets
- Templates for common agent tasks
- Paths for conditional branching (paid feature)
- Limited step-level transparency
Pricing comparison of AI agent builders
Cost modeling for individual and team plans
Cost modeling for enterprise plans
Key highlights:
- n8n: Charges per workflow execution: one run counts as a single execution, no matter how many nodes it includes.
- Agentkit: The cost is tied to API/model usage, where you pay for tokens and any used tools according to OpenAI’s rates; there is no separate charge for AgentKit itself.
- make: Charges per operation: each module in a scenario (Make’s term for a workflow) counts as one operation.
- Zapier: Charges per task: each action step after the trigger counts as one task.
For example, if a workflow has 10 nodes:
- make and Zapier would count that as 10 operations or tasks each time it runs.
- n8n would count it as one execution, regardless of the number of nodes it includes.
However, n8n’s pricing can be a bit confusing: even though operations aren’t counted individually, each plan still has a limit on the total number of executions (for example, 2,500 per month on the free tier).
n8n
n8n provides both self-hosted and cloud-hosted options. Both can run on your own infrastructure using Docker or Docker Compose.
The Community edition lacks a few enterprise-level features, including SSO, access controls, and global variables. Some of these missing features can be replaced by community-built nodes; for example, the n8n-nodes-globals package offers an alternative to global custom variables.
As of August 2025, n8n has removed active workflow limits across all its cloud plans, meaning you can have unlimited workflows, steps, and users in each plan.5
AgentKit
The cost is tied to API/model usage: you pay for tokens and any used tools per OpenAI’s rates; there is no separate charge for AgentKit.
make
make uses an operation-based pricing model:
That means even a moderate workflow can quickly consume your free quota. Suppose you build a daily AI agent that runs 3 times a day and uses 5 modules (e.g. fetch news, filter, call OpenAI, format result, send email). That’s 5 operations × 3 runs = 15 operations per day. Over 30 days, that’s ~450 operations.
- Free plan: includes 1,000 operations per month and allows up to 2 active scenarios.
- Paid plans: start at $9/month for 10,000 operations.
Because make bills per operation, workflows with more nodes or more frequent runs become costly.
Zapier
Zapier charges based on the number of tasks performed by its Zaps. A task corresponds to each data element processed by an action step in the workflow. For example, if a Zap adds one row to a Google Sheet, that counts as one task.
- Free plan: 100 tasks/month and 5 Zaps (workflows).
- Paid plans: Start at $19.99/month for 750 tasks/month.
Note that when you exceed your task limit, Zapier switches to pay-per-task billing at a higher rate to keep your Zaps running.
Zapier also offers AI agents as part of its AI orchestration package. These plans allow you to create AI-powered chatbots and agents. Free plan includes 400 activities/month.
Further readings
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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