No-code AI agent builder: compare n8n, Make, Zapier and more
A no-code AI agent builder lets you give a language model instructions, tools and knowledge, then test it, without writing the orchestration code yourself.
Three of the most common choices for automation builders are n8n, Make and Zapier. This guide shows what each one gives you, covers two dedicated agent builders the channel has recorded, then walks through building the same small agent so you can compare them on your own task.
- Zapier Agents if you want the most guided setup and your agent mainly acts inside apps Zapier already connects.
- Make AI Agent (New) if you already build Make scenarios and want an agent that can call them as tools.
- n8n AI Agent node if you want to self-host, mix agents into larger workflows, or drop into code when needed.
Every claim about each tool below links to that tool's own page.
What every agent builder needs
Whatever tool you pick, an agent has the same parts.
- Instructions. The job, the rules and the output format.
- A model. The language model that reads input and chooses actions.
- Tools. The actions the agent may take.
- Knowledge. Reference material such as docs or FAQs.
- A test loop. A way to send sample tasks and read what the agent did.
Judge each builder on how well it handles those five.
The three builders
Zapier Agents
Zapier describes Agents as AI teammates you equip with company knowledge, working across its app catalog, which it lists as 9,000+ apps. Its page also says you can create agents with help from Zapier Copilot.
The same page lists monitoring agent activity and chatting with an agent when needed. Zapier's task help page says Agents usage does not count as tasks and uses a separate activity quota.
Zapier help: How is task usage measured
The appeal is reach. If the apps your agent needs are already in Zapier, setup is mostly picking actions.
Make AI Agent (New)
Make's AI Agent (New) app lets you create an agent, add tools and knowledge, and chat with it to test before going live. Tools can be modules, scenarios or MCP tools.
Make help: Make AI Agent (New) app
Make lists it as available on all plans using Make's AI Provider, with custom AI provider connections on paid plans. It is in open beta, and Make notes that functionality and pricing may change.
The output panel shows the agent's execution steps and a token usage summary for each run. That is useful when you debug a wrong answer.
Make also supports sub-agents as tools, with one level of nesting.
Make help: Sub-agents and tool output filtering
With Make's AI Provider, the agent uses credits based on tokens and operations. With your own provider connection (paid plans), Make charges credits per operation and your provider bills the tokens.
n8n AI Agent node
In n8n, the AI Agent node connects a chat model and one or more tools, and the agent decides which tools to call. You must connect at least one tool.
Because the agent is one node in a workflow, you can put fixed steps before and after it. That makes it easy to validate the agent's output before anything happens.
You can run n8n in the cloud or self-host the Community edition.
n8n docs: Choose how to use n8n
Other no-code AI agent builders
The three tools above are automation platforms with agent features. These two are built around agents, and both have recordings on the channel.
OpenAI Agent Builder
OpenAI's docs describe Agent Builder as a visual canvas for building multi-step agent workflows. You start from templates or from scratch, connect nodes with typed inputs and outputs, and preview runs with live data.
To deploy, you publish the workflow and then embed it with ChatKit or download the Agents SDK code.
The same page says OpenAI is deprecating Agent Builder and that it is scheduled to shut down on November 30, 2026. Do not start a new long-term build on it.
Watch: testing OpenAI Agent Builder
Lindy
Lindy's docs describe it as an AI employee that completes work across your connected tools. You give it tasks in a conversation, and it can also run recurring work through routines.
The docs cover skills, memory, integrations and MCP connections.
Watch: Lindy AI agent tutorial
Side by side
| Compare | Zapier Agents | Make AI Agent (New) | n8n AI Agent node |
|---|---|---|---|
| Tools | Zapier app actions | Modules, scenarios, MCP tools, sub-agents | Tool sub-nodes, at least one required |
| Knowledge | Company knowledge and data sources | Knowledge files | Through tools and memory nodes |
| Testing | Activity monitor and chat (per Zapier Agents page) | Chat panel, execution steps, token usage | Executions view per run |
| Hosting | Zapier cloud | Make cloud | Cloud or self-hosted |
| Billing | Agents use a separate activity quota, not tasks | Credits (tokens with Make's AI Provider, operations with your own key) | Executions on n8n Cloud |
Check each pricing page for current numbers.
Build the same agent in each tool
Use one small agent as your test. A good one is an inbox helper that reads a customer message, looks up an order and drafts a reply.
Step 1: Write the instructions once
Write them in a plain text file so you paste the same text into each tool.
You help customers with order questions.
Use the order lookup tool when the message includes an order number.
Never promise refunds. Say a person will review refund requests.
Answer in JSON with fields: category, order_found, draft_reply.
Allowed categories: order_status, refund_request, other.
Step 2: Make one tool
Create a simple order lookup. A sheet with five fake orders is enough. Each builder can read a sheet through its own connector.
Step 3: Add knowledge
Add a short FAQ with your shipping and returns rules. Keep it to one page.
Step 4: Prepare ten test messages
Write ten realistic messages. Include two refund requests, two with an order number that does not exist and one that is off-topic.
Step 5: Run all ten in each tool
Record four things per message.
- The category the agent picked.
- Whether it called the lookup tool when it should have.
- Whether it promised a refund.
- Whether the output was valid JSON.
Step 6: Read the failures
Open the run log for every failed message. Zapier lists an activity monitor for agents, Make shows execution steps in the output panel, and n8n keeps a record of each execution.
n8n docs: Understand executions
Most failures trace back to unclear instructions or a tool that returns too much data. Fix one thing at a time and rerun.
Step 7: Pick
Choose the builder with the best pass rate on your checks, then weigh setup effort and cost.
Safety rules for any builder
- Start with read-only tools. Let the agent look things up before it can change anything.
- Keep a human on outbound actions. Drafts go to a person until you trust the pass rate.
- Limit tools. Each extra tool is another wrong choice the agent can make.
- Filter tool output. Return only the fields the agent needs. Make added tool output filtering for this reason.
What this guide does not cover
This guide makes no claims about which builder gives better answers. That depends on your model, instructions and data, which is why the test above matters.
For the difference between an agent and a fixed workflow, read this next.
How agentic AI differs from traditional automation
To compare the three tools for regular automation too, read this.
Sources
- Zapier Agents
- Zapier help: How is task usage measured
- OpenAI docs: Agent Builder
- Lindy docs: What is Lindy?
- Make help: Make AI Agent (New) app
- Make help: Sub-agents and tool output filtering
- Make help: Credits
- n8n docs: AI Agent node
- n8n docs: Choose how to use n8n
- n8n docs: Understand executions
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