If you’re trying to build an AI agent without writing code, two names come up again and again: n8n and Make (formerly Integromat). They look similar on the surface — both are visual, drag-and-connect automation tools — but once you start wiring up an actual agent that calls an LLM, remembers context, and uses tools, they feel like completely different products. We build agents on both every week, so here’s the honest, practical breakdown for 2026, not a feature-list copy-paste.
The short answer first
For building genuine AI agents — the kind that reason in a loop, decide which tool to call, and chain steps dynamically — n8n is the better pick in 2026. It has a native AI Agent node built on top of the LangChain framework, real memory options, and the ability to self-host for cheap or free. Make is excellent, often easier, and frequently the smarter choice for straightforward “if this happens, ask AI, then do that” automations — but it treats AI as one more module in a linear flow rather than as an autonomous reasoning engine.
So the real question isn’t “which tool is better” — it’s “what are you actually building?” Let’s make that concrete.
What “AI agent” really means here
People use “agent” loosely. Be honest about which of these you need, because it changes the answer:
- AI-assisted automation: A fixed sequence where one step calls an LLM. Example: a new support email arrives, GPT classifies its urgency, then it’s routed to the right Slack channel. The path never changes.
- A true agent: A model that’s given a goal plus a set of tools, then decides on its own which tools to call, in what order, and when it’s done. Example: “Answer this customer question” — and the agent chooses whether to search your knowledge base, look up the order in your database, or escalate to a human.
For the first kind, either tool works and Make is often faster to ship. For the second kind, the gap between them is large.
Where n8n pulls ahead for agents
1. A real, native AI Agent node
n8n’s AI Agent node is the headline feature. You drop it onto the canvas, attach a chat model (OpenAI, Anthropic, Google, Mistral, or a local model via Ollama), attach a memory node, and then attach tools as separate connected sub-nodes. The agent then runs the reasoning loop for you: it reads the goal, picks a tool, sees the result, and decides the next step. You’re configuring an agent, not faking one with branches and filters.
Critically, almost any n8n node can become a tool the agent calls — an HTTP request, a Google Sheets lookup, a Postgres query, a sub-workflow. That “any node as a tool” design is what makes n8n feel built for agents rather than retrofitted.
2. Memory that actually persists
Agents are only useful when they remember the conversation. n8n offers memory nodes out of the box — a simple in-session buffer for quick tests, or persistent stores like Postgres and Redis for production chatbots that need to recall a user across days. In Make, conversation memory is something you assemble yourself with data stores and manual record-keeping. Doable, but you’re building plumbing instead of an agent.
3. Self-hosting, cost control, and no operation caps
n8n is fair-code and can be self-hosted for free on a $5–10/month VPS or even a home server with Docker. Agents are chatty — a single user request can trigger five, ten, or twenty internal LLM and tool calls as the loop runs. On a per-operation pricing model that gets expensive fast. Self-hosted n8n bills you nothing per operation, so a noisy reasoning loop only costs you the underlying LLM API tokens. For an agent that runs thousands of times a day, this is the difference between a viable product and a runaway invoice.
4. Custom code when you hit a wall
No-code tools always have an edge case the UI can’t express. n8n’s Code node lets you drop into JavaScript or Python for that one tricky transform without abandoning the visual flow. You stay 95% no-code and buy yourself an escape hatch for the other 5%.
Where Make is genuinely the better choice
We’re not here to dunk on Make — for a huge share of real-world automations it’s the tool we’d actually recommend, and pretending otherwise would be dishonest.
- Polish and ease for beginners. Make’s canvas is more visual and arguably more intuitive. The way data bubbles flow module-to-module is easy to follow, and the error-handling and retry UI is genuinely friendly. If you’ve never automated anything, you’ll likely get your first win faster in Make.
- Breadth of pre-built app integrations. Make has a very deep catalogue of polished, ready-made app connectors with their actions mapped out for you. n8n has plenty too, but for some niche SaaS apps Make’s integration is more complete, so you click instead of hand-building an HTTP request.
- Fully managed, zero DevOps. No server to patch, no Docker, no uptime to babysit. For solo founders and marketers who never want to touch infrastructure, that peace of mind is worth real money.
- Linear AI automations. When the AI is one step in a fixed pipeline — summarize this, classify that, generate this caption — Make handles it cleanly and you don’t need agent machinery at all.
Make has also been adding more AI-native and agent-style features, so the gap is narrowing. But as of 2026, for autonomous, tool-using, memory-backed agents, n8n is still the more natural home.
Head-to-head comparison
| Factor | n8n | Make |
|---|---|---|
| Native AI agent node | Yes — built on LangChain, with tool-calling loop | Limited; AI is mostly a module in a linear flow |
| Conversation memory | Built-in (buffer, Postgres, Redis) | Manual, via data stores |
| “Any step as an agent tool” | Yes | No, not in the same agentic sense |
| Self-host / free option | Yes (Docker, fair-code) | No — cloud SaaS only |
| Pricing model | Per workflow execution (or free self-hosted) | Per operation (each module run counts) |
| Cost on chatty agent loops | Cheap, especially self-hosted | Can escalate quickly |
| Beginner friendliness | Good, slightly steeper | Excellent |
| Custom code escape hatch | JavaScript & Python node | More limited |
| Managed, zero-maintenance cloud | Yes (paid n8n Cloud) | Yes (this is its core model) |
A concrete example: building a customer-support agent
Say you want an agent that answers customer questions by pulling from your help docs and your order database. Here’s the rough shape in n8n:
- Add a Chat Trigger so the agent has a front-end to receive messages.
- Drop in the AI Agent node and write a system prompt: “You are a support agent. Use the knowledge base for product questions and the order tool for order status. Escalate if unsure.”
- Attach a chat model (e.g. an Anthropic or OpenAI model).
- Attach a Postgres memory node so it remembers the conversation per user.
- Attach tools: a vector store retriever pointed at your docs, and an HTTP Request tool that hits your order API.
- Hit test, chat with it, watch the execution log show exactly which tool it chose on each turn.
The agent decides at runtime whether a given message needs the docs, the order lookup, both, or neither. To replicate that in Make you’d manually build routers and conditional branches to simulate the decision-making — more modules, more operations billed, and a flow that’s harder to extend when you add a third tool. That’s the structural difference in a nutshell.
Now flip the scenario: you just want incoming reviews summarized and dropped into a spreadsheet with a sentiment label. No decisions, no memory, no tools. Build that in Make and move on — reaching for an agent node here would be over-engineering.
How to actually choose
- Pick n8n if: you’re building a true agent with tools and memory, you expect high volume and want to control costs, you’re comfortable running a small server (or paying for n8n Cloud), and you want a code escape hatch.
- Pick Make if: you want the smoothest possible no-code experience, your AI use is a step inside a linear automation, you’d rather never touch a server, and a specific app you depend on has a stronger ready-made Make connector.
- Honestly? Many teams use both. Make for clean app-to-app plumbing and quick linear AI tasks, n8n for the heavy agentic brain. They’re not enemies — they’re different tools for different jobs.
FAQ
Is n8n or Make cheaper for AI agents?
Self-hosted n8n is the cheapest by a wide margin because it has no per-operation fees — you only pay for the LLM tokens your agent consumes. This matters because agents make many internal calls per request. Make’s per-operation pricing is predictable and fine for low-to-moderate volume, but a busy reasoning loop can burn through your operation quota fast. If you expect heavy usage, self-hosted n8n wins on cost; for light, occasional automations the difference is minor.
Do I need to know how to code to build an agent in either tool?
No. Both are genuinely no-code for the core build — you connect nodes and fill in forms. n8n optionally lets you drop into JavaScript or Python for tricky steps, but that’s a bonus, not a requirement. The harder skill in both tools isn’t coding; it’s writing a clear system prompt and thinking through which tools your agent needs and when it should use them.
Can Make build AI agents at all, or only n8n?
Make can absolutely run AI-powered automations and is steadily adding more agent-style capabilities. The distinction in 2026 is depth: n8n ships a dedicated agent node with a built-in tool-calling loop and native memory, so autonomous, multi-tool agents come together more naturally. In Make you can approximate agent behavior with routers and branching logic, but it’s more manual and less flexible to extend.
Your next step
Don’t agonize over the decision — both have free starting points. Spin up a free self-hosted n8n (or its free cloud trial) and build the customer-support agent from the example above; it’ll take an afternoon and teach you how agentic reasoning actually behaves. In parallel, sign up for Make’s free tier and rebuild one simple linear AI automation there. After a couple of hours hands-on with each, the right tool for your project will be obvious — and you’ll have a working agent to show for it.