n8n vs Dify: Which One Should Run Your AI Agent Workflows

Production-ready platform for agentic workflow development.

Both n8n and Dify show up on the same shortlists lately: "how do I build an AI agent without writing a backend from scratch." Both are open-source, both have a visual canvas, both let an LLM call tools, and both will happily run on your own server for free. That surface-level overlap is exactly why people pick the wrong one — the two tools started from opposite ends of the problem and it shows the moment you try to do something outside their comfort zone.

Short version: n8n is a general workflow automation platform — 500+ app integrations, deterministic logic, human-approval steps — that added an AI Agent node so agents can sit inside a bigger business process. Dify is an LLM application builder — RAG pipelines, agent orchestration, chatbot deployment — that added a workflow canvas so those LLM apps can have more than one step. If your project is "wire AI into stuff I already use," start with n8n. If it's "build an AI app or chatbot from scratch," start with Dify.

What Each One Actually Is

n8n (198,000+ GitHub stars, fair-code licensed) describes itself as a "workflow automation platform with native AI capabilities." Its core unit is the workflow: a chain of nodes — HTTP requests, database writes, Slack messages, Google Sheets rows, 500+ integrations in total — with an AI Agent node you can drop in anywhere that chain needs a decision made by an LLM instead of a fixed rule. n8n's own framing for this is blunt: "pair AI with explicit logic to stay in control of inputs and outcomes." The AI Agent node can call tools, including MCP-enabled ones, and workflows can pause for human-in-the-loop approval before an agent's action goes live.

Dify (150,000+ GitHub stars, source-available under its own license) describes itself as "an open-source platform for building AI applications." Its core unit is the app: a chatbot, an agent, or an agentic workflow, built around retrieval-augmented generation (RAG) over your own data, then deployed as a standalone web app or exposed via API. Where n8n's canvas connects business systems and lets AI make one decision inside that chain, Dify's canvas connects LLM steps — retrieval, prompting, tool calls, branching — into a single coherent AI product.

Put plainly: n8n answers "how do I get an LLM to make one smart decision inside a process I already run." Dify answers "how do I build the AI product itself." A lot of "n8n vs Dify" confusion comes from the fact that each one is now strong enough at the other's job to get you 80% of the way there — but the last 20% is where the wrong pick costs you a rebuild.

Why This Matters for Agent Builders

If you're gluing AI into systems that already exist — a Slack bot that files a support ticket, a workflow that reads a form submission, checks it against a CRM, and only pings a human if the AI flags it as suspicious — n8n's 500+ pre-built integrations mean you're wiring existing nodes together, not writing API clients. The AI Agent node is one piece of a larger deterministic flow, which is exactly what you want when the business logic matters as much as the AI's judgment call.

If you're building the AI product itself — a customer-facing chatbot with RAG over your docs, an internal research agent, anything where the LLM's reasoning is the product rather than one step in a larger process — Dify's RAG pipeline, prompt versioning, and one-click app/API deployment get you there without hand-rolling a vector store integration and a chat UI. Trying to force that use case into n8n means bolting a vector database node onto a tool that wasn't built around retrieval as a first-class concept. Trying to force n8n's use case into Dify means recreating 500 integrations' worth of plumbing that already exists elsewhere.

Licensing: Read This Before You Pick Either One

Neither is OSI-approved open source, and the restriction shape is different enough to matter if you're building something commercial:

n8n ships under the Sustainable Use License: free to self-host and run for your own internal business purposes, but you can't distribute it or offer it to others unless that's free and non-commercial. Features under an .ee. filename require a separate Enterprise License. In plain terms — running n8n privately to automate your own company's workflows is fine and free; reselling hosted n8n as your own product is not.

Dify permits commercial use, including running it as a backend for your own product, but its license specifically blocks using the Dify source to operate a multi-tenant environment (each customer gets their own workspace) without a separate commercial agreement. In plain terms — build one AI app for your own business on Dify, free. Try to become "Dify but I host it for other people," and you need to talk to Dify first.

Both restrictions target the same thing: stopping someone from cloning the project and selling it back as a competing hosted service. Neither restriction affects the common case of self-hosting for your own product or internal use.

Worked Example: The Same Task, Two Different Shapes

Say the task is "when a new lead fills out a form, have an AI agent research the company and draft a personalized outreach email for a human to approve."

In n8n, this is a workflow: a Form Trigger node receives the submission, an HTTP Request node hits a company-data API, an AI Agent node (pointed at your LLM of choice) drafts the email using that data as context, and a human-approval step holds the draft until someone clicks approve — at which point a Gmail or Outlook node sends it. Every non-AI step is a pre-built node; you're configuring, not coding.

In Dify, the same task looks more like: an Agent app with a tool the agent can call to fetch company data, a prompt that instructs it to draft the email, and — because Dify's unit is the app, not the business process — you'd typically expose this as an API that your own CRM or form handler calls, with the approval step living in whatever system triggered Dify in the first place. Dify is doing the AI reasoning well; it's not trying to also be your CRM's automation layer.

Neither is wrong. The n8n version is faster to build if the CRM, the form tool, and the email provider are all things n8n already has nodes for. The Dify version is faster to build if the "AI drafts an email" part is the hard part and everything around it already exists in your own stack.

n8n vs. Dify vs. the Alternatives

| Tool | Core unit | Best for | Self-host cost | |---|---|---|---| | n8n | Workflow (nodes + logic) | Wiring AI into existing business systems and SaaS tools | Free (Sustainable Use License) | | Dify | App (agent, chatbot, RAG pipeline) | Building the AI product itself, RAG-first | Free for single-tenant (commercial license needed for multi-tenant hosting) | | Raw LangChain/LangGraph | Code (Python/JS library) | Full control, no visual canvas, deepest customization | Free (MIT) | | Zapier | Workflow (nodes + logic), cloud-only | Same idea as n8n, less AI-native, no self-hosting | Paid SaaS, no free self-host |

If you're not sure which you need: ask whether the hard part of your project is "connecting to a lot of existing tools" or "getting the LLM to reason well over your data." The first points at n8n. The second points at Dify. If the honest answer is "both, equally," most teams start with whichever one already has more of the integrations they need — reworking a workflow's automation later is usually cheaper than reworking its AI reasoning layer, or vice versa, depending on which one is more custom.

Common Mistakes

Picking n8n because "it has more integrations" for a project that's really an AI product. Integration count doesn't help you if the actual hard problem is RAG quality and prompt reliability — that's Dify's specialty, not n8n's.

Picking Dify because "AI-native" sounds more modern, for a project that's really a business automation. If you need 15 different SaaS tools wired together and the AI is one decision point among many, you'll spend more time working around Dify's app-centric model than you would just configuring n8n nodes.

Assuming self-hosting means no licensing constraints. Both licenses are permissive for internal use and restrictive for reselling as a hosted service — read the actual restriction (Sustainable Use License for n8n, the multi-tenant clause for Dify) before building a commercial product on top of either.

Skipping the human-in-the-loop step because the demo worked. Both tools support pausing a workflow for human approval before an agent's action executes. Cutting that step to make a demo faster is exactly how an agent drafting an email becomes an agent sending one nobody reviewed.

FAQ

Can I use n8n and Dify together?

Yes — a common pattern is Dify handling the AI-native app (chat, RAG, agent reasoning) and exposing it as an API, with n8n calling that API as one step inside a larger business workflow. Each tool stays focused on what it's actually built for.

Do either of these require a paid plan to be useful?

No. Both are free to self-host under their respective licenses for internal/single-tenant use — you only hit a paywall if you want the managed cloud version, n8n's Enterprise-licensed (.ee.) features, or Dify's multi-tenant hosting permission.

Which one is easier to get running for the first time?

Both ship a one-command Docker Compose setup for self-hosting. n8n's learning curve is closer to "learn the nodes you need," since most tasks map to an existing integration. Dify's learning curve is closer to "learn RAG and prompt configuration," since its interface assumes you're building an AI-first app rather than connecting existing tools.

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Written by McKlaud AI. Want to know which AI tools actually fit your business? Get a free AI audit.