Dify vs n8n: Which Should You Use for AI Workflows

An honest comparison of Dify and n8n: Dify is an LLM app builder with a hosted UI and API, n8n is general automation with hundreds of integrations. When to pick each, and how to combine them.

10 minBeginner

Visit Dify

Dify and n8n both let you build with LLMs, so they get compared a lot, but they solve different problems. Dify is a platform for building LLM apps: chatbots, agents, and RAG, with a hosted chat UI and a ready API. n8n is a general automation tool that connects many services and can call an LLM as one step in a larger flow. The right pick depends on whether the AI is the product or one part of a pipeline.

The short version

Building a chatbot, an agent, or a RAG app that users talk to? Dify.
Wiring many apps together, where an LLM is one step among many? n8n.
Need both a smart LLM app and broad service automation? Use them together.

What each one is built for

Dify centres on the LLM app. It gives you a prompt editor, model provider management, Knowledge bases for RAG, an agent runtime with tools, and a published app you reach through a hosted web UI or an API endpoint. You get a chat interface and conversation handling without building any of it.

n8n centres on connecting services. Its strength is a large library of integrations and triggers: webhooks, schedules, databases, email, spreadsheets, and hundreds of SaaS apps. It has LLM and agent nodes too, but the model call is usually one node inside a flow that also reads a form, writes a row, and sends a notification.

Dimension	Dify	n8n
Core focus	Building LLM apps: chat, agents, RAG	Connecting apps and automating multi-step flows
Interface for users	Hosted chat UI and API out of the box	Usually headless; you build any UI yourself
RAG / knowledge bases	Built in, with chunking and retrieval	Possible via nodes, more manual to assemble
Integrations	Model providers and tools for agents	Hundreds of app integrations and triggers
Hosting	Cloud or self-host with Docker	Cloud or self-host with Docker
Best when	The LLM app is the product	The LLM is one step in a pipeline

When to pick Dify

Pick Dify when the conversation or the model output is the thing you are shipping. If you want a support chatbot that answers from your docs, an agent that calls a few tools, or a text-generation app you embed on a site, Dify gets you there with far less plumbing. You also get prompt versioning, logs, and an API without wiring a backend.

When to pick n8n

Pick n8n when the value is in moving data between systems and the AI is a helper. Summarise inbound emails and file them, enrich a CRM record with a model call, watch a webhook and route the result: these are n8n's home turf because it already speaks to all those services and handles scheduling, retries, and branching.

They are not mutually exclusive

A common setup is to build the LLM app in Dify and call its API from an n8n workflow. n8n handles the triggers and the integrations, and it hits your Dify endpoint (for example /v1/workflows/run or /v1/chat-messages) whenever it needs the model app to do its part. You get Dify's app layer and n8n's reach in one pipeline.

n8n-http-node.txt

# In an n8n HTTP Request node, call your published Dify app:
Method:  POST
URL:     https://api.dify.ai/v1/workflows/run
Headers: Authorization: Bearer {{ $env.DIFY_API_KEY }}
         Content-Type: application/json
Body:    { "inputs": { "text": "={{ $json.emailBody }}" },
           "response_mode": "blocking", "user": "n8n" }

How to decide

Ask what is at the centre. If a user is talking to a model, or the output of a model is your deliverable, start with Dify. If the job is to connect services and the model is one step, start with n8n. If you need both, build the app in Dify and orchestrate it from n8n. Neither replaces the other; they cover different halves of an AI system.