How to Pick Dify or n8n by Who Edits the Agent, and Call Dify From n8n

Pick Dify when the people editing the agent are not developers. Pick n8n when the agent has to start from a webhook or a schedule and write into other systems. Dify runs the AI product, n8n runs the process around it. When a project needs both, they connect over plain HTTP, and I show the exact call. The six steps below follow the order I would decide in, with prices, license terms and docs read on Oct 23, 2026.

This is the deep dive on one pair from my three way comparison, Flowise vs Dify vs n8n, which also covers the Flowise shutdown. Here I stay with the two tools that are still shipping, whether you searched n8n vs dify, dify or n8n, or n8n vs dify for AI agents. The other pairs are in Flowise vs n8n and Dify vs Flowise.

Two limits on what follows. I started n8n 2.43.4 in Docker once on Oct 23, 2026 (arm64, 4 GiB Docker VM), so its footprint is one run on an empty instance, not a load test. I did not run Dify, so its figures come from its docs, pricing page and repository. I have not run the two together on a client project either, so Step 6 is built from the docs and I mark what I did not test.

Before you start, have a Dify workspace (Dify Cloud or your own install), an n8n instance, and a model provider key. A chatflow is Dify's conversational app type, a knowledge base is the documents an app searches, an execution is one full n8n workflow run, and a trigger event is a Dify run started by a schedule, webhook or integration.

Dify vs n8n in one rule

Dify is built around the AI app. You build a chatflow, workflow or agent, attach a knowledge base, publish it, and every published app doubles as a REST API that you call with an app key. n8n is built around the process. Its HTTP Request node calls any app or service with a REST API, and its pricing page lists agents, MCP and AI nodes on every cloud plan.

Ask which kind of change comes next. A prompt, a retrieval setting, a document set or a model swap is an AI side change. A trigger, a field mapping, a retry or the CRM record to update is a process side change.

Step 1: Decide who changes the agent after launch

My first question: when the answer is wrong at 4 pm on a Friday, who opens the tool? If it is a support lead or an operations person who edits documents and rewrites instructions, put the agent in Dify. Its Studio and Knowledge sections are where those people work, and the app publishes as a web app, an embed or an API.

If it is a developer who thinks in nodes, data and error branches, put it in n8n, next to the triggers and connectors it depends on, as in my n8n AI agent guide. If you are unsure an agent is needed at all, read when to use AI agents vs automation first.

Step 2: Check the license before you take client work

Both tools are free to self host, and both have a license clause that matters if you work for clients. This is my reading, and I am not a lawyer, so email the vendor on anything borderline.

Sources: Dify LICENSE, n8n license FAQ and n8n LICENSE.md.

My decision rule for client work is one install per client, on the client's own account or servers, so neither the Dify multi tenant clause nor the n8n hosted service limit applies. Charging for setup, maintenance and consultancy is fine under both. Avoid one shared install where many outside customers log in and build. n8n's FAQ also rules out letting external end users build their own workflows through your product, including via an API, MCP or an AI agent.

Step 3: Compare what each price actually counts

The cloud plans count different things, so a headline price comparison misleads. Dify charges per workspace per year and caps people, apps and knowledge. n8n charges per month and caps finished workflow executions, with unlimited steps in each.

Sources: dify.ai/pricing and n8n.io/pricing, read Oct 23, 2026. Dify Enterprise and n8n Enterprise are custom quotes.

Dify Professional is $590 divided by 12, about $49.17 a month for 3 members. n8n Pro is 50 euros times 12, 600 euros a year for 120,000 executions (10,000 times 12), or 0.005 euros per execution if you use every one. The currencies differ, so compare the shape and not the totals.

Headcount pushes you up Dify's plans, and run volume pushes you up n8n's. For example, a knowledge tool that 40 people edit hits Dify's Team plan on headcount before any volume limit, and a lead scoring flow that runs 30,000 times a month passes Pro's 10,000 executions and lands on Business, which is self hosted only. One limit is easy to miss: Dify's Sandbox plan lists an API rate limit of 5,000 a month, which an n8n workflow calling a Dify app could use up, while Professional and Team list none. Dify's message credits are for trying hosted models, and you can switch to your own API key afterwards, so model costs sit outside both plans. My AI agent cost calculator covers that part.

Step 4: Size the server you will actually run

Dify's Docker Compose guide lists 2 CPU cores and 4 GiB of RAM as the minimum, and tells Mac users to give the Docker VM 8 GiB. The compose file starts 7 core services plus 8 dependent components, including Postgres, Redis, Weaviate and two sandboxes. That is a lot of moving parts to patch and back up.

n8n's own Docker Compose guide also asks for at least 4 GB of RAM and 2 vCPUs, but that setup includes the sandbox stack for n8n Assistant, which runs Docker in Docker. The plain image is lighter at idle. I ran docker.n8n.io/n8nio/n8n:2.43.4 with one docker run, polled /healthz until it answered 200 (4.6 seconds), waited 30 seconds, then read docker stats --no-stream: 495.1 MiB of memory and 0.84% CPU, on the default SQLite database with no workflows and no assistant sandbox. A documented minimum and an idle reading are different things, and neither is a load test.

Dify alone is documented at 4 GiB, so I would not add n8n to that machine. Give Dify its own host, or use Dify Cloud and self host only n8n, the split I would price first for a small team.

Step 5: Check triggers and human approval

Sources: Dify triggers, Dify Human Input, n8n Schedule Trigger and n8n human in the loop for tools.

n8n gates an action the agent chooses: the workflow pauses before a specific tool runs and the reviewer approves or denies it. Dify's node pauses a point in a flow that you drew, and a person fills in a form. A draft a human edits fits Dify's form. An agent that might send an email or change a record on its own judgment fits n8n's tool level review. More on the idea in my entry on human in the loop.

Step 6: Connect n8n to a Dify app, and back

I found no native Dify node on n8n's integrations pages, so the connection is HTTP on both sides, and both routes are in the vendors' docs. I would start with n8n calling Dify, where n8n owns the trigger and the CRM and Dify owns the answer. It is the safer default when you need the result back, because Dify's Run Workflow endpoint returns the outputs in blocking mode (the call waits), while a Dify webhook trigger sends a response you configure when the run starts.

1. Publish a Workflow app in Dify and create its API key. Dify's Run Workflow endpoint needs a published workflow. The key is created from inside the app and scoped to it. In the workflow's User Input node, add a custom input field and note its variable name (I use query below).
2. Add an HTTP Request node in n8n. Method POST, URL https://api.dify.ai/v1/workflows/run on Dify Cloud, or your own API base URL when self hosted. Turn on Send Body, set Body Content Type to JSON, set Specify Body to Using JSON, and paste the body below.
3. Add the key as a credential. In the node's Authentication setting choose Generic Credential Type, then Header Auth, with the name Authorization and the value Bearer YOUR_APP_KEY. Call Dify from a backend like this, never browser code.
4. Read the answer. The blocking response nests the results under data.outputs, so a later node can use an expression such as {{ $json.data.outputs.result }}, where result is an output variable your workflow defines (Dify's example response uses that name).

{
  "inputs": { "query": "Score this lead: ..." },
  "response_mode": "blocking",
  "user": "n8n-lead-scoring"
}

The query key must match your input variable. Keep the user value stable per source, because Dify uses it to scope files and to resume runs. If several workflows call Dify, put the call in one n8n sub workflow so the credential, timeout and error handling live in one place.

The other direction takes three steps.

1. Add a Webhook node in n8n. Set the method to POST, set Respond to When Last Node Finishes if Dify needs the result back, and publish the workflow to get the production URL. The test URL only listens while you test in the editor.
2. Call it from Dify. Add an HTTP Request node with method POST and the production URL. Choose API key authentication of type Bearer or Custom to match your webhook. Send a JSON body that uses variables from earlier nodes in double curly braces.
3. Lock the webhook down. Require authentication on the Webhook node, and add an IP allowlist if Dify has a fixed address.

If a call fails, check four things first: the Dify workflow is published, the user value is consistent, the blocking call finishes inside the 100 second Cloudflare timeout noted on the Run Workflow page, and the Sandbox rate limit of 5,000 a month has not run out.

Which one for which job

If you would rather have this split designed and monitored for you, that is the work I do: see how I build AI agents.

Frequently asked questions

Is Dify better than n8n for AI agents?

Dify wins when non developers need to change prompts, documents and agent behavior. n8n wins when the agent connects to many systems, starts from triggers and retries on failure. I would pick on who changes it and what it connects to, and use both if the answers differ.

Can n8n call Dify?

Yes, over HTTP. Publish a Workflow app in Dify, create its app API key, and call the /workflows/run endpoint from n8n's HTTP Request node with a Bearer Authorization header. In blocking mode the response returns when the run finishes.

Can Dify call n8n?

Yes. A Dify workflow's HTTP Request node can post to an n8n Webhook node's production URL. Set the webhook to respond when the last node finishes if Dify needs the result back, and restrict it with authentication.

Can I host Dify or n8n for my clients?

Check first. Dify forbids a multi tenant environment without written authorization, where one tenant is one workspace. n8n allows automations for clients on your instance if clients cannot create or edit them, but not hosting it as a service where clients build workflows. My rule is one install per client.