# n8n vs Dify: Which AI Workflow Platform? (2026)

> Automation-first vs AI-native — n8n's 400+ integrations with agent nodes vs Dify's LLM-app platform with built-in RAG. Licenses, pricing shapes, and the fit test.

Direction of travel decides it. n8n is automation that gained AI: 400+ app integrations, with LangChain-based agent nodes slotting intelligence into business processes. Dify is AI that gained a canvas: an LLM-app platform with built-in RAG, prompt IDE, and agent nodes. Automating processes that include AI steps → n8n; building AI products visually → Dify.

n8n and Dify both put AI workflows on a visual canvas, which makes them look like rivals. Their DNA disagrees: **n8n is an automation platform that grew AI organs; Dify is an AI platform that grew automation limbs.** Which DNA matches your problem decides this in one question.

## The short answer

- **Workflows that start in business tools** — a webhook, an email, a CRM update — with AI as steps inside → **[n8n](/tools/n8n)**.
- **Apps that start in a model conversation** — chatbots over knowledge, AI tools with users and APIs → **[Dify](/tools/dify)**.
- **Either way, read the license** before building a business on top — both are conditional, not OSI open source.

## What each is

**[n8n](/tools/n8n)** brings a decade of automation muscle (~192k stars, 400+ integrations, a $5.2B valuation after SAP's May 2026 strategic investment) and added a serious AI layer: LangChain-based agent nodes (Tools, ReAct, Plan-and-Execute), conversation memory backends, vector-store nodes for RAG, every major model provider. Its killer property is that **the agent has hands**: the intelligence step slots between real triggers and real actions, and 900+ templates show the patterns. The 2.0 release (December 2025) hardened security defaults for exactly this run-arbitrary-workflows reality.

**[Dify](/tools/dify)** built the AI-app factory (~145k stars): a canvas for chatflows and agentic workflows where the LLM-specific machinery is native — a RAG pipeline from ingestion to retrieval, a prompt IDE with model comparison, agent nodes with 50+ tools, hundreds of models behind one panel, and LLMOps for the improve-from-production loop. Its killer property is the **publishing path**: canvas → working app → backend-as-a-service API your product embeds.

## Dimension by dimension

| | n8n | Dify |
| --- | --- | --- |
| DNA | Automation + AI nodes | LLM apps + workflow canvas |
| Integration moat | 400+ apps, triggers | Models, RAG, prompt tooling |
| RAG | Assembled from nodes | Built-in pipeline |
| App publishing | Workflows, webhooks | Apps with UIs + APIs |
| License | Fair-code (Sustainable Use) | Modified Apache-2.0 (conditions) |
| Cloud pricing shape | Per execution | Per workspace + credits |
| Self-host | Docker/npx, internal use free | Docker Compose stack, single-tenant free |

## How to actually choose

Run the **starting-point test** on your three nearest use cases: do they begin with an event in a business system (ticket created, form submitted, invoice received) or with a person talking to a model? Event-born workflows belong in n8n — you'll spend your life in its trigger ecosystem anyway. Conversation-born apps belong in Dify — the RAG/prompt/publish machinery you'd otherwise assemble is the product. Teams with both genuinely run both, webhooking between them; the platforms compose better than they compete. And if neither canvas fits because your orchestration logic is *code*, that's the signal you've outgrown low-code into [the framework tier](/guides/concepts/agent-frameworks-2026).

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_Source: https://agentscamp.com/guides/comparisons/n8n-vs-dify — Guide on AgentsCamp._