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.
Key takeaways
- n8n = automation-first: the agent node sits between a Gmail trigger and a Slack action, with 400+ integrations as the agent's hands.
- Dify = AI-native: the canvas exists to build LLM apps — RAG pipeline, prompt IDE, model management, and backend-as-a-service APIs are first-class.
- License fine print matters on both: n8n's Sustainable Use License (fair-code — free internal use, no reselling); Dify's modified Apache-2.0 (no multi-tenant offering, keep branding). Neither is OSI open source.
- Pricing shapes differ: n8n cloud meters executions (unlimited steps/users); Dify cloud meters workspaces with message credits — model costs are yours on both.
- The honest tiebreak: where does the workflow START — in your business tools (n8n) or in a model conversation (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.
- Apps that start in a model conversation — chatbots over knowledge, AI tools with users and APIs → Dify.
- Either way, read the license before building a business on top — both are conditional, not OSI open source.
What each is
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 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.
Frequently asked questions
- Can n8n do what Dify does, and vice versa?
- With effort, partially. n8n's AI nodes (agents, memory, vector stores) can assemble RAG chatbots — but you're wiring what Dify ships integrated, and there's no prompt IDE or app-publishing layer. Dify can call external services via tools/plugins — but 400+ polished integrations with triggers is n8n's moat. Each is mediocre at the other's center.
- Are n8n and Dify open source?
- Source-available, both — with real conditions. n8n's fair-code license permits free internal business use but not selling n8n-as-a-service or embedding it in paid products. Dify's modified Apache-2.0 forbids multi-tenant operation without a commercial license and requires keeping its branding. Internal self-hosting is effectively free on both; building a SaaS on either means licensing conversations.
- Which is better for a no-code team building AI features?
- If the features live inside existing processes (summarize tickets, draft replies, route leads), n8n — the team works where the triggers are. If the feature IS the app (a knowledge chatbot, an internal AI tool with users), Dify — the publishing path from canvas to working app with API is what it's for.
Related
- N8nFair-code workflow automation with native AI — a visual canvas plus code, 400+ integrations, and LangChain-based agent nodes; self-host free or cloud per-execution.
- DifyThe visual platform for LLM apps and agentic workflows — canvas-built chatflows, RAG pipeline, agent nodes with 50+ tools, and LLMOps, self-hosted via Docker.
- LangchainThe provider-agnostic agent framework, post-1.0: a standard create_agent loop on the LangGraph runtime, middleware hooks, and the largest integration ecosystem.
- Which Agent Framework in 2026? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK vs Claude Agent SDKA decision guide to the major AI agent frameworks — control vs. abstraction, multi-agent models, state and durability, and which fits your project.
- How RAG Actually Works: Ingestion, Chunking, Retrieval & RerankingA clear, practical walkthrough of the retrieval-augmented generation pipeline — what each stage does, where it fails, and how the pieces fit together.
- LangGraph vs CrewAI: Agent Frameworks Compared (2026)LangGraph vs CrewAI — explicit state-machine control vs role-based crew abstractions. Which agent framework fits your reliability bar and team.