n8n vs Dify: Which AI Agent Platform (2026)
n8n vs Dify compared for 2026: automation-first platform with AI vs AI-first agent platform. Which to choose for building agents and workflows.
TL;DR — Both are visual, self-hostable platforms, but they start from opposite ends. n8n is an automation platform (400+ integrations) that added AI — pick it when your agent’s value is connecting to many external services. Dify is an AI-first platform (built-in RAG, agent skills, model management) that added workflows — pick it when the AI interaction itself is the product. The deciding question: is AI the brain that needs many hands (n8n), or is AI the product (Dify)?
These two get compared constantly because both are visual, both self-host, and both have an “AI Agent” capability. But their centers of gravity are different, and that difference should drive your choice. For each tool’s full story see the n8n deep-dive and the Dify deep-dive.
The fundamental difference: automation-first vs AI-first
- n8n started as an open-source Zapier alternative — a workflow automation platform with 400+ app integrations. AI nodes (including an AI Agent node) were added on top. Its strength is connecting an agent to the real world: Slack, Jira, databases, APIs, email.
- Dify started as an LLM application platform — built-in RAG, model management, agent skills, observability. Visual workflows were part of it from early on. Its strength is the AI-native experience: building chatbots, knowledge bases, and RAG apps.
So the question isn’t “which is the better builder,” it’s “what is your agent’s value?” If the agent reasons and then needs to do things across 20 services, n8n is the hands. If the agent interaction itself (chat, RAG, knowledge retrieval) is the product, Dify is purpose-built for it.
Head-to-head
| Dimension | n8n | Dify |
|---|---|---|
| Origin | Automation platform + AI | AI platform + workflows |
| Core strength | 400+ service integrations | LLM/RAG/agent-native experience |
| AI is… | One capability among many | The primary focus |
| RAG | Via integrations (Pinecone, Qdrant nodes) | Built-in, first-class |
| Non-AI automation | Excellent (original purpose) | Not the focus |
| Triggers | Webhooks, cron, app events, manual | API call, conversation |
| Custom code | JavaScript/Python nodes | Python in sandboxed skills |
| Integration breadth | 400+ | Growing plugin marketplace |
| Self-hosted | Yes (fair-code) | Yes (Apache 2.0 core) |
| Per-execution cost | Zero (self-hosted) | Zero (self-hosted) |
| Best for | ”Agent needs to touch many services" | "Building an AI-native product” |
Which to choose
Pick n8n when:
- Your agent’s value comes from connecting to many external services (CRM, ticketing, messaging, databases)
- You also run non-AI automations and want one platform for both
- You need mature, battle-tested integrations with built-in auth and retry handling
- The AI is the brain; the integrations are the point
Pick Dify when:
- You’re building an AI-native product: chatbot, support agent, knowledge base
- You need built-in RAG without assembling a vector DB + embedding + retrieval pipeline
- The agent interaction itself is the product, not a step in a larger automation
- Non-developers need to build and iterate on AI apps fast
Use both when you need Dify’s agent design plus n8n’s integration breadth: Dify designs and runs the agent, and calls n8n via webhook to execute real-world actions across services. They compose cleanly.
Where they fit in the stack
Both are the agent-framework layer of the AI agent infrastructure stack, approached from different directions. If you’re choosing between visual and code-first more broadly, also see Dify vs LangGraph. Both n8n and Dify call models through whatever provider you configure, so a model gateway underneath gives you cost tracking and failover across both.
FAQ
Is n8n or Dify better for AI agents?
Neither universally. n8n is better when your agent’s value is connecting to many services (it’s an automation platform first). Dify is better when the AI interaction itself is the product (it’s AI-first, with built-in RAG). Match the tool to where your agent’s value lives.
Can n8n do RAG like Dify?
n8n can do RAG via integration nodes (vector DB nodes like Pinecone/Qdrant plus embedding nodes), but it’s assembled, not built-in. Dify ships RAG as a first-class, integrated feature. For RAG-heavy products, Dify is less work.
Do both support self-hosting?
Yes. n8n is fair-code (free self-host, can’t resell as a service); Dify’s core is Apache 2.0. Both run on your own infrastructure with zero per-execution fees — though the LLM token costs inside them are separate.
Which has more integrations?
n8n, by a wide margin — 400+ app integrations is its core identity. Dify has a growing plugin marketplace but far fewer pre-built service connectors; integrations are not its focus.
Can I use n8n and Dify together?
Yes, and many teams do. Dify designs and runs the agent; when the agent needs to act across external services, it calls an n8n workflow via webhook. You get Dify’s AI-native building plus n8n’s integration breadth.
Key takeaways
- n8n = automation-first (400+ integrations) with AI added; Dify = AI-first (RAG, agent skills) with workflows added.
- The deciding question is whether your agent’s value is broad service integration (n8n) or the AI interaction itself (Dify).
- Both self-host with zero per-execution fees; both call models through a configurable provider, so put a gateway underneath.
- Read the n8n and Dify deep-dives; for the code-first alternative see Dify vs LangGraph.
