n8n vs Dify: Which Should You Build On?

Updated 2026-10-09 · 1139 words

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Quick take: if what you're building is fundamentally an LLM app, a RAG-backed chatbot, a knowledge-base Q&A tool, an agent you want to ship as an API or embeddable widget, Dify's purpose-built knowledge base, prompt orchestration, and app-deployment tooling get you there with less wiring. If the AI piece is one step inside a larger automation that also needs to touch Slack, a CRM, a spreadsheet, or a dozen other apps, n8n's 400+ integrations and general-purpose canvas are the better foundation, with its AI Agent node handling the LLM part well enough. We built the same support-knowledge-base agent, ingest a set of docs, answer questions against them, escalate what it can't answer, in both to see where each one actually held up.

What n8n and Dify actually are

n8n is a node-based workflow automation platform. You wire together triggers, app integrations, logic branches, and, since its AI Agent node shipped, LLM-powered reasoning steps, all on the same canvas. It's been in active development since 2019, and AI-agent capability is one feature inside a much broader automation toolset that was never built around language models specifically.

Dify, from LangGenius, is a newer platform (2023) built the opposite way: LLM applications are the whole point, not a feature. It bundles a visual workflow builder for agent and RAG logic, a built-in knowledge base with automatic chunking and embedding, a prompt IDE for testing and versioning prompts, and a backend-as-a-service layer that turns whatever you build into a callable API or an embeddable chat widget with almost no extra work.

Comparison table

Building the same thing: canvas vs. app platform

In n8n, we built our support agent with an AI Agent node connected to a Vector Store Tool, after separately loading our docs through a chunking step and an embeddings node into Supabase. Getting that retrieval pipeline right took the bulk of our setup time, choosing a chunk size, picking an embedding model, confirming retrieval quality against real questions, because n8n gives you the pieces but not an opinion on how to assemble them.

In Dify, we uploaded the same documents directly into its knowledge base feature, which handled chunking and embedding automatically with sensible defaults we could still tune if needed. Wiring that knowledge base into an agent, writing the system prompt, and testing it in Dify's built-in prompt playground took roughly a third of the time the equivalent n8n setup did. Where n8n pulled ahead was the escalation step: routing an unanswered question to a Slack channel for a human to pick up was a native node away in n8n, while the same step in Dify meant calling out to an external webhook ourselves, since Dify's own app isn't built to reach into a dozen business tools the way n8n's node library is.

Integrations and ecosystem

This is the clearest dividing line. n8n's 400+ built-in nodes mean almost any everyday business app, CRMs, spreadsheets, messaging platforms, databases, is a drag-and-drop connection away. Dify's plugin and tool marketplace is real and growing, but it's built around extending what an LLM app can call as a tool, not around being a general integration hub, and it's noticeably thinner outside common LLM-adjacent use cases.

If your project's job is mostly reasoning over your own content and exposing that as an app or API, that gap barely matters. If it needs to actively touch a dozen different business systems as part of its logic, n8n's library is the faster, less improvised path.

Pricing reality check

Self-hosting either is free beyond your own server costs. On the cloud side, Dify's sandbox tier is genuinely free for light experimentation, with paid tiers that scale as usage grows; n8n's cloud plans start around $24/month for a starter tier with limited active workflows and execution volume. Neither platform fee includes the LLM API calls your agent actually makes, that's billed separately by whichever model provider you connect, and it's usually the larger cost once you're past the prototyping stage on either tool.

When NOT to use n8n

Skip n8n if the entire deliverable is an LLM-native app, chatbot, or knowledge-base assistant you want to ship fast as an API or widget. You'll spend real time rebuilding RAG plumbing and app-deployment scaffolding that Dify already gives you out of the box, for a use case n8n was never designed around in the first place.

When NOT to use Dify

Skip Dify if your project needs to reach deeply into business systems beyond the LLM app itself, a CRM sync, a multi-step approval chain across Slack and email, a scheduled report pulling from five different data sources. Dify's tool-calling can bridge some of that gap, but you'll be writing custom integrations for territory n8n already covers natively, and you lose n8n's broader workflow logic (branching, error handling across non-AI steps) that a pure LLM-app platform doesn't prioritize.

Which should you pick?

Choose Dify if the product is the LLM app itself: a support bot, a research assistant, a RAG-backed internal tool, and you want the knowledge base, prompt management, and API deployment handled for you. Choose n8n if the AI piece is a component inside a larger automation that also needs to move data between other business systems; our guide to building an AI agent in n8n and our RAG chatbot with n8n guide cover that build in detail.

Learn more directly from the source: n8n publishes full documentation and a large template library, and Dify covers its knowledge base and agent-building tools in its own docs, both worth reading before you commit infrastructure to either one.

If you're weighing n8n against another agent-first option rather than a general automation platform, our n8n vs OpenAI AgentKit comparison covers a similar trade-off from a different angle, and our n8n vs Langflow comparison covers the same question against a Python-first, LangChain-native builder instead. Our best no-code AI agent builders roundup widens the field further if neither of these is quite the right fit.

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