When comparing Flowise vs n8n, you’re essentially looking at two powerful but fundamentally different approaches to AI-powered automation. n8n is a comprehensive, source-available workflow automation platform that integrates AI as a powerful component within larger, multi-system processes. In contrast, Flowise is a specialized, open-source low-code tool designed specifically for building custom Large Language Model (LLM) orchestration flows and AI agents, often leveraging frameworks like LangChain. Your choice hinges on a key question: do you need a dedicated tool to build and prototype standalone AI agents (Flowise), or a robust platform to orchestrate AI within your entire business ecosystem (n8n)?
The Core Difference: The Specialist vs. The Generalist
Let’s be honest, the world of AI tools can feel a bit crowded. To cut through the noise, I like to use an analogy. Think of Flowise as a specialized chef’s knife—it’s exquisitely designed for one primary job: crafting and experimenting with LLM applications. It’s for developers who live and breathe LangChain and want a visual canvas to build chatbots, RAG (Retrieval-Augmented Generation) systems, and other AI-native apps quickly.
On the other hand, n8n is the ultimate Swiss Army knife for automation professionals, but this one comes with a seriously sharp, AI-powered blade. Its main purpose is to connect hundreds of different applications and services, from your CRM to your database to your favorite chat app. The AI capabilities within n8n are designed to be a super-intelligent gear in that larger machine. It’s not just about building an agent; it’s about what that agent can do across your entire tech stack.
So, are you building a self-contained AI brain, or are you building an automated business where an AI brain is one of the key employees?
A Head-to-Head Comparison: Flowise vs n8n
To make it even clearer, let’s break down their features side-by-side. I’ve spent time building with both, and here’s how they stack up in the areas that matter most.
| Feature | Flowise | n8n |
|---|---|---|
| Primary Use Case | Building standalone LLM apps, chatbots, and RAG systems. | Orchestrating end-to-end business workflows that include AI components. |
| AI Specialization | Deep. It’s the core focus, built on LangChain/LlamaIndex. | Broad. AI is a core feature with native nodes, LangChain support, and agentic tools. |
| Integration Library | Limited to AI-centric tools and APIs. | Extensive, with 400+ pre-built nodes for CRMs, databases, marketing tools, etc. |
| Learning Curve | Easier for AI-specific tasks, but has a learning curve for complex chains. | Moderate. The visual builder is intuitive, but the platform’s depth requires learning. |
| Custom Code | More limited. Focus is on connecting pre-built components. | Highly extensible. Supports custom JavaScript/TypeScript in Code nodes. |
| Deployment | Open-source for self-hosting; offers a cloud version. | Free community edition for self-hosting; multiple paid cloud tiers for scalability. |
When to Choose Flowise: The AI Prototyping Powerhouse
Flowise truly shines when your project is AI-first. It’s the platform you spin up when you have an idea for a new chatbot and want to get from concept to a working prototype in an afternoon. I’ve seen teams use it to rapidly test different prompts, models, and vector stores without getting bogged down in boilerplate code.
Ideal Use Cases for Flowise
- Rapid Prototyping: Quickly building and testing conversational AI and chatbots.
- Standalone RAG Applications: Creating knowledge base bots that can answer questions from a specific set of documents.
- Visual LangChain Development: For teams already committed to the LangChain ecosystem who want a visual interface to manage their chains.
However, it’s important to recognize its limitations. If your chatbot needs to do more than just chat—say, update a user’s record in Salesforce, process a payment via Stripe, and then add them to a Mailchimp sequence—you’ll quickly hit the boundaries of Flowise’s general integration capabilities.
When to Choose n8n: The All-in-One Automation Hub
n8n is where AI gets down to business. It operates on the principle of creating “agentic workflows,” a concept we live by at n8n. This means AI agents are not just unpredictable black boxes; they are controllable, reliable tools within a larger, deterministic workflow. You get the power of AI without sacrificing control and visibility.
A Real-World Example: AI-Powered Customer Support Triage
Imagine a support ticket arriving in your company’s inbox. Here’s how you’d build a solution in n8n that Flowise alone couldn’t handle:
- Email Trigger: An n8n workflow instantly starts when a new email arrives in your support mailbox.
- AI Agent Node: The email content is passed to an AI Agent. You configure it to perform three tasks:
- Categorize: Is this a
Billing,Technical, orSalesissue? - Prioritize: Is the sentiment
UrgentorNormal? - Summarize: Create a one-sentence summary of the user’s request.
- (Crucially, you use n8n’s Structured Output Parser to guarantee the AI returns this data in a clean, predictable JSON format every single time.)
- Categorize: Is this a
- Switch Node: This is the traffic cop of your workflow. It reads the AI’s output and routes the ticket down the correct path.
- Action Nodes:
- If
Billing: n8n creates a note on the customer’s Stripe profile and sends a Slack message to the#finance-alertschannel. - If
TechnicalandUrgent: It creates a high-priority ticket in Jira and assigns it to the on-call engineer. - If
Sales: It creates a new lead in HubSpot and uses the Gmail node to send an immediate acknowledgment to the potential customer.
- If
See the difference? The AI did the thinking, but n8n did the doing. That’s the power of integrating AI into a comprehensive automation platform.
The Best of Both Worlds? Using Flowise with n8n
Now, here’s where it gets interesting for advanced users. You don’t always have to choose. Some teams use both! You could build a highly sophisticated RAG agent in Flowise, fine-tuning its knowledge base and conversational flow, and then expose it as an API.
Then, inside your n8n workflow, you simply use the HTTP Request node to call your Flowise agent. This gives you the specialized AI-building power of Flowise combined with the enterprise-grade orchestration and integration power of n8n. It’s a pro move for complex, hybrid use cases.
The Final Verdict: Which Path Is Yours?
Choosing between Flowise vs n8n isn’t about which tool is objectively “better,” but which tool is better for your specific job.
Choose Flowise if… you’re an AI developer or hobbyist focused squarely on building LLM-powered applications. Your goal is to rapidly prototype and experiment with the AI models themselves, and you want a visual builder to accelerate that process.
Choose n8n if… you’re an automation specialist, developer, or business operator. Your goal is to solve real-world business problems by embedding AI into your existing systems. You need reliability, scalability, and the ability to connect to hundreds of applications to turn AI insights into concrete actions.
Ultimately, both tools are pushing the boundaries of what’s possible with AI and low-code. Your decision will define whether you’re building an AI, or building a business powered by one.
