We package Flowise as an AWS AMI, so weigh that against everything below. Flowise vs Langflow is a close call: both are visual LLM builders, both are permissively licensed (Apache 2.0 for Flowise, MIT for Langflow), and the real split is JavaScript versus Python. n8n is a different animal. It's a workflow automation platform where AI is one node among more than 1,500 integrations, and its license restricts commercial embedding in a way the other two don't.
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How to choose between them
Three questions cover most of it:
- Is the AI workflow the whole product, or one step in a bigger process? If it's the whole product, Flowise or Langflow fit better. If AI is one node inside a process that also touches your CRM, spreadsheets, and email, n8n's integration library does more of the surrounding work for you.
- Is your team JavaScript or Python? Flowise wraps LangChain.js, Langflow is native Python. Neither requires deep coding to use, but drop-to-code moments favor whichever language your team already knows.
- Are you planning to embed this commercially? If you're building a product on top of one of these and reselling it, or letting external users trigger workflows, check n8n's Sustainable Use License first, that specific use case needs a separate paid agreement, unlike Flowise or Langflow.
1. Flowise: drag-and-drop LangChain.js, Apache 2.0
Flowise's core is Apache 2.0, which allows commercial use and embedding. The one exception is the enterprise folder in its server package, which carries a separate commercial license (spelled out in the project's LICENSE.md). It's built as a drag-and-drop layer over LangChain.js, designed to abstract away the code most people write by hand when building a RAG pipeline or chatbot: connect a document loader, an embedding model, a vector store and an LLM node on the canvas, and Flowise handles the orchestration. You self-host it with npm, Docker, or a prebuilt server image. Workday acquired FlowiseAI in August 2025.
Key features
- Apache 2.0 core, commercial and embedded use allowed
- Drag-and-drop wrapper around LangChain.js, JavaScript/Node.js stack
- Fast path from a blank canvas to a working RAG chatbot
- Every chatflow gets a REST prediction endpoint, so other apps can call it
2. Langflow: Python-first, backed by IBM, MIT licensed
Langflow is open source under the MIT license, so commercial and embedded use is unconditional. It's built natively in Python, which suits data and ML teams who want to intervene in code when the visual canvas hits its limits, rather than staying purely drag-and-drop. DataStax acquired Langflow in April 2024, and DataStax itself was acquired by IBM in a deal that closed May 28, 2025, so Langflow is now effectively IBM-backed while staying under the same MIT terms. Managed hosting through third-party providers starts around $16/month if you'd rather not self-host.
Key features
- MIT licensed, unconditional commercial and embedded use allowed
- Native Python, built for teams that want to drop into code from the canvas
- Now backed by IBM via the DataStax acquisition, still open source
- Third-party managed hosting available from roughly $16/month
3. n8n: the automation platform AI got added to
n8n is best described as self-hostable Zapier with LangChain-based AI agent nodes added on top. Its README claims more than 1,500 integrations covering CRMs, spreadsheets, email, and most SaaS tools a business already runs. That breadth is the actual pitch: AI is one node type among many, useful when the workflow needs to touch several other systems, not just call an LLM. The catch is licensing. n8n's core ships under a Sustainable Use License, fair-code rather than open source, free for internal business use and non-commercial purposes, but embedding n8n in a product you sell, reselling automation as a service, or letting external users trigger workflows all require a separate commercial agreement and fees. n8n Cloud starts at 20 euros a month (Starter, billed annually) if you'd rather not self-host.
Key features
- 1,500+ integrations across CRMs, spreadsheets, email, and general SaaS tools
- LangChain-based AI agent nodes alongside traditional automation nodes
- Sustainable Use License: free for internal/non-commercial use, paid agreement required for embedding or reselling
- n8n Cloud hosting from 20 EUR/month, billed annually
Flowise vs Langflow: same job, different language
Put the two canvases side by side and they look almost interchangeable. Loaders, splitters, embeddings, vector stores, models and agents, wired left to right.
The difference shows up the first time a node doesn't do what you need. In Langflow, every component is Python you can open and edit from the canvas. In Flowise you're in the LangChain.js world, and a custom tool means writing a JavaScript function. So decide on language before features: a Node.js team will move faster in Flowise, and a team that lives in Python notebooks will be happier in Langflow. Licensing barely matters between these two, since Apache 2.0 and MIT both allow commercial use.
Flowise vs n8n: an LLM builder or an automation platform?
This is the pair people mix up most, judging by the searches that land on this page. Flowise is for building the AI thing itself: a chatbot over your docs, an agent with tools. n8n is for automating the process around it. A form comes in, a CRM record updates, a Slack message goes out, and somewhere in the middle an LLM summarises the ticket.
n8n's AI agent nodes are fine for simple agents. For a retrieval pipeline you want to tune chunk by chunk, Flowise gives you more control.
Flowise n8n integration: running both
You don't have to pick. n8n has no official Flowise node, but every Flowise chatflow exposes a prediction API, so an n8n HTTP Request node can POST a question to /api/v1/prediction/<chatflowId> and pass the answer on to the next step. Add an Authorization: Bearer header if the chatflow has an API key set. Langflow flows can be called the same way through its /api/v1/run endpoint, with the key in an x-api-key header.
Langflow vs n8n for AI agents
It comes down to where the agent sits. If the agent is the product (it reasons, calls tools, answers users), Langflow's canvas is built for exactly that, and you can drop into Python when a component falls short. If the agent is one step in a business process, n8n is the better home, because the steps before and after it are already in its integration library. Nothing stops you doing both: build the agent in Langflow and call it from n8n.
The licensing catch most roundups skip
This matters more than most comparisons admit. Langflow is MIT. Flowise's core is Apache 2.0, with only its enterprise folder under a commercial license. Neither cares how commercial your use case is. n8n's core is licensed under a Sustainable Use License: you can self-host and run it internally for free, and non-commercial redistribution is fine, but the moment you embed n8n inside a SaaS product, resell automation built on it as a service, or let external users trigger its workflows, you need a signed commercial agreement with n8n and owe them fees. That's not a criticism of n8n's model, fair-code licensing is a legitimate way to fund an open project, but calling all three of these tools equally "open source" without that distinction misleads anyone planning a commercial product on top of one.
Flowise vs Langflow vs n8n: feature comparison
| Feature | Flowise | Langflow | n8n |
|---|---|---|---|
| License | Apache 2.0 (enterprise folder under a commercial license) | MIT | Sustainable Use License, plus n8n Enterprise License |
| Free for commercial embedding | Yes (Apache 2.0 core) | Yes | No, requires paid agreement |
| Language / stack | JavaScript/Node.js, LangChain.js | Python | Node.js, general automation engine |
| Primary focus | RAG pipelines, chatbots, agents | LLM chains and agents, code-friendly | General workflow automation, AI as one node type |
| Integration count | Focused on LLM/vector store connectors | Focused on LLM/vector store connectors | 1,500+ across CRMs, SaaS, spreadsheets |
| Calling it from other apps | REST prediction API per chatflow | REST run API per flow | Webhook trigger nodes |
| Hosting | Self-host (npm, Docker, or an AMI) | Self-host, or third-party from ~$16/month | Self-host, or n8n Cloud from 20 EUR/month (annual) |
| Backing | Workday (acquired August 2025) | IBM (via DataStax acquisition) | n8n GmbH |
Flowise alternatives beyond these three
Some of the searches that land here aren't about Langflow or n8n at all.
Voiceflow vs Flowise
Voiceflow is a commercial, hosted platform for designing chat and voice agents, aimed at product and customer-experience teams more than developers. You don't host it and you don't touch the code. Flowise is the opposite trade: more wiring, but the server, the data and the prompts stay with you. For a support bot a CX team maintains day to day, Voiceflow is the easier fit. For anything that needs your own models or has to stay inside your VPC, Flowise. If voice is the real requirement, our self-hosted voice AI agent stack covers the open-source route.
Outside that, Dify is another self-hostable visual builder for LLM apps, closer to Flowise and Langflow than to n8n. Check its license terms the same way you would n8n's before building a commercial product on it.
Why we run Flowise for clients
Most teams that come to us wanting a visual AI builder are trying to ship a RAG chatbot or agent quickly, not automate their whole back office, which puts them squarely in Flowise or Langflow territory rather than n8n's. We picked Flowise specifically because it's JavaScript-native, which matches the stack most of our clients' engineering teams already run, and because its Apache 2.0 core means nobody has to check a commercial-use clause before shipping. We package it as a ready-to-launch AMI so the setup, Node.js, the vector store, environment variables for whichever LLM provider you're using, is done before the instance finishes booting.
Our Flowise developer guide and Flowise AWS Marketplace guide cover the setup in full. If you're pairing Flowise with a self-hosted model instead of a hosted API, our OpenWebUI with Ollama guide and Mistral developer guide cover running the model side yourself, and our self-hosted LLM comparison helps pick the model family.
Where I'd start
Building a RAG chatbot or agent as the product itself, and your team is JavaScript: Flowise. Same goal, but your team is Python and wants to drop into code: Langflow. Automating a broader business process where AI is one step among many, and you're not embedding it commercially: n8n, its integrations do real work the other two don't attempt.
If commercial embedding or reselling is part of the plan, read n8n's Sustainable Use License terms before building on it, that's the one decision here that isn't reversible after the fact.
Frequently Asked Questions
Flowise vs Langflow: which should I pick?
Flowise if your team works in JavaScript and wants the quickest drag-and-drop route to a RAG chatbot. Langflow if your team is Python-first and expects to edit component code. Both are permissively licensed (Apache 2.0 and MIT), so licensing won't decide it.
Flowise vs n8n: what's the real difference?
Flowise builds LLM apps: RAG pipelines, chatbots, agents. n8n automates business processes across 1,500+ integrations, with AI as one node type. n8n also ships under its Sustainable Use License, which restricts embedding and reselling, while Flowise's core is Apache 2.0.
Langflow vs n8n: which is better for building AI agents?
Langflow, if the agent itself is the product: its canvas is built for LLM chains and agent logic, and you can edit any component's Python. n8n wins when the agent is one step in a process that also updates a CRM, a spreadsheet or a ticket queue.
Is Flowise open source?
Yes. Flowise's core is Apache 2.0, which allows commercial use and embedding. The exception is the enterprise folder in its server package, which sits under a separate commercial license. Langflow is MIT. n8n is source-available under its Sustainable Use License.
Is n8n actually free for commercial use?
For running it internally to automate your own company's processes, yes. For embedding n8n inside a product you sell, reselling automation as a service, or letting external users trigger workflows, no, those uses require a signed commercial agreement and fees, per n8n's own Sustainable Use License terms.
Can Flowise and n8n work together?
Yes, though n8n has no official Flowise node. Point n8n's HTTP Request node at Flowise's /api/v1/prediction/{chatflowId} endpoint with the question in the JSON body, plus a Bearer key if the chatflow needs one. Langflow flows work the same way through /api/v1/run/{flow_id}.
Voiceflow vs Flowise: what's the difference?
Voiceflow is a commercial, hosted platform for chat and voice agents, aimed at product and CX teams. Flowise is a self-hostable, developer-oriented builder on LangChain.js. Voiceflow is easier for a support bot a CX team maintains. Flowise fits when the server and data must stay yours.
What are good Flowise alternatives?
Langflow is the closest swap: same visual approach, Python instead of JavaScript, MIT licensed. n8n suits automation-heavy work where AI is one step. Dify is another self-hostable visual LLM app builder. Voiceflow fits hosted, customer-facing chat and voice agents.
Which open source AI agent builder has no commercial strings?
Langflow (MIT) has none. Flowise's core is Apache 2.0 and also free for commercial use, with only its enterprise folder under a commercial license. n8n's Sustainable Use License restricts embedding and reselling, so it's the one that needs a legal read first.
What's a good visual llm builder for a non-Python team?
Flowise. It's built in JavaScript and Node.js on top of LangChain.js, and custom tools are JavaScript functions, so it fits a team already working in the JS ecosystem without adding a Python runtime alongside everything else.
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