Langflow vs n8n vs Make vs Flowise: Which No-Code AI Builder Should You Use in 2026?
Langflow, n8n, Make, and Flowise compared honestly. Find out which no-code AI workflow builder fits your project in 2026 — and why the differences matter.
AI ToolsNo-CodeLangflown8nWorkflow AutomationAI Agents
LLM
RAG
There are now four tools that show up in every conversation about no-code AI workflow building: Langflow, n8n, Make, and Flowise. The honest problem with most comparisons is that they treat all four as roughly equivalent alternatives and suggest you "pick whichever feels right." That framing misses the point. These tools are built on different assumptions about what you are trying to build, and choosing the wrong one will cost you weeks of rework.
This comparison is opinionated. It is also honest — every tool here is genuinely useful for the right context.
Quick Verdict
Tool
Best for
AI-native
Self-host
Starting price
Langflow
AI-first workflows, RAG pipelines, agents
Yes
Yes
Free (open source)
n8n
General automation + AI as a component
Partial
Yes
Free (self-host) / ~$20/mo cloud
Make
Non-technical teams, business process automation
No
No
Free tier / ~$9/mo
Flowise
Langflow-style AI flows, alternative UI preference
Yes
Yes
Free (open source)
The table tells most of the story. Langflow and Flowise are AI-native. n8n and Make are automation-first tools that have added AI capabilities. If AI is the core of what you are building, that distinction matters enormously.
Langflow is a visual canvas for building AI workflows, built on top of LangChain. You drag components onto a canvas — LLM nodes, vector store nodes, prompt templates, memory modules, agent executors — and connect them visually. The underlying code is LangChain, which means anything the LangChain ecosystem supports, Langflow exposes without requiring you to write Python directly.
It was built AI-first from day one. This is not a workflow automation tool that bolted on an "OpenAI" node. The entire component library is organized around AI primitives.
Strengths
Langflow has the deepest AI and LLM support of any visual builder. You can build retrieval-augmented generation pipelines by connecting a document loader to a text splitter to an embedding model to a vector store to a retrieval chain — entirely visually. Multi-agent setups, tool-using agents, and memory-persistent conversations are first-class citizens.
The visual debugger is particularly useful. When a chain fails, Langflow shows you exactly where in the graph the failure occurred and what data was flowing at that point. For LLM debugging, where the error could be in the prompt, the retrieval step, or the output parser, this is genuinely valuable.
Being open-source means you can self-host, inspect the code, modify components, and contribute. The community has grown significantly and there is a good library of pre-built flows shared publicly.
Weaknesses
Langflow is not the right tool for non-AI automation. If you need to watch a Gmail inbox, trigger on Slack messages, update a Salesforce record, and send a notification to Teams — that is a workflow automation problem, and Langflow is not designed for it. It has some integration capability but it is thin compared to n8n or Make.
Initial setup requires more technical comfort than Make. You need to either run it locally with Python or deploy it to a server. The hosted cloud version (Langflow Cloud) reduces this, but you are still working with a tool that assumes you understand what an LLM, an embedding, and a vector store are.
Best for
Builders who want to create AI-first workflows: document Q&A systems, customer support agents with memory, RAG pipelines over private data, multi-step reasoning agents. If AI is the product, Langflow is the tool.
n8n
What it is
n8n is an open-source workflow automation platform — the most honest comparison is self-hostable Zapier. It has over 400 integrations (Google Workspace, Slack, Salesforce, databases, HTTP APIs) and a visual node-based editor where you chain these integrations together. Code nodes let you drop JavaScript into any step when the visual nodes are not enough.
It added AI capabilities meaningfully over the past two years: LLM nodes (OpenAI, Anthropic, Gemini), AI Agent nodes that can use tools from its integration library, and vector store nodes for basic RAG. The AI additions are real, not cosmetic.
Strengths
The integration library is unmatched. If you need to combine AI with existing business systems — pull data from a CRM, enrich it with an LLM, write results back to a database, and post a Slack message — n8n does all of that in one flow. Make can do this too, but n8n gives you code flexibility when things get complex.
Self-hosting is a genuine advantage for teams with data residency requirements or who do not want to pay per operation. The community edition is free and capable.
Code nodes mean that when a node does not do what you need, you can write JavaScript to fill the gap. This is a meaningful escape hatch that Make does not have.
Weaknesses
AI support in n8n is an add-on, not the architecture. LLM orchestration is less mature than Langflow — you can call GPT-4o and chain a few steps, but building a proper RAG pipeline with sophisticated retrieval, re-ranking, and memory is considerably harder in n8n than in Langflow, and you will end up writing more code. Multi-agent orchestration is genuinely difficult.
The self-hosted version requires someone to maintain it. Updates, server costs, backups — this is real operational overhead that the cloud version of Make does not require.
Best for
Teams that want workflow automation as the primary use case and want AI to be one capable component within it. Engineering teams or startups with at least one developer who can maintain a self-hosted deployment. Anyone who needs the breadth of n8n's integration library and wants to add intelligent steps to existing automation.
Make (formerly Integromat)
What it is
Make is a cloud-native visual automation platform. It has been around since 2012 (as Integromat) and has built one of the largest integration libraries in the automation space. The interface is drag-and-drop, polished, and notably approachable for non-technical users.
Make's AI capabilities come through its OpenAI module, Anthropic module, and HTTP request module — you can call any LLM API and use the response in your automation. Some built-in AI-flavored modules (image recognition, text analysis) exist but are not deeply agentic.
Strengths
Make has the lowest learning curve of the four tools here. A non-technical operations manager can build a functional automation in an afternoon. The UI is the most polished, the documentation is excellent, and the templates library lets you start from working examples.
The integration breadth is comparable to n8n and, for some platforms, deeper — Make has nurtured enterprise software integrations for over a decade. If you need to connect a legacy CRM or a niche SaaS product, Make is often the first place to check.
Pricing for light to moderate use is reasonable, and there is no server to maintain.
Weaknesses
Make runs on Make's cloud only. There is no self-hosting option. For teams with data residency requirements or who process sensitive data through automations, this is a hard constraint.
AI capabilities are fundamentally HTTP calls to LLM APIs. You can build automations that classify text, generate content, or extract structured data from documents. You cannot build agents that maintain state, use tools autonomously, run multi-step reasoning loops, or do anything resembling what Langflow's agent nodes do. Calling this "agentic" would be misleading.
The pricing model charges per operation, which means complex automations with many steps can become expensive at scale.
Best for
Operations and marketing teams automating business processes where AI plays a supporting role. Classifying incoming support tickets and routing them. Generating first drafts of outbound emails. Summarizing documents and posting them to Notion. If the workflow is primarily about connecting business tools and AI is a useful step in the middle, Make is the easiest way to get there.
Flowise
What it is
Flowise is an open-source LangChain UI — functionally similar to Langflow. Like Langflow, it provides a visual canvas for building LLM chains, RAG pipelines, and agent flows. It is self-hostable, has an active community, and covers much of the same AI workflow territory that Langflow does.
Strengths
Flowise is genuinely capable for AI-native workflows. It has good RAG support, LLM chain building, and agent configuration. Being open-source and self-hostable means no vendor lock-in and no per-operation pricing.
Some users prefer Flowise's specific UI conventions — the way it organizes nodes and surfaces configuration options differs from Langflow in ways that feel meaningful once you have worked with both.
The community is active and the project has contributed back to the ecosystem meaningfully.
Weaknesses
The team behind Flowise is smaller than the team behind Langflow, and the gap in polish and feature velocity has widened over 2025-2026. Some features that have shipped in Langflow have taken longer to appear in Flowise.
Documentation is thinner in places, which means more time spent reading source code or community forums when you hit edge cases.
Best for
Teams who want Langflow-style AI workflow building and either prefer Flowise's specific UI or have existing Flowise deployments they do not want to migrate. Also useful for teams who want to contribute to the codebase or build on top of Flowise's internals directly.
A Three-Question Framework to Choose Your Tool
Rather than a feature matrix, these three questions will narrow down your choice faster than any comparison table.
Question 1: Is AI the core of what I am building, or is it a feature inside a larger automation?
If AI is the core — if you are building a document Q&A bot, a customer-facing agent, a RAG pipeline over proprietary data, or a multi-step reasoning system — then Langflow or Flowise is the right starting point. These tools were designed for exactly this.
If AI is one useful step inside a broader automation — classify this email, summarize this document, generate this copy — then n8n or Make will serve you better because the integration breadth and automation primitives are what you actually need.
Question 2: Do you want to self-host?
Self-hosting gives you data control, eliminates per-operation costs at scale, and removes vendor dependency. Langflow, n8n, and Flowise all support self-hosting with real production deployments. Make does not offer self-hosting at all.
If self-hosting is a requirement, Make is eliminated immediately.
Question 3: How technical is your team?
Non-technical teams who need to move fast and do not want to manage infrastructure should look at Make first, or Langflow Cloud if AI workflows are the priority. Both provide managed cloud deployments with minimal setup.
Technical teams — those with developers comfortable with Docker, server configuration, and Python/JavaScript — have access to all four tools. n8n and the self-hosted versions of Langflow and Flowise become viable and cost-effective.
What Langflow Does That Nothing Else Does
The practical difference between Langflow and the other tools in this list becomes clear when you try to build specific things.
RAG pipelines. Retrieval-augmented generation — connecting an LLM to your own documents so it can answer questions grounded in your data — is a native workflow type in Langflow. You have visual components for every step: loading documents, chunking them, embedding them, storing them in a vector database, retrieving relevant chunks, injecting them into a prompt, and generating a grounded answer. In n8n, you can approximate this with custom code. In Make, it is not really possible without significant workarounds.
Multi-agent orchestration. Langflow supports building systems where multiple AI agents collaborate — one agent researches, another synthesizes, a third formats the output. Agent-to-agent communication, tool assignment, and agent memory are all first-class concepts. This class of architecture is not available in Make and is early-stage in n8n.
LangChain ecosystem access. Because Langflow runs on LangChain under the hood, anything the LangChain Python library supports — every vector store integration, every embedding model, every retrieval strategy — is accessible in Langflow without writing code. The ecosystem has become very large.
Visual debugging for LLM chains. When a chain fails in Langflow, you can inspect exactly what prompt was sent, what the model returned, and where in the graph the failure occurred. Debugging LLM behavior is inherently harder than debugging deterministic code, and this visibility matters when you are trying to improve chain quality.
Why We Teach Langflow at explainx.ai
The tool we cover in our Langflow workshop is not arbitrary. After evaluating all four tools covered here, Langflow is the one that most directly maps to what people who are building AI-first products actually need to do.
The AI workflow space is moving fast. Langflow's team ships frequently, the open-source community is active, and the LangChain foundation means it benefits from a large ecosystem of model integrations, retrieval strategies, and community-contributed components.
For someone who wants to build real AI workflows — not just call an LLM API from a business automation — Langflow provides the most capable starting point without requiring you to write Python from scratch.
If you have read this far and Langflow sounds like the right tool for what you are building, the fastest way to get from zero to working flows is hands-on practice.
explainx.ai runs a focused Langflow workshop on September 7, 2026. In four hours, you will build three working AI flows:
A document Q&A bot using RAG
A multi-step agent with tool access
A conversational flow with persistent memory
By the end of the session, you will have working code you can deploy and extend, not just slides about what is theoretically possible.
If you are still evaluating whether Langflow is the right fit before committing to a workshop, the Langflow tutorial is the right place to start — it takes about an hour and produces a working document Q&A bot you can deploy yourself.
The Bottom Line
The no-code and low-code AI workflow space has matured enough that tool choice matters. Picking Make when you need agentic AI will leave you writing workarounds indefinitely. Picking Langflow when you need broad business automation will leave you building integration plumbing from scratch.
The honest summary:
Langflow if you are building AI-first workflows, RAG pipelines, or agent systems
n8n if you need broad automation with AI as a capable component and want self-hosting
Make if your team is non-technical and AI is a supporting step in business process automation
Flowise if you want Langflow-style AI flows and prefer its UI or community
Most teams building in 2026 will land on Langflow or n8n. The question is whether AI is the architecture or the feature.
