Langflow vs n8n: Which Fits Your AI Workflow?
Langflow is a visual builder for AI agents and flows that you run as a Python package, and n8n is a workflow platform with a visual canvas, 1500+ integrations and a fair-code license. Pick Langflow when the flow is the LLM app and you want to edit Python components. Pick n8n when events and connectors drive the work. If you would rather not draw anything, POLYROB is the third option: a self-hosted agent that is already built, and its autonomous runs stop at 150 steps and $2.00 by default (docs/[email protected]).
This page compares the two on what their own repositories and docs say. Every fact about Langflow and n8n was read on 2026-11-15 from a README, a license file or a docs page, and each one links to its source. POLYROB gets its own section near the end, including where it loses.
How do Langflow and n8n differ?
| Langflow | n8n | |
|---|---|---|
| What its README calls it | “a powerful platform for building and deploying AI-powered agents and workflows” | “Fair-code platform to build and deploy AI agents and workflows” |
| How you build | Visual builder, plus source code access to customize any component in Python | Visual canvas, combined with JavaScript, Python and npm packages |
| License | MIT | Sustainable Use License, plus an Enterprise License for .ee. files |
| Install | uv pip install langflow -U, then uv run langflow run; also Docker and a desktop app for Windows and macOS | An install script that needs Docker, or docker run; also n8n Cloud |
| Default address | http://127.0.0.1:7860 | http://localhost:5678 |
| Integrations | “a growing library of AI tools”, major LLMs and vector databases | “1500+ integrations” and “9,000+ workflow templates” |
| MCP | Both an MCP server and an MCP client | Not stated in the README |
Sources: the Langflow README and Langflow MCP docs, and the n8n README, all fetched 2026-11-15.
What is the main difference between Langflow and n8n?
Langflow starts from the LLM application. n8n starts from the workflow.
The Langflow README lists a visual builder, an interactive playground “to immediately test and refine your flows with step-by-step control”, multi-agent orchestration with conversation management and retrieval, and observability through LangSmith, LangFuse and other integrations. A finished flow can be deployed as an API, exported as JSON for Python apps, or served as an MCP server so that MCP clients can use it as a tool. The Langflow docs describe it as both an MCP server and an MCP client, with streamable HTTP and Server-Sent Events as transports (docs, version 1.13.x, 2026-11-15).
The n8n README leads with automation: a canvas, custom code where you need it, 1500+ integrations, and “multi-step AI workflows with logic, tool use, human approvals, and full observability”. An agent is one kind of node inside a larger flow that a webhook, a schedule or a new row can start.
In practice that means the same task looks different in each. “Answer questions over our documents and expose that as a tool” is a Langflow-shaped job. “When a form is submitted, enrich the lead, ask a model to classify it and post to the sales channel” is an n8n-shaped job.
When should you choose Langflow?
Choose Langflow if these describe your project:
- The product you are building is an LLM application, and you want to iterate on it visually and then call it from code.
- You want to open a component and change its Python. The README says source code access lets you customize any component.
- You need the flow as an API or as an MCP tool for other software.
- You want the most permissive license of the two. The Langflow repository ships an MIT license file.
- You want a desktop app. The README links Langflow Desktop for Windows and macOS.
Check the setup notes before you expose it. The README says official release packages and tagged source archives disable automatic login by default, and you set LANGFLOW_SUPERUSER_PASSWORD before starting. It also says the install needs Python 3.10 to 3.14.
When should you choose n8n?
Choose n8n if these describe your project:
- The work is event-driven and the AI step is one stage in a longer process.
- Connectors matter more than custom agent logic: the README counts 1500+ integrations.
- People who do not write code need to read and change the flow.
- You want a hosted option next to self-hosting. The README links a cloud login beside the Docker instructions.
Read the license before you build a product on it. The n8n LICENSE file says you may use or modify the software “only for your own internal business purposes or for non-commercial or personal use”, and that you may distribute it only free of charge for non-commercial purposes. Files with .ee. in the name need an n8n Enterprise License. That suits internal automation. If you plan to resell it, ask n8n about your case. We are quoting a license file, not giving legal advice.
Can you use Langflow and n8n together?
Yes, and the MCP support makes it direct. Langflow can serve a flow as an MCP server, which turns a retrieval flow into a tool that a client can call. n8n handles the triggers and the connectors around it, and its README says you can add JavaScript, Python and npm packages where the canvas runs out. The cost is two systems to learn, update and secure.
Where does POLYROB fit between Langflow and n8n?
POLYROB is neither a canvas nor a flow builder. It is the agent. You install it, describe a goal or a schedule in plain language, and it plans the steps, uses tools and reports back from a terminal, a web console or a chat app ([email protected]). It is MIT licensed ([email protected]).
curl -fsSL https://polyrob.dev/install.sh | bash
polyrob doctorA recurring job is one line, from docs/guide/[email protected]:
/job new "every day 09:00" Summarise the inbox and post the three items that need meWhat you get without building it, with the file behind each point:
- No flow-building tool underneath. The dependency list in
[email protected]names neither Langflow nor n8n, and the agent loop is native code. - Scheduled jobs, plus goals that run in the background once you switch the dispatcher on with
polyrob autonomy on(docs/[email protected]). - A cap on every autonomous run: 150 steps (
AUTONOMOUS_MAX_STEPS) and $2.0 (AUTONOMOUS_RUN_BUDGET_USD) by default (docs/[email protected]). The budget check fails open on a probe error, so treat it as a guard rail. - Eight chat surfaces: Telegram, email, WhatsApp, Discord, Slack, Signal, Feishu / Lark and DingTalk (
[email protected]). - An MCP client, so it can call tools from any MCP server, including one that serves a Langflow flow (
tools/mcp/@v1.7.10). The MCP client guide shows the setup.
Pick it if the job is “keep working on this and ask me before high-impact steps” and you would rather configure an agent than draw one. The self-hosted AI agent guide walks through a full install.
Where does POLYROB lose to Langflow and n8n?
Plainly:
- No visual builder. The console has five destinations: Chat, Inbox, Work, Money and Agent (
docs/guide/[email protected]). None of them is a flow editor. - Integrations. n8n lists 1500+. POLYROB ships tools for the web, files, code, a shell, git and GitHub, mail and MCP, and seven first-party packs (
[email protected],docs/[email protected]). Anything else comes through MCP or a skill you write. - Apps and hosting. POLYROB has a web console and a terminal, where Langflow ships a desktop app for Windows and macOS. The POLYROB README says there is no hosted version, while n8n offers a cloud.
- Community size. POLYROB is the newer project, with a smaller community (
docs/[email protected]). - Migration. Nothing converts a flow, so moving one means describing the job to the agent again.
How do you decide?
| If you need | Look at |
|---|---|
| A visual builder for an LLM app, with Python components | Langflow |
| A flow exposed as an API or an MCP tool | Langflow |
| Events and schedules that start steps across 1500+ services | n8n |
| A flow that non-developers can edit, with a hosted option | n8n |
| The most permissive license of the two | Langflow |
| A self-hosted agent that is already built and works toward goals | POLYROB |
If neither fits, see our lists of n8n alternatives and LangChain alternatives, or the n8n vs LangChain comparison.
Frequently asked questions
Is n8n becoming obsolete?
We do not judge age. On 2026-11-15 the n8n README titles the project “The Platform for AI Agents and Workflow Automation”, which shows it is positioned for agent work today. We did not measure usage.
Is there a better tool than n8n?
It depends on the job. Langflow fits when the flow is an LLM application you want to edit in Python and serve as an API or MCP tool. A ready-made agent such as POLYROB fits when you want something that works toward a goal without a flow to draw. For a longer list, see n8n alternatives.
Is Langflow completely free?
The Langflow code is under an MIT license, according to the license file in its repository on 2026-11-15, so you can install and run it yourself at no license cost. You still pay the model providers you connect, and we did not check any hosted Langflow offering.
What is the difference between n8n and LangChain?
n8n is a product you run, and LangChain is a library you build with. Our n8n vs LangChain page compares them from their own READMEs.
Sources
Fetched 2026-11-15: the Langflow README, the Langflow license file, the Langflow MCP server docs, the n8n README and the n8n LICENSE file. POLYROB facts are from the public repository at tag v1.7.10. Projects change quickly: check each link before you decide.
To try an agent that is already built, read how to install POLYROB.
POLYROB is an open-source AI agent you run and own. Install it or read the docs.