Langflow vs n8n: one builds the AI logic, the other runs the workflow

The short version

• n8n is a workflow automation platform that treats a model as one step in a business process.
• Langflow is a visual builder for AI agents and retrieval pipelines, with custom components written in Python.
• Choose n8n when most of the work is integration, and Langflow when most of the work is AI logic.
• Running both makes sense only when you have both problems, because it means two systems to host, patch and monitor.
• Both tools hold credentials to other systems, so a self-hosted instance of either needs fast patching and minimal public exposure.

What is the difference between Langflow and n8n?

n8n is a workflow automation platform. Langflow is a visual builder for AI agents and retrieval pipelines. n8n starts from the business process and treats a model as one step in it. Langflow starts from the AI logic and treats the rest of the business as something to call.

That starting point decides most of what follows. An n8n workflow begins with a trigger: a webhook, a schedule, a new row, an incoming email. It then moves data through connected apps, with credentials, retries, error handling and a record of every run as standard parts of the product. A Langflow flow begins with a prompt, a model, a retriever and some tools. The canvas is built for trying a different embedding model, chunking strategy or agent setup and seeing the result straight away.

The overlap is real. n8n ships an AI agent node and can talk to vector stores. Langflow can call external tools and expose a flow as an API. So both can run an agent. The useful question is which surrounding work your project needs more of.

How do Langflow and n8n compare side by side?

When should you choose n8n?

Choose n8n when most of the work is moving data between systems and the model is one step. Classifying inbound email, routing a support ticket, enriching a lead, drafting a reply for a human to approve: these are integration problems with a small AI step in the middle.

n8n earns its place on the unglamorous parts. It already knows how to authenticate against a large catalogue of business apps. It handles schedules and webhooks. When a step fails, you can see which one, with what input, and rerun it. A person who is not an AI specialist can read a workflow and follow what it does. Those properties decide whether an automation is still running next year.

It gets harder when the AI logic becomes the hard part. Retrieval tuning, multi-agent behaviour and systematic evaluation are possible, but a workflow canvas is not the natural place to iterate on them. If you find yourself fighting the agent node, move the AI logic elsewhere and keep n8n as the orchestrator.

When should you choose Langflow?

Choose Langflow when the AI behaviour is the hard part and you need to iterate on it. A retrieval assistant over internal documents is the typical case. The result depends on chunking, embedding choice, retrieval settings and prompts, and you want to change each one and compare.

Langflow suits Python teams. Components are Python, so a custom retriever or internal tool fits the way those engineers already work. A finished flow can be served as an API endpoint, so another system can call it without knowing how it works inside.

What Langflow does not aim to be is the operations layer around a business process. Scheduling across many systems, human approvals, per-step reruns and connectors to every business app are not its centre of gravity. If you run a whole process inside it, expect to rebuild those parts by hand.

Can n8n replace Langflow for AI agents?

For simple agents, yes. If the agent has a handful of tools, a clear prompt and a well-bounded job, the n8n agent node will do it, and you get n8n's triggers and run history as well.

For agents whose quality depends on retrieval or careful tuning, not comfortably. The reverse also holds. Langflow can stand in for n8n on a single process triggered by one API call, but not across a set of business automations. Choose by where your maintenance time will go, not by which demo looked better.

Neither canvas tells you whether the agent is right. That takes a test set of real cases and somebody who reads the failures.

Should you use Langflow and n8n together?

Yes when you have both problems, and no otherwise. The clean pattern is that n8n owns the trigger, the credentials to business systems and the final action, and calls a Langflow flow over HTTP for the AI step. Each tool stays on the work it is good at.

A worked example: a support email arrives. n8n picks it up, looks up the customer in the CRM and posts the text to a Langflow flow. Langflow retrieves from the knowledge base and returns a draft with its sources. n8n puts the draft in front of a human and logs the outcome. Either side can change without touching the other, as long as the API contract holds.

The cost is two systems to host, patch and monitor, and two places to look when something breaks. If one tool covers the job, use one. Two platforms for one small workflow is a maintenance bill with no return.

Are Langflow and n8n free to use?

Both can be self-hosted with no licence fee for many uses, but the terms differ and the running costs remain. Langflow is published under the MIT licence. n8n is released under its own Sustainable Use License, which is source-available rather than open source and restricts some commercial uses. If you plan to build n8n into something you sell, read the current licence text first.

The hosted picture differs too. n8n sells a hosted version directly. Langflow is less clear. langflow.org and IBM's product page refer to a Langflow Cloud, and IBM publishes no price for it. DataStax records changes to its own service in the Astra DB Serverless release notes, and those notes cover the DataStax service only. If a managed Langflow matters to your plan, get availability and price in writing before you commit.

Model costs are separate. Both products call a model provider, and you pay that provider either way. A tool with no licence fee can still produce a large bill when a loop runs unchecked.

Which is safer to self-host, Langflow or n8n?

Neither is safe to leave exposed and unpatched, and both had serious vulnerabilities reported in 2026. Two CVEs do not rank two products, so the honest answer is that your patching and exposure matter more than the logo.

On Langflow, Sysdig's threat research team deployed vulnerable instances across multiple cloud providers and regions and recorded exploit events from 6 unique source IPs over 48 hours in March 2026. Its write-up on CVE-2026-33017 is headlined 'How attackers compromised Langflow AI pipelines in 20 hours'. Sysdig is a security vendor reporting its own data, and a honeypot shows attacker interest, not how many real deployments were harmed.

On n8n, Censys published an advisory dated 7 January 2026 on CVE-2026-21858, an unauthenticated remote code execution issue nicknamed NI8MARE, using queries for hosts below the patched version, 1.121.0. Its scan data is a point-in-time snapshot, the page says exposed rather than confirmed exploited, and other scanners report different counts. So this post does not quote a number.

The shared lesson is about what these tools hold. Both keep credentials for the systems they connect to, such as model API keys, database logins and access to business apps, and both can run code. A compromised instance gives an attacker a route into everything it connects to, not only the tool itself.

What should you lock down before self-hosting either one?

• •Keep the editor off the public internet, behind a VPN or single sign-on.
• •Expose only the webhook or API paths a process actually needs.
• •Follow the project's security advisories and name the person who applies patches.
• •Give each integration a narrow credential, so one compromise does not reach everything.
• •Run the instance on a host that cannot reach systems it has no reason to touch.

When is neither Langflow nor n8n the right tool?

When the agent is the product, or the job is small enough for a plain script. A low-code canvas saves time while the flow is simple and the team is still exploring. It stops saving time when code review, proper tests and versioning become the main way you work. If a customer-facing product depends on agent behaviour, plain code is often easier to test and to own.

A fixed process that needs no judgement also needs no model. If a rule will do the job, use n8n without the AI step, or a scheduled script. Adding an agent to a deterministic process adds cost and a new way to fail.